This Complete Survey Results presentation focuses on observability environments and teams, full-stack observability capabilities, observability-related tasks, AI recommendation practices, GenAI copilots, AI observability benefits and challenges, observability effectiveness, and spending preferences.
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Organizations are looking for simplified and scalable platforms that enable faster AI development, improved data quality, and enhanced accessibility to trusted data that feeds AI. Informatica’s latest Intelligent Data Management Cloud (IDMC) updates, including CLAIRE AI, are set to significantly improve data readiness and advance data management for organizations pursuing AI. CLAIRE Copilot and GPT integrations, in particular, hope to solve the data issues organizations continue to face around data integration, data quality, and data governance.
To learn more, download the free brief, Delivering AI-ready Data With Informatica and CLAIRE AI.
As AI adoption accelerates, organizations are evaluating how AI-driven workloads impact end-user support, digital workspaces, and endpoint strategies. At the same time, IT and security teams face growing challenges related to “shadow AI”—unsanctioned AI tools used by employees. Enterprises must weigh the urgency of AI adoption against other IT priorities, balancing the need to stay competitive with a cautious approach to emerging AI hardware investments. TechTarget’s Enterprise Strategy Group recently surveyed business professionals to gain insights into these trends.
To learn more, download the infographic, AI at the Endpoint: The Impact of AI on End Users and Endpoint Devices.
As organizations race to develop generative AI solutions, the importance of data readiness has become increasingly evident. The need to prepare and manage enterprise data effectively for generative AI has placed a sharp focus on data quality, governance, and bias. These factors are now shaping how and when organizations bring their generative AI solutions to life. TechTarget’s Enterprise Strategy Group recently surveyed IT and data professionals to gain insights into these trends.
To learn more, download the free infographic, Data Readiness for Impactful Generative AI.
Databricks and Anthropic’s landmark partnership delivers Anthropic’s cutting-edge Claude models directly into the Databricks Data Intelligence Platform, empowering enterprises to build and deploy secure, high-performing AI agents on their own data. This directly addresses a critical market challenge around security, governance, and integration, accelerating the adoption of enterprise-class AI solutions across numerous industries.
To learn more, download the free brief, Databricks and Anthropic Partner for Enterprise AI Agents.
Pure Storage is doubling down on the enormous potential market around providing storage and data management for running AI at scale with the recent unveiling of FlashBlade//EXA, a high-performance storage solution designed to handle the most demanding AI and high-performance computing (HPC) workloads. The announcement underscores Pure’s intent to become a force among the large- and hyper-scale players creating storage and data solutions for next-generation AI workloads.
To learn more, download the free brief, Pure Storage Deepens AI-at-scale Focus With FlashBlade//EXA.
This Complete Survey Results presentation focuses on plans for PCs with local AI compute capabilities, as well as the use cases and associated benefits and challenges among early adopters of the technology from the perspective of both end users and the IT practitioners supporting these deployments.
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As AI adoption accelerates, organizations are evaluating how AI-driven workloads impact end-user support, digital workspaces, and endpoint strategies. While AI PCs introduce new possibilities for local AI computing capabilities, their integration into enterprise environments is at an early stage, with practical use cases still emerging. Understanding how businesses align these technologies with IT and business objectives is important for establishing a baseline of expectations, driving adoption, and maximizing their potential.
At the same time, IT and security teams face growing challenges related to “shadow AI”—unsanctioned AI tools used by employees. These tools may enhance individual or team productivity but also introduce security and data governance risks. Organizations must assess the scope of this trend, the potential benefits, and the necessary controls to mitigate risks while supporting innovation.
Additionally, AI PCs are entering the market amid widespread endpoint refresh cycles, driven by both Windows 11 upgrades and post-pandemic hardware replacement. Enterprises must weigh the urgency of AI adoption against other IT priorities, balancing the need to stay competitive with a cautious approach to emerging AI hardware investments.
To gain further insights into these trends, Enterprise Strategy Group surveyed 750 business professionals in North America (U.S. and Canada), including 330 IT professionals responsible for their organization’s endpoint computing environment who have insight into how AI is impacting these environments, as well as 420 corporate knowledge workers (i.e., end users) who use or will use AI as part of their daily workflows.
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This Complete Survey Results presentation focuses on AI adoption, data quality, data privacy and security, data governance and compliance, purchase decision-making, integration and interoperability, data readiness challenges, and attributes related to data readiness.
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As organizations race to develop generative AI solutions to enable data-driven decision-making, create unique customer experiences, improve efficiencies, and build competitive advantages, the importance of data readiness has become increasingly evident. The need to prepare and manage enterprise data effectively for generative AI has placed a sharp focus on data quality, governance, and bias. These factors are now shaping how and when organizations bring their generative AI solutions to life.
This research highlights a clear demand for generative AI driven by the fear of missing out. This is set against the backdrop of data readiness influencing the pace of adoption.
To gain further insight into these trends, Enterprise Strategy Group surveyed 385 IT and data professionals at organizations in North America (U.S. and Canada) involved with or responsible for the data governance and AI technologies, processes, and programs used to manage their organization’s data.
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Regardless of company size, robust cybersecurity programs are a business-critical element of any organization that doesn’t want to risk data exfiltration, ransomware, regulatory scrutiny, and other threats. However, recent research by Enterprise Strategy Group, now part of Omdia, revealed that organizational size often correlates with the degree of development and maturity of its cybersecurity programs, especially for those operating as midmarket firms. How can these firms keep up? Where should they focus first?
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As pivotal, operations-supporting technologies, securing cloud infrastructure and applications as well as the data stored in or traveling between them is a major priority for cybersecurity teams today. Recent research by Enterprise Strategy Group, now part of Omdia, revealed that despite their size, smaller enterprises often have mature cybersecurity programs, leveraging many of the same strategies used by much larger, better staffed enterprises. And their challenges align too, as small enterprises also struggle to keep up with increasingly more diverse attack surfaces, tools complexity, and cloud security operations.
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