AI Product Manager — Inference Phase

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Inference Specialization

AI Product Manager

AI PMs decide what gets built, why it matters, and how success is measured. One of the fastest-growing non-technical AI roles and the most underdeveloped skill in most AI teams today. +89% YoY hiring into the role.

Days

Projects

$165K

Median

↑89%

YoY Hiring

$120K–$220K

Salary Range

Week 1: AI Product Strategy and User Research · Days 1–10  

Learn to scope AI products, conduct user research, and define metrics that connect model quality to business outcomes.

D1–3

AI product strategy: when AI is genuinely the answer

Feasibility frameworks, build vs buy decisions, competitive moats, AI product maturity model

↗ AI Product Management Specialization — Duke/Coursera (free audit)

D4–6

User research for AI: edge cases and mental model mismatches

How users actually think about AI systems. Trust, fear, over-reliance, and mental model mismatches

↗ Human-Centered AI Design — IDEO/Coursera (free audit)

D7–9

AI metrics: connecting model KPIs to business KPIs

Accuracy to revenue, NPS, cost savings. Common metric pitfall patterns and how to avoid them

↗ AI For Everyone — Andrew Ng/Coursera (free audit)

🏗 Project — Day D10: AI Feature Product Requirements Document 

· Problem statement backed by 5 user research quotes

· 3 success metrics at both model and business level

· Technical feasibility assessment with engineering input

· Risk register with likelihood and impact estimates

· 1-page phased launch plan

Week 2: Technical Depth and Experimentation · Days 11–28  

Build just enough technical depth to lead AI engineering teams, run clean experiments, and ship responsible products.

D11–14

How LLMs actually work: the AI PM perspective

Tokens, context windows, RAG vs fine-tuning, hallucination — all explained for product decision-making

↗ Generative AI for Everyone — DeepLearning.AI/Coursera (free audit)

D15–18

AI A/B testing: running statistically valid model experiments

Statistical power, novelty effects, guardrail metrics, CUPED variance reduction for ML experiments

↗ Trustworthy Online Controlled Experiments — Kohavi (free excerpts)

D19–22

Responsible AI product decisions: bias, privacy, safety

Embedding ethics into feature decisions from day one. Red-teaming features, AI usage policies

↗ Responsible AI Practices — Google (free)

D23–27

Build a working AI prototype with no ML background

GPT-4o plus Streamlit to prototype a testable AI product in one day — user-testable in 48 hours

↗ Streamlit official documentation (free)

🏗 Project — Day D28: AI Feature Roadmap and Business Case 

· 6-month roadmap sequencing 3 AI features with dependencies

· Business case with 3-scenario ROI model

· Technical dependency map for engineering

· Risk register with mitigation strategies

· 15-minute executive presentation deck

Week 3: Execution, Launch, and Strategic Leadership · Days 29–40  

Run AI sprints, launch AI features responsibly, and develop the strategic AI product vision for long-term impact.

D29–32

AI sprint planning with ML engineering teams

Running sprints when the deliverable is a model. Estimation under uncertainty, definition of done for AI

↗ Agile for ML Teams — Google Cloud Skills (free)

D33–36

AI product launch: beta programmes and trust-building

Responsible rollout: gradual launch, beta programmes, managing user expectations, post-launch dashboards

↗ Reforge Product Launch frameworks (free blog content)

D37–39

AI strategy and CAIO-level thinking

AI transformation roadmaps, make vs buy decisions, board-level AI reporting, responsible scaling policies

↗ The AI Awakening — Erik Brynjolfsson/Coursera (free audit)

🏗 Project — Day D40: AI Product Strategy and Investor Pitch — Capstone 

· Full market analysis with TAM, SAM, and SOM

· User research synthesis from 5 or more interviews

· Complete PRD with 6-month roadmap

· 3-year financial model with assumptions

· 15-slide investor presentation deck

· Go-to-market strategy document