AI Product Strategy
Everyone is adding AI to their product. Most of them are building wrappers that will be commoditised in six months. This course teaches you how to evaluate AI opportunities with a strategic lens: when AI adds real value, what makes an AI feature defensible, how to price for usage-based costs, and how to ship AI responsibly without destroying user trust.
What you'll learn
Course outline
Free — no account needed
The AI Opportunity Map — Where AI Adds Real Value
A framework for identifying which problems in your product are worth solving with AI
Avoiding the AI Wrapper Trap
Why most "AI products" are one API change away from irrelevance — and how to build something defensible
Choosing the Right AI Primitive for Your Problem
RAG vs fine-tuning vs prompt engineering vs agents — match the technique to the problem
Full course — $49 one-time
Pricing AI Features — Usage-Based vs Flat Rate
How to build a pricing model that covers your API costs and still grows with customers
Building AI Defensibility — Data Moats and Flywheel Effects
The concrete mechanisms that make your AI product harder to copy over time
Evaluating AI Use Cases — The ROI Framework
Prioritise your AI roadmap using time saved, error reduction, and willingness to pay
Shipping AI Responsibly — Safety, Bias, and User Trust
The practical steps to ship AI features that do not damage your product reputation or user relationships
Get the full course
7 lessons — from AI opportunity mapping to shipping responsibly in production.
About this course
Building a successful AI product requires more than technical capability — it requires understanding where AI creates defensible value, how to avoid the commodity traps that kill most AI startups, and how to build products that improve as they accumulate data and feedback. This AI product strategy course covers the strategic frameworks that product leaders and founders need to make sound decisions about what to build, how to differentiate, and how to think about competition from the major AI labs themselves.
This course is for product managers, founders, and technical leads responsible for AI product decisions. After completing it you will understand how to identify AI use cases worth building, how to design feedback loops that make your product better over time, and how to think about defensibility in a landscape where the underlying models are rapidly improving.
Frequently asked questions
How is AI product strategy different from regular product strategy?
AI products have specific strategic dynamics that traditional product frameworks do not fully address: capability improvements come from the underlying model provider (not your team), output quality is probabilistic rather than deterministic, and defensibility often comes from data and workflow integration rather than the AI itself. This course adapts classic product strategy concepts to these AI-specific dynamics.
What is the biggest strategic mistake AI product teams make?
The most common mistake is building a product whose entire value proposition is "we use AI" — with no proprietary data, no workflow integration, and no switching cost. When a better model does the same task more cheaply, the product has nothing to defend. Defensible AI products use AI as a capability layer over proprietary data, deep workflow integration, or network effects — not as the product itself.
How do I think about competition from OpenAI and Anthropic?
The major AI labs compete in the horizontal layer — general-purpose models, chat interfaces, API access. They generally do not compete aggressively in vertical applications (legal AI, medical AI, construction AI) because domain expertise, workflow integration, and regulatory understanding are not their core strengths. The strategic question is whether you are building in the horizontal layer (where you will lose) or the vertical application layer (where you can win).
What does "data flywheel" mean for AI products?
A data flywheel is a self-reinforcing loop where more users generate more data, which improves the product, which attracts more users. For AI products: user interactions reveal where the model fails, those failures are used to improve prompts or fine-tune the model, and the improved model creates a better product. Building a data flywheel is the clearest path to an AI product becoming more valuable and defensible over time.
How do I measure the success of an AI feature?
AI feature metrics need to capture both usage and quality. Usage metrics: adoption rate, retention, and frequency of use. Quality metrics: task completion rate, user corrections (when users edit the AI output), thumbs up/down feedback, and A/B test impact on downstream business metrics. Avoid vanity metrics like number of AI interactions that do not tell you whether the AI is actually helping users accomplish their goals.