Problem solve

Problem solve

Get help with specific problems with your technologies, process and projects.

• 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

• 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

• 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

• 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

• 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

• 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

• 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 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

• 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

• 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

• Why enterprise AI needs diagnostic intelligence

  Enterprise AI detects anomalies but can't always explain why they happen. Next up for these systems is a focus on diagnosis to enhance trust, compliance and safety.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

• 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

• 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

• 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

• 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

• 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

• How to use LangChain for LLM application development

  LangChain helps developers do more with language models, linking them to tools, APIs and live data to build more capable and dynamic AI applications.Continue Reading

• Why does AI hallucinate, and can we prevent it?

  Despite their sophistication, LLMs still often produce inaccurate or misleading output. Will these hallucinations ever go away for good?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

• How bad is generative AI data leakage and how can you stop it?

  Mismanaged training data, weak models, prompt injection attacks can all lead to data leakage in GenAI, with serious costs for companies. The good news? Risks can be mitigated.Continue Reading

• A short guide to managing generative AI hallucinations

  Generative AI hallucinations cause major problems in the enterprise. Mitigation strategies like retrieval-augmented generation, data validation and continuous monitoring can help.Continue Reading

• Expanding explainable AI examples key for the industry

  Improving AI explainability and interpretability are keys to building consumer trust and furthering the technology's success.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

• Combating racial bias in AI

  By employing a diverse team to work on AI models, using large, diverse training sets, and keeping a sharp eye out, enterprises can root out bias in their AI models.Continue Reading

• Addressing 3 infrastructure issues that challenge AI adoption

  One of the biggest problems enterprises run into when adopting AI infrastructure is using a development lifecycle that doesn't work when building and deploying AI models.Continue Reading

• Tackling the AI bias problem at the origin: Training data

  Though data bias may seem like a back-end issue, the enterprise implications of an AI software using biased data can derail model implementation.Continue Reading

• How to troubleshoot 8 common autoencoder limitations

  Autoencoders' ability for automated feature extraction, data preparation, and denoising are complicated by their common problems and limitations in usage.Continue Reading

• Data science's ongoing battle to quell bias in machine learning

  Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it.Continue Reading

• Understanding how deep learning black box training creates bias

  Bias in AI is a systematic issue that derails many projects. Dismantling the black box of deep learning algorithms is crucial to the advancement and deployment of the technology.Continue Reading

• 7 last-mile delivery problems in AI and how to solve them

  Enterprises are discovering it's easier to build AI than it is to integrate it into existing processes. We examine seven 'last-mile' deployment problems when delivering AI.Continue Reading

• Here's how one lawyer advises removing bias from AI

  Avoiding bias in AI applications is one of the central challenges in using the technology. Here's some advice on deploying AI technologies in a way that is fair.Continue Reading

• Supercomputer consortium powering COVID-19 treatment research

  Supercomputers, AI and high-end analytic tools are each playing a key role in the race to find answers, treatments and a cure for the widespread COVID-19.Continue Reading

• Using AI in AML fights fraud while protecting privacy

  Money laundering and fraud remain a risk for financial institutions, but AI can act as a useful tool against a constantly evolving financial enemy.Continue Reading

• Supervise data and open the black box to avoid AI failures

  As AI blooms, marketers and vendors are quick to highlight easy positive use cases. But implementation can -- and has -- gone wrong in cases that serve as warnings for developers.Continue Reading

• Serverless machine learning reduces development burdens

  Getting started with machine learning throws multiple hurdles at enterprises. But the serverless computing trend, when applied to machine learning, can help remove some barriers.Continue Reading

• Collaborative robots' safety stalls enterprise implementation

  Cobots are promising big gains, especially in enterprises utilizing manual labor. However, due to a number of safety concerns, human workers are still at risk.Continue Reading

• How to solve deep learning challenges through interoperability

  The challenges of training and overseeing advanced neural networks is leading to an implementation bottleneck in deep learning technology.Continue Reading

• The future scope of chatbots begins with addressing flaws

  Chatbots are hot software in the enterprise, but to maintain longevity and relevance, developers need to take a look at the barriers to entry, interface options and NLP issues.Continue Reading

• Berklee uses SnapLogic for its AI in higher education needs

  Berklee College of Music needed intelligent integration for its two student portals after a merger with the Boston Conservatory. The college chose SnapLogic to connect the systems.Continue Reading

• AI for accessibility helps people with disabilities

  AI for people with disabilities is making a meaningful difference in their ability to navigate the world and participate in all the activities of daily life.Continue Reading

• Convert unstructured data to structured data with machine learning

  With access to powerful compute power and advances in machine learning, unstructured data is becoming easier and cheaper for businesses to turn into usable sources of insight.Continue Reading

• Algorithmic bias top problem enterprises must tackle

  No enterprise today would roll out an obviously biased AI tool, but many remain unaware of the risks of unconscious bias in algorithms, which can produce equally dangerous results.Continue Reading

• GDPR regulations put premium on transparent AI

  As the EU's GDPR regulations go into effect, enterprises must focus on building transparency in AI applications so that algorithms' decisions can be explained.Continue Reading