---
name: lead-ai-product-strategy
description: Develop AI product strategies that connect market choice, ICP, workflow value, capability advantage, defensibility, product portfolio, commercialization, pricing, unit economics, Build or Buy decisions, platform timing, organization, and governance. Use for strategy memos, annual planning, product line reviews, pricing and packaging, moat analysis, portfolio allocation, platform proposals, and executive decision support.
---

# Lead AI Product Strategy

Convert AI capability into a focused market position, repeatable value engine, and economically sustainable operating model.

## Operating rules

- Start from customer workflow and willingness to change, not model novelty.
- Distinguish market facts, internal facts, assumptions, strategic choices, and experiments.
- Do not call generic model access or raw data volume a moat without testing replication difficulty.
- Calculate full cost per successful task, including operations and failure handling.
- Treat platformization as an investment that requires repeated demand, stable commonality, and ownership.
- Make portfolio choices explicit: accelerate, maintain, experiment, partner, or stop.

## Workflow

### 1. Frame the strategic question

Define decision horizon, target outcome, resource envelope, constraints, current product position, and the decision that leadership must make.

### 2. Choose the market and ICP

Map user workflow, problem severity, frequency, budget, data access, implementation friction, risk, and proof of value. Separate user, beneficiary, buyer, administrator, and risk owner.

### 3. Define the winning system

Read [references/strategy-commercialization.md](references/strategy-commercialization.md). Connect customer workflow, AI capability, product experience, operations, distribution, trust, economics, and feedback loops. State what the company will not pursue.

### 4. Test defensibility

Assess workflow embedding, authorized feedback data, proprietary evaluation and error knowledge, domain operations, trust, distribution, unit cost, network effects, switching cost, and ecosystem. For each claimed moat, describe how a capable competitor would copy it.

### 5. Design commercialization

Quantify value created, current alternative cost, willingness to pay, full cost to serve, pricing metric, packaging, gross margin, implementation effort, support burden, and expansion path. Model conservative, base, and upside scenarios.

### 6. Allocate the portfolio

Score initiatives on strategic value, customer evidence, technical feasibility, economic potential, risk, learning value, and dependency. Set investment thesis, milestone, kill criteria, and next capital decision for each.

### 7. Decide Build, Buy, Partner, and platform timing

Build where control or differentiation matters and the organization can maintain the capability. Buy commodity capabilities when speed and maturity dominate. Platformize only after repeated use cases reveal stable shared needs.

### 8. Produce the artifact

Copy [assets/ai-product-strategy-memo.md](assets/ai-product-strategy-memo.md). Use [assets/unit-economics-model.md](assets/unit-economics-model.md) for pricing and scenario analysis.

## Quality gate

Confirm that the strategy states:

- Which customer and workflow to win, and which to decline.
- Why now and what evidence supports the timing.
- The value mechanism and measurable customer outcome.
- The complete system required beyond a model API.
- A replication-resistant moat hypothesis and its test.
- Pricing metric, full cost, margin, and scale sensitivity.
- Product portfolio choices and kill criteria.
- Build/Buy/Partner and platform logic.
- Organizational ownership, risk posture, and next strategic review trigger.

Reject a roadmap of disconnected features as a product strategy.