Features

Features

• GenAI data center infrastructure reshapes business processes

  Building and retrofitting data centers to support GenAI is not a matter of IF but WHEN, so the game plan must include power needs, grid connectivity, model training and permitting.  Continue Reading

• GenAI in product manufacturing cuts costs but adds risks

  In product design and manufacturing, GenAI systems are consolidating supplier data, improving processes and cutting significant software costs -- yet they raise unique concerns.  Continue Reading

• Overcome roadblocks to GenAI adoption and unlock ROI

  GenAI deployments can fall prey to unrealistic goals, misguided pilots, job loss fears, hidden costs and lack of trust. Governance and workforce readiness are keys to success.  Continue Reading

• Open source AI: What it means for enterprise innovation

  Open source AI is transforming enterprise innovation with greater flexibility and control, but organizations must address governance, security and operational challenges to scale effectively.  Continue Reading

• FinOps can manage AI computing costs, experts say

  FinOps is no longer just about optimizing cloud spending. Hear from experts on what makes FinOps a desirable option to help businesses manage their AI costs.  Continue Reading

• AI ethical red flags businesses must avoid

  AI tools are everywhere in businesses, but are ethical best practices keeping pace? Here are the ethical AI red flags business leaders are seeing, and how to avoid them.  Continue Reading

• Businesses face complex cost-cutting options with GenAI

  Some GenAI cost saving candidates might disappoint. The best approach to cost reductions vary according to the business function and an enterprise's strategic business priorities.  Continue Reading

• GenAI is ready for more autonomy, but supply chains are not

  Disruptions, vulnerabilities, late deliveries and customer interactions are among the supply chain concerns that GenAI could address autonomously -- if businesses can trust it.  Continue Reading

• GenAI's role in a return trek to the moon and beyond

  NASA's use of GenAI models in the Artemis space program and communication with Mars rovers provides valuable business lessons in governance and keeping humans in the loop.  Continue Reading

• Experts predict quantum utility by 2033

  Quantum computing is no longer science fiction. Experts at Nvidia GTC 2026 think quantum utility is possible by 2033, but not without challenges.  Continue Reading

• Smarter robots: Agentic and physical AI converge in business

  The robotics industry is entering a new era where, thanks to AI, robots learn, optimize and solve the world's most complex supply chain, logistics and labor challenges in real time.  Continue Reading

• Is the AI memory shortage stifling enterprise adoption?

  Enterprises bought up computer memory to fuel AI adoption. Now, amid a memory shortage, analysts share insights on what this means for enterprise AI adoption and what's next.  Continue Reading

• Is GenAI villain and hero in data center power drama?

  GenAI's infrastructure requirements drive up power demands for hyperscalers and other large data centers, but GenAI, and AI-based automation, could also help manage consumption.  Continue Reading

• AI-ready data needs its own set of rules, experts say

  At Gartner's 2026 Data and Analytics Summit, AI-ready data was top of mind. Explore these best practices from experts on achieving data readiness for AI systems.  Continue Reading

• C-suite shakeup: Demand for chief AI officers accelerates

  GenAI's infiltration into virtually every aspect of business demands the C-suite make room for a CAIO focused on AI development, strategy, implementation, education and governance.  Continue Reading

• Q&A: Prioritize AI adoption while safeguarding human skills

  In this Q&A, Gartner analyst Arun Chandrasekaran unpacks some of generative AI's invisible undercurrents and what to do about them.  Continue Reading

• GenAI streamlines enterprise knowledge management process

  Business leaders sharpen insights, speed decisions and boost productivity using GenAI-powered knowledge management systems with their ability to unify and synthesize data.  Continue Reading

• How GenAI deployments are redefining everyday work routines

  GenAI deployments are making dramatic strides in boosting employee productivity, strategic analysis, software development cycles and customer service -- but not without risks.  Continue Reading

• Is your business ready for an agentic AI team?

