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• What competitive edge do AI-native companies have?

  While AI has emerged as a powerful enterprise tool, many businesses struggle to integrate AI to its fullest potential. These lessons from AI-native companies can help. Continue Reading

• Physical AI security threats and how to mitigate them

  Malicious prompt injections, sensor manipulation, hardware tampering and vulnerable software take on greater urgency when robotic systems interact with humans and the real world. Continue Reading

• Physical AI in business gets smarter and more autonomous

  Leaping from the computer screen into dynamic real-world environments, physical AI and embodied robotics take on increasingly more complex and dangerous tasks. Continue Reading

• Supply chain AI agents: Where should autonomy begin and end?

  AI agents can scan emails, audit invoices, flag billing issues, develop transactional data and negotiate contracts along the supply chain -- but they can't execute the deal (yet). Continue Reading

• Robotics needs real-time edge AI: Why and where

  AI provides robotics the edge they need in dynamic environments ranging from manufacturing to healthcare, where fast response times and real-time decision making are critical. Continue Reading

• Definitions to Get Started

  • What is automated machine learning (AutoML)?
  • What is a data scientist? What do they do?
  • What are AI agents? Types and examples
  • What is an intelligent agent? Definition, use cases and benefits

  • What is natural language processing (NLP)?
  • What is a neural network?
  • What is edge AI?
  • What is AgentOps? What it does and how it powers AI agents

  View All Definitions

• The automated workforce: Physical AI's labor impact

  Humanoid robots, autonomous vehicles and other machines will augment some jobs, replace others and create new roles. Emerging use cases include logistics and worker safety.Continue Reading

• Can Nvidia's physical AI strategy replicate its AI dominance?

  Nvidia is building a broad physical AI ecosystem across training, simulation and deployment, but in a fragmented market, the chipmaker will need to coexist before it can dominate.Continue Reading

• Colossus data center buildouts: Can they ever be duplicated?

  SpaceX's fast pace, energy capacity and crafty permitting in building the Colossus 1 and 2 data centers provide some worthy lessons that can be emulated -- and others that can't.Continue Reading

• Physical AI deployment challenges and how to address them

  Physical AI can conjure up unique but solvable issues surrounding unpredictability, data quality, safety and cost overruns. Avoid business disruptions with these best practices.Continue Reading

• What it finally takes to make quantum commercially viable

  Turns out quantum computing still has several missing links on the road to commercial viability, including better fault tolerance, lower error rates and more qubits per system.Continue Reading

• To justify physical AI costs, choose high-value use cases

  Physical AI is starting to drive business value in industries like manufacturing and healthcare. But the ROI calculus is subtle and calls for a wide-angle view of metrics.Continue Reading

• Planning AI infrastructure upgrades: Four steps for success

  AI infrastructure upgrades require proactive planning. Steps include setting business goals, assessing existing infrastructure, and optimizing hardware, security and governance.Continue Reading

• Making space for spatial computing

  Construct spatial computing programs through evidence, not hype. Use this roadmap to ensure that workflow selection is the starting point, not device selection.Continue Reading

• A guide to AI chip architectures

  The diversity of today's AI processors can make their purpose, benefits and limitations confusing. This roundup can help businesses match their AI tasks to the right chip.Continue Reading

• AI agent security: How reliable is enterprise AI testing?

  Experts outline comprehensive testing practices before and after deployment of AI agents to prevent them from acting on their own beyond prescribed instructions.Continue Reading

• Embodied AI vs. physical AI: Why their differences matter

  When integrating physical AI into the workplace, successful planning and deployment depend on knowing robotics is more than an array of technologies embodied in a mechanical form.Continue Reading

• Power-hungry AI data centers weigh viable energy sources

  AI data centers are encountering political resistance in some communities, but they'll be built elsewhere. Survival depends on the power sources chosen and their availability.Continue Reading

• Why businesses need an AI agent kill switch

  Executives must understand the importance of AI safety to prevent reputational, financial and operational damage. Here's why an AI kill switch is the safety measure they need.Continue Reading

• The hybrid future of enterprise AI sovereignty

  With increasingly defined regulations to comply with and political tensions growing, business leaders are adopting AI sovereignty practices to weather the storm.Continue Reading

• Physical AI, robotics revive lagging U.S. shipbuilding

  Physical AI and robotics speed shipyard operations, improve production planning, increase shipbuilding volume and automate labor-intensive tasks like welding and surface prep.Continue Reading

• The best AI governance tools and platforms in 2026

  AI governance has become a boardroom priority. These tools and platforms can help leaders fulfill AI governance mandates.Continue Reading

• Virtual power plants could help solve the AI energy problem

  Virtual power plants can help enterprises and data centers overcome power limitations by unifying energy resources to stabilize grids, reduce costs and ensure energy availability.Continue Reading

• When orbital AI data centers become business reality

  Shrinking launch costs, hyperscaler validation, serviceable hardware, manageable insurance risks and earthly energy constraints will thrust data centers into orbit and beyond.Continue Reading

