Copyright TechTarget - All rights reserved https://cyber.law.harvard.edu/rss/rss.html Techtarget Feed Generator en Wed, 20 May 2026 10:29:17 GMT https://www.techtarget.com/searchenterpriseai editor@techtarget.com <p><i>Without relevant, AI-ready data, agents are doomed to fail.</i></p> <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/macmillan_andy.jpg" alt="Alteryx CEO Andy MacMillan">Andy MacMillan </div> <p><i>In response, as agentic AI became the focal point of many enterprises' development initiatives over the past couple of years, longtime data preparation specialist Alteryx made its mission under CEO Andy MacMillan, who </i><a href="https://www.techtarget.com/searchbusinessanalytics/news/366616939/Alteryx-names-Andy-MacMillan-CEO-amid-ongoing-change"><i>took over as the company's leader</i></a><i> in December 2024, to provide users with the canvas they need to make data AI-ready.</i></p> <p><i>Since then, however, Alteryx has expanded its ambitions under MacMillan.</i></p> <p><i>The emergence of AI over the past few years as a means of generating insights and automating business processes has forced many data management and analytics vendors to evolve. For example, data platform vendors Databricks and Snowflake now provide full-featured AI development environments. Database providers such as MongoDB and Couchbase similarly aim to become AI development platforms in addition to their historical focus. And analytics specialists including GoodData and Tableau are building on longtime semantic modeling capabilities to turn their platforms into context layers within agentic workflows.</i></p> <p><i>Alteryx, which <a href="https://www.techtarget.com/searchbusinessanalytics/news/252513750/Alteryx-makes-personnel-changes-to-navigate-cloud-journey">struggled to keep up with the pace of change</a> before being </i><a href="https://www.techtarget.com/searchbusinessanalytics/news/366563665/Alteryx-to-be-acquired-by-private-equity-firms-for-44-billion"><i>sold to a private equity firm</i></a><i> in December 2023 and taken private to reorganize away from the public spotlight, is likewise evolving. </i></p> <p><i>However, beyond shifting from a data preparation platform for BI to a focus on AI-ready data, the vendor aims to become a critical part of agent workflows by enabling business users to add </i><a href="https://www.techtarget.com/whatis/definition/business-logic"><i>business logic</i></a><i> -- rules, workflows and analysis -- to agents to help give them </i><a target="_blank" href="https://a16z.com/your-data-agents-need-context/" rel="noopener"><i>the contextual awareness they require</i></a><i> to properly carry out their specific work.</i></p> <p><i>Unlike many AI development platforms that provide developers and engineers with capabilities for building agents, Alteryx's new Agent Studio is designed to empower Alteryx's user base of business analysts -- the experts in their domains with first-hand experience -- to connect data, logic and governance with AI.</i></p> <p><i>Its decentralized approach to development could be a differentiator. But, with only 11% of respondents to a recent Alteryx survey expecting responsibility for AI workflows to move to line-of-business domains over the next three years, </i><i>this approach could be risky, according to consultant Donald Farmer, founder and principal of TreeHive Strategy, who noted that 11% is not a transformative number.</i></p> <p><i>In a recent interview before the start of Alteryx Inspire, the vendor's user conference in Orlando, Fla., MacMillan discussed the vendor's shift from a focus on data preparation for BI to AI-ready data. </i></p> <p><i>In addition, he spoke about Alteryx's different approach to developing agents, the struggles he sees from customers as they attempt to become </i><a href="https://www.techtarget.com/searchenterpriseai/feature/Businesses-gear-up-for-AI-agents-in-the-enterprise"><i>agentic enterprises</i></a><i>, and what might be major themes in data and AI when Alteryx hosts its user conference in 2027.</i></p> <p><b>Editor's note</b>: <i>This Q&amp;A has been edited for clarity &amp; conciseness</i>.</p> <p><b>When </b><a href="https://www.techtarget.com/searchbusinessanalytics/news/366617637/New-Alteryx-CEO-sees-platform-as-the-canvas-for-AI-prep"><b>we last talked</b></a><b> shortly after you took over as CEO, you spoke about evolving Alteryx so it becomes the canvas for customers to prepare data for AI -- how is making data AI-ready different than making it ready for BI?</b></p> <p>Andy MacMillan: They're not entirely different. But what is different about it is the speed that agents are going to interact with data and <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-and-AI-governance-must-team-up-for-AI-to-succeed">the governance around it</a>. With BI, you had snapshots. Everyone could look at the same dashboard for a week, and you could audit it. Now, if there is a <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol</a> endpoint for an agent, and anyone can ask that agent a question, there's a different level of predictability that's needed.</p> <p>Now, instead of going to a Tableau or Qlik dashboard as I might have five years ago, I'm going to go to ChatGPT. But I want ChatGPT to give me answers with the same certainty and clarity that I would have gotten from those dashboards, and I also want ChatGPT's new capabilities, which are the analysis and probabilistic nature that can do the reasoning. Right now, I think people feel like they're trading those off. They feel like they have the consistency of their dashboard or the reasoning of the agent. Our goal is to make sure there isn't a tradeoff.</p> <p><b>When customers use Alteryx to prepare data, are there different things they need to do differently when getting it ready for AI than they did when preparing it for BI?</b></p> <p>MacMillan: I don't think there are things they have to do differently. I think there are just things overall that we all have to make sure that we're doing better, such as the governance around it to make sure <a href="https://www.techtarget.com/searchenterprisedesktop/feature/The-next-enterprise-AI-problem-is-visibility">there's visibility</a> and understandability and thinking through the permutations of how people are going to interact with the data can be different.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Our ambitions have gotten bigger than simply data preparation. Now, [our ambitions] include providing some of the calculations and business logic that you could maybe argue is data prep, but is more than just cleaning data. </figure> <figcaption> <strong>Andy MacMillian</strong>CEO, Alteryx </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>What are different are the capabilities to build the workflow. Three years ago -- even two years ago -- Alteryx users could drag and drop tools onto a canvas and solve a problem, but you still had to know how to drag and drop all those tools. Now, you can type in what you're trying to do and watch it put the tools on the canvas that help you build the workflow. There's also the ability to use AI to interrogate the requirements. … At its core, the value proposition is empowering the person who knows the process the best, and knows the data the best, to be responsible for building [AI tools], and empowering them to keep it up to date.</p> <p><b>What does it require to be AI-ready?</b></p> <p>MacMillan: I think I have a different take than a lot of folks. There's been a lot of talk about <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Agents-semantic-layers-among-top-data-analytics-trends">a semantic layer</a>. That's not bad -- putting labels on data and making sure it's clean is all reasonable. But I think to be AI-ready, data has to have gone through the operational understanding of what and how to use the data and how to pull it together for use cases, and then the agent has to know which use cases to use the dataset for.</p> <p>When I talk to folks about this, I point out that when you talk to the best people in your company about a topic, and you ask them a question, they usually ask you questions back. If I say, 'What was the revenue for our stores in California?', they respond, 'Do you want all the stores, or do you want the online sales that came through California?' Or maybe they point out that there were two stores in California that are no longer in business, but were in business during the first quarter and ask if that should be included. That's not a semantic layer problem. That's a business logic problem. Getting data AI-ready is not just having a semantic layer. It's also having business logic in a callable place, where that logic is in the calculation and the AI can call that and get the answer. That <a href="https://www.techtarget.com/searchenterpriseai/post/AI-agents-are-only-as-smart-as-the-data-that-feeds-them">business logic is the missing piece</a>.</p> <p><b>How does business logic get connected with AI to inform agents?</b></p> <p>MacMillan: There's a misconception that business logic is in all of an organization's old applications, and, having worked at some of these big <a href="https://www.techtarget.com/searchcloudcomputing/definition/Software-as-a-Service">SaaS</a> providers, I don't know that it always is. What we don't want to do is consume AI only through those applications. You want to be able to get to the data and pull it all together so you're not orchestrating AI on top of applications, but instead building logic that talks directly to data. Maybe the data came from those applications, but you're implementing that logic at the data layer in a visible, understandable, repeatable, auditable environment and connecting it with AI.</p> <p>Now, with business logic, there's a powerful platform for making AI work and understand the actual business.</p> <p><b>Where is Alteryx in its evolution toward becoming the canvas for getting data AI-ready?</b></p> <p>MacMillan: We're definitely there as we launch the capabilities that we're shipping.</p> <p>But I would say that our ambitions have gotten bigger than simply <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Top-data-preparation-challenges-and-how-to-overcome-them">data preparation</a>. Now, [our ambitions] include providing some of the calculations and business logic that you could maybe argue is data prep, but is more than just <a href="https://www.techtarget.com/searchenterpriseai/tip/Clean-data-is-the-foundation-of-machine-learning">cleaning data</a>. We're helping the analysts and ops people of the world put their knowledge to work with the data in a bunch of different ways. Data prep is one of those ways, but building agents in Agent Studio is a lot more than data prep. That's taking prepared data and logic and activating it with AI.</p> <p><b>Beyond getting data AI-ready, what's a specific new role that Alteryx hopes to play?</b></p> <p>MacMillan: I think it's this business logic layer. What we're talking to customers about is making building AI agents simpler. Everyone today has access to AI, and most people that we talk to have access to a bunch of data. What we're trying to do is help them simply put that data to work for AI, and do that without trying to run through an application stack and without having some big orchestration project with 20 different platforms to get an agent to do the most basic sales and marketing things.</p> <p>All the data from sales and marketing applications is in a cloud data warehouse, and the sales and marketing operations team knows what that data means. We're letting them describe the logic, expose that logic to ChatGPT and create an agent. We're just trying to <a href="https://www.techtarget.com/searchenterpriseai/tip/Citizen-developers-are-redefining-enterprise-AI-development">provide our users a canvas to do that</a>.</p> <p><b>When you meet with customers, what are the biggest concerns you hear from them as they strive to become agentic enterprises?</b></p> <p>MacMillan: Data governance is a big one -- the tension between IT, <a href="https://www.techtarget.com/searchdatamanagement/definition/DataOps">DataOps</a> and the business. A lot of customers have a pristine data warehouse, but no one is allowed to use it, so that's not super helpful. Other customers have the opposite, where <a href="https://www.techtarget.com/searchdatamanagement/feature/Controlling-data-sprawl-requires-governance-discipline">data is everywhere</a> and they're struggling to manage it.</p> <p>The other big one that we're seeing is that the budget has shifted from AI being an IT-driven initiative to being a line-of-business initiative. With that shift has come responsibility, and with the responsibility has come a mandate to start using AI to solve actual problems, so customers are asking how to do that. They can't just use AI to write better emails. They have to use it to [improve operations]. That's the pressure I'm hearing at the moment from every one of our customers. That shift has happened quickly, and we're trying to be helpful as companies go through that shift.</p> <p><b>Things are evolving incredibly fast, so predicting the future is perhaps more difficult now than ever, but what do you think will be the major trends in data and AI a year from now?</b></p> <p>MacMillan: I think we're going to be talking about people modernizing their business logic away from their application portfolio and into an agent portfolio. That doesn't mean applications all go away, but I talk to so many people today that are constrained by their logic being kept in their enterprise resource planning and customer relationship management applications, and that constraint on their agentic growth is clearly going to be a problem.</p> <p>People are going to ask how to get to the data layer under that and go fast. People are going to realize they can build an agent when they get [constraints] out of the way and can just implement logic and go fast. We're going to move to an era when we agentify business logic, make it visible, understandable, repeatable, <a href="https://www.techtarget.com/searchdatamanagement/tip/Data-lineage-documentation-imperative-to-data-quality">auditable</a>, <a href="https://www.techtarget.com/searchbusinessanalytics/news/366618249/Trusted-data-at-the-core-of-successful-GenAI-adoption">trusted</a> and business-owned but running on the IT infrastructure environment. That's where we're headed.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> As the vendor grows to meet changing customer needs, CEO Andy MacMillan says its goals have expanded beyond its data prep roots to include connecting agents with proper context. https://cdn.ttgtmedia.com/rms/onlineimages/ai_g1182183209.jpg https://www.techtarget.com/searchdatamanagement/feature/Evolving-Alteryx-focusing-on-AI-ready-data-logic-for-agents Wed, 20 May 2026 09:30:00 GMT <p>With many enterprises struggling to build AI tools that can be trusted in production, Informatica's latest product development initiatives aim to provide users with a foundation for building reliable agents.</p> <p>Unveiled on Wednesday during Informatica World, the vendor's user conference in Las Vegas, new features in <a href="https://www.techtarget.com/searchdatamanagement/news/366633382/Informatica-launches-agents-adds-new-AI-development-tools">the Informatica Intelligent Data Management Cloud</a> (IDMC) are highlighted by Agentic Multidomain MDM, which is a set of AI agents that continuously perform <a href="https://www.techtarget.com/searchdatamanagement/definition/master-data-management">master data management</a> tasks such as cleansing, enriching and stewarding data to maintain its quality, consistency and relevance.</p> <p>In addition, Informatica launched <a href="https://www.techtarget.com/searchapparchitecture/tip/An-overview-of-headless-architecture-design">headless</a> data management capabilities -- including native support for Model Context Protocol (MCP) to connect agents with data sources -- that enable users to oversee their data from a central repository while leaving the data where it lives. New integrations, including some with Salesforce, which <a href="https://www.techtarget.com/searchcustomerexperience/news/366624960/Salesforce-to-acquire-Informatica-in-8-billion-deal">acquired Informatica in 2025</a>, round out Informatica's IDMC additions.</p> <p>Given that the new features accelerate master data management to ensure that data can be trusted to inform AI tools, and that they provide a new architecture for both human and agentic workflows, they are valuable additions for Informatica users according to William McKnight, founder and president of McKnight Consulting.</p> <p>"This is an evolution of Informatica's IDMC from human-directed middleware into more of an autonomous data workforce that continuously cleans and governs data," he said. "It introduces headless data management … to provide front-end tools with real-time, context-rich data. By minimizing the traditional data trust bottleneck, this could enable AI to safely execute workflows independently."</p> <p>Plenty of other providers are also adding agentic features, McKnight continued. However, Informatica's <a href="https://www.techtarget.com/searchenterpriseai/feature/How-to-ensure-AI-transparency-explainability-and-trust">focus on trust</a> and native integrations with key Salesforce capabilities help distinguish the vendor.</p> <p>"Informatica differentiates through a focus on trusted data to drive live, real-time business transactions and automated actions," McKnight said. "Natively pairing with Salesforce allows it to stream trusted master data directly into front-end workflows. This headless framework could enable Informatica to embed complex data management into everyday business applications more fluidly than competitors."</p> <p>Based in Redwood City, Calif., Informatica enables customers to integrate, prepare and govern data to ready it for analytics and AI. Peers range from fellow specialists such as <a href="https://www.techtarget.com/searchdatamanagement/news/366580432/Collibra-launches-AI-Governance-unveils-GenAI-capabilities">Collibra</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366632699/Fivetran-DBT-Labs-merge-to-add-complementary-capabilities">Fivetran</a> to hyperscale cloud providers that offer master data management capabilities, including AWS, Google Cloud and Microsoft.</p> <section class="section main-article-chapter" data-menu-title="A trust foundation"> <h2 class="section-title"><i class="icon" data-icon="1"></i>A trust foundation</h2> <p>As enterprises have <a href="https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026">increasingly invested</a> in AI development over the past few years, but <a href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf">struggled to build</a> tools trustworthy enough to move into production, data management and analytics providers have recently made enabling users to discover and operationalize contextually appropriate data a priority.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This is an evolution of Informatica's IDMC from human-directed middleware into more of an autonomous data workforce that continuously cleans and governs data. … By minimizing the traditional data trust bottleneck, this could enable AI to safely execute workflows independently. </figure> <figcaption> <strong>William McKnight</strong>President, McKnight Consulting </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Without contextually relevant data -- such as supply chain data for a supply chain optimization agent -- AI tools will fail to deliver trustworthy outputs and projects will fail. Vendors, however, have taken different approaches to improving data discovery and retrieval.</p> <p>For example, Databricks <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">introduced Instructed Retriever</a> in January as an alternative to traditional retrieval-augmented generation, which has proven limited. MongoDB and Teradata have added capabilities that refine <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">vector indexing and search</a> to make contextually relevant data easier to discover. And GoodData and Tableau have built <a href="https://www.techtarget.com/searchenterpriseai/tip/Exploring-the-context-layer-for-AI-systems">context layers</a> that similarly aim to make it easier to feed agents contextually relevant data.</p> <p>Like GoodData and Tableau that built on existing capabilities to aid AI development -- in their case semantic layers -- Informatica is building on existing master data management capabilities to provide users with a trusted data foundation for AI.</p> <p>Agentic Multidomain MDM includes a Data Steward Agent to automate the time-consuming work of resolving quality issues and matching records to ensure accuracy. Informatica Agentic Integration automatically <a href="https://www.techtarget.com/searchdatamanagement/opinion/Why-agentic-AI-demands-both-structured-and-unstructured-data">joins structured and unstructured data</a> and feeds it into appropriate AI pipelines, and Data Quality and Metadata Enrichment Agents automate governance.</p> <p>Meanwhile, headless data management, automated by <a href="https://www.techtarget.com/searchdatamanagement/news/366537106/Informatica-unveils-plan-to-infuse-Claire-with-generative-AI">Informatica's Claire AI engine</a>, provides the architecture for organizations to automate their AI workflows.</p> <p>Driven by a desire to improve efficiency through AI, demand for high-quality data is increasing exponentially, according to Gaurav Pathak, senior vice president of product management for Informatica. Developing Agentic Multidomain MDM and headless data management was a response to the increasing demand for trusted data to fuel applications that improve business efficiency, he continued.</p> <p>"We have always focused on increasing data management productivity, and both of these [increase productivity]," Pathak said. "With headless data management, we are making that productivity available across every surface. That's the main driver."</p> <p>Customer feedback showed that many AI initiatives are held back by systems that <a href="https://www.techtarget.com/searchdatamanagement/feature/Reconsider-the-AI-readiness-gap-in-data-and-analytics">aren't ready for AI</a>, he added, noting that the new features aim to help users overcome systemic barriers.</p> <p>"The idea is to work with data quality tools to make sure that bad data does not reach agents, that context is available, make sure that no private, sensitive data is available to agents," Pathak said.</p> </section> <section class="section main-article-chapter" data-menu-title="The user perspective"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The user perspective</h2> <p>One of the customers using Informatica as part of its AI pipeline is Yum! Brands, a multinational fast food corporation based in Louisville, Ky., that operates such brands as KFC, Pizza Hut and Taco Bell.</p> <p>Yum! has been an Informatica user for about a decade, and over that time its use of Informatica has evolved from extract, transform and load workloads to <a href="https://www.techtarget.com/searchdatamanagement/definition/Extract-Load-Transform-ELT">get data in and out of systems</a> to master data management and governance.</p> <p>Now, the company is using Informatica as the context layer for AI, according to Kartik Pillai, Yum!'s director of data strategy, master data management, AI and data governance.</p> <p>"For us, Informatica plays a role in providing that golden record," he said. "It's about a golden record with context for agents -- the governance, the policies, the lineage, the quality that provides the holistic 360-degree record for an agent to act on. That becomes a feeder for some of our more custom agents."</p> <p>One such agent is for forecasting labor needs at individual restaurants, a process that requires highly specific data, Pillai continued. Another is for financial reporting such as same-store sales growth.</p> <p>As Yum! has experimented with AI, Pillai noted one of the barriers to moving pilots into production has been the ideal nature of proof-of-concept projects versus the messy reality of production environments. Governance, therefore, <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-and-AI-governance-must-team-up-for-AI-to-succeed">has become critical</a> to ensure consistency so agents can be trusted.</p> <p>Agentic Multidomain MDM and headless data management, which Yum! has experimented with in recent weeks, could further enable the company to build production-ready agents, according to Pillai.</p> <p>"We have been conceptualizing with them, and I think definitely they will [help]," he said. "As far as getting things into production, it takes a bit of momentum to get it there."</p> </section> <section class="section main-article-chapter" data-menu-title="Additional capabilities and next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Additional capabilities and next steps</h2> <p>Beyond Agentic Multidomain MDM and headless data management for AI, Informatica unveiled the following new capabilities:</p> <ul type="disc" class="default-list"> <li>Data 360 Connector and Scanner, an integration with <a href="https://www.techtarget.com/searchcustomerexperience/news/366636353/Salesforce-adds-Informatica-to-Data-360-MuleSoft-fold">Salesforce Data 360</a> that enables the real-time, bi-directional flow of data between Salesforce Data 360 and any enterprise system.</li> <li>MDM Aware Data 360 to deliver trusted records to Salesforce Data 360 where users can activate data for real-time applications.</li> <li>Claire in Slack, providing agents for data quality, data discovery and governance -- among others -- in Slack conversations so users don't have to switch between environments to complete their work.</li> <li>Integrations with data platform and hyperscale cloud vendors simplify AI development, including availability of Informatica MCP servers in development environments such as <a href="https://www.theserverside.com/blog/Coffee-Talk-Java-News-Stories-and-Opinions/What-is-Amazon-Bedrock">Amazon Bedrock</a>, Databricks <a href="https://www.techtarget.com/searchbusinessanalytics/news/366625695/Latest-Databricks-tools-use-AI-to-simplify-AI-development">Agent Bricks</a>, Microsoft <a href="https://www.techtarget.com/searchenterpriseai/news/366616024/Microsoft-intros-Azure-AI-Foundry-for-building-AI-apps">Foundry</a> and Snowflake <a href="https://www.techtarget.com/searchdatamanagement/news/366625218/Snowflake-continues-to-add-AI-boost-Cortex-capabilities">Cortex AI</a>.</li> </ul> <p>The integrations with Salesforce provide evidence that Informatica and Salesforce are a complementary fit, according to McKnight. However, accelerating master data management with Agentic Multidomain MDM is the most valuable new feature, he continued.</p> <p>"As a long-time proponent of master data management, I would have to say that the accelerating of master data management with agents stands out for its efficiency gains in this important area," McKnight said.</p> <p>Looking ahead, Informatica's product development plans focus on making both agents and humans more productive and accurate, according to Pathak.</p> <p>Toward that end, initiatives include using <a href="https://www.techtarget.com/searchdatamanagement/tip/Metadata-management-standards-examples-that-guide-success">metadata</a> to make relevant data available to agents, providing users reusable data products that can aid AI development, and expanding <a href="https://www.techtarget.com/searchapparchitecture/tip/AI-Agents-role-in-IT-infrastructure-is-expanding">AI-assisted data management and stewardship</a>.</p> <p>"Our vision for AI to be governed 90% by AI and humans providing 10% by supervising and [implementing] governance and guardrails," Pathak said.</p> <p>McKnight, meanwhile, suggested that as more organizations put AI tools into production, Informatica could address emerging issues such as <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-navigate-data-sovereignty-for-AI-compliance">data sovereignty</a> and multi-system governance.</p> <p>"As they are about infrastructure and not a database or cloud company, they could do things like expanding on their recent framework for Microsoft Fabric Open Mirroring by building zero-copy governance bridges that span multiple competing cloud ecosystems simultaneously," he said. "They could launch local data sovereignty agents explicitly designed for localized infrastructure."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> Agents that continuously perform master data management tasks and a headless architecture for data management improve access to data that can be trusted to inform outputs. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a252657224.jpg https://www.techtarget.com/searchdatamanagement/news/366643437/Informatica-update-aims-to-provide-trust-foundation-for-AI Wed, 20 May 2026 09:00:00 GMT <p>Alteryx on Wednesday introduced new features that unite data with trusted business rules and workflows to aid customers building agents and other AI applications.</p> <p>Unveiled during the vendor's Inspire user conference in Orlando, Fla., Agent Studio and the Alteryx One MCP Server simplify converting data workflows into <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-build-your-first-agentic-AI-system">agentic AI systems</a>, enabling business analysts to use their expertise to build AI tools rather than rely on centralized IT teams.</p> <p>Launched <a href="https://www.techtarget.com/searchdatamanagement/news/366623973/Alteryx-One-launch-aims-to-unify-simplify-vendors-platform">in May 2025</a>, Alteryx One is Alteryx's platform for data management and insight generation, unifying previously disparate capabilities such as analytics automation and no-code data preparation.</p> <p>Agent Studio is a new feature within the Alteryx One platform that allows users to easily transform trusted datasets and <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a> -- rules, workflows and analysis -- into autonomous agents that can be deployed in Alteryx or fed into the agent orchestration frameworks now provided by third-party vendors. MCP Server is Alteryx's version of a <a target="_blank" href="https://modelcontextprotocol.io/docs/getting-started/intro" rel="noopener">Model Context Protocol</a> server to extend agents beyond Alteryx One into applications such as Slack and Microsoft Teams and external AI models so the agents can securely access information beyond Alteryx's environment.</p> <p>In addition, Alteryx introduced new workflow deployment options, governance capabilities, and an Alteryx One desktop application to unify Alteryx tools such as Designer and AI Tooling for desktop users.</p> <p>With many enterprises making AI development a priority, and others opting for the security and cost-control of on-premises workflows over the cloud, the new features are significant because they address the varying needs of Alteryx customers, according to David Menninger, an analyst at ISG Software Research.</p> <p>"These new features provide an agentic AI framework for Alteryx's users, which is important given the focus on AI in today’s market," he said. "In addition, there is a revival of interest in on-premises capabilities both for governance reasons and to address cost concerns. Several of these features address those concerns."</p> <p>Based in Irvine, Calif., Alteryx is a longtime data management provider that enables customers to integrate and prepare data for analytics and AI initiatives. After a clumsy transition to the cloud and slow revenue growth, <a href="https://www.techtarget.com/searchbusinessanalytics/news/366563665/Alteryx-to-be-acquired-by-private-equity-firms-for-44-billion">the vendor was acquired</a> by a private equity firm and taken private so it could reorganize out of the spotlight of the public markets.</p> <p>Competitors include <a href="https://www.techtarget.com/searchdatamanagement/news/366633382/Informatica-launches-agents-adds-new-AI-development-tools">Informatica</a> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366641671/Latest-Qlik-tools-target-helping-users-achieve-AI-goals">Qlik</a>, among others.</p> <section class="section main-article-chapter" data-menu-title="Fueling AI"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Fueling AI</h2> <p>As enterprises <a target="_blank" href="https://kpmg.com/us/en/media/news/q1-ai-pulse2026.html" rel="noopener">increase their investments</a> in AI development but <a target="_blank" href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf" rel="noopener">frequently struggle</a> to move AI initiatives past experimentation and into production, feeding agents and other AI tools the high-quality, relevant data they require to properly perform has been a common hurdle.</p> <p>In response, data management and analytics vendors such as <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">Databricks</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366637414/MongoDB-launches-latest-Voyage-models-to-aid-AI-development">MongoDB</a> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366642778/Tableau-repositions-for-AI-unveils-new-knowledge-layer">Tableau</a> have prioritized providing tools that help customers discover and deliver contextually appropriate data to AI tools.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> These new features provide an agentic AI framework for Alteryx's users, which is important given the focus on AI in today’s market. In addition, there is a revival of interest in on-premises capabilities both for governance reasons and to address cost concerns. Several of these features address those concerns. </figure> <figcaption> <strong>David Menninger</strong>Analyst, ISG Software Research </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>With Agent Studio and its MCP Server, Alteryx is similarly adding capabilities designed to help customers deliver trusted, relevant data to AI-powered systems in a move motivated by a combination of customer feedback and first-hand experience building agents, according to Alteryx CEO Andy MacMillan.</p> <p>"A lot of the AI capabilities, the idea that we want to have visible, trusted, auditable data in agents, has come from first-hand experience … being business analysts trying to bring data to AI," he said.</p> <p>However, by making Agent Studio and MCP Server part of Alteryx One -- a low-code/no-code platform for data management and insight generation -- Alteryx is taking a different approach to AI development than many other data management and analytics vendors. Instead of creating a development environment for centralized IT teams, it is <a href="https://www.techtarget.com/searchenterpriseai/tip/Citizen-developers-are-redefining-enterprise-AI-development">empowering business users</a> to build agentic AI tools.</p> <p>Beyond the first-hand experience MacMillan cited, an Alteryx survey of more than 1,400 business leaders showed that 11% of respondents expect responsibility for AI workflows to move to line-of-business domains over the next three years.</p> <p>"Agent Studio, MCP Server and a lot of the things we're talking about are designed around how to make AI trusted, and how to make it trusted is by empowering Alteryx users -- the business analyst -- to be the one to connect enterprise data, business logic and governance in a way that the business can depend on," McMillan said.</p> <p>Donald Farmer, founder and principal of TreeHive Strategy, noted that Alteryx is taking a novel approach by empowering business users to build <a href="https://www.techtarget.com/searchcustomerexperience/feature/The-front-office-is-being-rebuilt-around-AI-workflows">AI workflows</a>. However, while Alteryx is now providing the AI development capabilities it needs to remain viable, its approach is questionable, he continued.