  The race is on to implement agentic AI teams. But can your business successfully deploy and manage them before taking this next step in enterprise automation?  Continue Reading

• From security to trust: How AI is transforming the CISO's job

  Modern security officers must manage AI risks, safeguard enterprise data and ensure AI systems operate securely, expanding their role beyond traditional cybersecurity.  Continue Reading

• How businesses can close the AI engineering gap

  Amid growing concerns that AI is replacing jobs in several sectors, demand for AI engineers is surging as businesses employ several methods to overcome a severe skills shortage.  Continue Reading

• Battle of the bots: Best GenAI chatbots for business

  Businesses are deploying multiple GenAI tools to support productivity. Success requires choosing the right tools for the right use case and keeping a close eye on the outcomes.  Continue Reading

• The AI bias playbook: Mitigation strategies for CIOs

  From prioritizing data management to having a governance-first mindset, the C-suite can use the AI bias mitigation strategies in this playbook to limit AI bias risk.  Continue Reading

• 35 AI content generators to explore in 2026

  Stay abreast of the growing selection of artificially generated media that businesses can use to produce text, images, music and code.  Continue Reading

• Optimize AI models to generate more bang for your buck

  AI models must be fully optimized to align with business goals, provide valuable insights and produce positive ROI. Check out these proven, common sense model optimization methods.  Continue Reading

• AI deployments gone wrong: The fallout and lessons learned

  AI's deployment landscape is littered with good intentions, crushed projects and unintended consequences due to misaligned business goals, mistrust in AI and weak management.  Continue Reading

• How watermarking AI content benefits businesses

  AI watermarking is being used to identify deepfakes, enhance transparency and build trust in AI-generated content, but reliability issues and false positives present challenges.  Continue Reading

• 5 skills needed to become a prompt engineer

  With the rise of generative AI, prompt engineering has emerged as a new profession. Desired skills include refining prompts, analyzing AI output and ensuring alignment with business goals.  Continue Reading

• How to ensure AI transparency, explainability and trust

  Transparency, explainability and trust are critical for enterprise AI. Learn how organizations can embed these principles to build accountable, ethical and reliable systems.  Continue Reading

• 10 AI business use cases that produce measurable ROI

  When justifying their investments in AI deployments, business leaders know keeping up with the Joneses isn't enough without uncovering a positive ROI. Here's where to find it.  Continue Reading

• Spotlight on AI and emerging tech in 2026

  As we welcome 2026, AI continues to lead the charge in business innovation. Expect to see more about governance, physical AI and integration with emerging technologies.  Continue Reading

• Leading AI with ethics: The new governance mandate

  Ethical AI governance is now a boardroom priority, enabling organizations to curb bias, ensure accountability and build trust as a strategic advantage.  Continue Reading

• AI's business future: What's to come in the next 5 years

  Five years can seem like an eternity in a world where AI's technological advancements occur almost daily, but best to be prepared now and avoid "future shocks."  Continue Reading

• Humans and AI: The role of people in the new AI world

  With AI now poised to be a capable teammate rather than a mere tool, businesses are struggling to identify where and to what extent human-AI collaboration makes sense.  Continue Reading

• Context engineering takes prompting to a higher business level

  In the care and feeding of AI models and chatbot interfaces, prompting alone can be a fool's errand in strategic business planning without the proper context to interpret prompts.  Continue Reading

• The dollars and sense of implementing AI

  Calculating ROI of an AI project to prove business value requires a complicated mix of costs, including data prep, infrastructure, integration, staffing, training and power needs.  Continue Reading

• Regression in machine learning: A crash course for engineers

  Regression in machine learning helps organizations forecast and make better decisions by revealing the relationships between variables. Learn how it's applied across industries.  Continue Reading

• 11 emerging technologies to watch in 2026

  Technology never stands still, and 2026 could see it go full sprint. See our picks for emerging and developing technologies and tell us what tech your organization is watching.  Continue Reading

• How generative AI is changing creative work

  As generative AI accelerates concepting and production, creative teams are evolving, requiring new skills, governance models and stronger human insight to guide meaningful work.  Continue Reading

• The future of generative AI: 10 trends to follow in 2026

  GenAI's outlook focuses on ROI expectations, power demands, data quality, agentic orchestration, plug-and-play LLMs, ethical practices, stepped-up security and human oversight.  Continue Reading

• AI regulation: What businesses need to know in 2026

  Numerous local, regional and national AI regulations can help businesses govern AI, foster innovation and mitigate potential risks -- as long as their differences are understood.  Continue Reading

• How AI is transforming project management in 2026

  As the project management field increasingly embraces AI-powered software, the benefits can help organizations thrive -- but only if the risks are properly considered too.  Continue Reading

• Who wins -- and loses -- with Trump's AI executive order?

