Useful AI projects begin with a workflow, not a model. The best early opportunities usually involve repetitive information work, clear source material and a team that can judge whether the result is good enough.
Look for information bottlenecks
Research, classification, summarization, drafting and knowledge retrieval are often suitable because AI can accelerate the first pass while a person remains responsible for the final decision.
Define the human control point
Decide which actions can be automated, which require approval and what happens when confidence is low. The operating model is as important as the technical implementation.
Measure workflow outcomes
Track time saved, completion quality, rework, adoption and the effect on the wider process. Model accuracy alone does not prove that the solution creates business value.
Start narrow and learn
A focused pilot with real users reveals data gaps, edge cases and trust requirements. Use that evidence before expanding the workflow or adding autonomy.
Apply the decision to your own product context.
A focused conversation can clarify assumptions, risks and the most useful way to move forward.
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