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Choose the right first AI project to maximize learning and organizational momentum.
Read through the lesson, mark it complete when the concept is clear, then move to the next lesson in the sequence or jump back to the module map.
Most organizations choose AI pilots wrong. They either pick the most ambitious possible project ("let's replace our whole customer service team with AI") or they pick something so trivial it proves nothing.
The right pilot is: meaningful enough to build credibility, scoped enough to complete in 60-90 days, and instrumented to generate learnings that inform the next initiative.
Business impact — Must move a metric someone cares about. "Interesting experiment" is not a success criterion.
Feasibility — Can this realistically be built in 60-90 days with available resources? Don't pilot something that requires 18 months of infrastructure work first.
Measurability — Can you clearly measure success vs baseline? Without measurement, you can't prove value or justify the next investment.
Learning value — Will this generate insights applicable to future initiatives?
Low downside risk — If it fails, what's the damage? Pilot in a domain where a partial or failed implementation has limited negative consequences.
Score each candidate pilot (1-5): business impact × feasibility × measurability × learning value ÷ risk. Top scorers are your best candidates.
Big bang — "We'll replace the entire process." Scope it down. Pilot one step, not the whole thing.
Purely internal — if only your team uses it, learning is limited. Find pilots with external touchpoints (customers, partners) for richer feedback.
Unclear ownership — every pilot needs a named owner who is accountable for success. Shared accountability means no accountability.
Document everything: what worked, what didn't, what you'd do differently, what the actual ROI was vs projected. This becomes the organizational playbook for AI initiatives.