&nbsp;</p> <p>"The work on an MCP server is necessary," he said. "Alteryx needs this capability to remain credible. Its historical differentiation has been business logic captured in the workflow. Exposing that through MCP is a coherent move. Whether enterprises want to route their [large language model] traffic through Alteryx workflows rather than building pipelines elsewhere is an open question."</p> <p>In addition, basing a strategy on 11% of organizations expecting to decentralize agent development management could prove dubious, according to Farmer.</p> <p>"That's not a transformative number," he said. "In fact, it is well within the margin of simple organizational drift."</p> <p>Menninger, however, countered that Alteryx has built a differentiated business over the years that the empowerment of business users builds upon. Alteryx's main focus has been data preparation. In addition, however, it provides analytics operations capabilities that ensure consistency and governance within analytics workflows.</p> <p>"These new features bring Alteryx's unique capabilities to the world of agentic AI," Menninger said.</p> <p>Specifically, they enable users to integrate <a href="https://www.techtarget.com/searchdatamanagement/definition/deterministic-probabilistic-data">deterministic</a> Alteryx workflows established over time with probabilistic agent-based processes, he continued.</p> <p>"By providing an agentic framework, Alteryx customers can more easily bring these two types of processes together," Menninger said.</p> <p>Beyond Agent Studio and the Alteryx One MCP Server, new Alteryx capabilities include the following:</p> <ul type="disc" class="default-list"> <li>An Alteryx One desktop app for users that prefer a desktop environment to the web.</li> <li>New deployment options including Workspace Execution so users can run workflows in the cloud, Data Bridge to enable cloud-based workflows to securely connect with on-premises and private network data without moving it into the cloud, and Server Execution so analysts can view and manage server-based workflows from the cloud while running them on premises.</li> <li>Live Query and new connectors that allow users to work with data where it lives rather than moving it into Alteryx.</li> <li>Data Labels and asset certification that show where data comes from, who within an enterprise <a href="https://www.techtarget.com/searchdatamanagement/tip/The-data-ownership-blind-spots-putting-organizations-at-risk">is responsible for it,</a> and how it is being used to inform data and AI initiatives.</li> </ul> <p>Data Bridge and Server Execution -- which is not yet generally available -- are valuable additions, according to Farmer. However, he noted that by launching capabilities that enable workflow orchestration from the cloud before making Server Execution GA, Alteryx, while putting in time-consuming product development work, appears to be prioritizing its cloud business over its historical base of <a href="https://www.computerweekly.com/feature/Why-run-AI-on-premise">on-premises users</a>.</p> <p>"Cloud-managed orchestration of on-premises workflows is exactly what large customers have been asking for, [but] shipping the cloud-native execution path first suggests the cloud business is being prioritized over the existing customer footprint," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>As Alteryx plans future product development, the shift from <a href="https://www.techtarget.com/searchenterpriseai/tip/Compare-top-AI-coding-tools">agentic coding</a> to agentic building and further removing AI development from centralized teams are focal points, according to MacMillan.</p> <p>Agentic AI tools such as Claude and ChatGPT <a href="https://www.techtarget.com/searchenterpriseai/feature/35-AI-content-generators-to-explore-in-2026">can write code</a> that helps Alteryx customers build agents. However, not all business users are experts in coding. The next step, therefore, is to enable large language models to not just write code, but build Alteryx workflows.</p> <p>"I think that is coming, agentic building for non-coders into environments that make sense for them that they trust," MacMillan said. "That's a really big one for us."</p> <p>Menninger, meanwhile, noted that enterprises struggle to integrate deterministic and <a target="_blank" href="https://www.bosch.com/research/bcai/probabilistic-modeling/" rel="noopener">probabilistic processes</a>. Therefore, adding more capabilities that enable customers to combine the two would benefit existing users and perhaps appeal to potential new ones.</p> <p>"Alteryx can help play a role in bringing these two worlds together by continuing to extend its agent-to-agent capabilities and supporting a mixture of those two types of activities," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> Capabilities include an MCP server and a tool that helps transform trusted data and logic into agents, with potential differentiation lying in the empowerment of business users. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a279596285.jpg https://www.techtarget.com/searchdatamanagement/news/366643336/Latest-Alteryx-features-aim-to-boost-AI-powered-automation Wed, 20 May 2026 09:00:00 GMT <p>Streaming data specialist Confluent on Tuesday introduced new features for Confluent Cloud and Confluent Intelligence aimed at better enabling customers to build and secure AI applications fueled by real-time information.</p> <p>Revealed at the vendor's user conference in London, they include a fully managed <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol (MCP) Server</a> that acts as a control center for developers and agents to build and manage streaming operations for AI using natural language, and built-in machine learning capabilities that detect and redact <a href="https://www.techtarget.com/searchsecurity/definition/personally-identifiable-information-PII">personally identifiable information</a> (PII) in Confluent's <a href="https://www.techtarget.com/searchdatamanagement/definition/Apache-Flink">Apache Flink</a> streaming engine.</p> <p>In addition, new Confluent capabilities include support for the open source <a target="_blank" href="https://agentskills.io/home" rel="noopener">Agent Skills</a> framework to add best practices for AI-powered operations, support for new large language models, and support for vector search on Amazon DynamoDB.</p> <p>Two of the biggest barriers enterprises face when trying to move AI projects into production are security related to sensitive data such as PII and the operational complexity of managing <a href="https://www.techtarget.com/searchdatamanagement/opinion/Real-time-data-streaming-for-AI-invest-where-it-matters">streaming data infrastructures</a>, according to Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget.</p> <p>Given that Confluent's new capabilities focus on those barriers to successful AI development, they are significant additions.</p> <p>"They directly address the two biggest barriers preventing AI projects from reaching production," Catanzano said. "By embedding automated PII redaction and private connectivity alongside natural language operations, Confluent is essentially removing the friction that causes eight in ten companies to struggle with scaling AI."</p> <p>Founded in 2014 to commercialize the open source <a href="https://kafka.apache.org/">Apache Kafka</a> streaming platform and based in Mountain View, Calif., Confluent was recently <a href="https://www.techtarget.com/searchdatamanagement/news/366636098/IBM-acquiring-Confluent-to-boost-AI-development-capabilities">acquired by tech giant IBM</a> to add streaming data capabilities to its AI development platform.</p> <section class="section main-article-chapter" data-menu-title="Streamlining streaming for AI"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Streamlining streaming for AI</h2> <p>Enterprises continue <a href="https://kpmg.com/us/en/media/news/q1-ai-pulse2026.html">to invest in AI development,</a> but also continue to <a href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf">struggle to build AI tools</a> that can be trusted enough to move into production.</p> <p>In response, many data management and analytics vendors -- including Databricks, MongoDB and Tableau, among others -- have recently introduced tools aimed at improving AI pipelines and the data they feed AI applications so that outputs are more accurate and the tools can be trusted in production.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> These capabilities are a nice step forward for developers as they build and govern agentic applications. While they don't differentiate Confluent in the market, they do help it stay competitive. </figure> <figcaption> <strong>Kevin Petrie</strong>Analyst, BARC U.S. </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Now Confluent is similarly adding new capabilities aimed at refining AI development with its strategy shaped by customer feedback, according to Sean Falconer, the vendor's head of AI.</p> <p>"A big part of it came directly from customers," he said, noting that the biggest challenges customers face when building AI applications no longer relate to AI models, but instead relate to the accessibility and relevancy of the data informing AI applications. "We saw growing demand from teams trying to operationalize AI, especially around making real-time data easier to work with and easier to secure."</p> <p>In particular, developing the <a href="https://www.computerweekly.com/feature/Gartner-How-AI-will-transform-managed-network-services">fully managed</a> MCP server and adding support for Agent Skills were motivated by interactions with users, Falconer continued.</p> <p>"We saw strong adoption of our open source MCP server," he said. "Customers were already using it to manage and troubleshoot streaming infrastructure through AI tools, so the next logical step was giving them a fully managed experience that's easier to use in production."</p> <p>Specific new capabilities in Confluent Intelligence and Confluent Cloud include the following:</p> <ul type="disc" class="default-list"> <li>The fully managed MCP server and support for Agent Skills to manage streaming data for real-time AI tools.</li> <li>Automated PII detection and redaction in Flink SQL.</li> <li>Secure connectivity to Microsoft Azure-hosted services with support for Azure Private Link.</li> <li>An open source adapter that integrates Flink SQL on Confluent Cloud with <a href="https://www.techtarget.com/searchdatamanagement/news/366632699/Fivetran-DBT-Labs-merge-to-add-complementary-capabilities">DBT Labs</a> so data engineers can easily build and manage streaming data pipelines using a familiar framework.</li> <li>Support for new AI models from Anthropic and Fireworks AI to build real-time AI applications.</li> <li>Support for vector search on Amazon DynamoDB to expand Confluent's ecosystem.</li> </ul> <p>Collectively, Confluent's new capabilities, while they don't substantially differentiate Confluent from competing vendors providing AI development capabilities, keep Confluent competitive, according to Kevin Petrie, an analyst at BARC U.S.</p> <p>"I do believe these capabilities are a nice step forward for developers as they build and govern agentic applications," he said. "While they don't differentiate Confluent in the market, they do help it stay competitive."</p> <p>Perhaps the most critical of the new capabilities is automated PII detection and redaction, Petrie continued, noting that his firm's research shows that AI adopters prioritize data privacy above all other aspects of a <a href="https://www.techtarget.com/searchdatamanagement/feature/How-executives-can-build-a-responsible-AI-framework">responsible AI framework</a>.</p> <p>"Confluent's automated redaction of PII in Flink helps enforce privacy policies and satisfy regulatory requirements such as GDPR or CCPA while maintaining the real-time service levels that AI often needs," he said.</p> <p>In addition, Petrie noted that support for Agent Skills -- which was originally developed by Anthropic and <a target="_blank" href="https://www.bishoylabib.com/posts/claude-skills-comprehensive-guide" rel="noopener">made open source in December 2025</a> -- could give Confluent a temporary advantage.</p> <p>"Confluent has some early-mover advantage with its support of Agent Skills, which are fast becoming a must-have open format for providing AI applications with the context they need to deliver value," he said.</p> <p>Like Petrie, Catanzano called out the value of automated PII detection and redaction.</p> <p>"It solves the fundamental blocker that security teams face when deciding whether to allow data into AI pipelines," he said. "This single capability can unlock entire use cases in regulated industries like healthcare and financial services that were previously off-limits."</p> <p>Collectively, the new features are logically constructed to help customers more effectively build and secure real-time AI tools, Catanzano continued. However, <a href="https://www.computerweekly.com/feature/Why-AI-is-forcing-enterprises-to-rethink-observability">model monitoring</a> and <a href="https://www.techtarget.com/searchenterpriseai/tip/What-are-the-benefits-of-an-MLOps-framework">MLOps capabilities</a> are not included and could help customers as they continue to invest in AI development.</p> <p>"They've focused heavily on the data layer and security controls, but they haven't addressed model monitoring, drift detection, or other MLOps concerns that also plague production AI systems," Catanzano said. "[However], that may be intentional given their focus on being the streaming foundation rather than a complete AI platform."</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>As Confluent plans future product development, continuing to add and enhance features that help enterprises move AI initiatives into production at scale remains a focus, according to Falconer.</p> <p>"You'll continue to see us invest in areas like MCP, Agent Skills, agents and real-time context delivery so developers can more easily build AI applications and agents that stay connected to what's happening in the business right now," he said. "A lot of the industry is realizing that AI is only as useful as the quality and freshness of the context behind it."</p> <p>Security and <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-and-AI-governance-must-team-up-for-AI-to-succeed">governance</a> are also priorities, Falconer continued.</p> <p>"Enterprises want to move faster with AI, but they also need confidence that sensitive data is protected and that these systems operate within the right controls and policies, so a big part of our focus is making secure, governed real-time data access a built-in part of the platform," he said.</p> <p>Catanzano noted that from a competitive standpoint, Confluent provides a broader combination of streaming, governance and AI-native features than Kafka and some <a href="https://www.techtarget.com/searchdatamanagement/news/366640882/Redpanda-launches-streaming-engine-optimized-for-AI">other competing platforms</a>. To continue distinguishing itself from its peers, Catanzano suggested that Confluent add prebuilt capabilities such as industry-specific templates to further streamline real-time AI application development.</p> <p>"They could differentiate further by creating industry-specific templates and prebuilt streaming pipelines for common AI use cases -- fraud detection, personalization, predictive maintenance -- that combine their governance, connectivity and agent capabilities into turnkey solutions that reduce time-to-value for new customers in regulated industries," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> A fully managed MCP server and machine learning-powered data privacy capabilities aid customers attempting to move real-time AI applications into production. https://cdn.ttgtmedia.com/rms/onlineimages/cloud_g1223481405.jpg https://www.techtarget.com/searchdatamanagement/news/366643312/Latest-from-Confluent-streamlines-use-of-streaming-for-AI Tue, 19 May 2026 05:00:00 GMT <p>Alation on Monday unveiled AI Governance, a new suite purpose-built to help enterprises remain regulatory compliant as they move agents and other AI tools into production.</p> <p>After experimenting with AI over the past few years, many organizations have refined AI applications to the point that they can be trusted to deliver accurate outputs. However, as new <a href="https://www.techtarget.com/searchenterpriseai/tip/Global-AI-legislation-and-regulation-tracker">AI regulations emerge</a> and others change, compliance is a challenge.</p> <p>Alation, a metadata management specialist that <a href="https://www.techtarget.com/searchdatamanagement/news/366613016/Alation-launches-AI-governance-suite-to-meet-rising-need">already provides certain AI governance capabilities</a>, is now adding AI Governance to directly address compliance by providing customers with a system of record to demonstrate their adherence to government oversight.</p> <p>Components of the suite include a registry to inventory an organization's AI models and applications, cards that display vital information about each model -- including applicable regulatory requirements with all fields citing their sources -- and a dashboard for executives to monitor compliance.</p> <p>Given that AI Governance addresses <a href="https://www.computerweekly.com/feature/AI-and-compliance-Staying-on-the-right-side-of-law-and-regulation">a growing problem</a> for many enterprises, it is a valuable addition for Alation users, according to Stewart Bond, an analyst at IDC.</p> <p>"It is significant because it addresses a genuine and growing pain point -- the manual, fragmented process AI and data leaders in organizations face when trying to demonstrate AI compliance to boards and regulators," he said.</p> <p>With the responsibility for an organization's AI governance increasingly being assigned to data leaders, which is the target audience for Alation's <a href="https://www.techtarget.com/searchdatamanagement/feature/16-top-data-catalog-software-tools-to-consider-using">data catalog</a> and other data intelligence capabilities, the new suite is a logical addition for the vendor, Bond continued.</p> <p>"We have seen a shift of AI governance responsibility toward organizational data leaders, which also plays directly to Alation's existing strengths in data intelligence, making this a natural and well-timed extension of its core platform," he said.</p> <section class="section main-article-chapter" data-menu-title="Keeping compliant"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Keeping compliant</h2> <p>With successful AI development comes new problems.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> It is significant because it addresses a genuine and growing pain point -- the manual, fragmented process AI and data leaders in organizations face when trying to demonstrate AI compliance to boards and regulators. </figure> <figcaption> <strong>Stewart Bond</strong>Analyst, IDC </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>OpenAI's November 2022 launch of ChatGPT sparked <a target="_blank" href="https://kpmg.com/us/en/media/news/q1-ai-pulse2026.html" rel="noopener">surging enterprise interest</a> in AI development. In response, data management and analytics vendors whose platforms oversee the data that feeds AI tools the intelligence they need to perform have built environments to simplify AI development.</p> <p>Despite the investments many organizations have made in AI initiatives and the development tools provided by vendors, most AI projects <a target="_blank" href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf" rel="noopener">continue to fail</a> before reaching production. The reasons vary, but poor data quality and disorganized data estates that make it difficult to discover and retrieve relevant data are among them.</p> <p>Now, with many enterprises placing greater emphasis on data quality and better organizing their data, and with many vendors adding tools that improve data retrieval, some are <a target="_blank" href="https://www.gallup.com/workplace/691643/work-nearly-doubled-two-years.aspx" rel="noopener">successfully deploying agents</a> and other AI tools.</p> <p>Suddenly, the new challenge for them is staying on top of evolving AI regulations and not running afoul of any of them.</p> <p>GT Volpe, Alation's head of product management, noted that as Alation customers began to scale their AI initiatives, they had pieces of AI governance in place, but they were inconsistent and applied piecemeal through emails and spreadsheets. The new suite is designed to provide <a href="https://www.techtarget.com/searchapparchitecture/tip/Privacy-compliance-and-governance-are-changing-development">a consistent compliance layer</a>.</p> <p>"Alation already governs the data AI depends on -- quality, lineage, and policies," he said. "Extending that foundation to govern AI itself was a natural move. The substrate was already there. What was missing was the AI-specific layer."</p> <p>Alation's compliance-specific AI Governance suite includes the following features:</p> <ul type="disc" class="default-list"> <li>AI Asset Registry to provide a comprehensive, searchable inventory of an organization's models, agents and other AI tools.</li> <li>Model Cards generated from the <a href="https://www.techtarget.com/whatis/definition/metadata">metadata</a> of AI assets that demonstrate -- citing sources -- whether there is documented proof that the AI tools meet regulatory requirements or whether more human verification is needed.</li> <li>Agent-powered governance workflows that route high-risk AI assets to appropriate departments for remediation.</li> <li>A registry of regulations that provides built-in support for statutes such as the EU AI Act, <a href="https://www.techtarget.com/searchdatabackup/feature/AI-and-GDPR-How-is-AI-being-regulated">GDPR</a>, NIST AI RMF, and ISO 42001 to guide teams as they address compliance.</li> <li>A dashboard for chief data officers, chief information officers, chief revenue officers and chief compliance officers that displays the state of their organization's compliance.</li> </ul> <p>Governance -- or lack thereof -- is often a hindrance as enterprises scale AI initiatives, according to Michael Ni, an analyst at Constellation Research. As a result, Alation's AI Governance is significant for the vendor's users.</p> <p>"AI Governance is quickly becoming an operational bottleneck, not a documentation exercise," he said. "Enterprises are racing to deploy AI agents, but almost none can answer a regulator's simplest question: 'Show me every AI system you have, what data it uses, who approved it, and whether it complies.' Alation sees that governance gap becoming the next enterprise AI crisis."</p> </section> <section class="section main-article-chapter" data-menu-title="Competitive standing"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Competitive standing</h2> <p>Like Alation, data intelligence providers such as <a href="https://www.techtarget.com/searchdatamanagement/news/366580432/Collibra-launches-AI-Governance-unveils-GenAI-capabilities">Collibra</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366628006/Informatica-adds-MCP-support-spate-of-AI-fueled-features">Informatica</a> are among the many vendors now offering AI governance capabilities. In that respect, Alation's AI Governance is not differentiated, according to Bond. However, its agent-powered workflows could distinguish it to some degree.</p> <p>"Alation is not first to market, and the announcement carries some catch-up character," he said. "That said, the agentic workflow routing driven by regulation applicability is a meaningful differentiator, moving beyond static cataloging toward dynamic, automated governance."</p> <p>Bond added that Alation's five-component architecture for ensuring compliance with AI regulations, covering the full lifecycle from asset registration through workflow routing to executive oversight, is coherent. However, a risk-scoring engine that monitors <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-identify-and-manage-AI-model-drift">model drift</a> would strengthen AI Governance, he continued.</p> <p>"Alation already provides an Open Data Quality Framework and references model monitoring more broadly across its platform, so the gap may be more about surfacing those capabilities explicitly within the new AI Governance offering than building them from scratch," Bond said.</p> <p>Ni similarly noted that other vendors address compliance with their AI governance capabilities. However, being first to market is not what's important from a competitive standpoint, he continued. Instead, it's providing a trusted system for answering board and regulator demands.</p> <p>"Alation's announced capabilities are not purely catch-up, but also not a standalone category-defining leap," Ni said. "What Alation brings and is their opportunity is connecting AI governance to metadata, lineage, business context and evidence management in a way that data teams already use to govern data."</p> <p>The composition of AI Governance is a significant improvement over patchwork AI governance frameworks enterprises put in place on their own, Ni added. However, it could be improved by including capabilities that govern agent behavior beyond documentation.</p> <p>"What's still missing is the runtime layer," Ni said. "Alation's announcement is strong, [but] governance also has to address … governing AI behavior while those systems are operating. As AI-driven automation and agents execute more tasks, enterprises need to shift from documentation to runtime controls, governance and accountability."</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>Alation's product development plans are focused on delivering governance to the entire AI workflow, according to Volpe.</p> <p>That includes governing data, the capabilities that contextualize data, such as semantic models, and -- as Ni suggested -- agents and other AI tools in production.</p> <p>"The connective tissue between all three layers is feedback loops, human-in-the-loop checkpoints and measurement so that every decision logged and every outcome measured compounds accuracy and governance over time," Volpe said.</p> <p>Logging and measuring AI outcomes would be wise, according to Bond, who suggested that Alation could improve AI Governance by adding capabilities that <a href="https://www.computerweekly.com/opinion/Better-governance-is-required-for-AI-agents'">audit agents</a> in action rather than stopping at how agents were approved.</p> <p>"Extending the platform to cover agentic AI behavior … would address a fast-emerging governance gap that few vendors have solved," he said. "That capability would also be a credible differentiator that could attract net-new customers in sectors like financial services and healthcare, where agent accountability is becoming a hard requirement."</p> <p>Ni, meanwhile, advised Alation to build a <a href="https://www.techtarget.com/searchenterpriseai/post/How-CIOs-should-architect-trust-in-AI-not-just-govern-it">trust layer for AI</a>.</p> <p>"A trust layer goes beyond documenting AI systems to continuously validate whether they are using trusted context, following policy, and producing explainable outcomes," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> As enterprises launch agents and other cutting-edge applications into production, remaining compliant with evolving regulations is challenging. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1127196618.jpg https://www.techtarget.com/searchdatamanagement/news/366642977/Alation-intros-AI-governance-suite-to-ensure-compliance Mon, 11 May 2026 12:02:00 GMT <p>Rapid innovation in <a href="https://www.techtarget.com/searchenterpriseai/Ultimate-guide-to-artificial-intelligence-in-the-enterprise">AI</a> has fueled debate among industry experts about the existential threat posed by so-called intelligent machines that can perform tasks previously done by humans.</p> <p>Artificial general intelligence (<a href="https://www.techtarget.com/searchenterpriseai/definition/artificial-general-intelligence-AGI">AGI</a>) refers to machines that are able to think and experience the world like a human. Doomsayers argue that AGI will be upon us sooner than expected and have the capability to outwit people. Shorter term, they warn our overreliance on AI systems could spell disaster: Disinformation will flood the internet, terrorists will craft dangerous and cheap weapons, superintelligent AI models will lead to mass unemployment, automated AI could start a nuclear war and killer drones could run rampant.</p> <p>Even Geoffrey Hinton, who's referred to as the "godfather of AI" for his seminal work on neural networks, has expressed growing concerns over AI's threat to humanity, He <a href="https://www.techtarget.com/searchenterpriseai/news/366536381/Examining-AI-pioneer-Geoffrey-Hintons-fears-about-AI">issued a warning in 2023</a> about the rapidly advancing abilities of <a href="https://www.techtarget.com/searchenterpriseai/definition/generative-AI">generative AI</a> chatbots, like ChatGPT. "Right now, they're not more intelligent than us, as far as I can tell," he told the BBC. "But I think they soon may be." Hinton predicted it would take 5 to 10 years, rather than his previous timeline of 30 to 50 years.</p> <p>Rising concerns about AI's existential risks have led to calls for moratoriums on AI R&amp;D -- an <a href="https://www.techtarget.com/searchenterpriseai/news/365534127/The-call-for-an-AI-pause-points-to-a-major-concern">AI pause</a> -- from industry and academic experts, including executives at many companies fueling AI innovation. Yet even staunch American AI doomerists like Sam Altman and Elon Musk have accelerated their AGI efforts.</p> <div class="imagecaption alignRight"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/ottenheimer_davi.jpg" alt="Davi Ottenheimer">Davi Ottenheimer </div> <div class="imagecaption alignRight"> \ </div> <p>Others argue that this AI doom<i> </i>narrative distracts from more likely AI dangers enterprises need to heed: AI bias, inequity, inequality, hallucinations, new failure modes, privacy risks and security breaches. A big concern among people in this group is that a pause might create a protective moat for major AI companies, like <a href="https://www.techtarget.com/searchenterpriseai/definition/OpenAI">OpenAI</a>, maker of ChatGPT and an AI pause<i> </i>advocate.</p> <p>"Releasing ChatGPT to the public while calling it dangerous seems little more than a cynical ploy by those planning to capitalize on fears without solving them," said Davi Ottenheimer, vice president of trust and digital ethics at Inrupt, a secure data-sharing platform provider. A bigger risk might lie in enabling AI doomsayers to profit by abusing our trust, he said.</p> <p>The existential threat discourse "turned out to be a new form of mysticism, to rope in true believers who wouldn't question the premise," Ottenheimer argued. Every major AI company gave what he called "saccharin safety pledges," then abandoned them. For enterprise leaders evaluating vendors, Ottenheimer offered a diagnostic: "The tells are architectural. When a vendor says 'safety,' ask where the controls are enforced." If they can't show evidence within a trend line, the commitment is decorative.</p> <section class="section main-article-chapter" data-menu-title="Responsible AI or virtue signaling?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Responsible AI or virtue signaling?</h2> <p>Signed letters calling for an AI pause get a lot of media play, but they beg the question of what happens next.</p> <p>The late Abhishek Gupta, who founded the Montreal AI Ethics Institute, was one of many prominent AI ethicists who viewed the calls for an AI pause ineffective, if not disingenuous. "I find it difficult to sign letters that primarily serve as virtue signaling without any tangible action or the necessary clarity to back them up," Gupta said in an interview in 2023. Such letters are often counterproductive as they consume attention cycles without leading to any real change, he noted.</p> <p>Media-fueled doomsday narratives consume valuable time and resources and, in the process, they confuse the discourse and potentially put a lid on the levelheaded conversation required to make sound policy decisions, according to Gupta and like-minded ethicists. People seeking to manage AI risks are better off educating themselves on the actual risks instead of focusing on muddled musings about existential threats. They need to collaborate with technical experts who have practical experience in developing production-grade AI and machine learning systems, as well as with academic professionals who work on the theoretical foundations of AI.</p> <p>Building consensus on what needs to be tackled to ensure <a href="https://www.techtarget.com/searchenterpriseai/definition/responsible-AI">responsible AI</a> programs shouldn't be that hard.</p> </section> <section class="section main-article-chapter" data-menu-title="What are realistic AI risks?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>What are realistic AI risks?</h2> <div class="imagecaption alignRight"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/green_brian_patrick.jpeg" alt="Brian Green">Brian Green </div> <p>If AI doomerism isn't likely to prove useful in controlling AI risks, how should enterprises be thinking about the problem?</p> <p>It's helpful to frame AI risks as those that come from AI itself and risks that come from the use of AI by humans, said Brian Green, director of technology ethics at the Markkula Center for Applied Ethics at Santa Clara University.</p> <p>Risks from the AI technology itself range from simple errors in computation that lead to bad outcomes, to the existential threat of AI gaining a will of its own and deciding to attack humankind, Green said. At present, there's no clear way for the latter to happen, he said. Green is the co-author of <i>Ethics in the Age of Disruptive Technologies: An Operational Roadmap, </i>a <a href="https://mailchi.mp/scu/itec-handbook">handbook</a><i> </i>that lays out what he considers practical steps businesses can take to make ethical decisions</p> <p>Risks stemming from the human use of AI cover every action people can imagine. They could be automated and made more efficiently evil with AI, such as more centralized nuclear weapons with hair-trigger controls, <a href="https://www.techtarget.com/searchenterpriseai/feature/The-implications-of-generative-AI-for-trust-and-safety">more powerful disinformation campaigns</a>, <a href="https://www.computerweekly.com/news/366543333/AI-can-never-be-given-control-over-combat-decisions-Lords-told">deadlier biological weapons</a> and more effective planning for social control.</p> <p>"Everything horrible that human intelligence can do, artificial intelligence might be programmed to do as well as or better than humans," Green said. "There are vastly more chances that humans might use AI for existentially risky purposes than there are chances that AI would just pursue these goals on its own."</p> <p>Green said he believes the realistic AI existential risks we face are more mundane: <a href="https://www.techtarget.com/searchenterpriseai/feature/How-to-detect-AI-generated-content">AI-generated content</a> trained to catch our attention that inadvertently blinds us to important issues, or AI-based marketing apps trained to lure us into buying products and services detrimental to our well-being.