Evaluate

Evaluate

Weigh the pros and cons of technologies, products and projects you are considering.

• 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

• 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

• 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

• Generative AI ethics: 16 biggest concerns and risks

  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. Continue Reading

• Build an ethical AI framework: 12 top resources

  IEEE Global Initiative, World Economic Forum and Stanford Institute are among the best resources available to businesses building a viable ethical AI foundation for responsible AI. 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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 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

• 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

• Agentic AI explained: Key concepts and enterprise use cases

  Agentic AI refers to artificial intelligence systems that are capable of autonomous action and decision-making.Continue Reading

• How ReAct agents can transform the enterprise

  Humans solve complex problems with reasoning and actionable steps. Now, agentic AI can do the same. Are ReAct agents the future of enterprise problem-solving?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

• How agentic RAG supports effective business workflows

  AI agents can complete business tasks with little to no human intervention. What if they could access domain-specific knowledge when completing those tasks?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

• How to build your first agentic AI system

  Agentic AI systems can be a value add for many businesses -- but only if they're built properly. Development teams looking to build agentic AI can use this guide to get started.Continue Reading

• Compare AI agents vs. RPA: Key differences and overlap

  Choosing the right automation strategy isn't just about efficiency -- businesses need to consider adaptability and reliability too.Continue Reading

• What is AI orchestration? How it works and why it matters

  AI orchestration manages how models, data, and tools work together, using integration, automation and oversight to support complex systems.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

• 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

• 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

• AI vs. machine learning vs. deep learning: Key differences

  Artificial intelligence, machine learning and deep learning are terms often used interchangeably, but they aren't the same. Understand the differences and how each is used.Continue Reading

• Advantages and disadvantages of AI explained

  Is AI good or bad? AI will benefit society, according to experts, but only with the correct guidelines in place and a solid understanding of what AI systems can and cannot do.Continue Reading

• Does using DeepSeek create security risks?

  The Chinese AI chatbot, despite its efficiency and customizability, raises serious concerns about data privacy, censorship and security vulnerabilities for business users.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

• What is GenAI? Generative AI explained

  Generative artificial intelligence, or GenAI, uses sophisticated algorithms to organize large, complex data sets into meaningful clusters of information in order to create new content, including text, images and audio, in response to a query or ...Continue Reading

• The advantages and disadvantages of AI in cybersecurity

  To meet growing cybersecurity needs, many organizations are turning to AI. However, without the right strategies in place, AI can introduce risks alongside its benefits.Continue Reading

• GAN vs. transformer models: Comparing architectures and uses

  Discover the differences between generative adversarial networks and transformers, plus how the two techniques might combine in the future to provide users with better results.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

• Generative AI vs. machine learning: Key differences and use cases

  Generative AI differs from simpler forms of machine learning in several ways, but both can enhance efficiency, personalize customer experiences and drive revenue growth.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

• 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

• 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

• How to effectively manage AI projects in 12 steps

  AI is a high priority for companies but results often fall short of expectations. These 12 steps will help you successfully manage AI projects and deliver business value.Continue Reading

• How small businesses can take advantage of AI

  Small business owners see AI's potential for automation and transformation, yet concerns remain. This seven-step program will help AI implementations get off on the right track.Continue Reading

• Compare 7 top AutoML tools for machine learning workflows

  From cloud-based platforms to open source options, compare the pros and cons of leading AutoML tools, which automate key machine learning tasks to accelerate workflows.Continue Reading

• AI in banking: Benefits, risks, what's next

  AI technologies bring operational efficiency and customer benefits to banking. Learn how GenAI and other AI tools are transforming financial services and risks to watch out for.Continue Reading

• Explore the benefits and risks of AI in fintech

  Financial technology can benefit greatly from AI tools and strategies. But financial services companies looking to adopt AI need to understand the risks too.Continue Reading

• 15 AI risks businesses must confront and how to address them

  These risks associated with implementing AI systems must be acknowledged by organizations that want to use the technology ethically and with as little liability as possible.Continue Reading

• What is machine learning? Guide, definition and examples

  Machine learning is a branch of AI focused on building computer systems that learn from data.Continue Reading

• Tips to prevent machine learning scalability problems

  Addressing ML scalability challenges involves selecting the right models, planning resource usage and managing network connectivity to support expanding applications and data load.Continue Reading

• Data science vs. machine learning: What's the difference?

