Phase 2: Discovery & AI Product Strategy · 40 min · Jobs-to-be-done · Opportunity Solution Tree · Python

Spotting AI Opportunities

The model of the week is not an opportunity. A job the user is struggling to get done is.

Hiring signal: AI product sense is the first signal tested in the interview loop, ahead of technical AI knowledge — evaluators want to see a candidate reason from a user's job-to-be-done to a sized opportunity, not pitch a model capability looking for a use case. Portfolio-first hiring research is explicit that a user-research-to-opportunity-tree artifact is one of the three highest-leverage project types for candidates transitioning into AI PM roles.

What you will learn

Frame a candidate AI feature as a job-to-be-done rather than a model capability
Size an opportunity using frequency, pain intensity, and current-workaround cost
Distinguish a demo-driven feature idea from a real, sized opportunity
Build an opportunity scorecard that ranks candidate AI opportunities against each other

The Problem

A VP of Product at a mid-size logistics SaaS company comes back from a conference with a mandate: "We need an AI story for the board deck by Q3." The PM team spends two weeks brainstorming. Someone suggests a chatbot for the support page. Someone else wants an "AI insights" widget on the dashboard because a competitor just shipped one. A third idea is a document-summarization feature nobody asked for, because the engineering team already has a RAG pipeline half-built from a hackathon.

None of these started from a user's job-to-be-done. They started from "we have a model, what do we point it at" — and that's backwards. Six months later, two of the three features ship, adoption is under 4%, and the board asks why the "AI story" didn't move any real metric. This is the single most common failure mode in AI product work right now: teams that are excellent at shipping model capability and terrible at identifying which capability actually removes friction from a job a user is already trying to do.

The fix isn't a better model. It's discipline about where opportunities come from. An AI opportunity is not "a place we could use AI" — it's a job the user is already hiring some worse solution to do (a spreadsheet, a manual process, three tabs open at once, a coworker who's the "expert"), where AI closes the gap between what exists and what the job actually requires.

Jobs-to-be-Done, Applied to AI

Jobs-to-be-done (JTBD) reframes discovery around the progress a user is trying to make, not the feature they're asking for. The classic form is: when [situation], I want to [motivation], so I can [expected outcome]. Applied to AI opportunities, this framing does something specific and useful: it forces you to describe the job in a way that doesn't mention AI at all. If you can't state the job without saying "AI," you don't have a job yet — you have a technology.

Take the logistics company's support chatbot idea. The lazy version: "We should add an AI chatbot to support." The JTBD version: "When a customer's shipment is delayed and they can't find a tracking update, they want a fast, specific answer without waiting in a phone queue, so they can decide whether to reroute inventory." Notice what changed — the second version tells you what "good" looks like (fast, specific, no queue), which lets you evaluate whether a chatbot is even the right tool, versus a better-surfaced tracking page, a proactive delay notification, or nothing at all.

Teresa Torres' Opportunity Solution Tree makes this a structured practice: a desired outcome at the root, opportunities (unmet needs, pain points, desires uncovered through user research) as the branches, and solutions as leaves considered only after opportunities are mapped and prioritized. The discipline that matters for AI specifically is resisting the urge to skip straight from outcome to "add AI" as a solution — you still have to walk through opportunities, and most teams don't.

If the job survives without the word "AI," you have a real opportunity
Write the job-to-be-done for your candidate feature, then delete every instance of "AI," "chatbot," "model," or "GenAI" from the sentence. If what's left still clearly describes a real, specific job a user struggles with today, you have an opportunity worth sizing. If the sentence collapses into nothing without those words, you had a technology pitch wearing a JTBD costume — and it will show up later as low adoption, because you never actually validated that users had the underlying problem.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Sizing the Opportunity, From Opportunity List to Opportunity Scorecard, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.

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