</p> <p>"I would argue that both of these things are already happening, so <i>this</i> possible existential AI risk is already upon us and is, therefore, 100% real," Green said.</p> <p>Nell Watson, an AI ethics researcher and author, identified a risk that most frameworks miss: AI-automated psychological decomposition at scale. AI could use tactics similar to the <i>Zersetzung</i> approach of East Germany's Stasi secret police, she said, in which gaslighting, reputation destruction and social isolation were used to break individuals covertly.</p> <p><a href="https://www.techtarget.com/searchenterpriseai/definition/agentic-AI">Agentic AI</a> makes this scalable and personalized in ways that were previously impossible, Watson added. The infrastructure is already built; recommender systems optimized for engagement can profile individual vulnerabilities and craft personalized content to exploit them, she explained. This approach becomes effective when the optimization target shifts from attention to influence. A new field of psychosecurity is needed, according to Watson, dedicated to protecting cognitive integrity, in the same way cybersecurity protects digital infrastructure.</p> <p>It's important to keep on top of known problems, such as AI bias and misalignment with organizational objectives, Green said: "Immediate problems that are ignored can turn into big problems later, and conversely, it's easier to solve big problems later if you first get some practice solving problems now."</p> </section> <section class="section main-article-chapter" data-menu-title="Could AI stir up social unrest by displacing workers?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Could AI stir up social unrest by displacing workers?</h2> <div class="imagecaption alignRight"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/pery_andrew.JPG" alt="Andrew Pery">Andrew Pery </div> <p>How AI technology is changing the nature of work is an issue companies should focus on now, said Andrew Pery, AI ethics evangelist at Abbyy, an intelligent automation company. "With the commercialization of generative AI, the magnitude of labor disruption could be unprecedented," he said. The International Monetary Fund <a target="_blank" href="https://www.imf.org/en/blogs/articles/2024/01/14/ai-will-transform-the-global-economy-lets-make-sure-it-benefits-humanity" rel="noopener">predicted</a> that nearly 40% of global employment is exposed to AI.</p> <p>"Such a dramatic displacement of labor is a recipe for growing social tensions by shifting millions of people to the margins of society with unsustainable unemployment levels and without the dignity of work that gives us meaning," Pery said. Labor displacement could give rise to more nefarious and dangerous uses of GenAI technology that subvert the foundations of a rule-based order, he added.</p> <p>Fostering digital upskilling for new jobs and rethinking social safety-net programs will play a pivotal role in safely transitioning into an age of AI, Pery said. However, he added that there's an even deeper structural concern: GenAI is replacing apprenticeships, eliminating the roles through which people learn how to become experts. The 2026 Anthropic labor market study <a target="_blank" href="https://www.anthropic.com/research/labor-market-impacts" rel="noopener">found evidence</a> that hiring of younger workers has slowed in highly exposed occupations, even though aggregate unemployment hasn't yet significantly increased.</p> <p>Reskilling, Pery argued, has become the moral placebo of the AI age: It sounds responsible but often means little. Workers need wage insurance, transition stipends, portable benefits and mechanisms for sharing automation dividends. California's SB 53: Transparency in Frontier Artificial Intelligence Act and New York's Responsible AI and Safety Education Act impose obligations on frontier developers to publish safety frameworks and disclose catastrophic risk assessments, Pery said. They function as a de facto national benchmark in the absence of comprehensive federal legislation.</p> <div class="imagecaption alignRight"></div> </section> <section class="section main-article-chapter" data-menu-title="How enterprises can manage AI risks"> <h2 class="section-title"><i class="icon" data-icon="1"></i>How enterprises can manage AI risks</h2> <p>A key component of responsible AI is identifying and mitigating risks arising from AI systems. These risks can manifest in various forms, including data privacy breaches, biased outputs, AI hallucinations, deliberate attacks on AI systems, and concentration of power in compute and data.</p> <p>AI experts recommended enterprises and stakeholders take a holistic and proactive approach that considers the potential effect of each AI risk across different domains and stakeholders to prioritize these risk scenarios effectively. This approach requires a deep understanding of AI systems and their <a href="https://www.techtarget.com/searchenterpriseai/feature/6-ways-to-reduce-different-types-of-bias-in-machine-learning">algorithmic biases</a>, the data inputs used to train and test the models, and the potential vulnerabilities and attack vectors that hackers or malicious actors can exploit.</p> <h3>AI risk heat map</h3> <p>A practical approach applies the methods used in cybersecurity that evaluate risks according to their probability and severity. Many risks haven't been identified yet, so businesses would need to distinguish between areas of uncertainty and known potential risks to build their <a href="https://www.techtarget.com/searchbusinessanalytics/definition/heat-map">heat maps</a>. Uncertainty considers the unknown unknowns, while risk refers to assessment based on known unknowns.</p> <h3>Trustworthy AI pledge</h3> <p>Pery suggested that enterprises make a top-down organizational commitment to <a href="https://www.techtarget.com/searchenterpriseai/tip/What-is-trustworthy-AI-and-why-is-it-important">trustworthy AI principles</a> and guidelines. Trustworthy AI includes human-centered values of fairness of AI outcomes, accuracy, integrity, confidentiality, security, accountability and transparency associated with the use of AI.</p> <p>Organizations that offer frameworks for trustworthy AI include the following:</p> <ul type="disc" class="default-list"> <li>Organization for Economic Cooperation and Development.</li> <li>Berkman Klein Center at Harvard University.</li> <li>Stanford Center for Human-Centered Artificial Intelligence.</li> <li>AI Now Institute.</li> </ul> <p>In addition, the NIST AI Risk Management Framework provides a comprehensive <a target="_blank" href="https://www.nist.gov/itl/ai-risk-management-framework" rel="noopener">roadmap</a> for implementing responsible AI best practices and a model for mitigating potential AI harms. Other standards that businesses might consider include the ISO/IEC 23894:2023 framework and the EU-sponsored AI governance <a target="_blank" href="https://www.cencenelec.eu/areas-of-work/cen-cenelec-topics/artificial-intelligence/" rel="noopener">framework</a> by the European Committee for Standardization and the European Electrotechnical Committee for Standardization.</p> <h3>Checklist of questions for monitoring AI</h3> <p>Enterprises should implement measures to ensure continuous human monitoring of AI system performance. These steps can include identifying potential deviations from expected outcomes, taking remediation steps to correct adverse results and including processes for overriding automated decisions by AI systems.</p> <p>'Kimberly Nevala, strategic advisor at SAS, recommended companies consider the following questions:</p> <div class="imagecaption alignRight"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/nevala_kimberley.jpg" alt="Kimberly Nevala">Kimberly Nevala </div> <ul type="disc" class="default-list"> <li>How will and could this solution go astray or make errors?</li> <li>Is intentional misuse probable and in what circumstances?</li> <li>How might the system be inadvertently misunderstood or misapplied?</li> <li>What are the impacts, and how do they scale?</li> <li>Does the system's design exacerbate or attenuate the potential for misuse and misunderstanding?</li> <li>How might this system be integrated into or influence others within and beyond our scope of control?</li> <li>What might be the second- or third-order effects thereof?</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Will AI regulation help or harm?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Will AI regulation help or harm?</h2> <p>Governments worldwide are starting to draft <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-regulation-What-businesses-need-to-know">AI regulations</a> that might prevent some of the worst AI risks. But poorly drafted regulations could slow the adoption of AI applications that solve some of our more pressing problems in healthcare and sustainable development -- or they could create new problems. Some industry observers have argued that regulation could stifle innovation and create a regulatory moat around incumbent companies, limiting competition and disruption from startups.</p> <p>Effective AI regulation requires being specific about the processes and requirements to make <a href="https://www.techtarget.com/searchcio/tip/AI-transparency-What-is-it-and-why-do-we-need-it">AI transparent</a>, understandable and safe. Drafting broad laws that focus on whether a process is ultimately harmful won't take us nearly far enough.</p> <p>Discussions about regulating AI could take a cue from the regulations that helped us transition through the Industrial Revolution, such as minimum AI safety standards for work conditions, minimum pay requirements, child labor restrictions and environmental standards. The AI equivalent would address how processes should be regulated, including how they use data, whether and how they drive decisions, and how we can ensure AI's processes remain transparent, understandable and accountable.</p> <p>Tackling existential AI risks will require identifying and addressing the present dangers of the AI systems we're deploying today, Nevala reasoned. "This is an issue that will only be addressed through a combination of public literacy and pressure, regulation and law, and -- history sadly suggests -- after a yet-to-be-determined critical threshold of actual harm has occurred," she said.</p> <p><i>George Lawton is a journalist based in London. Over the last 30 years, he has written more than 3,000 stories about computers, communications, knowledge management, business, health and other areas that interest him.</i></p> </section> What should enterprises make of the recent warnings about AI's threat to humanity? AI experts and ethicists offer opinions and practical advice for managing AI risk. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a194810146.jpg https://www.techtarget.com/searchenterpriseai/feature/AI-existential-risk-Is-AI-a-threat-to-humanity Mon, 11 May 2026 11:48:00 GMT <p>As MongoDB expands beyond its database roots to create a unified data platform for running AI tools in production, the vendor is adding new vector indexing capabilities and improving the performance of its core platform.</p> <p>Vector embeddings are numerical representations of data that make both structured and unstructured data easy to discover through various search methods, including similarity and keyword. Such searches feed relevant data into pipelines that provide agents and other AI applications <a href="https://www.techtarget.com/searchenterpriseai/tip/Exploring-the-context-layer-for-AI-systems">the proper contextual knowledge</a> they need to deliver accurate outputs.</p> <p>Unveiled in preview on May 7, Automated Voyage AI Embeddings in MongoDB Vector Search automates creating vector embeddings via MongoDB's Voyage models, reducing the time it takes to build a search infrastructure from weeks when performed by humans to minutes.</p> <p>In addition, the launch of MongoDB 8.3, made generally available on May 7, improves database performance to meet the higher demands that AI workloads place on systems than traditional data management and analytics workloads. The new version delivers higher <a href="https://www.techtarget.com/searchapparchitecture/tip/Read-and-write-considerations-when-designing-APIs">reads and writes</a>, higher <a href="https://www.techtarget.com/searchdatamanagement/definition/ACID">ACID</a> transactions without requiring any changes in code, and is capable of handling more complex operations than MongoDB 8.0, according to the vendor.</p> <p>Together, the new and improved capabilities represent MongoDB's advancement toward becoming a unified data platform for AI, according to Mike Leone, an analyst at Moor Insights &amp; Strategy.</p> <p>"It's a step forward because the ingredients underneath are real," he said, noting that MongoDB's aspiration is grounded in its capabilities. "MongoDB owns a top-tier embedding model, the operational database, and now the wiring between them, and very few competitors can say all three are first-party and tightly integrated. That's makes the platform claim land for me instead of feeling like marketing."</p> <p>William McKnight, president of McKnight consulting, likewise noted that MongoDB's new capabilities are valuable for users and represent progress for the vendor. However, he also pointed out that MongoDB's competitors are <a href="https://www.techtarget.com/searchdatamanagement/news/366633117/Couchbase-ups-database-vector-search-indexing-capabilities">adding similar capabilities</a>.</p> <p>"These enhancements reduce manual plumbing and provide performance gains, allowing enterprises to deploy secure, high-speed AI agents with minimal operational complexity," McKnight said. "They could also be viewed as table stakes since all major platforms are similarly adding support for AI agents."</p> <section class="section main-article-chapter" data-menu-title="Data discovery for AI"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Data discovery for AI</h2> <p>Based in New York City, MongoDB is a longtime database vendor that has <a href="https://www.techtarget.com/searchenterpriseai/news/366627557/Database-vendor-MongoDB-embraces-GenAI">expanded beyond its roots</a> to create a data platform for AI workloads over the past few years in response to <a target="_blank" href="https://kpmg.com/us/en/media/news/q1-ai-pulse2026.html" rel="noopener">surging enterprise interest</a> in developing and deploying agents and other AI applications.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> It's a step forward because the ingredients underneath are real. MongoDB owns a top-tier embedding model, the operational database, and now the wiring between them, and very few competitors can say all three are first-party and tightly integrated. </figure> <figcaption> <strong>Mike Leone</strong>Analyst, Moor Insights &amp; Strategy </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Competing vendors including database specialists, data platform vendors and hyperscalers such as <a href="https://www.techtarget.com/searchdatamanagement/news/366640598/Oracle-AI-Database-update-aims-to-ease-developing-agents">Oracle</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366577632/Vector-search-and-storage-key-to-AWS-database-strategy">AWS</a> have also made it a priority to add features that enable customers to build and manage AI tools.</p> <p>MongoDB's new capabilities, however, keep the vendor current, and the simplicity of its platform provides some differentiation, according to McKnight.</p> <p>"While specialized rivals lead in raw vector latency, MongoDB offers operational simplicity and long-term memory management by eliminating the need to sync data between disparate systems," he said. "It also has high-end capabilities for JSON-styled data. Ultimately, it's a pragmatic choice that combines enterprise-grade reliability and high-performance JSON storage with integrated AI orchestration."</p> <p>Despite heightened enterprise interest in AI development and tools provided by vendors such as MongoDB and its competitors designed to simplify the complex process of building agents and other AI applications, most AI initiatives <a href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf">never make it into production</a>. The reasons for the high failure rate vary, but the inability to retrieve relevant data, without which AI tools can't be trusted to deliver accurate outputs, is among them.</p> <p>By automating the process of creating vector embeddings -- which follows <a href="https://www.techtarget.com/searchdatamanagement/news/366637414/MongoDB-launches-latest-Voyage-models-to-aid-AI-development">MongoDB's January release</a> of five Voyage AI embedding and reranking models -- MongoDB is addressing the data retrieval problems that plague many AI projects, according to Pete Johnson, the vendor's field chief technology officer.</p> <p>"Without consistent, high-accuracy retrieval, you can't trust the decisions that an agent makes, and without that trust, you can't put an agent into production," he said. "That's the sentiment we hear from customers."</p> <p>Despite the common sentiment that accuracy problems can be addressed by upgrading to a new large language model, inaccuracy based on irrelevant data is not an LLM problem, Johnson continued.</p> <p>"Bad AI often less an LLM problem and more of a retrieval problem," he said. "The LLM can only act on the information that it's given, if that information … is lacking the right context, then the output will inevitably be wrong."</p> <p>In addition to MongoDB, data management vendors unveiling new capabilities aimed at feeding agents and other AI tools with more appropriate context since the start of 2026 include <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">Databricks</a>, GoodData, Qlik and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366642778/Tableau-repositions-for-AI-unveils-new-knowledge-layer">Tableau</a>, among others.</p> <p>Given the need to discover contextually relevant data through <a href="https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation">retrieval-augmented generation</a> pipelines for AI, while platform performance improvements are valuable, the Automated Voyage AI Embeddings are the most significant of MongoDB's new capabilities, according to Leone.</p> <p>"It's the one because the embedding pipeline is where production RAG quietly dies," he said. "Teams ship something that demos beautifully, then six months later the data has drifted, the embeddings haven't, and the agent is confidently retrieving last quarter's reality. Closing that loop in the database keeps an agent trustworthy a year after it ships, and that's where the real customer value shows up."</p> <p>McKnight similarly noted the value of automating <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">vector embedding generation</a>.</p> <p>"Automated Voyage AI Embeddings have the potential to reduce deployment time by enabling semantic search quickly," he said. "By providing real-time data updates and top-tier retrieval accuracy, this feature ensures that AI agents operate with the most current and precise context available."</p> <p>Beyond automated vector embedding creation and added database performance with the launch of MongoDB 8.3, the vendor made a new integration with <a target="_blank" href="https://docs.langchain.com/oss/javascript/langgraph/overview" rel="noopener">LangGraph.js</a> generally available and added cross-region connectivity for AWS PrivateLink.</p> <p>Collectively, the new features are designed to advance MongoDB's goal of becoming a platform for AI, according to Ben Cefalo, the vendor's chief product officer for core products.</p> <p>"These updates advance automated retrieval and persistent agent memory as part of our mission to unify the agentic AI stack, strengthen the core database foundation for mission critical workloads and provide with the skills to deploy production AI," he said.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/how_a_vector_database_works-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/how_a_vector_database_works-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/how_a_vector_database_works-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/how_a_vector_database_works-f.png 1280w" alt="A graphic displays how a vector database works" data-credit="Informa TechTarget" height="196" width="560"> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>With so many <a href="https://www.techtarget.com/searchenterpriseai/feature/Businesses-gear-up-for-AI-agents-in-the-enterprise">enterprises building agents</a> and so many data and analytics providers trying to appeal to those enterprises by simplifying AI development, the vendors that best serve the needs of existing users and potentially capture new ones will be those that help customers see and fix problems quickly, according to Leone.</p> <p>"The next year is going to expose a lot of agents that looked great in a demo and quietly fail in production, and the vendors who win will be the ones who help customers catch that early," he said.</p> <p>Consequently, he suggested that MongoDB add <a href="https://www.techtarget.com/searchitoperations/podcast/AI-observability-Why-old-monitoring-fails-in-the-GenAI-era">agent observability</a> capabilities so developers and engineers can address potential issues with AI tools before they cause problems in production or get scrapped before they ever make it that far.</p> <p>"If I were MongoDB, I'd lean hard into agent observability and evaluation as a first-party capability, since that's the credibility layer behind every 'trust an agent at scale' claim they're already making," Leone said. "Owning that gives AI-native teams one less thing to stitch together from outside the platform."</p> <p>McKnight, meanwhile, suggested that MongoDB broaden its support for complex <a href="https://www.techtarget.com/searchdatamanagement/definition/data-structure">data structures</a>. He noted that the vendor excels at operational simplicity, but support for data structures such as tensors and matrices would enable it to better handle high-dimensionality data.</p> <p>"Furthermore, incorporating built-in search enhancements such as native spellcheck and real-time recommendations would bridge the gap between its current document-store roots and the specialized capabilities of pure-play search engines," McKnight said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> With enterprise data workloads feeding AI pipelines, the longtime database vendor is evolving -- along with competitors -- by building capabilities for cutting-edge development. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1019737194.jpg https://www.techtarget.com/searchdatamanagement/news/366642768/MongoDB-adds-new-vector-performance-capabilities-to-aid-AI Fri, 08 May 2026 14:27:00 GMT <p>Manufacturers were slower than some industries to adopt digital technologies, a gap that became more visible during the COVID-19 pandemic as companies faced supply chain disruptions and shifting operational demands. Chief production officers, CSCOs and other C-suite members who focus on manufacturing should learn about the most common AI use cases for manufacturing to determine whether any are a good fit for their companies.</p> <p>The pandemic "really exposed the lack of [digital] investments they've made over time," said Sachin Lulla, industrials and energy transformation leader at EY Americas. Companies grew through acquisitions, piling up legacy debt applications that were never integrated -- "and they obviously paid the price for it,'' he said.</p> <p>Now, Lulla said EY is seeing "a massive shift" in how manufacturing companies are thinking about digital and, more importantly, how they are thinking about having a digital and <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-formulate-a-winning-AI-strategy">AI strategy</a> that has "a clear ROI/business case."</p> <section class="section main-article-chapter" data-menu-title="AI becomes a board issue"> <h2 class="section-title"><i class="icon" data-icon="1"></i>AI becomes a board issue</h2> <p>With the blockbuster debut of <a href="https://www.techtarget.com/whatis/definition/ChatGPT">ChatGPT</a>, AI has become a board-level priority for manufacturers -- a trend reflected in the growing frequency with which manufacturing clients are contacting EY for guidance on AI, Lulla noted.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Every board is now asking CEOs, 'Do we need a strategy that leverages AI?' Our advice is ... an AI strategy should be linked to overall business outcomes, and every use case must have a clear business case before you pursue it. </figure> <figcaption> <strong>Sachin Lulla</strong>Consulting industrial products sector leader, EY Americas </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>"Every board is now asking CEOs, 'Do we need a strategy that leverages AI?'" Lulla said. "Our advice is: They don't need an AI strategy -- they need a digital and AI strategy that are in agreement. An AI strategy should be linked to overall business outcomes for the company, and every use case must have a clear business case before you pursue it."</p> <div class="imagecaption alignRight"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/lulla_sachin.jpg" alt="Sachin Lulla">Sachin Lulla </div> </section> <section class="section main-article-chapter" data-menu-title="Transformative power of generative AI in manufacturing"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Transformative power of generative AI in manufacturing</h2> <p>Manufacturers are paying attention to AI, particularly to the potentially transformative power of generative AI (<a href="https://www.techtarget.com/searchenterpriseai/definition/generative-AI">GenAI</a>), the technology underlying ChatGPT and other AI-powered assistants.</p> <p><a href="https://www.abiresearch.com/press/strategic-deployment-of-generative-ai-in-manufacturing-will-unlock-us105-billion-added-revenue-by-2033/" target="_blank" rel="noopener">According to ABI Research</a>, the manufacturing industry's investment in GenAI will generate "additional revenues with a significant spike of $4.4 billion from 2026 to 2029. By 2033, revenue added from the use of GenAI in manufacturing will reach $10.5 billion," the firm said.</p> <p>The top reason cited by global manufacturers to use GenAI, according to a <a href="https://go.abiresearch.com/lp-the-state-of-technology-in-the-manufacturing-industry" target="_blank" rel="noopener">2024 survey by ABI</a>, is to identify the root cause of production issues faster than currently possible, followed by the "faster creation of work instructions" and "improved workforce coding skill."</p> <p>AI and digital technologies -- combined with higher employee skill levels -- are already giving the sector a jolt, according to <a href="https://www.mckinsey.com/capabilities/operations/our-insights/adopting-ai-at-speed-and-scale-the-4ir-push-to-stay-competitive?cid=omcknsl-eml-nsl--mck-ext-----&amp;hlkid=1675660fe04a43e9bd30c1a6c03e88d3&amp;hctky=9142176&amp;hdpid=edf49aac-38fe-4a9a-bee4-2e6ee5142b43" target="_blank" rel="noopener">McKinsey</a>. The firm said growth in the U.S. manufacturing sector was stuck at 1.4% over the past two decades: "More recently, AI, digital technologies, sustainable features and higher skills have reinvigorated the market: Over the past five years, U.S. industrials companies have generated total shareholder returns about 400 basis points higher than in the previous 15 years."</p> <p>McKinsey also <a href="https://www.mckinsey.com/capabilities/operations/our-insights/operations-blog/harnessing-generative-ai-in-manufacturing-and-supply-chains" target="_blank" rel="noopener">predicted</a> that generative AI has the potential to make a big difference in several areas of manufacturing, including planning, productivity by using root cause analysis to predict failures and reduce defects, and delivery by helping to get products to customers on time and communicating with them via AI chatbots. "Paired with digital twins, GenAI can create warehouse designs and production scenarios faster,'' the consulting firm said.</p> <div class="imagecaption alignRight"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/hayden_reese.jpg" alt="Reece Hayden">Reece Hayden </div> <p>Still, it's important to note that manufacturing remains in a relatively early <a href="https://www.techtarget.com/searchenterpriseai/Ultimate-guide-to-artificial-intelligence-in-the-enterprise">AI deployment</a> stage, stressed Reece Hayden, senior analyst at Verdantix, including in the area of generative AI, where, at present, "the only realistic applications for GenAI are currently in the back office with human oversight," he said.</p> <p>However, traditional machine learning (ML) models, such as <a href="https://www.techtarget.com/searchenterpriseai/definition/machine-vision-computer-vision">machine vision</a> and graph-based <a href="https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP">natural language processing</a>, are beginning to scale, he said.</p> <p>"As the ROI [from AI tools] becomes clearer, the technology matures and manufacturers accelerate digital transformation strategies, these models are increasingly being deployed to support a variety of back-office and even operational use cases," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="AI use cases in manufacturing"> <h2 class="section-title"><i class="icon" data-icon="1"></i>AI use cases in manufacturing</h2> <p><a href="https://www.techtarget.com/searchenterpriseai/feature/GenAI-in-product-manufacturing-cuts-costs-but-adds-risks">Manufacturers are applying AI</a> across both operational and back-office workflows, with the strongest early use cases often focused on productivity, quality, documentation and forecasting.</p> <p>Specific use cases of AI in manufacturing for CPOs, CSCOs and other C-suite members to look into include the following.</p> <h3>1. Cobots and autonomous mobile robots</h3> <p>Collaborative robots (<a href="https://www.techtarget.com/whatis/definition/collaborative-robot-cobot">cobots</a>) and autonomous mobile robots, or AMRs, have already been adopted by manufacturers to enhance and complement the workforce, while reducing errors, increasing speed to value and improving quality, according to Rockwell Automation's "9^th Annual State of Smart Manufacturing" <a href="https://www.rockwellautomation.com/en-us/capabilities/digital-transformation/state-of-smart-manufacturing.html" target="_blank" rel="noopener">report</a>. Some 85% of respondents have already invested or plan to invest in AI/ML in these areas this year.</p> <p>Amazon has deployed hundreds of thousands of robots working in tandem with employees, including a robotic system called Sequoia. Amazon says Sequoia can identify and store inventory at fulfillment centers up to 75% faster and reduce order processing time through a fulfillment center by up to 25%, improving shipping predictability and increasing the number of products available for same-day or next-day shipping.</p> <p>Drones are also gaining traction in the manufacturing sector, according to ABI Research.</p> <div class="imagecaption alignRight"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/iversen_james.jpg" alt="James Iversen">James Iversen </div> <h3>2. GenAI in PLC coding</h3> <p>Manufacturers that are "extremely digitally mature" are adopting GenAI for programmable logic controller (PLC) coding, said James Iversen, PTC CXC at PTC.</p> <p>"With any use case, a company must have correct data inputs and employees who understand the risks of using GenAI," he explained. Not many smaller manufacturers have the right apps, data streams and outputs, he added.</p> <p>Even at sophisticated companies, GenAI must be scrutinized, Iversen warned. "When GenAI writes code for PLCs, it has to be double-checked and triple-checked by coders to make sure there are no hallucinations and it is not adding lines of code that are completely irrelevant."<br><br>Siemens and motion tech company Schaeffler have collaborated on Industrial Copilot to help Schaeffler's automation engineers generate code faster for PLCs and reduce time, effort and the probability of errors. The PLC code is generated through natural language inputs.</p> <p>ABI Research's aforementioned "The State of Technology in the Manufacturing Industry" survey found that 52% of U.S.-based manufacturers believe GenAI can help them fix bugged software code more quickly than currently possible.</p> <h3>3. GenAI in managing inventory levels and purchasing cycles</h3> <p><a href="https://www.techtarget.com/searcherp/tip/Benefits-and-use-cases-for-AI-in-inventory-management">Checking inventory levels</a> of raw materials components in warehouses is another big GenAI use case. "Manufacturers can look at the historical data of how much raw materials cost in the past and can suggest best period times for purchasing,'' Iversen said.</p> <p>AI can also be used to streamline warehouse operations, ensuring the right levels of inventory and that duplicate components are not being purchased, he said.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/8_ai_use_cases_in_manufacturing-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/8_ai_use_cases_in_manufacturing-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/8_ai_use_cases_in_manufacturing-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/8_ai_use_cases_in_manufacturing-f.png 1280w" alt="AI manufacturing use cases" height="255" width="560"> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <h3>4. Autonomous vehicles</h3> <p>The deployment of autonomous vehicles (AVs) is at varying stages. Automaker Rivian has integrated <a href="https://www.emergingtechbrew.com/stories/2024/06/06/rivian-ai-next-gen-vehicles-wassym-bensaid?mbcid=35630512.182543&amp;mblid=ab14e67d094b&amp;mid=0ee7b8bf9bc73b17e8ebf5a7f0dfe825&amp;utm_campaign=etb&amp;utm_medium=newsletter&amp;utm_source=morning_brew" target="_blank" rel="noopener">AI prediction technology</a> into its R1T pickup truck and R1S SUV and has initiatives underway to integrate traditional AI and GenAI inside its vehicles.</p> <p>However, the technology remains nascent for AVs due to AI's inability to make cause-effect challenges, according to <a href="https://www.autonews.com/mobility-report/ai-lacks-causal-inference-needed-av-edge-cases" target="_blank" rel="noopener">Automotive News</a>. General Motors, for example, has halted plans to develop its fully autonomous Cruise Origin, which was being designed without a steering wheel or other human controls.</p> <p>British automaker Bentley is also <a href="https://www.carscoops.com/2024/04/bentley-wont-offer-level-3-autonomous-system-because-they-deem-it-too-dangerous/" target="_blank" rel="noopener">exercising caution on AVs</a>, focusing on plans to implement Level 2 autonomous systems, which take over controls, such as assisting with remote parking, steering and managing speed, but require the driver to stay focused on the road at all times.</p> <div class="extra-info"> <div class="extra-info-inner"> <h3 class="splash-heading">What makes an AI use case practical in manufacturing?