The AI Adoption Compass
Start with a valuable decision, not an impressive demo.
AI becomes useful when it improves a real moment of work: a support agent finding an approved answer, a manager seeing a clearer summary, or a team reducing the time between a lead and a thoughtful response.
Ask four questions: What outcome should improve? Who needs the help? What information is safe to use? Where must a person remain responsible? If those answers are fuzzy, the project needs framing before it needs a model.
A workflow worth automating
Choose work that is repetitive, visible, and recoverable.
The best first workflow is frequent enough that people notice the change, bounded enough to test safely, and reversible enough that a mistake does not become a crisis. Think drafts before sends, suggestions before decisions, and retrieval before interpretation.
Make the first version smaller than your ambition. A narrow, trusted experiment teaches more than a broad system that nobody adopts.
Measure what earns trust
Track quality, speed, escalation, and confidence.
Usage can be a helpful signal, but it is not the finish line. A serious AI initiative should make its value legible: did output quality improve, did a task take less time, were uncertain cases escalated correctly, and do users know when to trust the result?
Those measures create a healthier conversation. They make it easier to decide whether to improve, integrate, pause, or expand a workflow.