Unit 10: Building your AI product strategy on a page

Learning objectives

Synthesize everything from this course into a one-page AI product strategy. Present AI strategy to stakeholders at different levels. Create an actionable plan that balances near-term execution with long-term vision.

Video script

Reading material

The one-page template in detail

Data advantage (2-3 sentences). Name the proprietary data assets that give your product a defensible position for AI features. If you don't have any yet, describe how you'll create them.

Customer problem portfolio (3-5 bullet points). Each bullet names a specific customer problem, the target user segment, and the evidence that validates the problem. These should be problems, not solutions.

Build-buy-wait assessment (table format). Three columns: Problem, Approach (build/buy/wait), Rationale. One row per problem from your portfolio.

Investment portfolio (brief allocation). Percentage of AI investment across mature (ship now), emerging (experiment), and early-stage (watch). Name the specific initiatives in each bucket.

Success metrics (table format). One row per initiative. Columns: Initiative, Layer 1-2 metrics (trust and quality), Layer 3-4 metrics (task completion and business impact), Success threshold.

Trust framework (3-4 principles). The design principles that will govern how your AI features interact with users. These should be specific enough to guide product decisions.

Strategy evolution

Your AI strategy should be stable for 6-12 months at the problem level. The customer problems you're targeting shouldn't change quarterly unless you receive significant new evidence. But the approach layer (build/buy/wait) and the investment portfolio should be reviewed quarterly because the technology landscape shifts fast.

Build a quarterly strategy review into your operating rhythm. Ask: which of our assumptions have been validated or invalidated? What technology changes affect our approach? Which experiments yielded results that should shift our priorities?

Practical exercise

Exercise: Write your AI product strategy

This is the capstone exercise for the course. Using everything you've learned and practiced across the ten units, write a one-page AI product strategy for a product you work on or know well.

Fill in all six sections of the template. Be specific. Use real data where you have it and clearly mark assumptions where you don't.

Then write a brief (one paragraph) version of how you'd present this strategy to three different audiences: executive leadership, your engineering team, and your design team.

This document is your portfolio piece. It demonstrates strategic thinking applied to AI products and shows that you can synthesize technical understanding, user insight, and business context into actionable direction.

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Course conclusion

Over these ten units, you've built the specialized skills needed to lead AI product initiatives. You can identify AI opportunities through adapted discovery techniques, reduce risk through assumption mapping, design for user trust, make informed build-buy-wait decisions, leverage your data as a strategic asset, collaborate effectively with ML engineers, measure AI product success across four layers, and experiment iteratively with probabilistic features.

The most important skill isn't any single technique. It's the judgment to apply the right approach at the right time. That judgment comes from practice. Take what you've learned here and apply it to your work. The frameworks will become intuition with repetition.