</h3> <p>The strongest manufacturing AI projects typically share a few characteristics:</p> <ul class="default-list"> <li>a clear operational or financial outcome</li> <li>high-quality, structured data</li> <li>defined human oversight requirements</li> <li>a realistic deployment path inside existing workflows</li> </ul> </div> </div> <h3>5. Forecasting demand to optimize the supply chain</h3> <p>During the COVID-19 pandemic, a food products distributor reimagined its supply chain by implementing <a href="http://www.accenture.com/content/dam/accenture/final/capabilities/strategy-and-consulting/supply-chain---operations/document/forward-looking-supply-chain-using-demand-forecasting.pdf" target="_blank" rel="noopener">demand forecasting</a> instead of relying on historical data. The company worked with Accenture to develop an AI system that utilizes new data and modeling techniques to improve demand sensing. Using internal data, such as sales and inventory, along with external data, including weather and restaurant reservations, the company gained more visibility and flexibility to anticipate supply chain disruptions. <br><br>The AI system has not only enabled the distributor to manage its supply chain more effectively, but also be better prepared for future disruptions.</p> <h3>6. GenAI for documentation</h3> <p>AI can be used to create frontline worker documentation -- i.e., a consolidated list of all machines and standard operating procedures on how to handle issues, Iversen said. A worker can audibly ask or type into a GenAI tool a question about what to do if a machine isn't operating at the correct output, and the tool gives a reason why, he said.</p> <p>"Let's say a machine is overheating, [the tool] will give you step-by-step instructions on here's what you should do,'' he said. "It's a time-saving mechanism to reduce errors in the manufacturing line as it pertains to machines."</p> <h3>7. GenAI in CAD product design</h3> <p>The first manufacturing use case for GenAI software was in computer-aided design (CAD) software, according to Iversen, and now, 70% of manufacturers are using the technology for discrete processes.</p> <p>Manufacturers are seeing a lot of productivity gains here mainly in time savings. "If I was designing a product and didn't want to start from scratch, I can upload the previous designs I've worked on that are similar and add parameters, such as 'don't exceed this amount of material,' or 'it has to be able to withstand this amount of sheer force and strain,'" he said.</p> <p>In response, the GenAI tool produces between one and 100 design solutions that accurately fit into those parameters.</p> <h3>8. Predictive maintenance</h3> <p>Predictive maintenance "is going to be a huge AI use case," Iversen said, and it's been rolled out by a handful of manufacturers. But it's not a top use case yet, in part because it <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Using-simulation-forecasting-in-business-analytics">does not typically require GenAI.</a> "If you have a robust [manufacturing execution system] or data analytics solution, you can already pretty effectively understand when a machine will have downtime, the root cause for why it's occurring and get some insight into how to fix the problem," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Challenges of implementing AI in manufacturing"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Challenges of implementing AI in manufacturing</h2> <h3>Data quality</h3> <p>Like in any industry, better data management is needed to fuel AI and empower teams. The Rockwell report found that respondents are "using data to fuel AI/ML and optimize processes. However, those surveyed believe their own organizations use less than half of collected data effectively."</p> <p>"AI needs terabytes of data generated by and collected from a broad range of sources: enterprise systems, machine sensors, connectivity infrastructure and human workers," according to the McKinsey report. Indeed, the most advanced front-runners in AI deployments are ahead because they "had the foresight to make investments and take on risks involved in building the data foundations that are needed to power AI technologies and unlock their potential impact," the report stated.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Focusing on building high-quality, clean, structured, application-specific data sets will help unlock various AI use cases. </figure> <figcaption> <strong>Reece Hayden</strong>Principal analyst, ABI Research </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>EY's Lulla agreed. To truly scale AI, you need accurate, trusted data, he said -- and you need to know which data is needed for the business case at hand. When implementing AI for clients, the first thing EY looks at is the business outcome. "Based on that, we define what data we need to deliver on the AI use case, including historical data and its quality," he said. "Most companies don't have the right data, or it takes a lot of manual effort to put that in place."</p> <p>He cited a company EY worked with that built protective sheets for kitchen countertops and was experiencing massive product recalls. "We needed a lot of different data, for example, conditions or parameters that affect the process," Lulla said, to do the analysis. This included temperature, pressure and speed, as well as configuration settings for the equipment, real-time sensor data, historical time-series data, operator event logs and final inspection results.</p> <p>"What we found was the final inspection was done manually and the quality inspectors did not capture reason codes for failure, such as color or gauge defects, and thus, the AI model could not be trained to predict quality problems accurately until the process was fixed to capture this data,'' Lulla said.</p> <h3>Operational risk</h3> <p>ABI Research's Hayden singled out operational risk as the biggest challenge of AI in manufacturing, especially <a href="https://www.techtarget.com/searcherp/tip/Use-cases-for-generative-AI-in-manufacturing">when generative AI is involved</a>.</p> <p>"Most manufacturing operations are critical, which means accuracy, reliability, security, privacy, availability and latency are all vital,'' he said. "Generative AI models remain immature, highly generalized with limited accuracy, which makes them ineffective for these applications."</p> <p>In addition, given the size and memory burden of generative AI models, it is challenging to deploy them at the edge, where most manufacturing applications are deployed, Hayden said, adding that, eventually, GenAI will scale for edge deployments.</p> </section> <section class="section main-article-chapter" data-menu-title="GenAI: Not there yet"> <h2 class="section-title"><i class="icon" data-icon="1"></i>GenAI: Not there yet</h2> <p>There are a lot of applications for GenAI in the areas of machine vision, industrial IoT and digital twins, but for now, Iversen's interviews with manufacturers indicated they are focused on the most "pragmatic use cases," he said -- projects that yield the fastest and best results. "And, right now, they are what will save employees time."</p> <p>He predicted it will be another six months to a year before companies broaden their use of GenAI.</p> <p>Hayden agreed. There are "very, very limited generative AI deployments outside of back office," he said, explaining that GenAI is "not yet suitable for mission-critical use cases." This is because data sets are not sufficient to train and fine-tune GenAI models. Additionally, GenAI is still reliant on humans, "given the high risk of deployment," Hayden said.</p> <p>Eventually, the data bottleneck will be addressed. In the meantime, he advised companies to get their data in order.</p> <p>"Focusing on building high-quality, clean, structured, application-specific data sets will help unlock various AI use cases," he said.</p> <p>Manufacturers are finding the most value in AI when use cases are tied to clear business outcomes, strong data foundations and realistic deployment plans. While some advanced applications remain early, practical uses in areas such as forecasting, documentation, design and maintenance are already showing where AI can improve manufacturing operations.</p> <p><strong>Editor's note:</strong> <em>This article was lightly updated to reflect current AI use cases, examples and deployment considerations in manufacturing. </em></p> <p><i>Esther Shein is a veteran freelance writer specializing in technology and business. Former senior writer at eWeek, she writes news, features, case studies and custom content.</i></p> </section> Manufacturers are moving from AI experimentation to practical use cases that improve productivity, product quality, maintenance and supply chain decision-making. https://cdn.ttgtmedia.com/rms/onlineimages/iot_g1212287865.jpg https://www.techtarget.com/searcherp/feature/10-AI-use-cases-in-manufacturing Thu, 07 May 2026 14:00:00 GMT <p>Like other forms of AI, generative AI can raise ethical issues and <a href="https://www.techtarget.com/searchenterpriseai/tip/Address-top-AI-privacy-concerns-with-this-checklist">risks pertaining to data privacy</a>, security, energy and other resource use, political impact and workforces. GenAI technology can also introduce new business risks, such as misinformation, hallucinations, plagiarism, copyright infringement and harmful content. Lack of transparency and the potential for worker displacement are additional <a href="https://www.techtarget.com/searchenterpriseai/tip/Generative-AI-challenges-that-businesses-should-consider">issues that enterprises might need to address</a>.</p> <p>Many of the risks posed by GenAI are "enhanced and more concerning" than those associated with other types of AI, said Tad Roselund, senior advisor and executive coach at consultancy BCG. Those risks require a comprehensive approach, including a defined strategy, good governance and a commitment to responsible AI.</p> <p>Companies that use GenAI should consider the following 16 issues:</p> <section class="section main-article-chapter" data-menu-title="1. Distribution of harmful content"> <h2 class="section-title"><i class="icon" data-icon="1"></i>1. Distribution of harmful content</h2> <p>Generative AI systems <a href="https://www.techtarget.com/searchenterpriseai/feature/How-to-detect-AI-generated-content">create content automatically</a> from people's text prompts. "These systems can generate enormous productivity improvements, but they can also be used for harm, either intentional or unintentional," said Bret Greenstein, chief AI officer at West Monroe, a business transformation consultancy. For example, an <a href="https://www.techtarget.com/searchsecurity/tip/Prepare-for-deepfake-phishing-attacks-in-the-enterprise">AI-generated email sent on behalf of the company</a> could inadvertently contain offensive language or issue harmful guidance to employees. GenAI should be used to augment but not replace humans or processes to ensure content meets the company's ethical expectations and supports its brand values, Greenstein advised.</p> </section> <section class="section main-article-chapter" data-menu-title="2. Taking harmful actions"> <h2 class="section-title"><i class="icon" data-icon="1"></i>2. Taking harmful actions</h2> <p>As AI systems move from generating content to taking action, accountability structures must evolve. Current accountability frameworks assume AI is a tool and liability flows to the humans who deployed it, said AI ethics researcher and author Nell Watson. However, as agentic systems make increasingly autonomous decisions, that model breaks down, and organizations should implement a "structured disagreement register," where both the AI system and the human decision-maker record their reasoning when they diverge, she said. This creates a corpus that reveals where each party adds value and where each introduces risk.</p> </section> <section class="section main-article-chapter" data-menu-title="3. Copyright and legal exposure"> <h2 class="section-title"><i class="icon" data-icon="1"></i>3. Copyright and legal exposure</h2> <p><a href="https://www.techtarget.com/whatis/feature/AI-content-generators-to-explore">Popular generative AI tools</a> are trained on massive image and text databases from multiple sources, including the internet. <a href="https://www.techtarget.com/searchsecurity/post/Best-practices-to-detect-and-mitigate-deepfake-attacks">When these tools create images</a> or generate lines of code, the data's source might be unknown, which could be problematic for a bank handling financial transactions or a pharmaceutical company relying on a formula for a complex molecule in a drug. Reputational and financial risks could also be substantial if one company's product relies on another's intellectual property. "Companies must look to validate outputs from the models," Roselund advised, "until legal precedents provide clarity around IP and copyright challenges."</p> </section> <section class="section main-article-chapter" data-menu-title="4. Data privacy violations"> <h2 class="section-title"><i class="icon" data-icon="1"></i>4. Data privacy violations</h2> <p>GenAI <a href="https://www.techtarget.com/whatis/definition/large-language-model-LLM">LLMs</a> are trained on data sets that might include personally identifiable information (PII) about individuals. This data can sometimes be elicited with a simple text prompt.</p> <p>Moreover, compared with traditional search engines, it can be more difficult for consumers to locate and request the removal of the information. Companies that build or fine-tune LLMs must ensure that PII isn't embedded in the language models and that it's easy to remove PII from these models in compliance with privacy laws.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/generative_ai_evolution.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/generative_ai_evolution_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/generative_ai_evolution_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/generative_ai_evolution.png 1280w" alt="Generative AI through the years." height="946" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Businesses are scrambling to maximize the benefits of today's generative AI while wrestling with inherent ethical issues. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="5. Sensitive information disclosure"> <h2 class="section-title"><i class="icon" data-icon="1"></i>5. Sensitive information disclosure</h2> <p>GenAI is democratizing AI capabilities and making them more accessible. This combination of democratization and accessibility, Roselund said, could potentially lead to a medical researcher inadvertently disclosing sensitive patient information or a consumer brand unwittingly exposing its product strategy to a third party. The consequences of unintended incidents like these could irrevocably breach patient or customer trust and carry legal ramifications. Roselund recommended that companies institute clear guidelines, governance and effective communication from the top down, emphasizing shared responsibility for safeguarding sensitive information, protected data and IP.</p> </section> <section class="section main-article-chapter" data-menu-title="6. Enterprise data contamination"> <h2 class="section-title"><i class="icon" data-icon="1"></i>6. Enterprise data contamination</h2> <p>A related concern is how AI-generated content could contaminate enterprise data. Rahul Jolly, vice president of AI and data at OSF Digital, a Salesforce consulting company, warned that as organizations use GenAI to create content, summarize interactions and enrich customer profiles, that output gets fed back into core systems. Over time, AI-generated data becomes indistinguishable from human-verified data. Competitive advantage will shift from having the most data to having the most trusted data.</p> </section> <section class="section main-article-chapter" data-menu-title="7. Compounded AI risks increase accountability challenges"> <h2 class="section-title"><i class="icon" data-icon="1"></i>7. Compounded AI risks increase accountability challenges</h2> <p>The accountability challenge is compounded by the way <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-existential-risk-Is-AI-a-threat-to-humanity">AI risks</a> interact. Compliance controls intended to manage privacy risk can spin up new databases of sensitive content that need protecting in their own right, said Hugh Mulligan, associate director of cyber risk and governance at S-RM, an intelligence and cybersecurity consulting firm. Cybersecurity teams that lock systems down too hard push users toward shadow AI that security teams can't see. Companies that will struggle most are the ones where AI risk sits in one department rather than being treated as an enterprise-level problem, he said.</p> </section> <section class="section main-article-chapter" data-menu-title="8. Amplification of existing bias"> <h2 class="section-title"><i class="icon" data-icon="1"></i>8. Amplification of existing bias</h2> <p>GenAI can potentially amplify existing bias. For example, data used to train LLMs can contain biases beyond the control of businesses that use these language models for specific applications. It's important for companies working on AI to have diverse leaders and subject matter experts to help identify bias in data and models, West Monroe's Greenstein said.</p> <p>Scott Zoldi, chief analytics officer at credit scoring services company FICO, identified two bias mechanisms that enterprises often overlook. First, most GenAI use involves prompting or fine-tuning a pre-existing LLM with training data that might not be sufficiently representative or balanced. Practitioners have no reliable way to assess those biases without access to the underlying data. Second, prompt engineering itself is a form of cognitive bias, shaping and constraining results in ways that reflect the practitioner's own assumptions. Zoldi recommended businesses <a href="https://www.techtarget.com/searchenterpriseai/tip/Why-small-language-models-are-on-the-rise">build small language models</a> on curated, auditable data sets rather than relying on prebuilt models with unknown provenance.</p> </section> <section class="section main-article-chapter" data-menu-title="9. Workforce roles and morale"> <h2 class="section-title"><i class="icon" data-icon="1"></i>9. Workforce roles and morale</h2> <p><a href="https://www.techtarget.com/searchenterpriseai/tip/Successful-generative-AI-examples-worth-noting">AI is being trained to do more daily tasks</a> that knowledge workers do, including writing, coding, content creation, summarization and analysis, Greenstein said. Although worker displacement and replacement have been ongoing since the first AI and automation tools were deployed, the pace has accelerated with <a href="https://www.techtarget.com/searchenterpriseai/tip/History-of-generative-AI-innovations-spans-9-decades">innovations in generative AI</a>. "The future of work itself is changing," Greenstein added, "and the most ethical companies are investing in this [change]."</p> <p>Ethical responses have included investments in preparing certain parts of the workforce for the new roles created by GenAI applications. Businesses will need to help employees develop skills such as prompt engineering. "The truly existential ethical challenge for adoption of generative AI is its effect on organizational design, work and ultimately on individual workers," said Nick Kramer, principal of AI and applied solutions at consultancy SSA &amp; Company. "This will not only minimize the negative impacts, but it will also prepare the companies for growth."</p> <p>In industrial environments, the accountability stakes are especially high. AI is increasingly operating inside mission-critical systems where decisions have real-world consequences, from keeping energy grids running to maintaining food supply chains, said Somya Kapoor, CEO of IFS Loops, an industrial <a href="https://www.techtarget.com/searchenterpriseai/definition/agentic-AI">agentic AI</a> platform. Ethical AI in the enterprise isn't just about preventing harm, but it's also about ensuring responsible execution in the systems that keep industries running, she said.</p> </section> <section class="section main-article-chapter" data-menu-title="10. Data provenance"> <h2 class="section-title"><i class="icon" data-icon="1"></i>10. Data provenance</h2> <p>GenAI systems consume tremendous volumes of data that could be inadequately governed, of questionable origin and used without consent or bias. Social influencers or the AI systems themselves can amplify inaccuracy to additional levels.</p> <p>"The accuracy of a generative AI system depends on the corpus of data it uses and its provenance," Zoldi said. <a href="https://www.nytimes.com/2026/03/04/climate/data-centers-electricity-trump.html"></a>AI vendors and companies are mining the internet without understanding its provenance, which can present accuracy problems.</p> <p>FICO, according to Zoldi, has been using generative AI for more than a decade to simulate edge cases in training fraud detection algorithms. The generated data is always labeled as synthetic, he said, so his team knows where it can be used. "We treat it as walled-off data for the purposes of test and simulation only," he said. "Synthetic data produced by generative AI doesn't inform the model going forward in the future. We contain this generative asset and don't allow it 'out in the wild.'"</p> <div class="youtube-iframe-container"> <iframe id="ytplayer-0" src="https://www.youtube.com/embed/gL-KFbnuQRY?autoplay=0&amp;modestbranding=1&amp;rel=0&amp;widget_referrer=null&amp;enablejsapi=1&amp;origin=https://www.techtarget.com" type="text/html" height="360" width="640" frameborder="0"></iframe> </div> </section> <section class="section main-article-chapter" data-menu-title="11. Lack of explainability and interpretability"> <h2 class="section-title"><i class="icon" data-icon="1"></i>11. Lack of explainability and interpretability</h2> <p>Many generative AI systems group facts together probabilistically, mirroring how AI has learned to associate data elements, Zoldi said. But these details aren't always revealed <a href="https://www.techtarget.com/searchenterpriseai/feature/Exploring-GPT-3-architecture">when using applications like ChatGPT</a>. Consequently, data trustworthiness is called into question.</p> <p>When interrogating GenAI, analysts expect to arrive at a causal explanation for outcomes. But machine learning models and generative AI search for correlations, not causality. "That's where we humans need to insist on model interpretability -- the reason why the model gave the answer it did," Zoldi said. "And truly understand if an answer is a plausible explanation versus taking the outcome at face value."</p> <p>Until that level of trustworthiness can be achieved, GenAI systems shouldn't be relied on to provide answers that could significantly affect lives and livelihoods.</p> </section> <section class="section main-article-chapter" data-menu-title="12. Process debt"> <h2 class="section-title"><i class="icon" data-icon="1"></i>12. Process debt</h2> <p>The lack of explainability feeds what AI ethics researcher Watson calls process debt: What happens when organizations choose dependency over comprehension. When nobody understands how core processes work without AI mediation, the ability to audit, recover or adapt is lost. The solution isn't less AI but AI designed for bilateral comprehensibility, she said, where both the human and the AI system can account for decisions in terms the other can verify.</p> <p>Watson's research has shown that standard safety alignment via <a href="https://www.techtarget.com/whatis/definition/reinforcement-learning-from-human-feedback-RLHF">reinforcement learning from human feedback</a> creates a thin surface coating on a model's behavior, one that can be easily stripped. An emerging approach called bilateral alignment learns a model's representation to guide it toward a better understanding of the world that holds up against malicious prompts, such as those that turned Microsoft's Tay chatbot racist and sexist in 2016, she said. It's the difference between surface-level paint that can be easily scraped off and color dyed into fabric, which is more difficult to get out.</p> </section> <section class="section main-article-chapter" data-menu-title="13. AI hallucinations"> <h2 class="section-title"><i class="icon" data-icon="1"></i>13. AI hallucinations</h2> <p>Generative AI techniques all use various combinations of algorithms, including autoregressive models, autoencoders and other machine learning algorithms, to distill patterns and generate content. As good as these models are at identifying new patterns, they sometimes struggle with teasing out important distinctions relevant to human use cases.</p> <p>This can include creating authoritative-sounding but inaccurate prose or producing pictures with realistic-looking imagery but misshapen human figures with extra fingers or eyes. With language models, these errors can show up as chatbots that inaccurately represent company policies, such as in the case of an Air Canada chatbot that misrepresented bereavement benefit policies. Lawyers using these tools have also been fined for filing briefs that cited nonexistent court cases.</p> <p>Newer techniques, such as <a href="https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation">retrieval-augmented generation</a> and agentic AI frameworks, can reduce these issues. However, it's important to keep humans in the loop to verify the accuracy of GenAI information to avoid customer backlash, sanctions or other problems.</p> </section> <section class="section main-article-chapter" data-menu-title="14. Environmental and electricity costs"> <h2 class="section-title"><i class="icon" data-icon="1"></i>14. Environmental and electricity costs</h2> <p>The environmental, energy and water costs of GenAI have grown significantly since the first wave of large model deployments. Training and running large AI models require enormous data center resources, driving up energy consumption, water use for cooling and emissions. Communities near data centers are increasingly feeling these effects and raising concerns. Employees of AI vendors have also identified instances where their employers have failed to address adverse effects on local communities. Improving an AI model to reduce these costs could be a net positive.</p> </section> <section class="section main-article-chapter" data-menu-title="15. Conflicting ethics frameworks"> <h2 class="section-title"><i class="icon" data-icon="1"></i>15. Conflicting ethics frameworks</h2> <p>The absence of a universal ethical framework for AI is a governance challenge. In the U.S. alone, there's no single coherent regulatory regime for AI: Federal baseline guidance and state-level legislation vary in scope and assumptions, S-RM's Mulligan said. A company operating across jurisdictions must comply with multiple rules and navigate frameworks built on contradictory philosophies. What companies need, Mulligan advised, is a tiered approach to governance that lets them make defensible decisions about which risks to accept and which to mitigate.</p> </section> <section class="section main-article-chapter" data-menu-title="16. Political impact"> <h2 class="section-title"><i class="icon" data-icon="1"></i>16. Political impact</h2> <p>The political impact of GenAI technologies is a fraught topic. On one hand, better tools have the potential to make the world a better place. At the same time, they could also enable various political actors -- voters, politicians and authoritarians -- to make communities worse. Social media platforms are an example of generative AI's negative effect on politics. They algorithmically promote or create divisive comments as a strategy to increase engagement and profits for their owners, rather than comments that find common ground but might not have the same click-through and sharing numbers.</p> <p>These issues will remain thorny for years to come as societies determine which GenAI use cases serve the public good and whether that should be the end goal.</p> <p><i>George Lawton is a journalist based in London. Over the last 30 years, he has written more than 3,000 stories about computers, communications, knowledge management, business, health and other areas that interest him.</i></p> </section> As adoption and use cases grow, generative AI is upending business models and driving ethical issues such as misinformation, brand integrity and job displacement to the forefront. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a279596285.jpg https://www.techtarget.com/searchenterpriseai/tip/Generative-AI-ethics-8-biggest-concerns Thu, 07 May 2026 13:00:00 GMT <p>With the introduction of the Autonomous Knowledge Platform, Teradata is planning to provide a new infrastructure for AI.</p> <p>Unveiled on Thursday, Teradata's new capabilities are designed to integrate AI development and management with analytics and data in a single system that can be deployed across cloud, on-premises and <a href="https://www.techtarget.com/searchcloudcomputing/post/Why-hybrid-cloud-architecture-is-becoming-the-default-for-AI">hybrid environments</a>.</p> <p>Capabilities of the Autonomous Knowledge Platform, among others, include Teradata AI Studio, which is a suite for developing and operating AI tools, a natural language interface for executing agentic workflows, and prebuilt agents that perform tasks such as infrastructure management and <a href="https://www.techtarget.com/searchenterpriseai/feature/FinOps-can-manage-AI-computing-costs-experts-say">cost optimization</a>.</p> <p>Because the new platform empowers agents and unifies previously disparate AI, analytics and data management capabilities, it is a significant addition for Teradata users, according to Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget.</p> <p>"The Autonomous Knowledge Platform represents a strong addition because it shifts from reactive to proactive infrastructure … that provides the business context and governance necessary for agents to sense, decide, and act reliably across enterprise environments, which wasn't previously possible in an integrated way," he said.</p> <p>Kevin Petrie, an analyst at BARC U.S., called the new platform, "important," noting that it will help Teradata compete in <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Top-11-business-intelligence-challenges-and-how-to-overcome-them">an evolving market</a> for data management and analytics vendors as traditional business intelligence is replaced by AI-powered insight generation and process automation.</p> <p>"This is an important addition," he said. "The Autonomous Knowledge Platform makes Teradata more competitive in this space and enables its customers to layer agentic AI capabilities onto their existing data environments."</p> <p>Based in San Diego, Teradata is a data management and analytics provider that has prioritized enabling users to build and deploy AI tools with recent product development initiatives.</p> <p>In January, the vendor <a href="https://www.techtarget.com/searchdatamanagement/news/366637641/Teradatas-AgentStack-aims-to-simplify-building-managing-AI">unveiled Enterprise AgentStack</a>, a suite scheduled for general availability by midyear, designed to simplify developing and governing agents. In March, Teradata added <a href="https://www.techtarget.com/searchdatamanagement/news/366639802/Teradata-updates-vector-indexing-suite-to-aid-AI-development">new vector indexing capabilities</a> to better enable users to discover and retrieve the relevant data agents require to perform properly.</p> <section class="section main-article-chapter" data-menu-title="Infrastructure for AI"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Infrastructure for AI</h2> <p>Throughout 2026, customer feedback has led data management and analytics vendors to add capabilities that enable enterprises to develop agents that can be trusted to deliver accurate outputs so they can be put into production.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The Autonomous Knowledge Platform represents a strong addition because it shifts from reactive to proactive infrastructure … that provides the business context and governance necessary for agents to sense, decide, and act reliably across enterprise environments. </figure> <figcaption> <strong>Stephen Catanzano</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Many organizations experimented with agents dating back to 2024, but few were able to build agents trustworthy enough to deliver any return on their investments. One of the reasons many AI initiatives <a target="_blank" href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf" rel="noopener">never made it past the pilot stage</a> was that the data retrieval processes used to feed AI pipelines couldn't discover and deliver enough high-quality, relevant data for agents to perform as intended.</p> <p>In response, vendors such as Databricks, Domo, GoodData, MongoDB, Qlik, Snowflake, Tableau and ThoughtSpot have all introduced new capabilities aimed at better enabling customers to <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-quality-fast-failures-and-quick-wins-key-to-AI-success">successfully build agents</a> <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-quality-fast-failures-and-quick-wins-key-to-AI-success"></a>rather than merely experiment with agentic AI development.</p> <p>Driven by customer feedback, Teradata is similarly aiming to improve the success rate of AI development initiatives, first with capabilities introduced earlier this year and now with the Autonomous Knowledge Platform, according to Sumeet Arora, the vendor's chief product officer.</p> <p>"Customer feedback was central," he said. "It came from hundreds of direct conversations with enterprises about how their relationship with data is changing -- who uses the platform, how they use it, and in what ways they need it to work differently as AI agents become part of daily operations. Those signals shaped every major element of the platform.</p> <p>Specific elements of the platform include the following:</p> <ul type="disc" class="default-list"> <li>AI Studio to provide a single place for organizations to build, deploy and <a href="https://www.techtarget.com/searchdatamanagement/feature/Data-and-AI-governance-must-team-up-for-AI-to-succeed">govern AI tools</a>, including an agent for hybrid data retrieval, end-to-end AI and machine learning pipelines, and model lifecycle management tools.</li> <li>Tera, an AI-powered workspace featuring a natural language interface where users can execute agentic workflows.</li> <li>Tera agents, which are prebuilt tools for specific tasks.</li> <li>Teradata Cloud, the Autonomous Knowledge Platform's first available deployment option featuring elastic compute and active compute capabilities to address the cost and performance of AI workloads and integrations with data sources to reduce data duplication.</li> <li>Teradata Factory to enable on-premises deployments for customers with <a href="https://www.techtarget.com/whatis/definition/data-sovereignty">data sovereignty</a> and <a href="https://www.techtarget.com/searchenterpriseai/tip/Global-AI-legislation-and-regulation-tracker">regulatory concerns</a>.</li> </ul> <p>"Underneath all of it is … AI moving closer to the data, not data moving to AI," Arora said. "That's an architectural principle that has shaped the platform from the ground up."</p> <p>From a competitive standpoint, even as a spate of other data management and analytics providers introduce capabilities aimed at improving the AI development process, Teradata's new suite is distinguished from those of competing vendors in some ways, according to Catanzano.</p> <p>In particular, he noted that capabilities which attempt to eliminate the need to choose either <a href="https://www.techtarget.com/searchdatacenter/tip/AI-capacity-planning-Balancing-flexibility-performance-and-risk">high performance</a> or low cost, and either cloud or on premises, are potential differentiators. In addition, the concept of autonomous knowledge -- the delivery of business context to agents -- is significant.