  AI companies are the major winners with Trump's new AI executive order. But what does it mean for end users and enterprises?  Continue Reading

• 9 key benefits of AI for business

  Leading AI experts sound off on some of the top areas where AI deployments can positively impact business operations and services.  Continue Reading

• How engineers can build a machine learning model in 8 steps

  Building a machine learning model is a multistep process involving data collection and preparation, training, evaluation and ongoing iteration. Follow these steps to get started.  Continue Reading

• How AI can improve 5G telecommunications for enterprises

  AI is helping make 5G networks more resilient, integrated and easy to implement. That makes private 5G networks for enterprises a clearly superior alternative to Wi-Fi.  Continue Reading

• Strategies to reduce machine learning bias

  Machine learning bias can distort predictions and harm trust. This guide explains types of bias, real-world cases and seven effective strategies to ensure fairness in ML models.  Continue Reading

• 10 AI and machine learning trends to watch in 2026

  AI is reshaping virtually every aspect of business operations. Stay ahead of the curve by exploring these 10 emerging AI trends for 2026.  Continue Reading

• Time to promote AI agents from "assistants to associates"?

  Are our humble AI agents turning into valuable AI associates? Learn about the growth of AI agents from experts at this year's EmTech MIT conference.  Continue Reading

• Managing trust in the age of AI influence

  Can AI technology foster trust and serve the common good, or will algorithms shape humanity's future for the worse? Experts at EmTech MIT wrestled with the nuances.  Continue Reading

• 10 real-life examples of AI in the agriculture industry

  From crop monitoring to autonomous machinery, AI is changing the agriculture industry. Hear from farm owners and domain experts on what use cases are having an effect in 2025.  Continue Reading

• 3 enterprise AI horror stories for CISOs from 2025

  In a race to adopt innovative technology, organizations across the globe made mistakes in enterprise AI deployment. What lessons can you learn from this year's AI horror stories?  Continue Reading

• AI's effect on jobs not what expected, Yale study finds

  A recent Yale study finds AI is reshaping jobs, automating tasks and boosting demand for analytical and collaborative skills, with overall employment remaining stable.  Continue Reading

• Ethical considerations of agentic AI and how to navigate them

  Beyond the risks associated with traditional AI, agentic AI poses specific ethical concerns, including diminished human oversight, privacy erosion and misaligned outcomes.  Continue Reading

• 11 real-world agentic AI examples and use cases

  AI that doesn't just follow instructions but figures out how to get things done -- that's the promise of agentic AI, an emerging approach that's already changing some sectors.  Continue Reading

• AI agent frameworks: A guide to evaluating agentic platforms

  Navigate the agentic AI tool landscape and accelerate successful deployment with this comparison of AI agent frameworks, platforms and capabilities.  Continue Reading

• Agentic AI compliance and regulation: What to know

  Agentic AI's autonomous nature and its ability to access multiple data layers bring heightened risk. Learn how to ensure its deployment meets compliance standards.  Continue Reading

• Compare GPUs vs. CPUs for AI and machine learning use cases

  Choosing which AI hardware option makes more sense for an AI or machine learning project depends on multiple factors, including speed, memory and cost.  Continue Reading

• Security risks in agentic AI systems and how to evaluate threats

  Agentic AI changes workflows, boosts productivity and introduces new security risks. Learn what agentic AI can do and how to make this intelligent automation system secure.  Continue Reading

