Getting Started With GenAI Tools for Enterprise AI Use Cases Getting started with GenAI tools is easy if the goal is to run a demo. It is much harder if the goal is to create an enterprise AI use case that employees can rely on in real work. The starting point should be a specific [...]
Read MoreGenAI Tools in Enterprise AI: What New Teams Should Understand First New enterprise AI teams often enter the market believing that GenAI tools are mostly interchangeable interfaces around large language models. In practice, the surrounding operating design matters as much as the model. Data access, retrieval, permissions, evaluation, workflow integration, human review, and post-go-live support [...]
Read MoreBeginner's Guide to GenAI Tools for Enterprise AI Programs Teams beginning an enterprise AI program often focus first on which GenAI tools to buy. That is understandable, but tool selection is rarely the hardest part. The bigger challenge is deciding which business information the tool may use, what it is allowed to produce, how users [...]
Read MoreChoosing the Right Role for AI and Analytics in Enterprise Decision-Making Enterprise decision-making fails when leaders cannot separate three needs: trusted facts, forward-looking estimates, and interpretation of complex information. Analytics, machine learning, and generative AI can each help, but they should not be given the same role. Choosing the right role for AI and analytics [...]
Read MoreHow AI and Analytics Strengthen Decision Support Across Business Teams Business teams rarely suffer from a complete lack of data. They struggle because information is fragmented, reports arrive at different times, definitions vary, and people spend too much effort turning raw signals into a decision. AI and analytics can strengthen decision support by shortening that [...]
Read MoreAI and Analytics in Decision Support: Where Each Adds Business Value Executives often group AI and analytics together, but the two create business value in different ways. In decision support, analytics is strongest when leaders need a reliable view of what happened, what is changing, and how performance compares with agreed definitions. AI is useful [...]
Read MoreWhere AI Can Support Operations Management in Finance, Sales, and Customer Support Operations leaders are under pressure to improve speed and visibility without adding more manual coordination. AI in operations management can help, but only when it is applied to work where data, decisions, and repeatable actions are clear enough to support dependable execution. Finance, [...]
Read MoreUsing AI in Operations Management Across Finance, Sales, and Support Workflows Using AI in operations management across finance, sales, and support works best when the program is designed around the handoffs between functions. Businesses often deploy separate assistants for each department, then discover that the same customer, order, product, and policy information is being interpreted [...]
Read MoreAI in Operations Management: Where It Fits Across Finance, Sales, and Support AI in operations management is most useful when it helps leaders coordinate work across functions without blurring who owns the underlying decisions. Finance, sales, and customer support all produce signals that affect daily execution: overdue receivables can change account priorities, sales commitments can [...]
Read MoreEvaluating Customer Support AI Around Accuracy, Escalation, and Human Review Evaluating customer support AI requires three controls to work together: accuracy, escalation, and human review. Accuracy determines whether the system can handle a class of requests reliably. Escalation determines whether it recognizes the cases it should not handle alone. Human review determines whether those exceptions [...]
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