AI Product Strategy

Why AI Product Strategy matters for AI Product Managers

AI Product Strategy turns model capabilities into business outcomes. It helps you decide what to build, why it matters, when it ships, and how it drives revenue or savings. With a clear strategy, you can align stakeholders, budget, data, and technical effort around measurable impact—not hype.

• Find high-impact AI use cases tied to business goals
• Decide build vs buy vs partner with cost, speed, and risk in mind
• Set a compelling AI product vision and north-star metrics
• Analyze competitors and pick a defensible position
• Plan a portfolio and roadmap that balances quick wins and big bets
• Monetize AI features with sustainable unit economics

Who this is for

• AI/ML Product Managers defining AI features or platforms
• Founders and PMs adding generative AI to existing products
• Analysts and DS/ML leads stepping into product strategy

Prerequisites

• Basic product management concepts (problem/solution fit, metrics, MVP)
• Familiarity with ML/AI fundamentals (supervised vs generative, data quality, evaluation)
• Comfort with spreadsheets and simple metric math (ROI, CAC, LTV)

Learning path

1. Map business goals to AI opportunities
   Identify top company objectives and pain points. Draft 5–10 candidate AI use cases aligned to measurable outcomes.
2. Evaluate and prioritize use cases
   Score by user value, feasibility, data readiness, risk, and time-to-impact. Pick 1–2 for near-term delivery and 1 strategic bet.
3. Decide build, buy, or partner
   Compare total cost of ownership, speed, differentiation, compliance, and vendor risks.
4. Define vision and metrics
   Write a crisp vision statement, a north-star metric, and a metric tree (leading/lagging metrics).
5. Analyze market and competition
   Assess alternatives, moats (data, distribution, workflow lock-in), and your positioning.
6. Plan roadmap and portfolio
   Sequence quick wins, enablers, and big bets. Add risks, assumptions, and learning milestones.
7. Price and monetize
   Choose a pricing model, estimate unit economics, and design metering and limits.

Worked examples

1) Prioritizing AI use cases with value, feasibility, and risk

Scenario: You have 3 ideas for a support product: auto-draft replies, intent routing, and knowledge gap detection.

• Auto-draft replies: Value 5, Feasibility 4, Data Readiness 4, Risk 3, Time-to-Impact 4
• Intent routing: Value 4, Feasibility 5, Data Readiness 5, Risk 2, Time-to-Impact 5
• Knowledge gap detection: Value 3, Feasibility 3, Data Readiness 2, Risk 4, Time-to-Impact 2

Score formula (simple): Score = 0.35*Value + 0.25*Feasibility + 0.2*Data + 0.1*(6-Risk) + 0.1*Time

def score(v,f,d,r,t):
    return 0.35*v + 0.25*f + 0.2*d + 0.1*(6-r) + 0.1*t
print("Auto-draft:", score(5,4,4,3,4))
print("Routing:", score(4,5,5,2,5))
print("Gap detect:", score(3,3,2,4,2))

Decision: Intent routing wins as a near-term ship; auto-draft is second; gap detection becomes a research spike.

2) Build vs buy vs partner for an LLM-powered feature

Goal: Add meeting summary in-app.

• Build: Highest differentiation, but 4–6 months, infra cost, model ops, compliance scope
• Buy: Fast 2–4 weeks, per-usage fees, vendor lock-in risk
• Partner: Co-marketing, shared roadmap; dependency on partner stability

Decision guide:

• If speed-to-market and low maintenance are critical → Buy
• If the summary is core to your moat or needs deep domain tuning → Build
• If distribution and co-selling matter → Partner

# Annualized rough cut
build_engineering = 2.5  # FTE
ml_ops_infra = 0.5       # FTE equivalent
inference_cost = 0.04    # $ per meeting
vendor_buy_cost = 0.06   # $ per meeting
volume = 1_000_000
build_tco = (3.0*180000) + volume*0.04   # FTE cost assumed $180k
buy_tco   = volume*0.06
print(build_tco, buy_tco)

3) Writing an AI product vision and north-star metric

Vision: "Every support reply is accurate, empathetic, and instant, with AI as the default co-pilot."

