Start with the decision, not the technology
A useful AI initiative begins with a specific task: what decision needs support, who owns it, and what happens after the output is produced? A broad ambition to “use AI” rarely provides enough direction for a reliable implementation. Map one workflow from input to action before selecting a model.
Make the baseline visible
Document how the work is done today. Time per task, error patterns, review effort, and the cost of rework help establish a fair comparison. A prototype that feels impressive is not necessarily an improvement once human review and system integration are included.
Build evaluation into the work
Create representative examples, including difficult cases and failures. Decide what an acceptable result looks like and who reviews it. Evaluation should cover factual usefulness, task completion, access boundaries, and the behavior of the surrounding workflow.
Design the handoff
People need to know when to trust an output, when to verify it, and how to escalate an exception. Give users relevant sources and a clear way to correct mistakes. For consequential actions, an explicit approval step may be essential.
Treat launch as the beginning
Production systems need monitoring, ownership, maintenance, and a route to improvement. Agree who handles failures, how changes are tested, and when the business case will be reviewed. The objective is a dependable capability, not an isolated demonstration.