Phase 2: Discovery & AI Product Strategy · 45 min · RICE Framework · Python

AI Roadmapping

A crawl/walk/run rollout isn't a smaller version of the finished feature — it's a smaller version that still gets you a real answer about whether the big version is worth building.

Hiring signal: AI PM interview loops explicitly test prioritization under uncertainty as one of the four core signals — a candidate who can sequence AI bets with a documented framework (RICE plus staged rollout) instead of ranking by loudest stakeholder is demonstrating exactly the judgment a hiring panel is screening for.

What you will learn

Apply RICE (Reach, Impact, Confidence, Effort) scoring to AI opportunities, including the AI-specific confidence discount
Design a crawl/walk/run staged rollout plan for an AI feature, defining what each stage actually validates
Sequence dependent AI bets so foundational work (data readiness) is scheduled before work that depends on it
Communicate roadmap uncertainty to stakeholders honestly, without either overpromising a fixed timeline or hiding behind vagueness
Distinguish a roadmap stage gate that should block progression from one that's a soft checkpoint

The Problem

A PM has four AI opportunities that all cleared feasibility triage from the last lesson: a policy-lookup assistant, an escalation-risk flagger, an automated onboarding Q&A bot, and a full autonomous refund-handling agent. Leadership wants to know which one ships first, and the honest answer is "whichever one the last person to pitch in the planning meeting argued for most persuasively" — which is not a roadmap, it's a coin flip dressed up as a decision.

Worse, the plan for the one that does get picked — the refund-handling agent, because it sounds the most impressive — is "build it, then launch it." There's no staged plan. If the agent's refund-approval logic has a flaw that only shows up on edge cases, the team finds out when it's already live and issuing incorrect refunds to real customers, not before.

Both problems have the same root cause: no structured way to compare opportunities against each other, and no structured way to de-risk the one that gets chosen before it's fully live. This lesson gives a PM both: RICE scoring (adapted for AI's specific uncertainty) to sequence which bet goes first, and crawl/walk/run staging to de-risk how any single bet actually rolls out.

RICE Scoring for AI Opportunities

RICE scores an opportunity on four factors — Reach (how many users/events does this touch in a given period), Impact (how much does it move the needle per user touched, usually a rough scale like massive/high/medium/low/minimal mapped to a number), Confidence (how sure are you about the Reach and Impact estimates, as a percentage), and Effort (person-months to build) — and combines them as (Reach × Impact × Confidence) / Effort. Higher score, higher priority. This isn't unique to AI; it's a standard product prioritization framework (see the Intercom resource below for the original methodology).

What's specifically different for AI opportunities is the Confidence factor deserves an extra discount that a typical feature doesn't need. For a normal feature, confidence uncertainty is usually about market/user behavior — will people actually use this? For an AI feature, there's a second layer of uncertainty stacked on top: will the model actually perform well enough on this task, at this scale, with this data? A PM should discount Confidence for an AI opportunity based on two things beyond normal market uncertainty: how validated the feasibility triage from the last lesson actually was (a feature that cleared triage cleanly deserves a smaller discount than one that cleared with several scoped mitigations), and how novel the AI approach is for the team (a fourth RAG feature built by a team that's shipped three before is more predictable than a team's first agentic feature).

A high RICE score with low confidence is a bet worth staging, not skipping
Don't let a low-confidence AI opportunity fall off the roadmap just because its RICE score looks weaker than a safer bet — a high-Reach, high-Impact opportunity with real uncertainty about AI performance is often exactly the kind of bet that's worth taking, as long as it's staged so the first investment is small and produces a real answer about whether the bigger investment is justified. The RICE score should influence sequencing (what goes first, and how big a first bite to take), not act as a binary yes/no filter that kills anything uncertain.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Crawl/Walk/Run: Staging a Single Bet, Sequencing Dependent Bets, Communicating Roadmap Uncertainty Honestly, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Agent