What AI product strategy actually means
AI product strategy is not deciding which LLM to use. It is deciding which user problems AI can improve, which workflows it should touch, what human review looks like, and how you measure whether it helped.
Most 'AI products' fail not because the model is wrong but because the product strategy is — they automate the wrong thing, skip the review layer, or measure activity instead of outcomes.
The right starting questions
- Which decisions or workflows in your product are bottlenecked by speed, context, or consistency?
- Where does a human currently spend time doing something pattern-based?
- What is the cost of an AI mistake in this workflow?
- Who reviews AI output and how do we know when to stop trusting it?
Productized AI vs. AI features
A productized AI offering — like CanonIQ or FounderIQ — is built around a specific user journey, structured inputs, and a repeatable output. It is not a prompt wrapper. It is a workflow with design, data management, review gates, and a clear value moment.
AI features (a summarize button, a draft generator) are valid, but they are not a strategy.
Where B. focuses
- Identifying the right AI leverage points inside your product or org
- Designing the operating infrastructure AI can be grounded in (decision logs, data policy, review gates)
- Building productized AI offerings: scoping, pricing, delivery, and iteration
- Avoiding the common failure modes: hallucination risk in customer-facing flows, data leakage, and measurement debt