  Data science and machine learning both play crucial roles in AI, but they have some key differences. Compare the two disciplines' goals, required skills and job responsibilities.Continue Reading

• 10 popular libraries to use for machine learning projects

  Machine learning libraries expedite the development process by providing optimized algorithms, prebuilt models and other support. Learn about 10 widely used ML libraries.Continue Reading

• Supervised vs. unsupervised learning explained by experts

  Learn the characteristics of supervised learning, unsupervised learning and semisupervised learning and how they're applied in machine learning projects.Continue Reading

• Choosing between a rule-based vs. machine learning system

  Deciding between a rule-based vs. machine learning system comes down to complexity and organizational needs. Compare the advantages, drawbacks and use cases for each AI approach.Continue Reading

• Beyond AI doomerism: Navigating hype vs. reality in AI risk

  As AI becomes increasingly widespread, viewpoints featuring both sensationalism and real concern are shaping discussions about the technology and its implications for the future.Continue Reading

• AI and compliance: Which rules exist today, and what's next?

  The AI regulatory landscape is still racing to catch up with the fast pace of industry and technological developments, but a few key themes are starting to emerge for businesses.Continue Reading

• What does generative AI mean for the legal sector?

  Generative AI tools such as ChatGPT are entering law practices, promising more efficiency and less time spent on rote tasks. But risks remain around accuracy, ethics and privacy.Continue Reading

• What are the risks and limitations of generative AI?

  As enterprise adoption grows, it's crucial for organizations to build frameworks that address generative AI's limitations and risks, such as model drift, hallucinations and bias.Continue Reading

• The data privacy risks of third-party enterprise AI services

  Using off-the-shelf enterprise AI can both increase productivity and expose internal data to third parties. Learn best practices for assessing and mitigating data privacy risk.Continue Reading

• Assessing the environmental impact of large language models

  Large language models like ChatGPT consume massive amounts of energy and water during training and after deployment. Learn how to understand and reduce their environmental impact.Continue Reading

• The readiness of AI and LLM technology

  Generative AI technology has already disrupted how enterprises work. While many companies are uneasy with the fast-evolving technology, that's expected to change.Continue Reading

• New skills in demand as generative AI reshapes tech roles

  With generative AI adoption on the rise, employers are prioritizing creativity and problem-solving alongside technical skills for roles in software development and data science.Continue Reading

• How AI changes quality assurance in tech

  AI and automation have become more commonplace across business processes. In the tech industry, for example, the use of both can enhance quality assurance.Continue Reading

• AI needs guardrails as generative AI runs rampant

  Generative AI hype has businesses eager to adopt it, but they should slow down. Frameworks and guardrails must first be put in place to mitigate generative AI's risks.Continue Reading

• The rise of automation and governance in MLOps

  MLOps can make many of an organization's operations more efficient, but only when its automation capabilities are paired with effective governance strategies.Continue Reading

• AI use cases in banking create opportunities, improve systems

  AI has become increasingly more common in the banking industry and has found a home sifting through data, improving back-end systems and assisting with customer service.Continue Reading

• Interpretability and explainability can lead to more reliable ML

  Interpretability and explainability as machine learning concepts make algorithms more trustworthy and reliable. Author Serg Masís assesses their practical value in this Q&A.Continue Reading

• Solving the AI black box problem through transparency

  Ethical AI black box problems complicate user trust in the decision-making of algorithms. As AI looks to the future, experts urge developers to take a glass box approach.Continue Reading

• 5 ways AI bias hurts your business

  A biased AI system can lead businesses to produce skewed, harmful and even racist predictions. It's important for enterprises to understand the power and risks of AI bias.Continue Reading

• Synthetic data for machine learning combats privacy, bias issues

  Synthetic data generation for machine learning can combat bias and privacy concerns while democratizing AI for smaller companies with data set issues.Continue Reading

• Bias in machine learning examples: Policing, banking, COVID-19

  Human bias, missing data, data selection, data confirmation, hidden variables and unexpected crises can contribute to distorted machine learning models, outcomes and insights.Continue Reading

• Are giant AI chips the future of AI hardware?

