AI Product Strategy
Decide where AI creates durable value, and where it does not, with a live decision tool and a practical framework.
Good AI strategy is not a list of AI features. It is a clear view of where AI creates durable value for your users and business, and the discipline to say no everywhere else. Start with the decision tool below, then go deeper with the strategy guide and the frameworks this track is organised around.
Answer four questions about your idea
Guides and tools
Everything in the AI Product Strategy track. Live pages open now, more are on the build roadmap.
AI product strategy, in practice
A framework for deciding where AI creates value, and when not to use it.
Read guide →Build vs buy vs fine-tune
Which parts of your AI stack are commodity, and which are yours.
In progressPricing AI features
Why usage-based pricing fits AI, and how to think about margin.
In progressThe data flywheel
How to design products so every user makes the next one better.
In progressWhen not to use AI
The strategic case for reaching for a simpler solution.
In progressFrameworks built for AI products
Generic prioritisation still matters, but AI decisions need their own tools. These are the ones this track is organised around.
Common questions
Frequently asked questions
What is AI product strategy?
A clear view of where AI creates durable value for your users and business, plus the discipline to say no elsewhere. It differs from normal strategy because outcomes are probabilistic and data advantage compounds.
How does the decision tool work?
Answer four yes or no questions about your idea, covering whether a simpler rule would do, data readiness, the quality bar, and whether it builds advantage. The tool returns a verdict on whether AI is a strong fit.
Is choosing not to use AI a strategy?
Yes. Deciding that a simpler rule serves users better is a sound strategic choice, not a failure of ambition. Strong AI strategists are willing to kill AI ideas that do not earn their place.