</p> <p>"Autonomous knowledge that embeds business context, semantics and lineage directly into the platform gives agents trusted, governed understanding rather than just data access, setting it apart from vendors offering basic AI infrastructure," Catanzano said.&nbsp;"It seems to be a new, unique approach."</p> <p>Regarding the configuration of the Autonomous Knowledge Platform, he added that it seems logically built. However, Catanzano suggested that more features that create <a href="https://www.techtarget.com/searchbusinessanalytics/news/252515720/Gartner-Augmented-analytics-ecosystem-for-BI-now-key">a data and AI ecosystem</a> through integrations would add further effectiveness.</p> <p>"More clarity on real-time integration capabilities with existing enterprise systems and third-party tools would strengthen confidence in its ability to operate seamlessly across complex, heterogeneous environments," he said.</p> <p>Like Catanzano, Petrie called out the value of giving users the option to deploy their AI systems on premises, noting that BARC's research shows enterprises are expressing greater <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-navigate-data-sovereignty-for-AI-compliance">concern about data sovereignty</a> driven by regulatory mandates and US political developments.</p> <p>"While not unique in the industry, the Factory option for on-prem deployments is critical," he said. "Data platform vendors must meet [data] sovereignty requirements to compete in the global arena."</p> <p>In addition, Petrie noted that Teradata's new cost control and model lifecycle management capabilities help the Autonomous Knowledge Platform stand apart from competing AI development and management suites.</p> <p>"Many AI adopters struggle to anticipate and measure their consumption of AI tokens, which -- as with cloud compute -- can lead to budget-breaking bills," he said. "I also like Teradata's model lifecycle management capabilities. .... The more Teradata can help data and AI teams optimize how they build, train, and iterate ML models, the better they reduce complexity and speed AI projects."</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>After introducing the Autonomous Knowledge Platform, one of Teradata's next initiatives is to deepen the platform's capabilities to improve its ability to handle agentic <a href="https://www.techtarget.com/searchhrsoftware/feature/Beyond-Containment-Structuring-IT-for-enterprise-AI-at-scale">AI workloads at enterprise scale</a>, according to Arora.</p> <p>In addition, adding industry-specific context for AI similar to what ThoughtSpot is doing with its <a href="https://www.techtarget.com/searchbusinessanalytics/news/366640328/ThoughtSpot-domain-specific-Spotter-agents-target-AI-success">domain-specific Spotter agents</a> is part of Teradata's product development roadmap, Arora continued.</p> <p>"The frame for the next six months [is] serving enterprises with agents, and agents themselves [with agents]," he said. "Both are customers of this platform."</p> <p>Focusing on industry-specific agentic capabilities is wise, according to Catanzano.</p> <p>&nbsp;"Teradata could expand its ecosystem by developing industry-specific agent templates and prebuilt autonomous workflows tailored to verticals like healthcare, finance, and manufacturing," he said.</p> <p>A marketplace for agents developed by third parties and integrations with <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms">agentic platforms</a> would also serve the needs of Teradata's users and perhaps attract new customers, Catanzano added.</p> <p>"Creating a marketplace for third-party agents and integrations would attract new users seeking rapid deployment while giving existing customers more flexibility to customize autonomous intelligence for their unique business processes," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> As many enterprises prepare to move past experimenting with agents, the vendor's new platform is purpose-built to help users move pilots into production. https://cdn.ttgtmedia.com/rms/onlineimages/iot_g1199144987.jpg https://www.techtarget.com/searchdatamanagement/news/366642649/Teradatas-latest-targets-putting-agentic-AI-into-production Thu, 07 May 2026 08:30:00 GMT <p>As institutions like the United Nations (U.N.) call on people and organizations around the globe to come together to take action before the climate crisis worsens, climate terminology comes to the forefront, as well as how certain terms differ from one another.</p> <p>For example, <i>greenhouse gas emissions</i> and <i>carbon emissions</i> are often used interchangeably, but they have important distinctions.</p> <section class="section main-article-chapter" data-menu-title="What are greenhouse gas emissions?"> <h2 class="section-title"><i class="icon" data-icon="1"></i><a name="_1ijkdj1d3vke"></a>What are greenhouse gas emissions?</h2> <p>Put simply, a <a href="https://www.techtarget.com/whatis/definition/greenhouse-gas">greenhouse gas</a> (GHG) is a type of vaporous matter -- or gas -- in a planet's atmosphere that traps heat. There are several greenhouse gas types. On Earth, these include carbon dioxide, methane, nitrous oxide, fluorinated gases and water vapor.</p> <p>Emissions are the release of such gases into the atmosphere.</p> <p>So, greenhouse gas emissions are the release of greenhouse gases such as carbon dioxide and methane into Earth's atmosphere, which is meant to protect it from space.</p> </section> <section class="section main-article-chapter" data-menu-title="What are carbon emissions?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>What are carbon emissions?</h2> <p>In the simplest terms, carbon emissions are just a specific category -- carbon dioxide emissions -- of greenhouse gas emissions. Carbon emissions are sometimes referred to as carbon pollution.</p> </section> <section class="section main-article-chapter" data-menu-title="Why do people confuse the terms?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Why do people confuse the terms?</h2> <p>The reason people might confuse terms such as <i>greenhouse gas emissions</i>, <i>GHGs</i>, <i>carbon emissions</i>, <i>carbon dioxide</i> and <i>carbon pollution</i> has to do with two main areas:</p> <ul class="default-list"> <li>Carbon as the main driver of rising greenhouse gas emissions.</li> <li>How most people best understand concepts around climate change.</li> </ul> <h3>Carbon emissions drive rising GHGs</h3> <p>Carbon emissions, which come primarily from burning fossil fuels, receive so much attention because they're the main driver of climate change and global warming. Because of that, <i>carbon</i> is often used as shorthand to mean greenhouse gas emissions.</p> <p>Here is a cheat sheet for some common carbon-related terms:</p> <ul class="default-list"> <li><b>Carbon.</b> The shortened way to refer to carbon dioxide.</li> <li><b>Carbon dioxide equivalent.</b> A common unit to describe different greenhouse gases based on their global warming potential; also called <a href="https://www.techtarget.com/sustainability/feature/CO2-vs-CO2e-What-is-the-difference-and-why-does-it-matter">CO2e</a>.</li> <li><b>Carbon emissions.</b> The discharge of carbon dioxide into the atmosphere.</li> <li><b>Carbon footprint.</b> The total amount of greenhouse gases that an individual or organization generates.</li> <li><b>CO2.</b> Scientific shorthand for the chemical compound carbon dioxide.</li> </ul> <h3>Confusion around climate change communication</h3> <p>People who become familiar with climate change terminology might forget to tailor their communication in a way their audience will best understand.</p> <p>For example, although the terms <i>greenhouse gas emissions</i>, <i>carbon emissions</i> and <i>carbon pollution</i> all appear in association with climate change, people in the U.S. associate the terms <i>carbon emissions</i> and <i>carbon pollution</i> more with human and environmental harm, compared with the term <i>greenhouse gas emissions</i>, according to the 2023 study "Evaluating Terms Americans Use to Refer to 'Carbon Emissions,'" published in the journal <i>Environmental Communication</i>.</p> <p>People are also more likely to understand that fossil fuels create carbon pollution and emissions, rather than greenhouse gas emissions.</p> <p>Studies like this suggest that language matters, and <a href="https://www.techtarget.com/sustainability/feature/Key-sustainability-communications-strategies-for-businesses">explaining sustainability issues</a> in terms that a nonscientific public can understand is critical.</p> </section> <section class="section main-article-chapter" data-menu-title="How GHGs and carbon emissions heat the planet"> <h2 class="section-title"><i class="icon" data-icon="1"></i>How GHGs and carbon emissions heat the planet</h2> <p>Greenhouse gases, such as methane, nitrous oxide, fluorinated gases and carbon dioxide, are increasing and are responsible for global warming and climate change.</p> <p>Here's how it works: Greenhouse gases trap heat in the atmosphere by absorbing and reemitting infrared radiation back toward Earth's surface, which then keeps the planet warmer than it would be otherwise. This is what's known as the greenhouse effect.</p> <p>In an actual greenhouse, sunlight can pass through the glass walls, enabling the plants within to absorb it. The plants and soil then emit some of the absorbed heat energy as infrared radiation, which the glass absorbs and emits back into the greenhouse. This helps the greenhouse retain heat and stay warmer than it otherwise would be.</p> <p>Greenhouse gases such as methane and carbon work similarly. The sun's radiation passes through the atmosphere and is absorbed by Earth's surface. Some of that energy is emitted back into the atmosphere as infrared radiation. Some of the radiation passes back into space. But GHGs absorb some of the radiation and reflect it back to Earth again to heat the planet.</p> <p>Ideally, greenhouse gases keep Earth's temperature balanced -- not too cold and not too warm.</p> <p>The problem is that with the start of the Industrial Age, around the mid-1700s, people have increasingly mined, extracted and burned fossil fuels such as coal, oil and natural gas. These fossil fuels have increased and disrupted the level of greenhouse gases and, in turn, driven climate change.</p> <p>The scientific community is virtually united in agreement that climate change is real, that humans have caused and continue to worsen it, and that all stakeholders must take decisive and major action.</p> <p>The Intergovernmental Panel on Climate Change, a U.N. global scientific body commonly known as the IPCC, calls on everyone to <a target="_blank" href="https://www.un.org/en/actnow/ten-actions" rel="noopener">get involved</a> in addressing climate change to secure a livable future.</p> <p>Business and IT professionals, in particular, have a number of ways to get involved, including <a href="https://www.techtarget.com/sustainability/tip/Ways-to-reduce-an-organizations-digital-carbon-footprint">reducing the digital carbon footprint</a>, creating a <a href="https://www.techtarget.com/sustainability/feature/Green-office-ideas-businesses-can-explore">more sustainable office</a> and implementing solid <a href="https://www.techtarget.com/sustainability/tip/How-companies-can-improve-their-carbon-accounting-practices">carbon accounting practices</a>.</p> <p><i>Diann Daniel is a former executive editor who oversaw a number of sites within Informa TechTarget's Enterprise Software and Services group, including Sustainability and ESG.</i></p> <p><i>Jacob Roundy is a freelance writer and editor with more than a decade of experience in a variety of tech topics, such as data centers, business intelligence and sustainability.</i></p> </section> The terms 'greenhouse gas' and 'carbon' are often used interchangeably. Learn what the meaning of each is and how the two relate to climate change and global warming. https://cdn.ttgtmedia.com/rms/onlineimages/esg_g1440816946.jpg https://www.techtarget.com/sustainability/feature/Understand-greenhouse-gas-emissions-vs-carbon-emissions Thu, 07 May 2026 00:00:00 GMT <p>Tableau, which has long been one of the most respected platforms for business intelligence, is in a time of transition as its customers' data needs evolve from traditional BI to AI.</p> <p>The vendor, which is a subsidiary of Salesforce, was part of a small group of vendors last decade, including Qlik and Microsoft with its Power BI platform, that enabled users to develop vibrant data visualizations that made data accessible to <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Top-4-self-service-BI-benefits-for-organizations">self-service users</a>&nbsp;in addition to trained analysts and data scientists.</p> <p>The advent of the cloud and limited AI capabilities such as limited natural language processing (NLP) and decision intelligence brought new competition including ThoughtSpot and Domo, but Tableau's platform was still consistently recognized as <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Top-business-intelligence-tools-to-know-about">one of the best for BI</a>.</p> <p>AI has dramatically altered the paradigm for traditional analytics vendors.</p> <p>True NLP that allows anyone to query and analyze data, and autonomous agents that surface insights and execute business workflows, have lessened enterprises' emphasis on traditional data products such as reports and dashboards. Consequently, BI vendors such as Tableau are in flux, trying to serve their customers by finding a new role in what is no longer an analytics workflow, but instead an AI workflow.</p> <p>Tableau on Tuesday <a href="https://www.techtarget.com/searchbusinessanalytics/news/366642778/Tableau-repositions-for-AI-unveils-new-knowledge-layer">unveiled the Agentic Analytics Platform</a>, a new set of features including a knowledge engine designed to feed agents and other AI tools the contextually relevant data they require.</p> <p>With the platform, Tableau is positioning itself not as an endpoint for analysis, but an underlying layer for AI-fueled actions. And by doing so, Tableau is demonstrating its attempt to remain viable by providing a different kind of value to its customers than in the past, according to Micheal Ni, an analyst at Constellation Research.</p> <p>"Tableau hasn't lost relevance, but it has shifted from setting the pace to trying to reassert its role in a market that's moved from being defined by insights to being defined by AI-first interaction models," he said.</p> <p>However, Tableau is no longer part of a small group of BI vendors that is significantly more advanced than its competition, Ni continued.</p> <p>"Tableau has adapted by adding Tableau Next, Model Context Protocol servers, agentic analytics [and pushing its] semantic layer, but in response to the market rather than as the innovation pacesetter."</p> <p>Demetri Salvaggio, vice president of customer experience and operations at Engine, a travel platform provider based in Denver, and a Salesforce user for about eight years, similarly noted that Tableau is evolving, and so far doing so in step with its customers with features such as <a href="https://www.techtarget.com/searchbusinessanalytics/news/366571065/Tableau-launches-Pulse-a-GenAI-fueled-insight-generator">Tableau Pulse</a> and the new knowledge layer for AI.</p> <p>"Tableau Pulse, natural language querying and the Agentic Analytics Platform have all landed in roughly the same window we've been scaling," he said. "The platform is moving in the same direction we are, and that alignment matters more than any single feature."</p> <section class="section main-article-chapter" data-menu-title="Addressing customer needs"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Addressing customer needs</h2> <p>Founded in 2018, Engine provides a travel platform built for small and medium-sized businesses, enabling its customers to book and manage business trips.</p> <p>Almost from its inception, Engine used Salesforce to capture <a href="https://www.techtarget.com/searchcustomerexperience/definition/CRM-customer-relationship-management">customer relationship management</a> (CRM) data. More recently, it became a Tableau customer as well, driven by an increasing investment in the Salesforce ecosystem that also includes the CRM giant's Data Cloud and <a href="https://www.techtarget.com/searchcustomerexperience/news/366640897/Salesforce-Agentforce-head-discusses-future-of-AI-agents">Agentforce</a>, according to Salvaggio.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Tableau hasn't lost relevance, but it has shifted from setting the pace to trying to reassert its role in a market that's moved from being defined by insights to being defined by AI-first interaction models. </figure> <figcaption> <strong>Michael Ni</strong>Analyst, Constellation Research </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Engine was using BI tools before adopting Tableau, and it looked at vendors in addition to Tableau when re-evaluating whether its analytics layer was set up to support its growth.</p> <p>"Tableau won because of the ecosystem fit, not because of a feature checklist," Salvaggio said. "Native Salesforce integration, Data Cloud as a shared foundation, and a roadmap that was clearly heading toward agentic analytics -- that combination meant we could consolidate rather than stitch."</p> <p>Now, Tableau's addition of capabilities that help <a href="https://www.techtarget.com/searchdatamanagement/opinion/Why-data-semantics-matters-for-context-aware-systems">deliver context to agents</a> is keeping Engine a customer, he continued.</p> <p>Tableau is not abandoning its BI platform. In fact, the vendor recently launched a new premium version of Tableau Desktop. In addition, enabling <a target="_blank" href="https://www.tableau.com/community#:~:text=The%20Tableau%20Community%20is%20the%20official%20home,questions%20and%20get%20answers%20from%20the%20community" rel="noopener">the user community</a> that the vendor calls its DataFam to see and understand data remains at the core of its mission, according to <a href="https://www.techtarget.com/searchbusinessanalytics/feature/New-Tableau-leader-talks-vendors-evolution-in-era-of-AI">Mark Recher</a>, who was named Tableau's new executive vice president and general manager in March.</p> <p>To remain relevant to its customers as <a target="_blank" href="https://kpmg.com/us/en/media/news/q1-ai-pulse2026.html" rel="noopener">AI becomes more ubiquitous</a>, Tableau needs to do more than merely provide analytics capabilities, he noted. Toward that end, it is adding the enablement of users to take action to its mission of helping customers see and understand data, Recher said.</p> <p>That addition -- or transition -- is key, according to William McKnight, president of McKnight Consulting.</p> <p>"Tableau is in the middle of a high-stakes transformation," he said. "It still holds its reputation as the gold standard for visual storytelling and deep data exploration, but the AI era has challenged its role as the center of the analytics universe [and it is] fighting to become more of the 'brain' of the enterprise, as opposed to the great dashboard builder."</p> <p>Similarly, Salvaggio said that <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Top-11-business-intelligence-challenges-and-how-to-overcome-them">evolving beyond traditional BI</a> to become an enabler of AI is critical for Tableau to retain customers and serve their growing AI needs.</p> <p>Engine is building an agentic enterprise, he noted. To date, it has developed EVA, a virtual support assistant that helps customers book flights, hotels and rental cars. EVA currently handles half of all chat support cases without human intervention, and Engine plans to develop expand its use of AI.</p> <p>As a result, it needs tools that enable its agents to <a href="https://www.techtarget.com/searchcustomerexperience/answer/5-ways-to-improve-call-center-agent-performance">act appropriately</a>.</p> <p>"The biggest unlock test is for Tableau to keep closing the loop between insight and action," he said. "That's where we want it to go, and so far, the trajectory is right."</p> </section> <section class="section main-article-chapter" data-menu-title="Part of the pack"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Part of the pack</h2> <p>Although Tableau is meeting the changing needs of customers such as Engine, it is not the only BI provider to evolve beyond its roots as AI has continued to evolve rapidly over the past few years.</p> <p>Microsoft and Google are each taking a similar approach to Tableau, according to McKnight. As Microsoft <a href="https://www.techtarget.com/searchdatamanagement/news/366631393/Microsoft-adds-new-AI-development-OneLake-tools-to-Fabric">builds out Fabric</a>, an AI-fueled platform for data management and analytics that includes Power BI, and Google <a href="https://www.techtarget.com/searchbusinessanalytics/news/366615533/Googles-Looker-taking-an-agentic-approach-to-generative-AI">adds new functionality to Looker</a>, both are aiming to position their tools as APIs that push intelligence to the rest of user AI ecosystems.</p> <p>"Tableau is positioning itself as the authoritative API that feeds the rest of your AI ecosystem, which is a forward-thinking move [but] not innovative," McKnight said. "It is the entry stakes for staying relevant in a workplace where people no longer want to browse for insights."</p> <p>In addition to hyperscalers Microsoft and Google, traditional BI specialists GoodData and ThoughtSpot are adding capabilities that are designed to discover contextually relevant data for agents and other AI applications.</p> <p>GoodData in March <a href="https://www.techtarget.com/searchbusinessanalytics/news/366640059/GoodDatas-Context-Management-aims-to-make-AI-trustworthy">launched Context Management</a>, a layer like Tableau's Agentic Analytics Platform that is built on semantic modeling to discover and deliver the data that enables AI to produce accurate outputs. ThoughtSpot has similarly emphasized its semantic layer in recent product development initiatives, and in March <a href="https://www.techtarget.com/searchbusinessanalytics/news/366640328/ThoughtSpot-domain-specific-Spotter-agents-target-AI-success">unveiled agents</a> with industry-specific contextual awareness to engender trust in AI.</p> <p>Still other traditional BI vendors such as Qlik -- perhaps Tableau's closest competitor of the past -- and Domo have evolved to become more full-featured data platform providers.</p> <p>Qlik added a data integration platform prior to the dawn of the AI era and has since created an environment for customers to <a href="https://www.techtarget.com/searchbusinessanalytics/news/366641671/Latest-Qlik-tools-target-helping-users-achieve-AI-goals">build AI tools</a>. Domo similarly now <a href="https://www.techtarget.com/searchbusinessanalytics/news/366640792/Domo-doubles-down-on-AI-with-latest-platform-additions">provides a development environment</a> that enables users to build agents and other AI applications.</p> <p>"Tableau's Agentic Analytics Platform is credible and competitive, but it reflects convergence with the market more than clear separation from it," Ni said. "Tableau's platform is strong in intelligence, providing depth in semantics and governance to serve as a trusted decision input, while letting competitors focus on areas like AI-native user experiences and building analytic applications."</p> <p>Matt Aslett, an analyst at ISG Software Research, similarly noted that Tableau appears to be making an effective transition from providing BI capabilities to playing a role in AI. Key to Tableau's evolution -- and the evolution of all former BI specialists -- will be facilitating the understanding of relationships between data and enabling federated querying across sources such as <a href="https://www.techtarget.com/searchdatamanagement/news/366545117/Lakehouse-architecture-the-best-fit-for-modern-data-needs">data lakehouses</a> without forcing users to move data into a single system.</p> <p>"The analytics providers that are first to deliver this combination of functionality to market will be in pole-position to lead the race towards agentic analytics, and also fend off growing competition from data platform providers attempting to disintermediate analytics providers with conversational and analytics functionality of their own," Aslett said.</p> <p>For Engine, the key to remaining with Tableau will be how it evolves to fit into Engine's growing data and AI architecture, according to Salvaggio. A keen observer of not only Tableau but also its competitors, he noted that Power BI and ThoughtSpot each have added impressive <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Experts-predict-NLP-to-be-biggest-BI-trend-this-year">NLP capabilities</a> and agentic AI tools.</p> <p>"The question isn't, 'What's the best standalone analytics product?' It's, 'What fits the architecture we've already committed to?' Salvaggio said. "We run on Salesforce, Data Cloud and Agentforce. Putting a non-native analytics layer on top of that stack would create exactly the integration and governance overhead we deliberately moved away from. The Agentic Analytics Platform widens that gap rather than closes it."</p> </section> <section class="section main-article-chapter" data-menu-title="Remaining relevant"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Remaining relevant</h2> <p>While the Agentic Analytics Platform represents evolution for Tableau, it's only part of what the vendor must do to serve the needs of its customers as they transition away from traditional BI reports and dashboards to AI-powered insight generation and actions.</p> <p>For example, Engine has a list of features it would like to see Tableau add as it makes AI enablement a focus in addition to BI.</p> <p>In the near future, Salvaggio said it would like tighter integration between <a href="https://www.techtarget.com/searchbusinessanalytics/news/366622614/Tableau-enters-the-agentic-AI-era-with-the-launch-of-Next">Tableau Next</a> -- an agentic platform that integrates AI into workflows -- and Agentforce so that analytics findings can directly trigger agents to take action or review workflows without human intervention. In addition, it is hoping that Tableau adds agent observability capabilities that proactively detect and surface anomalies.</p> <p>Longer-term, Engine wants Tableau Next to evolve into a decision layer for agents, Salvaggio continued.</p> <p>"Tableau Next [should be] the unified decision layer across the agentic enterprise -- service, sales, supply, finance, all feeding into the same governed substrate," he said. "We're already moving that direction architecturally. We'd love the analytics layer to meet us there."</p> <p>McKnight noted that Tableau is wisely taking advantage of its existing <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Agents-semantic-layers-among-top-data-analytics-trends">semantic modeling</a> capabilities to develop tools that feed AI applications with contextual awareness.</p> <p>"Tableau needs to shift from a visual destination to a governed semantic engine that grounds AI agents in trusted, consistent logic," he said.</p> <p>However, Tableau's transition from an interface for BI to an infrastructure layer for AI won't be easy, he continued.</p> <p>Aslett similarly pointed out that transitioning to <a href="https://www.techtarget.com/searchenterpriseai/post/AI-agents-are-only-as-smart-as-the-data-that-feeds-them">a context layer for AI</a> is a logical evolution for BI providers such as Tableau and its peers.</p> <p>Ni, meanwhile, suggested that Tableau focus on providing vital information that enables customers to understand why things are happening within their business so they can act on that understanding.</p> <p>"If Tableau wants to win the next phase by evolving from 'trusted knowledge' to a 'trusted decision' system, it needs to help operators answer which decisions actually moved the business, and which didn't," he said. "That means shifting the question from 'margin declined in region X' to 'this pricing change improved margin by Y%.'"</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> With its new context layer for AI, the vendor is attempting a needed evolution as agents and other cutting-edge tools reduce enterprise reliance on traditional analytics. https://cdn.ttgtmedia.com/rms/onlineimages/code_g684641103.jpg https://www.techtarget.com/searchbusinessanalytics/feature/Tableau-in-transition-as-AI-forces-BI-vendors-to-evolve Wed, 06 May 2026 15:06:00 GMT <p>AI needs data centers, but many residents don't want them.</p> <p>Developers are building data centers across the country to support growing cloud demand and AI infrastructure needs. What started as mundane industrial development has snowballed into a charged public debate, raising questions about how data center development affects the communities around them. Through town hall shouting matches, ballot petitions, rallies and sometimes even violent altercations, communities are coming together to oppose development.</p> <p>According to a <a target="_blank" href="https://www.datacenterwatch.org/report" rel="noopener">recent report</a> from Data Center Watch, local, bipartisan opposition blocked or delayed $64 billion worth of data center projects between March 2024 and March 2025. As of March 2025, at least 142 activist organizations existed across 24 states -- and experts have suggested that number has grown in the past year as data center opposition gathers even more traction.</p> <p>"Community groups are not going away," said Jessica Sharp, a Wilmington, Ohio, resident who's organizing efforts to oppose the proposed $4 billion AWS data center campus in her city. "You're not just going to steamroll us. We've built too much momentum by working together … and we're not giving up the fight and our way of life."</p> <p>Many residents feel they're owed more than what operators are offering in exchange for accepting data centers in their towns and cities. Others feel that data center development should be halted or significantly reduced. And many more say that, above all else, the system for data center development is broken, as new developers too often ignore stakeholder concerns to bullishly push their builds forward.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Community groups are not going away … we're not giving up the fight and our way of life. </figure> <figcaption> <strong>Jessica Sharp</strong>A Wilmington, Ohio, resident </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>As more businesses adopt and deploy AI, they will increasingly depend on these data centers. Business leaders consequently need to consider how concerns about data center development can affect their own AI strategies -- from delayed or canceled projects to escalated costs to reputational backlash. Businesses have a role to play in advocating for input, transparency, participation and collaboration from all affected parties, especially residents, to help move the needle on the issues surrounding AI data center development today.</p> <section class="section main-article-chapter" data-menu-title="AI data centers seek new homes"> <h2 class="section-title"><i class="icon" data-icon="1"></i>AI data centers seek new homes</h2> <p>Burgeoning interest in AI has led to the proliferation of new data centers in recent years. They house the servers, networking equipment and other IT infrastructure that power AI. While similar to traditional data centers, the current crop of <a href="https://www.techtarget.com/searchdatacenter/tip/Balancing-automation-with-human-oversight-in-AI-data-centers">data centers supporting the AI boom</a> has unique needs, such as specialized hardware and vast computing power.</p> <p>Recent McKinsey &amp; Company research <a target="_blank" href="https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-cost-of-compute-a-7-trillion-dollar-race-to-scale-data-centers" rel="noopener">estimated</a> that <a href="https://www.techtarget.com/searchenterpriseai/feature/Communities-call-for-transparency-in-AI-data-center-deals">AI data centers</a> will require $5.2 trillion in Capex by 2030 to meet global demand for AI. Many companies are already making the necessary investment; S&amp;P Global <a target="_blank" href="https://www.spglobal.com/market-intelligence/en/news-insights/articles/2025/12/record-breaking-data-center-investments-m-a-in-2025-amid-ai-demand-96002955" rel="noopener">said at the end of 2025</a> that data center M&amp;A and investment hit over $61 billion worldwide, with the U.S. leading the way in data center growth.</p> <p>Much of that growth comes from massive cloud service providers such as AWS and Google. Often called hyperscalers, these companies operate specialized data centers for extreme scale. A hyperscale data center <a target="_blank" href="https://www.ibm.com/think/topics/hyperscale-data-center" rel="noopener">can house</a> more than 5,000 servers and use upward of 100 megawatts (MW) of power, covering 10,000 or more square feet and requiring massive energy and cooling systems.</p> <p>"We need more data centers because AI is taking off," said Darrell West, a senior fellow at the Brookings Institution's Center for Technology Innovation. "AI is being deployed virtually in every area … and it requires very high computational power, so data centers provide that type of computer processing."</p> <p>To build these data centers, developers are turning to rural areas with less advanced technology. Large land masses in states like Virginia, Texas and Ohio are prime targets. As of April 2026, according to Data Center Map, <a target="_blank" href="https://www.datacentermap.com/usa/virginia/" rel="noopener">Virginia alone</a> has 598 data centers, many of which make up what's called the Data Center Alley in Ashburn, Va. Texas sits at 439, and Ohio has 203.</p> <p>Data center locations usually depend on the availability of land, electricity, water and other resources, as well as the incentives and other unique advantages a community or state might offer, West said. For example, Virginia offers proximity to federal government agencies, which are increasingly using AI, especially for defense, along with the state's land and energy resources, he said.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/jessica-sharp_planned-site-for-data-center-f.jpg"> <img data-src="https://www.techtarget.com/rms/onlineimages/jessica-sharp_planned-site-for-data-center-f_mobile.jpg" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/jessica-sharp_planned-site-for-data-center-f_mobile.jpg 960w,https://www.techtarget.com/rms/onlineimages/jessica-sharp_planned-site-for-data-center-f.jpg 1280w" alt="Photo of field that is the potential location of the AWS data center in Wilmington, Ohio." data-credit="Jessica Sharp" height="420" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Potential location of the $4 billion AWS data center being considered in Wilmington, Ohio. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Data center operators promise community benefits"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Data center operators promise community benefits</h2> <p>Community leaders often pursue and partner with data center operators, welcoming development of these facilities for their potential benefits, such as job growth and tax revenue.