• AI and robotics yield bumper crops down on the farm

  Autonomous tractors roam the fields 24/7, while AI, computer vision and machine learning harvest fruits, increase milk production, limit pesticides and boost crop yields.Continue Reading

• AI in law offices: How it's being used and the risks

  Document discovery, court motions, patent protection, focus groups and client calls are tasks where AI saves law firms significant time and costs, while keeping humans in the loop.Continue Reading

• Controlling AI models is harder than building them

  Before companies safely grant autonomy to AI agents deployed in their business processes, they must first establish an infrastructure of governance, accountability and control.Continue Reading

• AI is supposed to cut lots of jobs? Not so fast

  Workers have long feared AI will one day displace them. But emerging evidence suggests otherwise: AI might preserve jobs, even as it reshapes roles and skill requirements.Continue Reading

• 5 advantages of a semantic layer for enterprise AI

  Semantic layers are an increasingly pivotal architecture for enterprise AI, enabling AI systems to more accurately identify relationships between conflicting data points.Continue Reading

• Autonomous warehouse drones streamline inventory control

  Autonomous indoor AI drones join the workforce, moving about factories and warehouses to track inventory, identify missing products and predict equipment maintenance.Continue Reading

• Humanoid robots not quite ready for primetime

  Don't be fooled by a robot's impressive display of movement and dexterity during onstage demos in controlled environments. Humanoid exploits in the real world can disappoint.Continue Reading

• Futurists predict what's next for AI and emerging technology

  The clock is ticking for businesses to start researching and investing in a range of emerging technologies. Experts weigh in on the tech they think will shape the future.Continue Reading

• Is AI replacing jobs? How 17 job types feel the effects

  AI has threatened various jobs since ChatGPT came out. New data shows where the threat is real and where it's imagined.Continue Reading

• 10 top AI knowledge management platforms for businesses

  AI knowledge platforms use search and GenAI to surface and monetize enterprise information, but comparisons show wide variance in capabilities, integration, governance and scale.Continue Reading

• How to choose the right IKMS in a fast-moving AI market

  Poor platform selection is a leading cause of failed knowledge management system deployments. Product demos don't tell nearly enough. Five key factors can determine IKMS success.Continue Reading

• Beautiful insanity: AI buildouts lead to long lead times

  As hyperscalers build out their AI infrastructure, enterprises are seeing the effects in long waits for equipment and a shift to distributed architecture.Continue Reading

• Artificial general intelligence: So close yet so far?

  Before signing off on their AI strategies, businesses should weigh the anticipated arrival and potential impact of artificial general and super intelligence on future operations.Continue Reading

• Educating the future AI-savvy human workforce

  As the need for skilled AI workers approaches desperation levels for many businesses, unique and more personalized teaching methods are preparing tomorrow's AI workers.Continue Reading

• AI drones undertake high-risk jobs along the supply chain

  From power line and bridge inspections to remote and heavy cargo deliveries, autonomous AI drones are becoming the supply chain's version of air traffic controllers.Continue Reading

• Can quantum-inspired algorithms help you now?

  Quantum-inspired algorithms mimic quantum computing principles on classical hardware. Experts share how businesses can benefit from this innovative technology.Continue Reading

• GenAI and synthetic data: What can go wrong in business?

  GenAI's ability to improve everything it touches, including synthetic data, is undeniable, but when misused, simulated data can amplify bias, taint data and compromise security.Continue Reading

• How to build AI skills across your workforce

  AI skills are no longer an optional investment. Business leaders who know how to build AI skills throughout their organizations can unlock AI's full potential.Continue Reading

• Democratizing AI in business: The good, bad and ugly

  AI democratization planned and introduced correctly can profoundly increase AI's business value -- but inadequate AI tools and poor workforce training can bring it all down.Continue Reading

• How to use AI agents for infrastructure management

  AI agents enable businesses to automate a variety of tasks, including infrastructure development. However, left unchecked, they can wreak havoc at the infrastructure layer.Continue Reading

• How to start a career in AI: Advice from experts

  AI careers don't follow one path. As AI becomes part of everyday work, more professionals are entering the field by building on what they already do and focusing on real results.Continue Reading

• Business vs. provider: AI software restrictions to know

  How much control do businesses have over the use of AI platforms they buy? Vendor acceptable use contracts and policies can determine an AI deployment's success or failure.Continue Reading

• Infrastructure requirements for physical AI systems

  Physical AI systems are becoming a priority for companies because of their strategic and operational advantages. But businesses must contend with their infrastructure needs.Continue Reading

• GenAI in accounting: Chat BDO's ride from pilot to production

  Accounting and advisory firm BDO built and deployed a proprietary AI platform so its tax and audit pros could automate routine tasks and significantly increase client-facing time.Continue Reading

• Exploring the context layer for AI systems

  To meet the needs of AI's increasing agency, context layers capture the missing reasoning, business rules and decision rationale that power truly aligned, autonomous AI systems.Continue Reading

