Artificial intelligence adoption often begins with a compelling demonstration. The real challenge comes next: deciding what deserves to reach production, who is accountable for the outcome, and how risk will be controlled.
Start with the decision, not the tool
A viable use case connects a technical capability to a specific institutional decision or process. Before choosing models or vendors, align on four elements:
- the expected business outcome;
- the information that may be used;
- the permitted level of autonomy;
- and the person accountable for the result.
Treat risk as part of the design
Privacy, security, explainability, and continuity are not final reviews. They are architectural constraints. Bringing them in early reduces rework and gives teams clear boundaries within which to move.
Scale evidence, not enthusiasm
A pilot should measure quality, cost, time, adoption, and operational risk. Without a baseline metric, there is no serious basis for deciding whether the initiative should grow.
The opportunity created by AI is real. So is the need for executive direction to turn isolated experiments into institutional capability.