Manage

Manage

Learn to apply best practices and optimize your operations.

• Controlling AI sprawl: A practical guide for the C-suite

  As AI tools proliferate across enterprise teams, leaders must ensure AI use isn't redundant or unsanctioned. Do you know all the AI tools your business uses? Continue Reading

• Best practices for integrating third-party AI with local systems

  Third-party AI components are increasingly common across industries and domains, but many businesses don't know how to effectively integrate them with their local systems. 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 apply FinOps to optimize agentic AI costs

  FinOps gives businesses the operating model for visibility, accountability and value management. But traditional FinOps models will need to be reworked for agentic AI. Continue Reading

• Why AI systems need a boundary between reasoning and execution

  Tightly coupled AI systems create control gaps. A reasoning-execution boundary enables policy-bound, traceable and auditable execution at scale across enterprise workflows. Continue Reading

• AI FinOps requires a different approach than cloud

  While AI's utility will continue to generate revenue for businesses, its cost grows with it. FinOps is a key strategy and governance tool that ensures AI delivers measurable ROI.Continue Reading

• Agentic governance must go beyond traditional IT practice

  Industry experts agree: Agentic AI requires new governance methods, not traditional IT playbooks. The current agentic governance gap threatens to derail enterprise AI adoption.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

• Dell exec: Balance safety and speed with agentic AI ethics

  At this year's Dell Technologies World conference, Dell executive John Roese shared his thoughts on agentic AI and how its ethics can be compatible with timely innovation.Continue Reading

• 8 AI costs leaders don't always budget for but should

  The hidden costs of enterprise AI often emerge after deployment, through stalled pilots, compliance burdens, workforce disruption, model upkeep and reputational risk.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

• Why AI pilots fail and how to move them into production

  While AI pilots thrive in sandboxes, production demands clear ownership, robust data infrastructure, failure testing and runtime monitoring throughout the system's lifecycle.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

• Combating the new wave of AI crimes and threats

  Attackers of any skill level can now use open source tools to target businesses for AI crimes. Here's how to cover organizations' expanded attack surface and prevent disaster.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

• 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

• Build and organize an effective machine learning team

  Putting together an ML team requires a business understand why it needs one and the core roles involved in making all aspects of the machine learning process work.Continue Reading

• How businesses use KPIs to measure AI's performance

  Business decision-makers under heavy pressure to justify the value of their AI projects look to key performance indicators for salvation. KPIs reveal AI's efficiency and ROI.Continue Reading

• 8 trends powering machine learning's dynamic new roles

  Machine learning is evolving rapidly, driving trends like smaller models, edge computing, generative AI convergence, governance and ML health monitoring.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

• 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

• AI agents are only as smart as the data that feeds them

  Using business semantics is key to making AI agents deliver accurate insights. Aligning an organization's data with its business logic ensures trust and smarter decisions.Continue Reading

• 4 strategic approaches to effective shadow AI governance

  Shadow AI is a technological risk and a governance challenge. Organizations must combine structured frameworks, continuous visibility and education to control these risks.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

• 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

• Managing drift in AI models and data

  The training data and algorithms used to build AI models have a shelf life. Detecting and correcting model drift ensures that these systems stay accurate, relevant and useful.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

• 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

• 7 best practices to avoid AI vendor lock-in

  While lock-in is sometimes unavoidable, it's the very definition of risk. Businesses can use these best practices to mitigate lock-in risk with AI vendors.Continue Reading

• Time to rethink cloud architecture for enterprise AI

  Enterprise AI systems demand cloud architectures that emphasize persistent state, governance and adaptive infrastructure to ensure long-term reliability.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

• How to preprocess different types of data for AI workloads

  Data preparation for AI differs by data type. However, common themes include improving data quality, ensuring consistency, reducing computing demands and enhancing model performance.Continue Reading

• Build accountability into AI to drive business value

  In a perfect world, AI systems work 100% of the time, but that's not the case in the real world. AI accountability ensures someone takes responsibility when AI fails.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

• 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

• Tools and techniques for optimizing AI data pipelines

  Enhancing the five critical stages of AI data pipelines is essential; it determines whether AI drives business value or becomes a drain on resources.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

• 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

• 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

• Reproducibility: Is your AI project doomed?

