AI product strategy, in practice
Not a list of AI features. A framework for deciding where AI creates durable value, how to evaluate a bet, and when the right answer is not to use AI at all.
AI product strategy is normal product strategy with three extra pressures: models are probabilistic, data is a moat, and the ground keeps moving as new models arrive. A 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 your business, and the discipline to say no everywhere else.
What makes AI strategy different
- Outcomes are probabilistic, so you plan for a range of behaviour, not one.
- Data advantage compounds: better products gather better data, which makes better products.
- Capability shifts fast; a bet that is hard today may be trivial in six months.
- The cost of being wrong is higher, because AI bets are expensive to build and to unwind.
Evaluating an AI opportunity
Before committing, pressure-test the bet against four questions.
| Question | What you are checking |
|---|---|
| Is AI the right tool? | Would a simpler rule or heuristic get most of the value? |
| Is the data there? | Do you have, or can you gather, the data the model needs? |
| Can it be good enough? | Is the quality bar reachable, and what happens below it? |
| Does it build advantage? | Does shipping it strengthen your data flywheel and moat? |
Build, buy or fine-tune
Most AI capability today can be bought or accessed through an API, so the strategic question is rarely can we build a model. It is which parts are commodity and which parts are yours. Buy the commodity, invest where your data or your workflow gives you an edge, and fine-tune only when a general model genuinely falls short for your users.
Pricing and data advantage
AI features carry real marginal cost, so pricing has to reflect usage, not just seats. And because data advantage compounds, the best AI strategies are designed so that every user interaction improves the product for the next user. That data flywheel, not any single model, is the durable moat.
Frequently asked questions
What is AI product strategy?
AI product strategy is 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, data advantage compounds, and model capability shifts fast, which raises the cost of being wrong.
How do you evaluate an AI opportunity?
Test the bet against four questions: is AI genuinely the right tool over a simpler rule, is the data available, can quality reach a good-enough bar and what happens below it, and does shipping it strengthen your data advantage. If it fails these, it is usually not worth building.
Should you build, buy or fine-tune an AI model?
Most capability can be bought or accessed by API, so buy the commodity parts, invest where your data or workflow gives an edge, and fine-tune only when a general model clearly falls short for your users. The strategic question is which parts are truly yours, not whether you can build a model.
When should you not use AI in a product?
When a simpler rule or heuristic captures most of the value, when the data is not there, or when the quality bar cannot be reached safely. Choosing not to use AI in those cases is a sound strategic decision, and interviewers and executives both value that judgement.
What is a data flywheel and why does it matter?
A data flywheel is a loop where every user interaction improves the product for the next user, which strengthens the product, which attracts more use and more data. Because data advantage compounds, this loop, rather than any single model, tends to be the durable moat in AI products.