</p> <p>An AI data center can create hundreds of jobs, said Douglas Swain, president of Logistix Property Group, a land development company specializing in data center land entitlement. What's more important is the quality of jobs, he added, noting that jobs at these facilities typically pay 50% more than a state's average wage. They're tech jobs in a growing tech industry.</p> <p>Data centers particularly promote job growth during construction, said Dan Diorio, vice president of state policy at the Data Center Coalition. For example, from May 2023 to May 2024, the U.S. Census Bureau <a target="_blank" href="https://www.naiop.org/research-and-publications/magazine/2024/fall-2024/business-trends/diving-deeper-into-construction-spending/" rel="noopener">measured</a> that data center construction spending increased by 69%, he said. And in 2023 alone, the U.S. data center industry contributed over four million jobs and $400 billion in labor income, according to the Data Center Coalition's <a target="_blank" href="https://static1.squarespace.com/static/63a4849eab1c756a1d3e97b1/t/67b38f78e9cf125daf756112/1739820925392/Data+Center+Economic+Contribution+Study+2025_Final.pdf" rel="noopener">2025 study</a>, "Economic contributions of data centers in the United States."</p> <p>"[Workers] make careers out of these short-term jobs," Diorio said. These jobs give them the experience and education they need to move on to more projects.</p> <p>For campus data centers -- which are particularly large and often hyperscaler-funded -- community benefit is even more fruitful, Swain added. Those projects take longer and involve multiple buildings and long-term vendors, so the jobs are more sustainable. That, in turn, can lead to more demand and investment in nearby housing and other infrastructure. "There's a lot of spin-off benefits in terms of eating, living and spending money within the community," he said.</p> <p>Data centers also sometimes offer a cleaner alternative to existing industrial infrastructure. For example, a <a target="_blank" href="https://mainemorningstar.com/2026/04/22/as-gov-mills-weighs-data-center-ban-projects-mixed-on-what-the-impact-would-be/" rel="noopener">data center project</a> in Jay, Maine, is on the site of a former paper mill. The data center would use less water than the paper mill did, and developers plan to replace the on-site gas-fired power plant with a solar field. Because the state anticipated the project to be significantly beneficial to the town, Gov. Janet Mills sought to exempt it from a bill proposing a temporary moratorium on data center development in March 2026; Gov. Mills subsequently vetoed that bill in April, and the state legislature was <a target="_blank" href="https://themainemonitor.org/jay-data-center-can-proceed/" rel="noopener">unable to override that veto</a>.</p> <p>Another benefit that spurs community involvement is property tax revenue. Operators can pay massive property taxes back to the cities and towns, money that these communities can use for infrastructure and other needs, West said. This is already happening in certain areas. For example, Covington, Ga., <a target="_blank" href="https://www.wsbtv.com/news/local/newton-county/covington-city-council-passes-resolution-end-property-taxes-fund-budget-with-data-center-revenue/CZ5C5HSBUBDZTC7M5IPTMVJECY/" rel="noopener">plans to eliminate</a> residential property taxes entirely due to the revenue it expects to receive from an AWS data center under construction there, Diorio said.</p> <p>In exchange for the tax revenue AI data centers promise and to compete with offers from other municipalities, community leaders have historically offered operators tax abatements or other incentives. These can include sales tax exemptions, tax incremental financing (TIF) agreements and payments in lieu of taxes (PILOTs). These financial incentives ensure that AI data centers return predictable, long-term revenue.</p> <p>However, some residents said tax incentives aren't always in the community's best interest. Quintin Kroger Kidd, a Wilmington resident, is part of active protests against the AWS data center campus, which is in the initial stages of development. AWS sought a 30-year, 100% property tax abatement for the data center. Under the <a target="_blank" href="https://www.wyso.org/news/2025-12-02/amazon-proposes-4b-data-center-in-wilmington-seeks-tax-abatement" rel="noopener">proposed TIF agreement</a>, the city and various city organizations, such as the Wilmington City Schools, would receive a PILOT that amounts to only about 30% of the property tax AWS would have to pay without the abatement, Kidd explained.</p> <p>Many residents, including Kidd, view these tax breaks as a free handout to billionaire developers. And sentiment against tax abatement is prevalent in other areas where data centers are going in.</p> <p>"These big tech companies think that they have their choice of the land, and they have these small rural towns, whose officials don't know better, tripping over themselves to hand out things like tax abatements," Sharp said. "It stems from misunderstandings and also data center lobbyist groups acting like this is a clean industry and no public health impacts, which couldn't be further from the truth."</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> [Communities] are now demanding that companies pay them. </figure> <figcaption> <strong>Darrell West</strong>Senior fellow, Brookings </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Prior to the AI boom, data center development often facilitated a symbiotic relationship between developers and communities. However, given rising concerns about the negative effects that hyperscale data centers can potentially have on communities, residents are realizing they have more bargaining power with data center developers than they might have originally thought, Brookings' West said. For that reason, tax abatements are becoming less common than they were even six months ago.</p> <p>"Instead of paying the companies by offering tax incentives, [communities] are now demanding that companies pay them -- that they pay their full taxes and also provide other financial benefits in the community to quell the public concerns that have developed about data centers," West said.</p> </section> <section class="section main-article-chapter" data-menu-title="What makes data centers so controversial?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>What makes data centers so controversial?</h2> <p>Despite the allure of economic growth, many residents are concerned about living near an AI data center. There's often a fundamental ideological difference between community members and what a data center represents, West said. In rural areas, residents particularly enjoy the rugged landscape; they would much rather look out their window at farmland instead of a large data center.</p> <p>Aside from data centers being an eyesore, residents cite a range of concerns, starting with their utility bills. Because AI data centers require massive amounts of electricity, residents worry they'll drive up regional electricity demand, causing bills to skyrocket. Another fear is that increased demand could cause grid overload that leads to supply issues, especially in hot summer months. "On social media, you see a lot of posts about people complaining about their utility bills," Kidd said.</p> <p>These facilities can sometimes <a target="_blank" href="https://hls.harvard.edu/today/how-data-centers-may-lead-to-higher-electricity-bills/" rel="noopener">use more energy</a> than large cities. That, coupled with the rapid growth of data centers in concentrated areas, could cause bills to rise -- either because of distributed increases in utility infrastructure costs or because demand is rising faster than energy supply. Data center operators often disclose plans to bear the cost of new infrastructure or demand, but verifying these commitments and operator compliance can be difficult due to confidential contracts.</p> <p>"We had an [electricity] rate increase last year, and AEP Ohio just announced there's another 40% interest increase coming this year," said Nikki Gerber, a resident of Adams County, Ohio. "We don't even have a data center here yet -- we are paying for the demand needed up at the ones in Columbus and New Albany," two other Ohio cities with data center development.</p> <p>There's also growing concern over water use. AI data centers often consume up to <a target="_blank" href="https://www.eesi.org/articles/view/data-centers-and-water-consumption" rel="noopener">five million gallons</a> of water per day for cooling. Many residents anticipate that water will primarily come from surrounding freshwater sources, leading to increased strain and even shortages. For Gerber, water is a particular concern. She has repeatedly asked officials and developers what damage the proposed data center in Adams County will cause the town's aquifer and has suggested they conduct impact studies. She said she hasn't received a response.</p> <p>Water use has also raised red flags for environmental conservationists. For example, a proposed data center in Urbana, Ohio, is two miles from Cedar Bog Nature Preserve, a protected wetlands area. Many residents, along with the Cedar Bog Association, have <a target="_blank" href="https://www.cedarbognp.org/urgent-request-on-opposition-to-data-center-in-urbana-oh/" rel="noopener">opposed the development</a>, citing concerns over how groundwater disruption could affect Cedar Bog. The Urbana city council <a target="_blank" href="https://www.springfieldnewssun.com/news/urbana-neighbors-cedar-bog-advocates-push-back-against-data-center/article_ef8b79ef-69ee-52f1-a625-70058877177e.html" rel="noopener">recently passed</a> a 12-month moratorium on data center development to study its potential impact.</p> <p>The proposed AWS data center in Wilmington has sparked similar concerns about its effects on wildlife. The <a target="_blank" href="https://www.nbc4i.com/news/local-news/columbus/ohio-epa-weighs-allowing-data-centers-to-release-wastewater-into-rivers/" rel="noopener">Ohio Environmental Protection Agency</a> is currently deciding whether to permit data centers to release untreated wastewater and stormwater into Ohio rivers and streams. <a target="_blank" href="https://natureforward.org/data-centers-and-water-use/" rel="noopener">Data center water pollutants</a> include biocides, corrosion inhibitors and heavy metals, such as lead. Many people are anxious about how introducing these pollutants into the water systems will affect wildlife and potable water in Wilmington and across the state, Kidd said.</p> <p>Living close to a hyperscale data center can also cause adverse health effects from air pollution, noise and other sources, <a target="_blank" href="https://www.frontiersin.org/journals/climate/articles/10.3389/fclim.2026.1648912/full" rel="noopener">according to a recent study</a> of Virginia's Data Center Alley. Effects range from steady light pollution that keeps residents awake at night to long-term health outcomes like respiratory and cardiovascular diseases.</p> <p>Data Center Coalition's Diorio questioned the methodology of that study, noting how it, and much of the rhetoric on health effects, overestimated the time data center generators are on. Those generators operate only during emergencies and short testing and maintenance periods, he said, citing Virginia's Joint Legislative Audit and Review Commission (VJLARC) <a target="_blank" href="https://jlarc.virginia.gov/pdfs/reports/Rpt598-2.pdf" rel="noopener">2024 study</a> that found generators in the Data Center Alley area were used at most twice a year and only for a few hours each time. Diesel generators are a "relatively small contributor to regional air pollution," the study said.</p> <p>At the AWS data center in Wilmington, Ohio, the plans include 252 generators that will be used for backup power. Each generator is expected to run for about ten hours per year for testing, maintenance and emergencies, said John Werkman, economic development manager with Amazon, at a <a target="_blank" href="https://www.wnewsj.com/2026/03/27/updated-article-planning-commission-tables-revised-aws-data-center-site-plan/" rel="noopener">recent special town meeting</a>. That's higher use than the VJLARC study found.</p> <p>Sharp's backyard faces the proposed Wilmington AWS data center. She said one of her top concerns is how the long construction period will affect her family. "That's going to be a huge burden, especially felt by the families in the closest proximity," she said.</p> <p>Data centers often emit low-frequency noise pollution, Sharp said. "In the air-cooled facilities like AWS uses, the fans are extremely noisy and create a lot of low-frequency noise," she said. "With ambient noise being so quiet in a rural community, that's going to be really pervasive and irritating."</p> <p>Sharp is also concerned about air pollution, noting that the hyperscale facility could negatively affect air quality compared to the current open field at the proposed data center location. "My husband has asthma, so I worry about him and our kids having disproportionate health impacts," she said.</p> <p>For some residents, lackluster job growth and housing are also issues. The jobs promised by data center developers either aren't enough to justify the development or are given to outsiders more often than to locals.</p> <p>"On some of the new data centers that are being developed, we found in our research that they typically might generate 500 construction jobs over a period of several years, and then once the data center is up and running, it may require just 100 jobs to operate," West said.</p> <p>Fluctuating job growth leaves residents weary. A 2025 <a target="_blank" href="https://www.businessinsider.com/data-centers-tax-subsidies-jobs-ohio-2025-5" rel="noopener">Business Insider analysis</a> found that, on average, even the largest U.S. data centers employ fewer than 150 permanent workers. Moreover, many residents worry that, because there's no mandate to hire from within the community, outsiders will move into the area seeking jobs, raising rents and reducing housing availability -- as was the case in Abilene, Texas, where Stargate's data center expansion <a target="_blank" href="https://time.com/7362401/ai-stargate-data-center-abilene-housing-crisis/" rel="noopener">caused a serious housing crisis</a> last year.</p> <p>In more rural areas, community members cite home value depreciation from the moment a data center is announced. "There are 'For Sale' signs all over my neighborhood," Sharp said. "I've seen houses on the market since December with no movement -- and this is in what was once a desirable area in Wilmington. It's not normal here to see a house on the market for five to six months, and some of these are brand new homes."</p> </section> <section class="section main-article-chapter" data-menu-title="Lack of transparency adds to growing resentment"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Lack of transparency adds to growing resentment</h2> <p>For residents, the top worry about data center development often isn't housing issues, rising utility bills or even adverse environmental impacts. Their main concern is that they're too often left out of the conversation entirely.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> They're … completely missing the point of what it means to be a good neighbor in a community. </figure> <figcaption> <strong>Jessica Sharp</strong>A Wilmington, Ohio, resident </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>In the initial stages of data center development, agreements between operators and local governments are typically confidential. Local officials and groups sometimes sign nondisclosure agreements, or NDAs, that bar them from discussing proposed developments. The rationale for these agreements is that developers need them to protect competitive advantages and proprietary data, and to ease the flow of information between local officials and infrastructure providers, Diorio said.</p> <p>Some states are even enshrining this sort of confidentiality in law: The recently passed <a target="_blank" href="https://search-prod.lis.state.oh.us/api/v2/general_assembly_136/legislation/hb184/07_EN/pdf/" rel="noopener">Ohio House Bill 184</a> included a confidentiality clause, Sec. 9.66(D), which stated that any business information submitted to a political subdivision for economic development assistance is confidential and can't be disclosed to the public, whether anonymized or not.</p> <p>This clause was particularly upsetting, Kidd said. HB 184 concerned an unrelated issue -- limitations on intercollegiate athlete contracts -- and the inclusion of the confidentiality clause caught residents off guard. In the context of data center development, it means that deals between operators and local government entities remain secret. Many feel this goes against the intention of Ohio's Sunshine Laws.</p> <p>Sharp shared a similar sentiment about the closed-door nature of data center development in Wilmington. The Clinton County Port Authority rushed the process under NDAs, and the data center deal was basically complete before it was public knowledge, she said. This involved changing zoning codes to allow for data center development in light industrial zones and then rezoning a parcel of land for light industrial use -- all without community knowledge.</p> <p>Gerber dealt with similar transparency issues. She spent the past three years working on rerouting the Buckeye Trail -- a famous, more than 1,400-mile trail loop in Ohio -- to make Manchester, Ohio, a trail junction village where hikers could resupply, boosting the local economy. She received approval for the project in January 2026 and was surprised a week later when the village announced that a data center would be built on the land, and her reroute couldn't proceed.</p> <p>It was like having the rug ripped out from under her, she said. "They just completely obliterated everything I was working on," she added.</p> <p>The polarizing nature of massive data center development and the secrecy surrounding some of the deals have left many residents feeling like the status quo must change.</p> <p>"They've taken away our seat at the table to decide what we want for our communities," Sharp said. "From a PR standpoint, they're failing … and completely missing the point of what it means to be a good neighbor in a community."</p> </section> <section class="section main-article-chapter" data-menu-title="How residents are fighting data center development"> <h2 class="section-title"><i class="icon" data-icon="1"></i>How residents are fighting data center development</h2> <p>Residents opposed to data center development are making their thoughts known in town halls across the country. The National Conference of State Legislatures <a target="_blank" href="https://www.ncsl.org/fiscal/which-states-are-banning-data-centers" rel="noopener">lists 14 states</a> that are considering some form of a data center development ban. Port Washington, Wis., passed the country's first anti-data center <a target="_blank" href="https://www.politico.com/news/2026/04/08/wisconsin-city-passes-nations-first-anti-data-center-referendum-00863432?utm_medium=bluesky&amp;utm_source=dlvr.it" rel="noopener">referendum</a> last month. Public opposition is no doubt putting pressure on officials to act.</p> <p>Some residents are taking matters into their own hands. Take Gerber, whose name is on a proposed Ohio constitutional amendment to ban the development of data centers larger than 25 MW in the state. The Ohio Residents for Responsible Development, a grassroots group of concerned citizens, is leading this initiative to amend the state constitution.</p> <p>"We have 72 counties covered out of 88 counties in the state … with county leads taking signatures," she said of the effort to get the more than 400,000 signatures needed to put the amendment on the November state ballot.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/barry-blankenship_gorman-farm-signing-event-f.jpg"> <img data-src="https://www.techtarget.com/rms/onlineimages/barry-blankenship_gorman-farm-signing-event-f_mobile.jpg" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/barry-blankenship_gorman-farm-signing-event-f_mobile.jpg 960w,https://www.techtarget.com/rms/onlineimages/barry-blankenship_gorman-farm-signing-event-f.jpg 1280w" alt="Photo of drive-through petition signing event in Trenton, Ohio." data-credit="Barry Blankenship" height="746" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Drive-through petition signing event at the Gorman Farm in Trenton, Ohio, where residents are helping get the more than 400,000 signatures needed to put a data center ban on the state's November ballot. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <p>Sharp is another Ohio citizen fighting data center development. She's the lead organizer for the Wilmington Residents for Responsible Development group. She recently <a target="_blank" href="https://www.wnewsj.com/2026/04/23/residents-file-lawsuit-over-proposed-aws-data-center-in-wilmington/" rel="noopener">filed a lawsuit</a> against Wilmington for its lack of transparency regarding the proposed AWS data center. The complaint alleges the city didn't follow required notice procedures when rezoning the development site -- which is next to residential homes, including Sharp's -- for data center development.</p> <p>The suit also raises other concerns, such as a request for damages to be awarded to the plaintiffs should the rezoning go through, and it calls for more explicit communication and transparency moving forward. Sharp's group also secured enough signatures to get a referendum on the upcoming November ballot so residents can vote on whether to rezone 500 acres for industrial data center development.</p> <p>Joseph Miller, director of PauseAI UK, the British arm of the PauseAI nonprofit that coordinates local groups to mitigate AI risks, said informing people of the risks and local organization are key to getting the attention of governments and decision-makers.</p> <p>One of PauseAI's biggest achievements, Miller said, was organizing the largest AI safety protest in London, drawing 300 people. The U.S. is seeing similar protests, though on a smaller scale. In March 2026, <a target="_blank" href="https://abc7news.com/post/sf-protesters-call-ai-pause-anthropic-openai-xai-white-house-pushes-national-framework-trump-seeks-liability-limits/18752242/" rel="noopener">demonstrators in San Francisco</a> called on AI companies to halt development, citing existential threats the technology could bring.</p> <p>In a few cases, growing resentment toward data centers and AI technology in general has led to violence. In April 2026, an Indianapolis <a target="_blank" href="https://www.cbsnews.com/news/indianapolis-councilor-ron-gibson-home-shooting-data-centers-note/" rel="noopener">city council member</a> woke up to find 13 bullet holes in his home and a note on his front door reading "No Data Centers." A few days later, someone targeted the home of Sam Altman, OpenAI's CEO, with a <a target="_blank" href="https://www.theguardian.com/technology/2026/apr/18/sam-altman-house-attack-ai" rel="noopener">Molotov cocktail</a>.</p> <p>Many community members aren't going to such extremes, however. For some, like Gerber and Sharp, the goal isn't to stop data center development entirely but to force officials to be more transparent about it. Neither Gerber nor Sharp is opposed to AI itself but rather to the communication tactics of data center developers, operators and local officials. This is an opinion many data center developers and advocacy groups actually share -- more communication and collaboration are needed between local communities and developers.</p> </section> <section class="section main-article-chapter" data-menu-title="The need for communication and sustainable development"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The need for communication and sustainable development</h2> <p>Bruno Berti, senior vice president of global product management at NTT Global Data Centers, a data center operator, said engagement with residents and prioritization of sustainable practices are the secret ingredients in his company's latest data center development projects.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Community engagement is going to be bigger for us. </figure> <figcaption> <strong>Bruno Berti</strong>Senior vice president of global product management, NTT Global Data Centers </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>An NTT data center project in Gainesville, Va., was ultimately approved for construction after NTT representatives attended local meetings and spoke with residents, Berti said. "It's becoming a lot more prevalent that we have to answer these questions," he said. "Community engagement is going to be bigger for us."</p> <p>Community engagement provides NTT with an opportunity to "demystify narratives" about data centers that are most concerning to residents, Berti added. Take, for example, potential concerns about the rising cost of electricity driven by data center consumption. While studies show that AI data centers can put a massive strain on power grids, that doesn't mean they're always the only culprits behind electricity rate increases.</p> <p>Increases in electric rates can stem from other factors, such as inflation and President Trump's tariffs, but people often attribute them to data center development, Brookings' West said. "The public is connecting data centers to electric rate increases, either fairly or unfairly, and blaming the data center developers for that."</p> <p>The Data Center Coalition's Diorio said grid capacity and electric rates typically are the responsibility of utility providers and depend on the grid infrastructure. Because of outdated systems and providers that haven't invested in their utility infrastructure, some communities can face negative consequences from hosting data centers.</p> <p>There are communities where data centers don't fit residents' needs, Diorio added. Generally, it's the responsibility of developers and operators to ensure they build in areas where they can operate without negatively affecting the people who live there. Practicing this foresight and respecting local communities' interests can demonstrate a commitment to sustainable and responsible development and improve public sentiment.</p> <p>NTT Global and other data center developers are working to engage with residents on their concerns about grid load and electricity rates, Berti said. This includes implementing peak shaving, in which data centers use backup power generation when the community's power load reaches a certain level. This enables data centers to continue operating without drawing excess energy from the grid, preventing brownouts and higher electricity costs for residents and businesses in the area.</p> <p>Increased generator use circles back to concerns about air pollution. Many data centers use diesel generators for backup power, which can harm the surrounding environment. However, some developers are turning to innovative, green technologies, such as fuel cells and renewable energy, Berti said. Fuel cells produce electricity without expelling harmful emissions because they rely on electrochemical reactions rather than combustion.</p> <div class="youtube-iframe-container"> <iframe id="ytplayer-0" src="https://www.youtube.com/embed/yGkfBo2iSiI?autoplay=0&amp;modestbranding=1&amp;rel=0&amp;widget_referrer=null&amp;enablejsapi=1&amp;origin=https://www.techtarget.com" type="text/html" height="360" width="640" frameborder="0"></iframe> </div> <p>Diorio mentioned that more data centers are using solar, wind and geothermal sources of energy to power operations. Operators are also exploring nuclear power as small modular reactors are being developed in the U.S. According to S&amp;P Global Energy, hyperscalers <a href="https://www.spglobal.com/en/research-insights/special-reports/look-forward/data-center-frontiers/data-center-sustainability-faces-challenges-amid-growth">outpaced</a> other industries in clean energy procurement in 2024 and 2025; however, they're unlikely to meet the decarbonization goals set for their data centers.</p> <p>While many data centers place substantial strain on water systems, others have managed to <a target="_blank" href="https://www.fwpcoa.org/content.aspx?page_id=5&amp;club_id=859275&amp;item_id=130961" rel="noopener">cut freshwater use</a> by as much as 70% with more sustainable closed-loop cooling systems, according to the Florida Water and Pollution Control Operators Association. Closed-loop systems consume water once and then reuse it to cool their systems, reducing runoff that drains into local ecosystems or contaminates groundwater.</p> <p>However, closed-loop systems still use chemicals and aren't necessarily a silver bullet. And developers' arguments don't always hold sway with residents. For example, Prologis, which is building a data center in Trenton, Ohio, <a target="_blank" href="https://trentonoh.gov/DocumentCenter/View/2403/Prologis-and-Staff-Public-Forum-Agenda-?bidId=" rel="noopener">told the local community</a> that increased industrial utility revenue from its data center could offset any residential rate increases; however, its explanations haven't done much to quell residents' concerns.</p> <p>Not all data center development is created equal, and developers that are trying to use sustainable practices have an imperative to communicate that more effectively to the communities involved. Even with these nuances, however, much of today's hyperscale data center development forgoes renewables and isn't as transparent as it could be, leaving residents in the dark about any semblance of benefits or valuable trade-offs.</p> </section> <section class="section main-article-chapter" data-menu-title="Businesses have a say in this data center debate"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Businesses have a say in this data center debate</h2> <p>Business leaders have a role to play in the future of data center development with their wallets and their advocacy. As workloads continue to scale and demand more data center resources, businesses must assess which data center operators they want to invest in and what they expect from them in terms of transparent, ethical development.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> There's no single right answer for how to engage with data center development. </figure> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Companies face the potential for disruption if they don't take data center controversies seriously. For example, if a business uses a facility that becomes a target of a local protest, it could experience delayed AI model training or deployment, performance issues due to restricted energy or water use, or higher operational costs as data center operators navigate permit modifications, regulatory compliance and litigation defense. Equally worrisome is the potential for reputational damage. If the data center an enterprise is using becomes the target of negative publicity, it could find itself also targeted by proxy.</p> <p>Businesses can mitigate these risks by partnering with responsible data center operators. These operators prioritize community engagement; use sustainable approaches to energy production and cooling; commit to bolstering community benefits and mitigating harm; and step away from NDAs and confidentiality to increase much-needed transparency.</p> <p>NTT's Berti suggested that NDAs might no longer be necessary for developers. "Part of the secrecy was trying to make sure that people didn't know where the data centers are located. I don't think that's an option anymore. Everybody knows where data centers are," he said. "The other reason we used to do it was we didn't want our competitors to know where we were building a data center."</p> <p>NDAs provided competitive advantages, Berti said, such as preventing competitors from buying up land and negotiating power agreements with local utility companies. However, many of those reasons aren't valid anymore, he added. "If you really look at the industry … there's enough capacity for all of us to be successful," he said. "I think working together is actually more important."</p> <p>While Diorio still sees security and competitive advantage as legitimate justification for data center developers' secrecy, he said he agrees that community engagement pushes the industry forward. Unfortunately, newer developers and operators entering the marketplace are using resources and engaging with community stakeholders irresponsibly, he said.</p> <p>There's no single right answer for how to engage with data center development. For better or worse, data centers are an economic and technology necessity -- not only for AI, but for cloud computing, data storage and everyday devices. However, a divide is quickly emerging in the industry between developers that seek to work with communities and those that develop regardless of community wishes and well-being.</p> <p>It's up to businesses and business leaders to push for the former over the latter, financially incentivizing operators to champion sustainable, transparent strategies, and constructively communicate and collaborate with local communities. To do this, companies can incorporate responsible data center procurement into their <a href="https://www.techtarget.com/searchenterpriseai/feature/Leading-AI-with-ethics-The-new-governance-mandate">wider ethical AI</a> and green AI initiatives, both of which are increasingly important as responsible AI practices become a must-have in the enterprise.</p> <p><i>Everett Bishop is an associate site editor for Informa TechTarget's AI &amp; Emerging Tech group, covering AI, quantum computing and other emerging technologies. He graduated from the University of New Haven in 2019.</i></p> <p><i>Olivia Wisbey is a site editor for Informa TechTarget's AI &amp; Emerging Tech group. She has experience covering AI, machine learning and other emerging technologies.</i></p> </section> Your company might not be building an AI data center, but your AI workloads likely run in them. Here's what you need to know about the land-use war around data center development. https://cdn.ttgtmedia.com/rms/onlineimages/storage_g1197646065.jpg https://www.techtarget.com/searchenterpriseai/feature/Communities-call-for-transparency-in-AI-data-center-deals Wed, 06 May 2026 13:46:00 GMT <p><i>As Tableau evolves to meet the changing needs of customers in the emerging era of AI, the vendor is doing so under a new leader.</i></p> <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/recher_mark.jpg" alt="Mark Recher, executive vice president and general manager, Tableau">Mark Recher </div> <p><i>Mark Recher, who has been with </i><a href="https://www.techtarget.com/searchbusinessanalytics/news/252490246/Tableau-Salesforce-integration-now-in-full-swing"><i>Tableau's parent company Salesforce</i></a><i> for more than 12 years, was named executive vice president and general manager of Tableau when Ryan Aytay, who was appointed CEO of Tableau in May 2023, stepped down after nearly </i><a href="https://www.techtarget.com/searchbusinessanalytics/news/366536579/Ryan-Aytay-named-new-CEO-of-Tableau-filling-vacant-slot"><i>three years in the role</i></a><i>.</i></p> <p><i>Recher takes over Tableau's leadership during a time of transition, not only for Tableau but for all data and analytics vendors.</i></p> <p><i>Tableau, along with competing vendors such as Qlik and Microsoft with its Power BI platform, was hailed as one of the innovators of the self-service analytics era, enabling users to create dazzling data visualizations that made it easy to digest and understand data. In recent years, in step with technological advances, Tableau evolved to include decision intelligence features and, most recently, AI capabilities including agents.