• Vertical AI agents explained: The future of enterprise tech

  The need for ever more AI expertise and performance has spawned vertical AI agents -- a new breed of autonomous agents purpose-built for specific industries. Learn how they work.  Continue Reading

• What GPT model limitations mean for the future of GenAI

  With concerns like limited reasoning and hallucination risk, will AI developers turn away from the GPT model and toward alternative model types?  Continue Reading

• ChatGPT advanced AI, but OpenAI faces hurdles in keeping lead

  Model makers disrupted AI, pushing Google to innovate and boosting Microsoft. Yet four years later, enterprises remain loyal to cloud providers, leaving the survival of the startups uncertain.  Continue Reading

• Claude vs. ChatGPT: How do they compare?

  Compare Anthropic's Claude vs. OpenAI's ChatGPT in terms of features, model options, costs, performance and privacy to decide which generative AI tool better suits your needs.  Continue Reading

• AI agents increasingly viable for enterprise use

  AI agent use is ramping up, with development environments leading the charge. Governance and agent building are also on the rise.  Continue Reading

• Hands-on with QuillBot AI: Review and guide

  Learn how one professional author uses QuillBot AI for writing tasks -- and how it stacks up to other popular options like Grammarly.  Continue Reading

• How AI is changing telemedicine in 2025

  AI is virtually expanding, reshaping and personalizing telehealth practices in triaging, image analysis, patient diagnoses, treatment planning, monitoring and mental health.  Continue Reading

• How AI helps Henry Schein One's accounts payable team

  The big healthcare product company's dental software division turned to AI automation startup Pipefy to transform a paper-based system into a digital one.  Continue Reading

• AI upskilling strategies that center workers, not tech

  At MIT's Sloan CIO Symposium, experts emphasized that AI upskilling starts with what humans do best, pairing hands-on learning with inclusive, contextualized strategies.  Continue Reading

• How Harvard is approaching AI in the classroom

  Generative AI is already changing classrooms and student behavior. At Harvard, that's prompting faculty to reexamine how they teach and how they assess real learning.  Continue Reading

• How business leaders are measuring generative AI's ROI

  At EmTech AI, generative AI's value was top of mind. But real ROI depends on business fundamentals like change management and clear success metrics.  Continue Reading

• Why enterprise AI is moving toward customization

  At MIT's EmTech AI conference, tech leaders emphasized the value of purpose-built systems designed to support real business workflows.  Continue Reading

• ChatGPT vs. GPT: How are they different?

  Although the terms ChatGPT and GPT are both used to talk about generative pre-trained transformers, there are significant technical differences to consider.  Continue Reading

• AI existential risk: Is AI a threat to humanity?

  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.  Continue Reading

• Explore real-world use cases for multimodal generative AI

  Multimodal generative AI can integrate and interpret multiple data types within a single model, offering enterprises a new way to improve everyday business processes.  Continue Reading

• Data quality in AI: 9 common issues and best practices

  Building a useful, reliable AI system requires trustworthy, well-managed data. Here's how to find and fix data issues that can drag down AI projects.  Continue Reading

• GANs vs. VAEs: What is the best generative AI approach?

  The use of generative AI is taking off across industries. Two popular approaches are GANs, which are used to generate multimedia, and VAEs, used more for signal analysis.  Continue Reading

• AI engineer vs. data scientist: What's the difference?

  AI engineers and data scientists both shape AI projects, but their roles aren't the same. Learn how their jobs differ and why it matters.  Continue Reading

• CNN vs. GAN: How are they different?

  Convolutional neural networks and generative adversarial networks are both deep learning models but differ in how they work and are used. Learn the ins and outs of CNNs and GANs.  Continue Reading

• 12 top resources to build an ethical AI framework

  Examine the standards, tools and techniques available to help navigate the nuances and complexities in establishing a generative AI ethics framework that supports responsible AI.  Continue Reading

• GPT-4o vs. GPT-4: How do they compare?

