A short lesson series on spotting high-value, low-risk places to start.
Key takeaways
Most firms that struggle with AI adoption made their first decision backwards — they picked a tool first and then went looking for a problem to justify it. This course flips that order: five lessons spent mapping your own operations for genuine friction points before any tool enters the conversation, so whatever you eventually choose is solving a real, prioritized problem.
The mapping exercise itself tends to be valuable independent of what you do with AI afterward, since it usually surfaces operational friction that leadership had stopped noticing simply because it had always been there.
Lesson one is a structured operations walk-through to generate a long list of candidate friction points across every function. Lesson two applies an impact-versus-effort framework to narrow that list. Lesson three stress-tests the top candidates against data availability and team readiness. Lesson four scopes the top three into concrete, fundable initiatives. Lesson five builds the sequencing plan for rolling them out in order.
Each lesson is designed to be worked through by a leadership team together rather than by one person alone, since the friction points and the readiness to address them are rarely visible to just one function or role.
Three is a deliberate number. One use case leaves no fallback if the first pilot underperforms or takes longer than expected; ten spreads attention and budget too thin to build real momentum on any of them. Three ranked candidates give you a primary bet, a credible second option, and enough breathing room to sequence thoughtfully rather than betting everything on a single outcome.
By the end of the course, those three candidates aren't just ideas — they're scoped enough to feed directly into a business case, with the impact estimates and effort assessment already done.
Teams that finish this course typically move straight into building the business case for their top-ranked candidate, using the scoping work from lesson four as the starting foundation. The second and third candidates aren't wasted work either — they become the natural next bets once the first initiative proves out.
The mapping framework itself is reusable. Firms that revisit it annually as their operations evolve tend to keep finding new candidates worth evaluating, rather than treating opportunity mapping as a one-time exercise.
A 30-minute call is enough to tell you whether AI pays for itself here.