</i></p> <p><i>Now, however, as enterprises make AI </i><a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026" rel="noopener"><i>the main focus of their development initiatives</i></a><i> and agentic AI becomes the new interface for business intelligence, removing analysis and insight generation from reports and dashboards and autonomously delivering actionable information to users in any system, Tableau and its peers must evolve. With their platforms no longer needed the way they were in the past, analytics vendors are trying to find new ways of serving a purpose in data and AI workflows.</i></p> <p><i>Tableau, with the introduction of </i><a href="https://www.techtarget.com/searchbusinessanalytics/news/366642778/Tableau-repositions-for-AI-unveils-new-knowledge-layer"><i>the Agentic Analytics Platform</i></a><i> on Tuesday at the vendor's user conference in San Diego, is attempting to become a knowledge layer for AI, enabling agents and other AI tools to access the trusted, high-quality data they need to have the </i><a href="https://www.techtarget.com/searchdatamanagement/opinion/Why-data-semantics-matters-for-context-aware-systems"><i>contextual awareness</i></a><i> to perform properly.</i></p> <p><i>In his first public comments since taking over as Tableau's new leader, Recher discussed his vision for the vendor in an exclusive interview. In addition, Recher spoke about goals he has in his new role, the challenges he's hearing about from customers as he meets with them for the first time and how Tableau plans to address them, and the opportunities the pace of innovation presents.</i></p> <p><b>Editor's note</b>: <i>This Q&amp;A has been edited for clarity and conciseness</i>.</p> <p><b>In the past when I'd speak with Tableau about product development, the message was always that the vendor's aim was to make data as easy to consume for as many people as possible. As we evolve into a new era of AI, what do you view as Tableau's mission as you become its leader?</b></p> <p>Mark Recher: I think this is the most exciting time in data and analytics, and I think the future is very exciting.</p> <p>For Tableau, the vision and where we're taking it is grounded in the original mission, which was helping everyone to see and understand their data. We're renewing that with a slightly different vision, which is helping everyone see, understand and act on their data. When I think about the future and where we're taking Tableau with the Agentic Analytics Platform and our knowledge engine, it's the same Tableau that our customers and the [<a target="_blank" href="https://www.tableau.com/community#:~:text=The%20Tableau%20Community%20is%20the%20official%20home,questions%20and%20get%20answers%20from%20the%20community" rel="noopener">Tableau community</a> called the] DataFam love, but it's built for the agentic era.</p> <p><b>How does Tableau Desktop, which includes a free version of your platform, play into your vision as you take over as Tableau's leader?</b></p> <p>Recher: Our incredible product, customer base, brand and community was built on one other thing [beyond trying to help everyone understand their data], which is the mission to democratize data.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> For Tableau, the vision and where we're taking it is grounded in the original mission, which was helping everyone to see and understand their data. We're renewing that with a slightly different vision, which is helping everyone see, understand and act on their data. </figure> <figcaption> <strong>Mark Recher</strong>Executive vice president and general manager, Tableau </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Something that we did 45 days ago that I'm not sure people really understand is that we launched Tableau Desktop Premium. The response has been staggering with 115,000 sign-ups. Tableau Desktop is an enormous part of what built Tableau, and we're re-grounding ourselves in that. We're going to bring all [of our] <a href="https://www.techtarget.com/searchbusinessanalytics/news/366622614/Tableau-enters-the-agentic-AI-era-with-the-launch-of-Next">agentic capabilities</a> there. Democratizing access is a core value that Tableau was founded on, and it's an important part of our future. Giving everyone the ability to see, understand and act so they can explore, build and be curious helps us build a better product and serve our customers better.</p> <p>That's our vision -- it's grounded in where it was founded, but with the extension of giving people the ability to use it for free.</p> <p><b>Every leader has their own ideas about what a company should be, so as you take over as Tableau's GM, what are some of your goals for the company?</b></p> <p>Recher: A little background on me is that I have been obsessed with data since I can remember. I was the kid looking at box scores on the sports page, trying to understand why <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Baseballs-Twins-deploy-Databricks-to-improve-analytics-power">the Twins</a> were trying to do something. I was obsessed with Bill James. <i>Moneyball </i>[by Michael Lewis] is my favorite book. In my career, I've been at Salesforce for 12 years and for the last seven have been chief operating officer of our largest businesses, and I ran those businesses on Tableau. My day, every day, starts with coffee and Tableau, so there's a kinship.</p> <p>When I think of my goals, coming into the role, one is delivering on that mission we just talked about. Second is everything in our culture being customer-obsessed and community-obsessed. It's re-grounding and delivering on our mission and making sure that, as a company, we are … obsessed on delivering on that mission through innovation.</p> <p><b>What is one other goal?</b></p> <p>Recher: It's a more exciting time to be <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Generative-AI-wont-replace-data-analysts">a data analyst</a> than ever, but those roles are evolving in an empowering way that is going to increase the impact that they make in their organizations. They're going from a data analyst to a knowledge architect to a decisions architect to an agentic analytics architect. Their ability, from Tableau, to drive their company's entire data and analytics strategy, and drive alerts across the organization when things change, is an exciting chapter, and I want to be a steward to the DataFam as they make that transition.</p> <p><b>In this time of transition, what are some of the biggest issues you are hearing about from customers as you meet with them for the first time as Tableau's leader?</b></p> <p>Recher: Every customer that I've spoken to since I've taken the role, and also in my last year in my previous role, is trying to figure out <a target="_blank" href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf" rel="noopener">how to get value from AI</a>. Specifically, since I've been talking to Tableau customers, the discussion we're having is about the difference between data and knowledge, and the importance of having a knowledge layer that <a href="https://www.techtarget.com/searchenterpriseai/tip/Exploring-the-context-layer-for-AI-systems">has the context of their business</a> so that AI can give accurate responses. There's a statistic that 89% of leaders have seen inaccurate responses from AI, and my response to that is, 'Show me the other 11%,' because when I play around with AI, I see [inaccuracy] all the time. That's one. The second issue is [wondering] how to use the investment they've made as a CIO or CEO in data to actually take action, to drive productivity, speed and value, whether that's for their organizations internally or the way they serve their customers. A lot of that has informed our strategy and evolution with Tableau.</p> <p>They all want to get value out of AI, they all are trying to translate data into knowledge, and they're all trying to figure out how to take action with that.</p> <p><b>Beyond the Agentic Analytics Platform, what are some other ways Tableau is trying to help customers get the value they're looking for from AI?</b></p> <p>Recher: Without giving away our roadmap, there will be certain domains that we continue to focus on very intentionally, places like knowledge and ontology, places like <a href="https://www.techtarget.com/searchbusinessanalytics/definition/decision-intelligence">decision intelligence</a>, and the action layer. Those are probably the three spaces, as we continue to move forward, that you will see more announcements and very interesting cool new capabilities.</p> <p><b>What have been your biggest challenges since taking over as Tableau's leader?</b></p> <p>Recher: My biggest challenge has been time. Any time I've gone into an organization or a business or a product, I get as deep as possible with the data, our customers, the community and all of that. The biggest challenge is that there's 24 hours in a day. In my very first meeting [as Tableau's leader], I showed a slide showing that there were 55 days until Tableau Conference and what we needed to focus on in those 55 days. My one ask of the team was pace and speed, because things are moving very fast right now and we're going to move faster than the market.</p> <p>It's been an amazing first two months, but as you come into a new role and try to understand every dimension of the business, you wish there were more than 24 hours in a day.</p> <p><b>How challenging is how fast technology is evolving and the need for organizations, whether data and analytics vendors such as Tableau or any company in any other industry, to keep pace?</b></p> <p>Recher: You could think of it as a challenge because of the speed of the market, or you could see it as an opportunity.</p> <p>Some of the things that are happening in the market should provide inspiration, but you also need to stay convinced of your strategy and your strengths and your advantages and what your customers want. That's how we're balancing it, and that's important for any company -- how to find inspiration and the ability to do things faster but not allow it to distract you from what matters most. I don't think of the <a href="https://www.techtarget.com/searchenterpriseai/feature/Businesses-gear-up-for-AI-agents-in-the-enterprise">speed of the market</a> as a challenge. For me, in two months, it's been a huge opportunity.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> With longtime data and analytics providers pivoting to find their role within AI workflows, Mark Recher takes over during a time of transition for the vendor. https://cdn.ttgtmedia.com/rms/onlineimages/ai_g1183318665.jpg https://www.techtarget.com/searchbusinessanalytics/feature/New-Tableau-leader-talks-vendors-evolution-in-era-of-AI Tue, 05 May 2026 11:13:00 GMT <p>A persistent gap exists between executive expectations of AI and its actual value, and the problem is not the technology. The primary constraint is workforce readiness.</p> <p><a href="https://www.techtarget.com/searchenterpriseai/tip/Questions-every-CIO-should-ask-before-investing-in-AI">Enterprise investment in AI</a> has accelerated at an unprecedented pace, with CIOs, CTOs and IT leaders under pressure to translate that spending into measurable business outcomes. Many organizations continue to approach AI as a tool deployment rather than a transformative operating model. This mindset limits AI's effect. Employees are often left to experiment without guidance, governance frameworks lag behind adoption and leaders overestimate how quickly teams can integrate AI into daily workflows.</p> <p>The answer is to reframe AI from a tool deployment to a strategic operating model that reshapes how work gets done. This mindset establishes human-machine collaboration as a core enterprise capability rather than an automation or research tool. It requires building AI literacy and skills to realize value.</p> <p>The risks of failing to shift include the following:</p> <ul class="default-list"> <li>Stalled ROI.</li> <li><a href="https://www.techtarget.com/searchsecurity/tip/Shadow-AI-How-CISOs-can-regain-control-in-2026">Shadow AI use</a>.</li> <li>Governance exposure.</li> <li>Fragmented adoption.</li> <li>Missed innovation opportunities.</li> <li>Exposure to compliance and security issues.</li> </ul> <p>IT leaders can set strategic goals for workforce readiness if they develop collaboration models, establish an employee training framework, identify common collaboration challenges and define best practices.</p> <section class="section main-article-chapter" data-menu-title="Understanding human-machine collaboration models"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Understanding human-machine collaboration models</h2> <p>IT leaders face an emerging mandate: Build a scalable AI workforce strategy instead of implementing random AI systems. To do so, organizations must evolve from treating AI as a productivity tool to viewing it as a collaborator, with a final implementation as a force multiplier embedded in workflows.</p> <p>The two most common collaboration models are the following:</p> <ul class="default-list"> <li><b>Additive.</b> Side-by-side use to increase general productivity.</li> <li><b>Multiplicative.</b> Collaborative and integrated, resulting in compounding benefits.</li> </ul> <p>It takes intentional design and management to unlock the <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-build-an-AI-augmented-workforce-The-CIOs-guide">value of employees and AI working together</a>.</p> <p>The following collaboration approaches can help leaders understand and quantify AI's contributions, listed in order of increasing value:</p> <ul class="default-list"> <li><b>AI consumers.</b> Employees use AI for basic productivity gains.</li> <li><b>AI collaborators.</b> Knowledge workers integrate AI into daily workflows and decision-making.</li> <li><b>AI orchestrators.</b> Leaders design human-AI workflows, governance and operating models.</li> <li><b>AI builders.</b> Technical teams develop, deploy and maintain AI systems.</li> </ul> <p>Deliberate designs help employees and their workflows progress through these collaboration lanes, gradually increasing employees' skills and AI tools' capabilities.</p> <p>These roles extend across the enterprise. All job roles require foundational AI literacy, with employees gradually moving between these collaboration lanes. Business leaders can guide this process with a maturity progression from experimental adoption to fully integrated, AI-enabled operations.</p> <p>Organizations that scale value successfully align workforce development and capabilities with these collaboration lanes.</p> </section> <section class="section main-article-chapter" data-menu-title="The AI training framework"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The AI training framework</h2> <p>Aligning workforce development with AI collaboration requires continuous learning rather than an ad hoc, one-time training initiative. Instead, organizations need a <a href="https://www.techtarget.com/searchcio/feature/AI-workforce-training-How-BDO-USA-trains-its-employees">tiered capability stack aligned to specific business outcomes</a> and tailored for job roles.</p> <p>The successful program integrates IT, HR, business leadership and departmental teams to identify necessary skills and goals.</p> <h3>Foundational AI literacy</h3> <p>All employees need a baseline understanding of AI to use it responsibly and effectively. This foundational layer is the minimum organization-wide standard for AI literacy.</p> <p>Building this literacy involves the following:</p> <ul class="default-list"> <li>Mandatory onboarding modules and micro-learning programs.</li> <li>In-app guidance embedded within AI tools.</li> <li>Clear references to what AI can and <a href="https://www.techtarget.com/whatis/feature/Jobs-that-AI-cant-replace-and-why">cannot do</a>.</li> <li>Human judgment requirements and oversight responsibilities.</li> <li>Basic prompt engineering techniques and safe usage practices.</li> </ul> <h3>Applied AI skills</h3> <p>Some employees require additional role-specific training to integrate AI into their daily workflows and decision-making processes. This level focuses on developing practical AI skills tied to business activities.</p> <p>This level of training includes the following:</p> <ul class="default-list"> <li>Role-based workshops and hands-on training sessions.</li> <li>Peer learning groups and collaborative experimentation.</li> <li>Simulated tasks that mirror real-world workflows.</li> <li>Advanced prompting techniques tailored to specific functions.</li> <li>Methods to evaluate outputs for accuracy, bias and relevance.</li> <li>Clear <a href="https://www.techtarget.com/searchdatacenter/tip/Balancing-automation-with-human-oversight-in-AI-data-centers">escalation paths for human review</a> when needed.</li> </ul> <h3>Strategy and governance</h3> <p>At the leadership level, enterprises must build the capability to design, manage and scale human-machine collaboration responsibly. Align this skillset with AI initiatives and business strategy.</p> <p>This level of training includes the following:</p> <ul class="default-list"> <li>Executive education programs and cross-functional forums.</li> <li>Case study analysis of successful human-machine collaboration models.</li> <li>Frameworks to design AI-enabled workflows.</li> <li>Change management strategies to drive controlled adoption.</li> <li><a href="https://www.techtarget.com/searchdatamanagement/feature/How-executives-can-build-a-responsible-AI-framework">Responsible AI principles</a> and ethical guidelines.</li> <li>Governance models that address risk, compliance and accountability.</li> <li>Established metrics to measure ROI, adoption and business effects.</li> </ul> <h3>Development and deployment</h3> <p>Technical teams require deeper expertise to build, integrate and maintain AI systems. This layer supports the operational backbone of the AI workforce and keeps it aligned with business requirements and goals.</p> <p>This level of training includes the following:</p> <ul class="default-list"> <li>Hands-on labs, <a href="https://www.techtarget.com/whatis/feature/10-top-artificial-intelligence-certifications-and-courses">certification programs</a> and vendor-led training.</li> <li>Model evaluation, testing and performance monitoring.</li> <li>Integration with enterprise systems and business workflows.</li> <li>Security, privacy and compliance implementation.</li> <li>Ongoing lifecycle management and system optimization.</li> </ul> <h3>Measurement and scalability</h3> <p>Feedback loops can continuously refine skills development and help leaders adapt training content as AI evolves and business needs change.</p> <p>Additionally, leaders must also evaluate build vs. buy vs. partner approaches to scale training programs. For example, a training partner may offer better foundational AI literacy, which the business can complement with custom-built programs for specialized roles. When learning is tied into tools and workflows, it can drive sustained behavior change.</p> <p>Metrics can identify successes and opportunities for improvement. Common metrics include the following:</p> <ul class="default-list"> <li>Adoption rates.</li> <li>Productivity and efficiency gains.</li> <li>Risk reduction and compliance metrics.</li> <li>Decision-making effectiveness.</li> <li>Workforce capability and AI literacy.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Common challenges of human-machine collaboration"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Common challenges of human-machine collaboration</h2> <p>Most <a href="https://www.techtarget.com/healthtechanalytics/feature/Challenges-of-AI-integration-in-healthcare-and-their-remedies">AI integration challenges</a> are organizational rather than technical. Left unaddressed, they directly affect cost efficiency, risk exposure and competitive positioning. The challenges fall into two categories: Operational and leadership.</p> <p>Operational and organizational challenges include the following:</p> <ul class="default-list"> <li>Employee resistance and fear driven by concerns about job displacement and a lack of clarity about AI's role.</li> <li>Skills gaps and learning curves that slow adoption and reduce confidence in AI outputs.</li> <li>Disparate tech stacks leading to fragmented experiences and inconsistent usage.</li> <li>Data quality and governance issues that undermine trust in AI systems.</li> </ul> <p><a href="https://www.techtarget.com/searchcio/feature/Why-AI-backlash-is-a-leadership-problem-not-a-tech-one">Leadership and structural challenges</a> include the following:</p> <ul class="default-list"> <li>Lack of clear ownership for AI workforce strategy across IT, HR and business units.</li> <li>Failure to recognize or reward teams for AI-enabled workflows.</li> <li>Increased shadow AI use, creating security and compliance risks.</li> <li>Over-reliance on tools without redesigning underlying processes and workflows.</li> <li>Disconnect between executive expectations and operational realities, leading to unrealistic timelines and missed ROI targets.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Best practices for human-machine collaboration"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Best practices for human-machine collaboration</h2> <p>IT leaders can direct strategy and operations to successfully integrate AI into workflows. They should begin with strategic actions, then drive adoption and scale before establishing continuous improvement practices.</p> <p>Strategic actions include the following:</p> <ul class="default-list"> <li>Assess workforce <a target="_blank" href="https://www.forrester.com/blogs/your-employees-arent-ready-for-ai-and-its-a-problem/" rel="noopener">readiness</a> to baseline skills, tools and adoption gaps.</li> <li>Define a clear AI operating model, including roles, responsibilities and collaboration opportunities across the organization.</li> <li>Establish clear AI policies and governance frameworks to guide responsible, consistent use.</li> <li>Mandate role-specific training tailored to actual workflows and business outcomes.</li> </ul> <p>The following actions can drive adoption and scale:</p> <ul class="default-list"> <li>Begin with high-impact, high-visibility use cases to demonstrate value quickly.</li> <li>Embed AI capabilities directly into core workflows rather than positioning them as optional tools.</li> <li>Establish internal AI evangelists to accelerate peer adoption.</li> <li>Align incentives and performance metrics to encourage AI usage and innovation.</li> <li>Recognize that change management is as essential as technology deployment.</li> </ul> <p>Add continuous improvement practices, including the following:</p> <ul class="default-list"> <li>Measure and iterate using defined metrics covering adoption, productivity and ROI.</li> <li>Balance speed with control when scaling AI across the enterprise.</li> <li>Continuously refine governance, training and workflows based on actual internal use.</li> <li>Treat AI collaboration as a long-term capability rather than a one-time initiative.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Wrap up: From AI adoption to AI advantage"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Wrap up: From AI adoption to AI advantage</h2> <p>Human-machine collaboration is emerging as a durable competitive advantage and core operating capability. Organizations that prioritize AI workforce readiness and strategic governance will outperform those focused solely on technology deployment. CIOs, CTOs and IT leaders must lead the transition from AI experimentation to enterprise-wide capability integration.</p> <p>AI investments bring value when paired with people who can use them effectively. The imperative is immediate: Workforce readiness is not optional; it is foundational to realizing an AI-driven transformation that drives revenue and innovation.</p> <p><em>Damon Garn owns Cogspinner Coaction and provides freelance IT writing and editing services. He has written multiple CompTIA study guides, including the Linux+, Cloud Essentials+ and Server+ guides, and contributes extensively to TechTarget Editorial, The New Stack and CompTIA Blogs.</em></p> </section> Organizations must shift from viewing AI as just a tool to a strategic collaborator. This requires workforce readiness and skills developed through training and collaboration. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a311883856.jpg https://www.techtarget.com/searchcio/tip/AI-augmented-teams-Training-for-human-machine-collaboration Tue, 05 May 2026 09:00:00 GMT <p>Once a driver of human knowledge through data, longtime analytics provider Tableau on Tuesday unveiled new capabilities designed to feed agents and other AI applications the contextual knowledge they need to be trusted by enterprises to autonomously perform in production.</p> <p>The Agentic Analytics Platform was introduced during Tableau Conference, the vendor's annual user conference in San Diego.</p> <p>Designed as a knowledge layer that automatically feeds agents and other AI tools the <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Talend-CEO-discusses-importance-of-mining-relevant-data">relevant data</a> they require to operate as intended, the Agentic Analytics Platform unifies proprietary data, the metadata that describes data to make it identifiable, and business logic <a href="https://www.techtarget.com/searchdatamanagement/opinion/Why-data-semantics-matters-for-context-aware-systems">through semantic modeling</a> to prepare data for discovery.</p> <p>Among others, key features of Tableau's knowledge layer for AI include data knowledge engine based on 20 years of semantic modeling, conversational analytics delivered through a natural language interface and security and governance.</p> <p>Business intelligence platforms such as Tableau were historically passive, enabling users to derive insights from visualizations that led to strategic decisions. The Agentic Analytics Platform is a significant addition because it marks Tableau's evolution from passive analytics to an AI-driven knowledge engine that fuels insights and actions, according to Matt Aslett, an analyst at ISG Software Research.</p> <p>"The Agentic Analytics Platform builds on Tableau's established functionality for existing users but evolves Tableau into a knowledge engine that can provide trusted context to enable human and agentic decisions and actions with advanced recommendations, summarization and automated actions," he said.</p> <p>In addition, the new suite will help Tableau, a subsidiary of CRM giant Salesforce, compete as other vendors similarly add capabilities aimed at fueling <a href="https://www.techtarget.com/searchenterpriseai/post/How-CIOs-should-architect-trust-in-AI-not-just-govern-it">trusted business decisions and actions</a>, Aslett continued.</p> <p>"Tableau … is already ahead of many of its rivals in terms of the delivery of AI-driven analytics," he said. "Its lead will be maintained by the new Agentic Analytics Platform capabilities even as all analytics software providers are in the process of adding AI-powered functionality to their products to support conversational and agentic analysis."</p> <section class="section main-article-chapter" data-menu-title="Intelligence for AI"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Intelligence for AI</h2> <p>While enterprises <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026" rel="noopener">continue to invest</a> in developing agents and other AI tools aimed at transforming their business by making employees better informed and processes more efficient, <a target="_blank" href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf" rel="noopener">many are struggling</a> to build tools that can be trusted enough to put into production.</p> <p>The problems preventing AI initiatives from moving past the pilot stage vary, but one of the main hindrances to date has been discovering and delivering the high-quality, contextually relevant data AI tools need to operate as intended.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The Agentic Analytics Platform builds on Tableau's established functionality for existing users but evolves Tableau into a knowledge engine that can provide trusted context to enable human and agentic decisions and actions with advanced recommendations, summarization and automated actions. </figure> <figcaption> <strong>Matt Aslett</strong>Analyst, ISG Software Research </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Throughout 2026, numerous data and analytics vendors have introduced capabilities designed to improve the discovery and delivery of data that will provide chatbots, agents and other AI applications with proper context.</p> <p>For example, <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">Databricks</a>, GoodData, MongoDB and <a href="https://www.techtarget.com/searchdatamanagement/news/366639802/Teradata-updates-vector-indexing-suite-to-aid-AI-development">Teradata</a> have all added capabilities that aim to improve data retrieval for AI. Now, Tableau is doing the same with its knowledge layer for AI.</p> <p>Mark Recher, who was appointed general manager of Tableau in March after <a href="https://www.techtarget.com/searchbusinessanalytics/news/366536579/Ryan-Aytay-named-new-CEO-of-Tableau-filling-vacant-slot">Ryan Aytay</a> departed following three years as the vendor's CEO, noted that the Agentic Analytics Platform represents Tableau's growth beyond self-service and augmented analytics to agentic analytics.</p> <p>"It's taking actions -- pairing insights with actions and the ability, in your organization, to surface information someone needs to know before they even know they need to know it," he said.</p> <p>Key is the knowledge engine, he continued. Connecting AI to data is not enough. AI <a href="https://www.techtarget.com/searchenterpriseai/tip/Exploring-the-context-layer-for-AI-systems">requires context to be effective</a>, and Tableau's new capabilities are designed to provide that needed context.</p> <p>"We've had a knowledge layer -- a semantic layer -- inside Tableau for decades," Recher said. "What we're announcing is a knowledge graph. You cannot provide agentic analytics without trusted knowledge which actually understands the context of your business."</p> <p>Tableau's Agentic Analytics Platform includes the following:</p> <ul type="disc" class="default-list"> <li>An engine that delivers trusted knowledge based on more than 20 years of providing semantic modeling tools.</li> <li>A natural language interface that enables users to query and analyze data within any dashboard.</li> <li>A decision engine that turns insights into action by surfacing insights and triggering workflows.</li> <li>Agentic analytics anywhere through an open architecture that enables organizations to deliver contextually relevant data, through <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol (MCP) servers</a>, to custom-built external AI tools, as well as public large language models such as Anthropic's Claude and OpenAI's ChatGPT.</li> <li>A command center for agentic analytics that combats <a href="https://www.techtarget.com/searcherp/feature/Orchestration-is-becoming-enterprise-AIs-real-test">agent sprawl</a> by serving as the primary interface for an organization's agentic analytics strategy.</li> <li>The combined governance and security of Salesforce and Tableau.</li> </ul> <p>Conversational analytics capabilities and some MCP servers are now generally available. The knowledge engine will be GA in June and the command center will be GA in the fall.</p> <p>Like Aslett, William McKnight, president of McKnight Consulting, noted that the Agentic Analytics Platform is an important addition for Tableau because it uses context to move beyond rear-facing analysis to AI-powered insight generation and action.</p> <p>"Tableau's Agentic Analytics Platform introduces new capabilities that transform the software from a passive visualization builder into an autonomous system capable of taking trusted, proactive actions," he said. "By leveraging a company's existing business logic, it empowers … while maintaining centralized governance."</p> <p>In addition, the Agentic Analytics Platform features capabilities that could help distinguish Tableau as data and analytics vendors <a href="https://www.techtarget.com/searchdatamanagement/opinion/The-race-to-build-the-ultimate-data-platform">reposition themselves</a> for AI-powered analysis and process automation, McKnight continued.</p> <p>"Most competitors treat AI as a feature inside their own walled garden," he said. "You have to use their chatbot to access their data. Tableau is taking a different path by positioning itself as an authoritative data service. Through MCP, it enables external agents to reach in and retrieve trusted numbers."</p> <p>However, there are more capabilities that Tableau could include with the platform&nbsp; to <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Baseballs-Rangers-seek-analytics-edge-with-Tableau">enable its users</a> to build and manage agents, McKnight continued.</p> <p>"The new platform appears to provide a robust governance framework, [but] the platform is missing resolution of multiple agent logic overlap and the incorporation of unstructured data context into the agent processing," he said.</p> <p>Aslett, meanwhile, noted that by focusing on context, Tableau's Agentic Analytics Platform is in line with the capabilities that <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Top-business-intelligence-tools-to-know-about">competing data and analytics vendors</a> are adding to their own platforms.</p> <p>"The leading providers are already looking beyond conversational interfaces and guided analytics for existing reports and dashboards," he said. "They're providing a context layer that captures established enterprise knowledge and semantic understanding and enables analytics via agents as well as external AI tools and applications."</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>While Tableau's Agentic Analytics Platform shows that the vendor is evolving to keep up with competitors and provide what enterprises require to operationalize AI, engagement with Tableau users provided part of the impetus for the context layer, according to Recher.</p> <p>Tableau has a large <a target="_blank" href="https://www.tableau.com/community#:~:text=The%20Tableau%20Community%20is%20the%20official%20home,questions%20and%20get%20answers%20from%20the%20community" rel="noopener">user community</a>, and its feedback is what drives the vendor's product development.</p> <p>"We use them for information on where we're taking product strategy," Recher said. "It wasn't just hearing it from our customers, it was hearing it from the community, the people who are working it Tableau and having good understanding of what they need capability-wise to be as productive as possible in the AI era."</p> <p>Future product development will focus on improving the capabilities of Tableau's knowledge graph, adding more <a href="https://www.techtarget.com/searchbusinessanalytics/definition/decision-intelligence">decision intelligence</a> to Tableau and enabling users, including agents, to act based on their insights, he continued.</p> <p>"Those are probably the three spaces … that you will see more announcements," he said.</p> <p>McKnight, meanwhile, suggested that Tableau not only continue to invest in semantic modeling capabilities, but also add features that ease customers' transition from using the vendor's platform as a front-facing tool for analysis to an underlying layer for AI.