  OpenAI's GPT-4o promises improved multimodal capabilities and increased efficiency. Explore the differences between GPT-4o and its predecessor, GPT-4.  Continue Reading

• Explore the role of AI in the U.S. federal government

  Reported federal AI use cases jumped from 710 in 2023 to 2,133 in 2024. The staggering growth rate could continue to rise under the Trump administration.  Continue Reading

• 20 real-world GenAI applications across leading industries

  Generative AI is predicted to add up to $16 trillion to the global economy by 2030. Learn how 20 industries, from finance and legal to healthcare and manufacturing, are using it.  Continue Reading

• How has generative AI affected cybersecurity?

  Generative AI helps security teams defend against threats, but it also lets bad actors infiltrate organizations. Learn about how each side is tapping into GenAI to gain an edge.  Continue Reading

• How will generative AI reshape the enterprise?

  Organizations are using generative AI to reimagine processes. Discover 10 ways the technology is transforming employees' jobs and their interactions with customers.  Continue Reading

• How generative AI can augment human creativity in business

  From personalized marketing and prototyping to business workflows and planning, generative AI can augment creativity as a new collaboration tool for humans in enterprise settings.  Continue Reading

• How AI is shaping the future of the travel sector

  The travel industry is using AI to increase efficiency and personalize experiences, but challenges include data privacy, legacy integration and the need for a human touch.  Continue Reading

• 2025 will be the year of AI agents

  2025 could bring more agentic AI developments. Enterprises could embed agents in their workflows. It could also lead to an orchestration infrastructure and better reasoning models.  Continue Reading

• A look at expectation vs. reality of generative AI in 2024

  Last year, expectations for generative AI for the coming year centered on regulation, open source and multimodality. While some predictions were accurate, others were wrong.  Continue Reading

• The year in AI: Catch up on the top AI news of 2024

  As the new year approaches, the pace of AI development shows no signs of letting up. Get up to speed for 2025 by catching up on TechTarget Editorial's top AI news stories from 2024.  Continue Reading

• AI in medical diagnostics and decision-making

  AI technologies enable doctors to improve diagnostic accuracy, personalize treatment plans and streamline clinical workflows. However, they also pose challenges worth considering.  Continue Reading

• 8 metrics to measure GenAI’s performance and business value

  When gauging the success of generative AI initiatives, metrics should be agreed upon upfront and focus on the performance of the model and the value it delivers.  Continue Reading

• How to detect AI-generated content

  AI- or human-generated? To test their reliability, six popular generative AI detectors were asked to judge three pieces of content. The one they got wrong may surprise you.  Continue Reading

• Hyperparameter tuning techniques to optimize ML models

  Hyperparameters play a key role in shaping how machine learning models learn from data. Learn how adjusting these settings can improve model accuracy and performance.  Continue Reading

• Privacy and security risks surrounding Microsoft Recall

  Microsoft's Recall feature promises AI-powered convenience, but it raises significant security and privacy concerns that the company must address before a public release.  Continue Reading

• A state-by-state guide to AI laws in the U.S.

  In lieu of federal regulation, U.S. states are proposing and enacting AI laws of their own. This up-to-date breakdown by state can help the C-suite keep tabs on developments.  Continue Reading

• Compare 9 prompt engineering tools

  To get the most out of generative AI models, developers and other users rely on prompt engineering techniques to achieve their desired output. Explore nine tools that can help.  Continue Reading

• Survey: Enterprise generative AI adoption ramped up in 2024

  Nearly a third of organizations are now using generative AI in production. But data quality, the AI skills gap and an evolving regulatory landscape continue to present hurdles.  Continue Reading

• What is enterprise AI? A complete guide for businesses

  Enterprise AI tools are transforming how work is done, but companies must overcome various challenges to derive value from this powerful and rapidly evolving technology.  Continue Reading

• Interpretability vs. explainability in AI and machine learning

  Understanding how AI models make decisions is challenging, but two concepts -- interpretability and explainability -- can shed light on model outputs.  Continue Reading

• Lessons on AI-driven marketing at EmTech MIT 2024

  In addition to generating personalized advertising content, marketing professionals are using large language models as new personas to shape brand perception and strategy.  Continue Reading