  Giant AI chips like the Cerebras WSE are dazzlingly fast and could transform AI models, but how soon is the question for CIOs. Experts mull the merits of small vs. big AI chips.Continue Reading

• How far are we from artificial general intelligence?

  Developers and researchers are currently debating the extent to which artificial general intelligence needs to mimic the human brain. Explore the two schools of thought.Continue Reading

• The state of AI defined by global adoption and regulation

  Cognilytica reports on AI adoption by both countries and companies across the globe, as well as the former's overall strategies and regulation frameworks.Continue Reading

• How to build a chatbot with personality and not alienate users

  Adding personality to a chatbot can push it toward the uncanny valley and raises ethical questions. But enterprises can make their bots more engaging, while avoiding these hurdles.Continue Reading

• How Getty Images reduces bias in AI algorithms to avoid harm

  In applications from internal job recruiting to law enforcement technology, AI bias is a widespread issue. Here's what enterprises can do to reduce bias in training and deployment.Continue Reading

• Ethical concerns of AI call growing adoption into question

  AI tools are getting easier to use every day, putting powerful tools into the hands of potentially malicious users. The time to think about the ethics of AI advances is now.Continue Reading

• AI gig economy sets workers and bots on collision course

  The future of work has shifted toward a gig economy, with high-value, short-term workers on demand for organizations. The fast turnover and high volume demand AI to reduce friction.Continue Reading

• Clashes between AI and data privacy affect model training

  Enterprises' lax data rules reveal weaknesses around AI and model training -- particularly machine learning's reliance on unrestrained big data collection.Continue Reading

• Enterprises work toward AI trust and transparency

  If ignored, a lack of trust in AI algorithms could diminish user adoption. To remedy this risk, enterprises are working to make their applications more transparent and explainable.Continue Reading

• Chatbots in customer service find success with focused goals

  Chatbots can be a great adjunct to customer service, but a successful rollout requires careful planning, flexibility and clear objectives.Continue Reading

• Use of AI in government makes agencies smarter

  Government agencies are starting to embrace some of the same AI technologies that typical enterprises use, and many are finding increased efficiencies along the way.Continue Reading

• Mapping adoption of artificial intelligence in the enterprise

  As many enterprises focus on use case research, the mature uses of AI are concentrated in supervised learning, structured data extraction and using a small number of platforms.Continue Reading

• Enterprise consumer relationships are building trust in AI

  Transparency is an increasingly important component of consumer trust. If you want to win over consumers whose data is being collected, start with explainability and collaboration.Continue Reading

• How AI in physical security makes public places safer

  Deep learning-based tools are increasingly finding a home in physical security to enhance the protection of real-world assets and make public spaces safer.Continue Reading

• AI in the legal industry focuses on augmenting research

  From billable time invoice generation to patent attribute data mining, the implementation of AI in law firms has aided in reducing low-value, time-consuming paralegal work.Continue Reading

• How data privacy and marketing coexist when influencing the public

  The author of a new book on the intersection of advertising and marketing with AI and data privacy talks about influencing the public with technology.Continue Reading

• How to address the hidden risks of algorithmic decision making

  From biased customer interactions to harmful treatment recommendations, some risks of automated decisions have yet to be resolved. A Wharton School professor has a way out.Continue Reading

• Interpretable AI has benefits beyond compliance

  Making AI models more interpretable has a wide range of benefits and shouldn't just be thought of as a compliance exercise, experts at the H2O World conference said.Continue Reading

• AI regulation stirs as unrestricted AI booms in China

  Governments need to start regulating AI as the technology advances, experts say in part one of a three-part series on AI ethics issues around regulation, control and bias.Continue Reading

• Explainability is no solution to problem of bias in AI

  Explainability has been touted as a solution to the problem of biased AI models, but experts say that approach only gets you part of the way to bias-free applications.Continue Reading

• Compliance.ai uses Tibco Cloud Mashery for API mastery

  Compliance.ai, a compliance and regulatory management platform vendor, uses Tibco Mashery to help manage and compile many different APIs and data sources.Continue Reading

• What do businesses do with the top machine learning platforms?

  Take a deep dive into machine learning, including decision trees, clustering, reinforced learning, neural networks, as well as supervised and unsupervised machine learning.Continue Reading