Open this lesson in your favourite AI. It'll walk you through the why, explain the demo, and quiz you on the try-it list.
Individual feature ideas are easy; a coherent view of where AI helps across your whole product is what separates a strategy from a pile of demos. An opportunity map plots candidate AI features against impact and effort (and the three taxes), so you can see the quick wins, the big bets, and the traps in one picture. This is the artifact you take to leadership instead of 'let's add a chatbot', and it forces prioritization grounded in user jobs rather than hype. Building this map is the synthesis of everything in this module — it's the deliverable a PM actually ships before any code.
Score each candidate on impact and effort, then bucket into quick wins, big bets, fill-ins, and money pits — a portfolio view rather than one-off ideas.
Use these three in order. Each builds on the one before.
In one paragraph, explain what an AI opportunity map is and how it helps prioritize features.
Walk me through how to build an impact-vs-effort opportunity map for AI features in a product.
Given an opportunity map with one big bet and three quick wins, how would you sequence the roadmap and justify it to leadership?
def quadrant(impact, effort):
hi_imp, lo_eff = impact >= 3, effort <= 2
if hi_imp and lo_eff: return "QUICK WIN — do first"
if hi_imp and not lo_eff: return "BIG BET — plan deliberately"
if not hi_imp and lo_eff: return "FILL-IN — only if idle capacity"
return "MONEY PIT — avoid"
candidates = [
("ticket auto-summary", 4, 2),
("semantic help search", 4, 3),
("AI onboarding chatbot", 2, 4),
("smart default reply", 3, 1),
]
for name, imp, eff in candidates:
print(f"{name:24} -> {quadrant(imp, eff)}")python3 main.py