</p> <p>Its embrace of <a href="https://www.techtarget.com/searchdatamanagement/news/366631576/New-consortium-to-aid-AI-by-standardizing-semantic-modeling">the Open Semantic Interchange</a>, an open standard that provides a universal structure for defining data so that semantics are the same across systems, will help. But changing Tableau's purpose within the data and AI workflow could still be challenging for users.</p> <p>"Tableau needs to shift from a visual destination to a governed semantic engine that grounds AI agents in trusted, consistent logic," McKnight said. "By adopting open standards … the platform ensures its business rules remain the 'single source of truth' across a headless data stack, but the transition may be brutal pivot from roots as a beloved visual interface to a background infrastructure and trust engine."&nbsp;</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> The Agentic Analytics Platform is designed to help users operationalize contextually relevant data for agents and demonstrates the vendor's ongoing evolution. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a199952058.jpg https://www.techtarget.com/searchbusinessanalytics/news/366642778/Tableau-repositions-for-AI-unveils-new-knowledge-layer Tue, 05 May 2026 08:00:00 GMT <p>Following closely on the heels of its March acquisition of master data management specialist Reltio, SAP on Monday revealed that it plans to acquire data lakehouse vendor Dremio and AI model developer Prior Labs to better enable AI development and management.</p> <p>As enterprise data management workloads evolve to become enablers of AI-powered insight generation and process automation, vendors such as SAP -- which <a href="https://www.techtarget.com/searchsap/news/366619376/SAP-data-cloud-Databricks-integration-aims-to-unify-AI-data">provides data platform capabilities</a> as part of its array of offerings beyond Enterprise Resource Planning (ERP) -- are attempting to simplify AI development and management for their customers.</p> <p>SAP's acquisition of <a href="https://www.techtarget.com/searchdatamanagement/news/366619541/Reltio-adds-real-time-data-delivery-to-fuel-fast-decisions">Reltio</a>, which SAP disclosed on March 27, adds master data management capabilities that enable users to unify their data to make it more easily accessible for AI and analytics workloads. The acquisition of Dremio, which provides a data lakehouse platform optimized for open source <a target="_blank" href="https://iceberg.apache.org/" rel="noopener">Apache Iceberg</a> tables that make data interoperable between platforms that support the format, will similarly simplify access to data for enterprise workloads.</p> <p>Purchasing Prior Labs, meanwhile, adds tabular foundation model capabilities that enable organizations to use data stored in tables to fuel&nbsp;<u>predictive AI initiatives</u>.</p> <p>"These acquisitions make sense strategically," Kevin Petrie, an analyst at BARC U.S., said. "SAP specializes in structured data, which remains the leading input for AI initiatives, and Prior Labs enriches the predictive AI capabilities that SAP users can apply to that data."</p> <p>Financial terms of the purchases were not disclosed, and each remains subject to regulatory approval and other customary closing requirements. SAP's <a href="https://www.techtarget.com/searchdatamanagement/news/366634167/Dremio-Cloud-An-autonomous-lakehouse-powered-by-AI-agents">Dremio</a> acquisition is expected to close during the third quarter of this year, while its purchase of Prior Labs is expected to close during second or third quarter of 2026.</p> <p>Founded in 2015 and based in Santa Clara, Calif., Dremio had raised $410 million in venture capital funding before its acquisition. Prior Labs, founded in 2024 and based in Berlin, had raised nine million Euros in funding.</p> <section class="section main-article-chapter" data-menu-title="Additive capabilities"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Additive capabilities</h2> <p>Although enterprises <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026" rel="noopener">continue to invest heavily</a> in developing agents and other AI applications aimed at making employees better informed business processes more efficient, many are <a target="_blank" href="https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf" rel="noopener">struggling to build AI tools</a> that can be trusted to properly perform in production.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> These acquisitions make sense strategically. SAP specializes in structured data, which remains the leading input for AI initiatives, and Prior Labs enriches the predictive AI capabilities that SAP users can apply to that data. </figure> <figcaption> <strong>Kevin Petrie</strong>Analyst, BARC U.S. </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Feeding such applications with high-quality, relevant data is one of the main barriers that halt AI initiatives.</p> <p>Through integrations developed since the start of 2025, SAP now enables users of its Business Data Cloud to access data in Snowflake and Databricks. The acquisition of Dremio's <a href="https://www.techtarget.com/searchdatamanagement/news/366545117/Lakehouse-architecture-the-best-fit-for-modern-data-needs">lakehouse capabilities</a> will extend the reach of SAP's Business Data Cloud to a new array of external data sources -- including on-premises databases -- without forcing users to move data into SAP.</p> <p>"With Dremio, we're able to add the modern lakehouse architecture. … We're super excited about this opportunity," Irfan Khan, president and chief product officer of SAP data and analytics, said during a virtual press conference.</p> <p>David Menninger, an analyst at ISG Software Research, noted that the significance of the acquisition is that it demonstrates SAP's ongoing evolution toward enabling access to more than just <a href="https://www.techtarget.com/whatis/video/An-explanation-of-SAP-ERP">SAP's ERP data</a> for its users to build effective AI and analytics tools.</p> <p>"Dremio furthers SAP's recognition that enterprise customers have lots of data that is not in SAP and represents a commitment to provide support for those data sources," Menninger said.</p> <p>Petrie, meanwhile, noted that Dremio's lakehouse platform is particularly valuable to SAP customers because of its data federation capabilities. Data federation is a data management strategy that creates virtualized views of data so it doesn't have to be moved or copied, which eliminates the cost and risk of migrating data between platforms as well as any <a href="https://www.techtarget.com/whatis/definition/data-sovereignty">data sovereignty</a> concerns.</p> <p>"The Dremio acquisition … is critical because migration complexity and rising sovereignty concerns prevent organizations from moving all their analytics and AI inputs into SAP," Petrie said, adding that Dremio rates highly in BARC's user evaluation surveys.</p> <p>The acquisition of Prior Labs is aimed at better enabling SAP users to build models that fuel <a href="https://www.techtarget.com/searchenterpriseai/tip/Generative-AI-vs-predictive-AI-Understanding-the-differences">predictive AI initiatives</a>, according to Philipp Herzig, SAP's chief technology officer.</p> <p>"Similar to large language models for unstructured data, we want to democratize the access for predictive AI, a market which is as large as the generative AI market," Herzig said during the press conference. "That is exactly where Prior Labs comes in."</p> <p>Menninger noted that enterprises have struggled to use LLMs as part of their AI pipelines given that the non-deterministic nature of LLMs can lead to different outputs based on the same input. Tabular foundation models produce more reliable outputs.</p> <p>"It is hard to govern processes where the output may change each time the process is run," Menninger said. "Prior Labs is focused on delivering foundation models for structured, tabular data. That way, users can have the flexibility of LLMs applied in a deterministic way."</p> </section> <section class="section main-article-chapter" data-menu-title="Consolidation continues"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Consolidation continues</h2> <p>Beyond better enabling SAP users to develop agents and other AI tools, SAP's acquisitions of Dremio and Prior Labs are part of <a href="https://www.techtarget.com/searchdatamanagement/feature/4-trends-that-will-shape-data-management-and-AI-in-2026">a growing consolidation trend</a>.</p> <p>Consolidation tends to come and go in cycles.</p> <p>For example, there was significant consolidation among data and analytics vendors in 2007 when IBM acquired Cognos, Hyperion was bought by Oracle and SAP purchased BusinessObjects. More followed in 2019 when Tableau was acquired by Salesforce and Google bought Looker within days of each other.</p> <p>Beginning with <a href="https://www.techtarget.com/searchcustomerexperience/news/366624960/Salesforce-to-acquire-Informatica-in-8-billion-deal">Salesforce's acquisition of Informatica</a> in a deal that ultimately closed in November 2025, another consolidation wave seems to be rising. As enterprises attempt to simplify complex AI pipelines and lower the high cost of AI development by reducing the number of tools and vendors needed to create AI workflows, independent vendors are finding it more difficult to compete.</p> <p>Last November, data integration vendor Fivetran and data transformation specialist DBT Labs&nbsp;<a href="https://www.techtarget.com/searchdatamanagement/news/366632699/Fivetran-DBT-Labs-merge-to-add-complementary-capabilities">agreed to merge</a>. In December, <a href="https://www.techtarget.com/searchdatamanagement/news/366636098/IBM-acquiring-Confluent-to-boost-AI-development-capabilities">IBM acquired streaming data vendor Confluent</a>. Now, first with Reltio and then Monday's moves to buy Dremio and Prior Labs, SAP is boosting its AI development capabilities through acquisitions of previously independent companies.</p> <p>"The acquisitions are part of an inherent cycle in the software industry," Menninger said, while noting that there is usually room for a few independent vendors in each market segment. "Large companies acquire smaller companies as a way to supplement their R&amp;D efforts. As markets become more mature, such as the data management market, it's natural for many of these companies to get acquired."</p> <p>Petrie similarly noted that acquisitions reflect platform vendors such as Salesforce, IBM and SAP taking advantage of the market to add capabilities that enable them to capture more of their <a href="https://www.computerweekly.com/microscope/news/366596056/Gartner-AI-is-driving-customer-spending">customers' spending</a>. In addition, such vendors are adding capabilities that provide competitive advantages by enabling access to competitors' platforms.</p> <p>"They recognize that migrations are tough and they need to help customers manage heterogeneous data environments," he said.</p> <p>SAP, without being specific, continues to look for further acquisition opportunities, according to Herzig.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> The tech giant's latest purchases add a data lakehouse that enables users to access data across systems and tabular foundation models that fuel predictive AI. https://cdn.ttgtmedia.com/rms/onlineimages/money_g1050046190.jpg https://www.techtarget.com/searchdatamanagement/news/366642794/SAP-acquisitions-of-Dremio-Prior-Labs-target-AI-development Mon, 04 May 2026 10:24:00 GMT Fri, 01 May 2026 08:46:00 GMT <p>Mike Capone is stepping down from his role as CEO of Qlik.</p> <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineImages/capone_mike.jpg" alt="Qlik CEO Mike Capone">Mike Capone </div> <p>Mike Lipps, Qlik's board chair, has been named interim CEO while the vendor's board conducts a search for a permanent successor, according to a Qlik spokesperson.</p> <p>Capone's move comes just over two weeks after Qlik held Connect, its annual user conference in Kissimmee, Fla., where it <a href="https://www.techtarget.com/searchbusinessanalytics/news/366641671/Latest-Qlik-tools-target-helping-users-achieve-AI-goals">unveiled new features</a> collectively aimed at helping customers deploy the vendor's AI tools to generate insights as well as develop, deploy and manage agents and other AI applications of their own.</p> <p>"After more than eight years as CEO of Qlik, I've made the difficult decision that now is the right time for me to step down from the role," Capone posted on LinkedIn. "Leading Qlik has been a true privilege. I've been fortunate to work alongside friends and colleagues who care deeply, customers who pushed us to be better, and partners who have helped take Qlik further than we could have alone."</p> <p>Based in King of Prussia, Penn., Qlik is a longtime analytics vendor that under Capone's leadership evolved into a more full-featured data platform provider <a href="https://www.techtarget.com/searchbusinessanalytics/news/252526899/Qlik-launches-new-cloud-based-data-integration-platform">featuring data integration</a> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366638938/Qlik-launches-agentic-experience-to-fuel-AI-powered-analysis">AI capabilities</a>.</p> <section class="section main-article-chapter" data-menu-title="Leadership in changing times"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Leadership in changing times</h2> <p>Capone was appointed Qlik's CEO in January 2018 after serving three years as chief operating officer at Medidata Solutions.</p> <p>His resignation comes at a time when the data management and analytics industries are evolving. Capone joined Qlik during an era when self-service analytics fueled by robust data visualizations represented the cutting edge of business intelligence (BI).</p> <p>Qlik was viewed as one of the leading BI vendors, but was acquired by private equity firm Thoma Bravo in 2016 for $3 billion and taken private so the vendor could transform for the cloud away from the scrutiny of the public market. Qlik launched Qlik Sense Cloud Business in 2016, which was replaced in 2018 by <a href="https://www.techtarget.com/searchbusinessanalytics/news/252471573/Qlik-Sense-Business-improves-Qliks-cloud-AI-capabilities">Qlik Sense Business</a>, a fully managed SaaS version of the vendor's enterprise analytics platform deployed on Qlik's own cloud.</p> <p>By January 2022, after a 5-year process to reorganize and expand by adding data integration capabilities through a series of acquisitions, <a href="https://www.techtarget.com/searchbusinessanalytics/news/252511695/Qlik-planning-an-IPO-files-application-with-the-SEC">Qlik filed paperwork</a> with the Securities and Exchange Commission for an initial public stock offering and a return to the public markets. However, economic uncertainty delayed Qlik's plans, and then OpenAI's November 2022 launch of ChatGPT sparked a seismic change for all data management and analytics vendors.</p> <p>Suddenly, with customers <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026" rel="noopener">turning their attention to AI development</a> rather than traditional BI tools such as reports and dashboards, data management and analytics providers had to become enablers of AI rather than analytics.</p> <p>Qlik evolved to meet the needs of its customers, making AI its focal point over the past few years. Going forward, as <a href="https://www.techtarget.com/searchbusinessanalytics/news/366610199/Qlik-AutoML-update-targets-trust-with-visibility-simplicity">Qlik continues to progress</a>, it will do so under the leadership of a new CEO.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> [Capone] inherited a well-regarded BI vendor and walked out with a full-stack data, integration and AI platform that the majority of the world's largest enterprises actually depend on. The piece that impressed me most was the agentic pivot over the last 18 months. </figure> <figcaption> <strong>Mike Leone</strong>Analyst, Moor Insights &amp; Strategy </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Perhaps Qlik's most significant growth under Capone came with respect to agentic AI, according to Mike Leone, an analyst at Moor Insights &amp; Strategy who followed Qlik throughout Capone's tenure.</p> <p>"[Capone] inherited a well-regarded BI vendor and walked out with a full-stack data, integration and AI platform that the majority of the world's largest enterprises actually depend on," Leone said. "The piece that impressed me most was the agentic pivot over the last 18 months, which landed faster and with more coherence than what I've seen from most of his peers in the legacy analytics space."</p> <p>In addition, revenue growth, successfully integrating an array of acquired companies and building a strong leadership team are among Capone's noteworthy accomplishments, he continued.</p> <p>Donald Farmer, founder and principal of consulting firm TreeHive Strategy and Qlik's vice president of innovation and design from 2011-16, similarly noted that Capone took over as the vendor's CEO at a difficult time and led the vendor's expansion into data integration and AI.</p> <p>"Mike Capone led Qlik through an unenviable period," he said. "Leveraged buyouts by private equity are a notoriously uncomfortable experience for staff, customers and partners. And with changes in market conditions, this has been a particularly complex time. … Capone has sustained Qlik as a largely cohesive company and technology stack, which must have been a great challenge."</p> <p>In addition to Qlik, established data and analytics vendors such as Alteryx, <a href="https://www.techtarget.com/searchbusinessanalytics/news/365535741/Sisenses-Orad-stepping-down-Katz-named-new-CEO">Sisense</a>, <a href="https://www.techtarget.com/searchbusinessanalytics/news/366571855/Snowflake-CEO-Slootman-steps-down-Ramaswamy-takes-over">Snowflake</a>, Tableau and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366612092/ThoughtSpot-taps-Salesforce-exec-Karkhanis-to-be-new-CEO">ThoughtSpot</a> have all changed their CEOs over the past few years, as the market has transitioned from a focus on traditional analytics to AI as a means of managing, exploring and analyzing data, and vendors have had to shift their strategic focus.</p> <p>Meanwhile, others including Confluent and Informatica were acquired -- and Fivetran and DBT Labs <a href="https://www.techtarget.com/searchdatamanagement/news/366632699/Fivetran-DBT-Labs-merge-to-add-complementary-capabilities">elected to merge</a> -- as the high cost of AI development makes it more difficult for independent specialists to compete with full-featured platform vendors.</p> <p>"As AI and agentic technologies reshape the world, the organizations that seize this opportunity will be those that can turn trusted data into meaningful action," Capone wrote on LinkedIn. "I am confident that Qlik is strongly positioned to help customers do exactly that."</p> <p>One of Capone's most significant accomplishments as Qlik's CEO was to help the vendor to do just what he suggested other organizations do, which is to <a href="https://www.techtarget.com/searchbusinessanalytics/news/366623963/Qlik-evolves-to-keep-up-with-latest-AI-analytics-trends">evolve with the times</a>, according to David Menninger, an analyst at ISG Software Research.</p> <p>"Capone has led a two-pronged evolution of Qlik," he said. "First, he oversaw the transition from on-premises software delivery and perpetual licensing to cloud-based software-as-a-service-model subscription licensing. Second, he oversaw the extension of Qlik from an analytics-only focus to a data and analytics focus and eventually into AI as well."</p> </section> <section class="section main-article-chapter" data-menu-title="The next CEO"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The next CEO</h2> <p>While Qlik declined to specify what traits and experience it will be looking for in its next CEO, it's likely that someone committed to Qlik's role as the connective layer for AI will be the vendor's next leader, according to Leone.</p> <p>When Snowflake CEO Frank Slootman departed in February 2024, Sridhar Ramaswamy, who had been Snowflake's senior vice president of AI for nine months after joining Snowflake in 2023 when&nbsp;<a href="https://www.techtarget.com/searchdatamanagement/news/366538520/Snowflake-acquisition-of-Neeva-to-add-generative-AI">the vendor acquired Neeva</a>, was named its new leader. Since then, Snowflake, which was slower to embrace AI than some of its competitors, has <a href="https://www.techtarget.com/searchdatamanagement/news/366638535/Snowflake-launches-new-AI-tools-unveils-OpenAI-partnership">aggressively added AI capabilities</a> and built an environment for customers to develop AI tools.</p> <p>Similarly, among others, <a href="https://www.techtarget.com/searchbusinessanalytics/news/366640328/ThoughtSpot-domain-specific-Spotter-agents-target-AI-success">ThoughtSpot</a> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366622614/Tableau-enters-the-agentic-AI-era-with-the-launch-of-Next">Tableau</a> have focused on AI and kept up with the latest trends in AI development as their leadership has changed.</p> <p>"I'd want someone fluent in the full data and AI stack as a system, with depth that reaches well beyond analytics," Leone said. "Qlik's competitive ground now is the connective tissue between data integration, governance, and agentic execution, and the next leader has to be able to hold that whole picture and prioritize across it."</p> <p>Priorities for the new CEO should include continuing Qlik's momentum with agentic AI, upgrading governance features that engender trust in SI outputs, and improving messaging related to its data engineering and lakehouse capabilities, he continued.</p> <p>Menninger likewise suggested that Qlik's next CEO will need to focus on properly positioning the vendor with respect to AI given that competition includes not only other data and analytics vendors but also <a href="https://www.techtarget.com/searchenterpriseai/tip/How-to-choose-the-right-LLM-for-your-needs">large language model developers</a>.</p> <p>"Qlik and other analytics providers face competition from multiple fronts," he said. "In addition to guiding Qlik through the competitive landscape, the new leader will also have to navigate the financial markets, creating a path for investors to recoup their investments."</p> <p>Qlik, however, is not alone among analytics providers with respect to needing to find its role as <a href="https://www.techtarget.com/searchenterpriseai/feature/Businesses-gear-up-for-AI-agents-in-the-enterprise">AI continues to gain prominence</a>, Menninger continued.</p> <p>"The key to Qlik's future, and many other analytics vendors, is how they tackle the AI world," he said. "In much the same way that Qlik had to manage the transition from on premises to cloud, they need to successfully manage the transition from BI to AI."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> With AI the dominant trend in data and analytics, the vendor's leader leaves after guiding it through its cloud transition and additions of data integration and AI capabilities. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1304896250.jpg https://www.techtarget.com/searchbusinessanalytics/news/366642652/Qliks-Capone-departs-after-eight-years-as-CEO Thu, 30 Apr 2026 15:02:00 GMT <p>With AI workloads requiring higher performance, fresher data and more complete transparency than traditional analytics workloads, vector database specialist Qdrant launched new features in Qdrant Cloud to address the demands of AI development.</p> <p>Accelerated indexing via <a href="https://www.techtarget.com/searchdatacenter/tip/How-do-CPU-GPU-and-DPU-differ-from-one-another">graphics processing units</a> (GPU) addresses performance by building the vector indexes that enable AI tools to retrieve relevant data substantially faster than was previously possible with Qdrant Cloud. Meanwhile, multi-AZ clusters guarantee that data is always available by replicating data across three availability zones and audit logging captures all operations performed through the Qdrant API <a href="https://www.techtarget.com/searchenterpriseai/feature/How-to-ensure-AI-transparency-explainability-and-trust">to provide transparency</a>.</p> <p>Given that the new features address practical issues that directly result in whether an AI tool can perform well enough to move into production, they are significant additions for Qdrant Cloud customers, according to Devin Pratt, an analyst at IDC.</p> <p>"This release is about making Qdrant Cloud more production-ready," he said. "It should help customers move faster, reduce operational risk and put stronger controls around AI retrieval."</p> <p>Vectors are numerical representations of data, including unstructured data such as text and audio, that make data searchable by agents and other automated systems so it can be discovered and used to inform AI and analytics applications.</p> <p>Qdrant, based in Berlin and New York City, is a vector database vendor that competes with fellow specialists such as <a href="https://www.techtarget.com/searchdatamanagement/news/366631366/Vector-database-vendor-Pinecone-eyes-future-under-new-CEO">Pinecone</a> and Weaviate as well as broad-based data management providers that offer vector database capabilities including <a href="https://www.techtarget.com/searchdatamanagement/news/366577632/Vector-search-and-storage-key-to-AWS-database-strategy">AWS</a>, Databricks and <a href="https://www.techtarget.com/searchdatamanagement/news/366583139/Oracle-adds-vector-search-capabilities-to-database-platform">Oracle</a>.</p> <section class="section main-article-chapter" data-menu-title="Performance for production"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Performance for production</h2> <p>Vector databases <a target="_blank" href="https://www.linkedin.com/pulse/why-vector-databases-now-hot-topic-abhishek-soni-fvacc/" rel="noopener">were introduced</a> in the early 2000s but remained a niche feature until OpenAI's November 2022 launch of ChatGPT marked significant improvement in generative AI (GenAI) technology and sparked <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026" rel="noopener">surging interest in AI development</a>.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This release is about making Qdrant Cloud more production-ready. It should help customers move faster, reduce operational risk and put stronger controls around AI retrieval. </figure> <figcaption> <strong>Devin Pratt</strong>Analyst, IDC </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>AI tools such as chatbots and agents require far more relevant data to be accurate than traditional data products, including reports and dashboards. In addition, they benefit from real-time data, so the outputs they deliver include input from the most current available information.</p> <p>With unstructured data representing most of all data, vector databases help provide the data volume AI tools demand. In addition, they can process data at high speed to guarantee the freshness of the data being fed into AI pipelines.</p> <p>As a result, throughout 2023 and 2024, <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">the popularity of vector databases exploded</a>. However, most data management tools, including vector databases, were not designed for AI.</p> <p>They were usable when enterprises were experimenting with AI, developing pilot initiatives to learn and refine their plans for AI before putting tools into production. But vector indexing alone did not deliver high enough accuracy for most projects to move past experiments, nor did vector databases have enough power to maintain performance under the scale of AI workloads.</p> <p>Now, to address the different demands of AI development, numerous vendors are replacing their capabilities with those designed to better enable enterprises to move AI projects into production.</p> <p>For example, Databricks <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">launched Instructed Retriever</a> and MongoDB introduced <a href="https://www.techtarget.com/searchdatamanagement/news/366637414/MongoDB-launches-latest-Voyage-models-to-aid-AI-development">new embedding and reranking models</a> to improve the data retrieval process, GoodData and InsightSoftware -- among others -- added and improved semantic modeling and other tools that address <a href="https://www.techtarget.com/searchdatamanagement/opinion/Why-data-semantics-matters-for-context-aware-systems">the context fed to AI</a>, and vendors including Actian and Teradata have added vector databases to address AI workloads.</p> <p>Now, Qdrant is similarly adding capabilities designed to improve AI development with the additions of GPU-accelerated indexing, multi-AZ clusters and audit logging in Qdrant Cloud.</p> <p>Like Pratt, Kevin Petrie, an analyst at BARC U.S., similarly noted that the new features address the needs of AI developers and are therefore valuable additions.</p> <p>"These features strengthen Qdrant's position as a vector search specialist that helps AI developers build sophisticated agentic applications," he said. "Qdrant seems to be thriving in this niche."</p> <p>Better performance and increased transparency are especially valuable for <a href="https://www.techtarget.com/searchcloudcomputing/tip/Is-your-compute-strategy-ready-for-AI-workloads-in-the-cloud">AI workloads</a>, Petrie continued.</p> <p>"Faster indexing helps operationalize applications in less time, which is critical as enterprises move into full-scale production with agentic AI," he said. "Audit logging is critical because AI adopters are finally starting to take governance seriously. They need transparent, explainable workflows to comply with internal policies and external regulatory requirements."</p> <p>GPUs are chips that provide the compute power that systems require to carry out workloads. Traditionally, many systems were built with central processing units, but GPUs provide substantially more power and are therefore better suited for <a href="https://www.computerweekly.com/microscope/news/366634677/AI-driving-GPU-demand">the demands of AI</a>.</p> <p>Multi-AZ clusters assure a system's reliability by replicating data across different availability zones within a region so that if availability in one zone goes down, the system still operates in the others with no delay and no need for users to act. And audit logging provides a trail that users can follow to address <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-regulation-What-businesses-need-to-know">AI's unique compliance</a> and security requirements.</p> <p>All were added to address the different demands AI workloads place on vector databases, according to Bastian Hofmann, head of product at Qdrant.</p> <p>"Vector search is running in production at scale for our enterprise customers," he said. "Multi-AZ and audit logging came directly from customer requirements -- higher uptime … and compliance visibility are essential when vector search sits on the critical path of your application."</p> <p>GPU-accelerated indexing was made available in Qdrant's open source database in 2025. Now, with CPUs not providing enough performance to power enterprise AI workloads at scale, Qdrant is adding power to its fully <a href="https://www.techtarget.com/searchnetworking/definition/managed-network-services">managed service</a>.</p> <p>"As production datasets and write volumes have grown, CPU-only indexing is no longer sufficient for certain workloads," Hofmann said. "Bringing GPU indexing to Qdrant Cloud means customers can run these heavier workloads in production without managing GPU infrastructure themselves."</p> <p>Beyond aiding existing Qdrant Cloud customers, the new features could help Qdrant distinguish its vector database capabilities from those of <a href="https://www.techtarget.com/searchdatamanagement/tip/Top-vector-database-options-for-similarity-searches">competing platforms</a>, according to Pratt.</p> <p>In particular, he noted that with high availability and audit logs becoming commonplace, the performance enabled by GPU-powered indexing -- speeding up how quickly Qdrant's database can prepare large or changing datasets for search -- could prove to be a competitive advantage.</p> <p>"The most differentiated capability in this release is faster indexing," Pratt said. "The availability and audit features matter, but they are quickly becoming enterprise expectations."</p> <p>Petrie similarly noted that the new features help Qdrant Cloud stand apart from other vector search offerings. However, vector search alone has proven insufficient for feeding AI and <a href="https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation">retrieval-augmented generation</a> (RAG) pipelines. Adding more retrieval methods could therefore further differentiate Qdrant from competitors, Petrie continued.</p> <p>"AI and RAG workflows need broader retrieval capabilities," he said. "They need to search text via keyword matching, find table values via SQL queries, identify relationships via knowledge graphs, and so on. So …. I would recommend that Qdrant broaden its retrieval methods and source data types to remain competitive in an increasingly multimodal world."</p> <div class="imagecaption alignLeft"> <img src="https://cdn.ttgtmedia.com/rms/onlineimages/how_a_vector_database_works-f.png" alt="A graphic shows how a vector database works.">Informa TechTarget </div> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>Just as Qdrant's latest features are aimed at fueling AI workloads, the vendor's product development roadmap is focused on further improving scalability, performance and <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Talend-CEO-discusses-importance-of-mining-relevant-data">search relevance</a>, according to Hofmann. In addition, Qdrant plans to add more transparency and <a href="https://www.techtarget.com/searchnetworking/tip/End-to-end-network-observability-for-AI-workloads">observability capabilities</a>, he continued.</p> <p>"On Qdrant Cloud, we're focused on operational simplicity -- easier cluster management, fewer manual steps -- and deeper integrations into enterprise systems so teams can plug Qdrant into their existing infrastructure without friction," Hofmann said.</p> <p>Pratt, meanwhile, suggested that Qdrant address how easy it is to use specialized vector search as part of a broad data ecosystem.</p> <p>It doesn't need to become a full-featured data platform that provides all the capabilities itself, he noted. But deeper integrations with <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms">AI development frameworks</a>, data warehouses, lakehouses, cloud platforms, <a href="https://www.techtarget.com/searchdatamanagement/feature/Why-enterprise-AI-initiatives-fail-without-governance">AI and data governance tools</a> and other capabilities that make up a data and AI stack would be beneficial.</p> <p>"One of Qdrant’s opportunities is to make specialized vector search easier to use inside the enterprise data platforms customers already rely on," Pratt said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> As customers look to move past experimentation and put pilots into production, the vendor's new features better prepare its platform for modern enterprise workloads. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1133705410.jpg https://www.techtarget.com/searchdatamanagement/news/366642580/Qdrant-boosts-performance-reliability-to-meet-AI-needs Wed, 29 Apr 2026 14:18:00 GMT 60 webmaster@techtarget.com