• Beyond the chatbot: Engineering the agentic enterprise

  Agentic AI is taking over where chatbots leave off. These systems let enterprises automate workflows, driving operational change and doing real work.Continue Reading

• 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

• The business case for a multi-cloud AI strategy

  Using a single cloud provider for AI infrastructure can have its limitations. Discover the benefits businesses can realize by opting for a multi-cloud AI strategy.Continue Reading

• How to choose a data center for AI workloads

  Variables like power and network capacity affect the ability of data centers to support AI workloads. But not all AI workloads require the most powerful data center capabilities.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

• 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

• History of generative AI innovations spans 9 decades

  Rapid GenAI advances are reshaping industries, sparking legal battles and driving embodied AI innovations, paving the way for transformative business processes and tools.Continue Reading

• LLM build vs. buy: A decision framework for LLM adoption

  When deciding whether to build or buy a large language model, businesses must consider costs, customization, governance, risk and readiness to determine the best AI approach.Continue Reading

• AI risk management: A strategic guide for enterprise leaders

  Enterprises across business sectors are adopting AI technologies, but are they prepared to deal with the risks? Our AI risk management guide can help.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

• Does your organization need an AI ethics committee?

  Ethics oversight ensures high-risk AI use is accountable, responsible and compliant. Not all organizations need one; some incorporate this oversight into risk or legal teams.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 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

• 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

• Time for AI: The 'too busy' problem is a software-age hangover

  Higher education must prioritize enterprise AI as a strategic shift, not just tool buying. Learn how governance, alignment and a five-year plan can transform institutions.Continue Reading

• Why and how enterprises should build a green AI initiative

  Executives making AI decisions must consider ethics, compliance and now environmental impact. Learn how green AI aligns governance and sustainability to improve digital strategies.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

• How CIOs should architect trust in AI -- not just govern it

  When designing trustworthy enterprise AI applications, platform architecture, not policy alone, is the best way to minimize long-term risk and ensure compliance and sustainability.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

• Is neuromorphic computing the future of AI hardware?

  AI hardware is hitting its limits. Is neuromorphic computing with its reduced energy use and latency a viable alternative? Hear one expert's take on this emerging technology.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

• How agentic AI is changing work, strategy and competitiveness

  AI agents are transforming work, business strategy and global competitiveness by enabling autonomous workflows, innovation and value creation in enterprises.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

• 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

• Best practices for building scalable AI infrastructure

  Scalability is vital for AI platforms. These 10 best practices can help businesses build scalable infrastructure that supports AI workloads and adapts to fluctuating demands.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 business leaders can prepare for quantum-AI integration

  AI completely changed the way we work, and quantum computing has the potential to upend our work completely. How can these technologies integrate to achieve further innovation?Continue Reading

• How to navigate data sovereignty for AI compliance

  As AI systems consume data for training and decision-making, businesses must navigate complex regulations, geopolitical boundaries and vendor risks.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

• 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

• 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

• Humanoid robots: The next frontier in physical AI

  Once seen as merely science fiction, humanoid robots are becoming increasingly tangible due to advancements in AI and other technologies.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

• Why business leaders must explore post-quantum pre-quantum

  With the continual advance of quantum technology comes threats to traditional cryptographic standards. Discover how to prepare for PQC pre-quantum.Continue Reading

• AI governance can make or break data monetization

  As pressures mount for businesses to get the most value from their data, AI governance ensures the data to be monetized is secure, protected, trustworthy and used responsibly.Continue Reading

• Optimizing hybrid cloud architecture for AI workloads

  Hybrid cloud architecture can help with the unique scalability, hardware and governance needs of AI workloads. But is it the right choice for your business?Continue Reading

• 4 types of machine learning models explained

  Rigorous experimentation is key to building machine learning models. Learn about the main types of ML models and the factors that go into picking and training them for a specific task.Continue Reading

• 9 top generative AI tool categories for 2026

  Agentic systems and AI data centers are driving forces shaping the development of GenAI tools and frameworks to build LLMs, orchestrate processes and integrate physical AI.Continue Reading

• What is automated machine learning (AutoML)?

  Automated machine learning (AutoML) is the automated process that applies machine learning models to real-world problems.Continue Reading

• What is a data scientist? What do they do?

  A data scientist is an analytics professional who collects, analyzes, and interprets data to transform it into actionable insights that can facilitate decision-making.Continue Reading

• Integrate and modernize legacy systems with AI

  Complete replacement of, or over-reliance on, legacy systems can be disastrous. Organizations that modernize gradually will thrive in markets where AI is vital.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

• Meet quantum needs with hybrid digital infrastructure

  The potential for quantum computing remains unparalleled. Does your organization understand the demands quantum computing will have on digital infrastructure?Continue Reading

• What are AI agents? Types and examples

  AI agents are autonomous intelligent software components that form the foundation of artificial intelligence (AI) systems.Continue Reading

• Generative models: VAEs, GANs, diffusion, transformers, NeRFs

  From powering conversational chatbots to realistic image creators, generative models are critical for many AI applications.Continue Reading