• North Star: % of tickets resolved within 1 hour without escalation
• Drivers: AI suggestion adoption rate; AI-corrected resolution accuracy; CSAT for AI-assisted replies
• Inputs: Knowledge freshness; prompt quality; model latency

4) Market sizing: TAM → SAM → SOM for an AI add-on

Product: Generative AI email assistant for SMB marketing teams.

• TAM: 10M SMB marketers × $10/month = $1.2B/year
• SAM (English-speaking, reachable): 4M × $10 = $480M/year
• SOM (first 2 years @1% share): 40k × $10 = $4.8M/year

def som(users, price, share):
    return users * price * share
print(som(4_000_000, 10, 0.01))  # $/month
print(som(4_000_000, 15, 0.008)) # higher price, lower share

5) Pricing a generative feature with unit economics

Assume average user generates 200 prompts/month; average prompt cost is $0.002; support + overhead $1/user/month.

• Cost per user ≈ 200 × $0.002 + $1 = $1.40
• Target 70% gross margin → Price ≥ $4.67
• Package as a $5 add-on or bundle into Pro tier priced +$6

prompts = 200
cost_per_prompt = 0.002
overhead = 1.0
gross_margin_target = 0.7
cpu = prompts*cost_per_prompt + overhead
price = cpu / (1 - gross_margin_target)
print(round(cpu,2), round(price,2))

Drills and exercises

• List 5 AI use cases for your product. For each, write the user problem, success metric, and rough value ($ or time saved).
• Score each use case (Value, Feasibility, Data, Risk, Time) and select a near-term and a strategic bet.
• Draft a one-sentence AI product vision and a metric tree (north star, drivers, inputs).
• Fill a Build/Buy/Partner matrix for your top use case.
• Create a 2-quarter roadmap: one quick win, one enabler, one big bet. Add risks and learning milestones.
• Choose a pricing model; compute unit economics and write guardrails (rate limits, abuse handling).

Common mistakes and how to avoid them

• Starting with models, not outcomes: Always anchor to a business metric and user value before picking a model.
• Ignoring data readiness: Check data legality, coverage, freshness, and label quality early.
• Over-rotating to quick wins: Keep a balanced portfolio; schedule enablers that unlock future leverage.
• Vendor lock-in without exit plan: Document switching costs, API parity, and export paths.
• Unpriced inference: Model costs can spike. Add metering, caching, and budgets from day one.
• Vague success criteria: Define offline metrics, online A/B metrics, and qualitative guardrails before launch.

Mini project: Ship an AI-assisted feature end-to-end

Goal: Launch AI-assisted reply suggestions for the support inbox in 6–8 weeks.

1. Define success: Raise % of 1-hr resolutions from 45% to 60%.
2. Scope MVP: Top 3 intents only; English; manual review required in week 1.
3. Decide approach: Buy a vendor for phase 1; plan a build path if adoption > 40%.
4. Data plan: Sample 10k past tickets; label 1k for evaluation; redact PII.
5. Evaluation: Target 80% helpfulness (human rater), latency < 1.5s.
6. Rollout: 10% → 50% → 100% with A/B; add rate limits and feedback buttons.
7. Monetization: Include in Pro tier; monitor gross margin weekly.

Subskills

• Identifying AI Use Cases And Value — Find opportunities tied to measurable outcomes and prioritize them by impact and feasibility.
• Build Buy Partner Decisions — Compare total cost, speed, differentiation, and risk to pick the best approach.
• Defining AI Product Vision — Craft a clear vision, north-star metric, and metric tree aligned to user value.
• Competitive And Market Analysis — Size the market, evaluate competitors and moats, and position your product.
• Roadmap And Portfolio Planning — Balance quick wins, enablers, and strategic bets with risks and learning milestones.
• Aligning AI With Business Goals — Map use cases to OKRs and quantify expected value creation.
• Pricing And Monetization For AI — Choose pricing models, estimate unit economics, and set metering and guardrails.

Next steps

• Work through the subskills below and complete the mini project.
• When ready, take the skill exam. Everyone can take it for free; logged-in learners get saved progress.
• Apply the roadmap to your product and iterate based on real metrics.

Skill exam

Test your understanding with realistic scenarios. Score 70% or higher to pass. You can retake the exam anytime.