AI creates operating value when it removes repeatable friction from important work. The best starting points are rarely the flashiest demonstrations. They are usually workflows that consume meaningful labor, delay decisions, create rework, or depend too heavily on a few experienced people. Start with workflow economics, not tools A useful AI opportunity has a clear business problem, a defined owner, enough volume to matter, and a measurable before-and-after condition. Instead

AI ROI should not be calculated by multiplying an optimistic time-saving estimate across an entire workforce. It should be measured at the workflow level using an agreed baseline, actual usage, complete operating cost, and a result the business recognizes. Establish the baseline before implementation Record the current volume, labor time, cycle time, error or rework rate, service level, and cost of delay. The baseline does not need to be perfect, but it must be defined before

Once AI moves beyond individual experimentation, the organization needs to know what it operates. An AI asset registry is the controlled record of the assistants, automations, model-enabled workflows, knowledge systems, and approved tools that affect company work. Treat the workflow as the asset The asset is not merely the model or vendor account. It is the complete operating arrangement: the business purpose, users, instructions, data sources, integrations, controls, review

A company should not manage AI as a collection of unrelated ideas. It should manage a portfolio of operating opportunities, each with a business owner, expected value, implementation path, risk profile, and decision date. Inventory workflows, not software wishes Begin by interviewing the people who own revenue, cost, delivery, risk, and customer experience. Ask where work stalls, where information is repeatedly re-entered, where specialists become bottlenecks, and where quali

Employees can leave an AI workshop impressed, motivated, and still unsure what they are allowed to use on Monday morning. The problem is usually not the quality of the presentation. It is the absence of a designed workflow around the training. The gap between a demonstration and daily work Generic training teaches features and techniques. Adoption requires employees to know which task to perform, which information may be used, which tool is approved, what output standard appl