An enterprise AI agent sounds appealing when a team is drowning in follow-ups. It can read records, compare them, prepare a next step, and sometimes act through connected systems. The useful question for a business leader is narrower: which part of a real workflow should the agent handle, and how will we know it made
Enterprise AI Agent: How to Choose a Business Workflow Worth Building Read More »
AI business use cases are useful only when they connect a business problem to suitable data, a real workflow, a measurable result and a person who remains accountable. A chatbot, prediction model or AI agent is not a business case by itself. The business case is the improvement it can produce, the evidence that supports
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Human in the loop means placing a person at a defined point in an AI-supported process to review evidence, make a judgment, approve an action, correct an error or stop the process. It should identify a real decision and a person with authority. Simply adding an approval button does not create meaningful human oversight. I
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What is MCP? Model Context Protocol is a common way for an AI application to discover and use approved tools, data and services. It can help an assistant move beyond answering from its existing knowledge, but it does not automatically make the assistant accurate, secure or authorized to act. The easiest comparison is a standard
What Is MCP? A Plain-English Guide for Business Leaders Read More »
If you want to learn how to build an AI agent, the easiest place to start is not with code. Start with one recurring business task, give ChatGPT the instructions and files required to perform it, define where it must stop, and test whether it behaves consistently. I used that approach to build an accounts-payable
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AI agents for business are being presented as the next major change in how work gets done. Some descriptions make them sound like digital employees capable of running entire departments. Others use the word “agent” for what is essentially a chatbot with a new label. Business leaders need a more practical explanation. An AI agent
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An AI operating model determines who selects AI opportunities, who owns the business outcome, who builds reusable capabilities, who accepts risk, and who keeps the product working after the pilot ends. Without those decisions, organizations can have an AI strategy, modern platforms and talented teams while still struggling to move useful ideas into production. I
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An AI maturity assessment should tell leaders what the organization can do reliably today, what is holding it back, and which action deserves funding next. It should not produce a flattering score that places the company in a fashionable category while teams continue running disconnected pilots with unclear business value. I have seen maturity assessments
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An AI strategy is a set of choices about where artificial intelligence should change the business, what role it should play, and what evidence must exist before the company invests further. It is not a list of AI projects. It is also not a technology roadmap with the word AI added to the title. I
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Data governance best practices should help a company settle important data decisions, prevent recurring failures and prove that agreed controls are working. If governance mainly produces meetings, glossaries and ownership spreadsheets, it may look mature while the same business problems continue underneath it. I have seen this gap across ERP programs, data warehouses, analytics platforms,
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