Phase 2: Discovery & AI Product Strategy · 130 min · Opportunity Solution Tree · RICE Framework · Python

Project: AI Opportunity Assessment

Every AI PM interview loop tests product sense on a real opportunity. This is the memo that proves you have it, on a feature you'll keep building for the rest of this course.

Hiring signal: The build plan's portfolio-hiring research names 'user research to opportunity solution tree' and 'model comparison analysis' among the highest-leverage project types for a transitioning AI PM candidate — this capstone produces both in one memo: a JTBD-framed, sized opportunity plus a defended build/buy/model-selection recommendation. Pick a real product or company here, and this becomes the single feature you carry through the PRD (Phase 4), the eval plan (Phase 5), the responsible AI review (Phase 7), and the final case study (Phase 9) — exactly the coherent, single-feature portfolio arc the research found outperforms a resume-led application.

What you will learn

Size a real AI opportunity for a real product or company using jobs-to-be-done framing and the frequency/pain/workaround-cost scorecard from Lesson 1
Produce a defensible build-vs-buy-vs-fine-tune recommendation with a total cost of ownership comparison for the chosen opportunity
Select and defend a specific candidate model using the cost/latency/quality/context-window matrix from Lesson 3
Run and document a feasibility and risk triage that would satisfy a skeptical engineering lead
Sequence the opportunity onto a roadmap with RICE scoring and a crawl/walk/run staged rollout plan
Assemble all of the above into one coherent memo an executive sponsor and an engineering lead would both accept

The Problem

Every lesson in this phase built one piece of a real AI product decision: sizing an opportunity with jobs-to-be-done, choosing a sourcing path with a TCO comparison, selecting a model with a weighted matrix, triaging feasibility before committing a roadmap slot, and sequencing the bet with RICE and staged rollout. In a real company, none of these happen as separate, disconnected exercises — they happen as one continuous thread of reasoning that starts with "here's a problem worth solving" and ends with "here's what we're building, how, and in what order," documented in a single artifact an executive sponsor and an engineering lead can both read and trust.

That artifact is an opportunity assessment memo, and it's also one of the highest-leverage things you can put in a portfolio when you're trying to break into AI product management. The research behind this course found that project-led portfolios outperform resume-led ones for candidates transitioning into the role, and that a real, defended opportunity assessment — the kind that shows judgment, not just vocabulary — is exactly the evidence a hiring panel is looking for when they ask "walk me through how you'd evaluate this AI opportunity" in a product-sense interview.

This project asks you to do the real thing, once, completely, on a product or company of your choosing — not a toy exercise, and not five separate homework assignments. Pick something you know well enough to reason about honestly: your current employer, a company whose product you use regularly, or one you've researched enough to make credible assumptions. The specificity matters more than the size of the company. A sharply-reasoned assessment for a mid-size SaaS product beats a vague one for a famous brand.

Why This One Feature Matters for the Rest of the Course

Here is the structural decision that makes this project different from a standalone assignment: the opportunity you choose and size here is the exact feature you will carry through the rest of this course. The PRD project in Phase 4 turns this opportunity into a full AI product requirements document. The evaluation plan project in Phase 5 builds a scoring rubric for this same feature. The responsible AI review project in Phase 7 assesses risk and fairness for this same feature. The final capstone case study in Phase 9 assembles all four of these into one portfolio piece, alongside a mock interview presentation.

This means the choice you make in this project is not low-stakes. Pick an opportunity that's rich enough to sustain four more rounds of real work — something with genuine complexity in the build-vs-buy decision, a real feasibility risk worth triaging, and a business case substantial enough that a PRD, an eval plan, and a responsible-AI review would each have real content to say, not a thin restatement of this memo. A trivial opportunity ("add an AI-powered dark mode toggle") won't hold up across five projects. An opportunity with a real data dependency, a real precision/recall tradeoff, and a real cost-at-scale question will.

Choose the feature you're building for the next four projects, not just this one
Spend real time on this choice before you start scoring. Ask: does this opportunity have a genuine build-vs-buy tension (not an obvious "just call the API" case), a feasibility risk worth triaging (not a slam dunk), and stakes high enough that a responsible-AI review later in the course would have something substantive to assess? If the honest answer to any of those is no, pick a different opportunity now — reworking this choice in Phase 4 because the original one turned out too thin costs far more time than getting it right here.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers What the Memo Needs to Contain, Why This Format, Specifically, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.

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