  AI governance focuses on mitigating risks from generative AI's unpredictability. Key strategies include reproducibility, explainability and comprehensive controls.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

• The ethics that make human-AI agent collaboration work

  Skepticism and distrust are common with new technology. Executives must develop a culture of trust with AI to ensure effective human-AI agent collaboration.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

• 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

• Top AI KPIs that business leaders need to know

  AI-specific KPIs can indicate model performance, customer satisfaction and overall business value. Explore these 25 AI KPIs to elevate the management of any AI initiative.Continue Reading

• What to know about synthetic data as a business advantage

  Synthetic data mimics real data without sensitive information. It offers cost-effective options and competitive advantages for AI training, software testing and data monetization.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

• 7 best practices for leading and managing agentic teams

  Agentic AI teams are an evolution in professional partnerships. Business leaders can ensure agents succeed with clear objectives, strong governance and disciplined management.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

• Turn your AI center of excellence from sidelines to impact

  AI CoEs face a challenging mission -- aligning AI investments with business strategy across multiple functions -- and many obstacles complicate their ability to deliver value.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

• Building governance for machine speed: The path to trusted AI autonomy

  Governance systems need to match the speed of the AI agents they govern with real-time monitoring and dynamic enforcement. That's how companies balance autonomy and accountability.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

• 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

• How to prepare your business for agentic AI adoption

  Agentic AI adoption can go awry without a clear plan in place. Use this guide to ensure AI agents are deployed correctly and optimized for successful business outcomes.Continue Reading

• 10 AI topics for 2026 that enterprise leaders need to know

  Use these trending AI topics to inform your organization's strategy for 2026 and position it for future success.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

• 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

• 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 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

• Top 4 AI chatbot privacy concerns and how to mitigate them

  Do you know how your AI chatbot is storing your data, and who they are sharing it with? Discover mitigation strategies to prevent your chatbots from revealing sensitive data.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

• 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

• Agentic AI architecture: An enterprise guide

  Agentic architectures can work independently to automate and orchestrate complex tasks in manufacturing, retail operations and supply chains but they present several challenges.Continue Reading

• What is AI agent memory? Types, tradeoffs and implementation

  AI agents with short- and long-term memory leapfrog simple task-oriented assignments into areas of autonomy, pattern recognition, personalization and strategic business planning.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

• 5 types of AI agents and how to choose the right one

  AI agents vary in design and purpose. Use this guide to explore different types of AI agents and determine which ones are best suited for various use cases.Continue Reading

• Why managing GenAI models is like owning a paddleboard

  Lessons on GenAI model management can come from unlikely sources -- even from buying a paddleboard for the first time.Continue Reading

• IBM's enterprise AI vision for customization, collaboration

  IBM focuses on the enterprise by making its WatsonX platform customizable for different cloud environments and applications.Continue Reading

• Understanding the limitations and challenges of RAG systems

  RAG reduces hallucinations and enables access to external data, but that doesn't mean it's perfect. These eight RAG limitations are essential for organizations to prepare for.Continue Reading

• AI implementation: 13 steps to achieve success in your business

  AI technologies can enable and support essential business functions. But organizations must have a solid foundation in place to bring value to their business strategy and planning.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

• Agentic AI workflows: Trends, examples and best practices

  Implemented correctly, agentic AI workflows can make business process management more responsive, flexible and autonomous.Continue Reading

• How to use change management for better AI adoption

  Is your business ready for AI -- or just adding expensive tools without adapting workflows to maximize value and minimize risk?Continue Reading

• 5 MCP security risks and mitigation strategies

  MCP facilitates interactions between AI models and external services. But at what security cost?Continue Reading

• How to evaluate LLMs for enterprise use cases

  What defines success is business value -- not just benchmark scores.Continue Reading

• 8 agentic AI governance strategies: A complete guide

  AI agents demand scrupulous data governance, such as permission policies and human oversight. This comprehensive guide can help organizations develop effective practices.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

• Generative AI security risks: Best practices for enterprises

  Despite its benefits, generative AI poses numerous -- and potentially costly -- security challenges for companies. Review possible threats and best practices to mitigate risks.Continue Reading

• Does AI-generated code violate open source licenses?

  Unresolved questions about how open source licenses apply to AI models trained on public code are forcing users, vendors and open source developers to navigate legal gray areas.Continue Reading

• What comes after LLMs? The next wave in generative AI

  Large language models ushered in a new era of AI, but they're not without their fair share of limitations. What technology comes next?Continue Reading

• RAG best practices for enterprise AI teams

  Many organizations are already using commercial chatbots driven by large language models, but adding retrieval-augmented generation (RAG) can improve accuracy and personalization.Continue Reading

• Address top AI privacy concerns with this 7-point checklist

  AI systems raise complex privacy and security issues. Use this practical checklist to protect sensitive data and stay compliant.Continue Reading