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E.F AI Product Design Thinking

AI product design starts with the user problem, not the model capability. A feature is worth building only when value, cost, risk, and user experience can be explained.

See the Decision Loop First​

AI Product Decision Matrix

AI Product Experiment and Metrics Loop

The first product habit is to make trade-offs explicit before implementation starts.

Run a Tiny Prioritization Score​

ideas = [
    {"name": "AI Tutor", "value": 9, "cost": 6, "risk": 4, "ux": 8},
    {"name": "AI Customer Service", "value": 8, "cost": 5, "risk": 5, "ux": 7},
    {"name": "AI Code Review", "value": 7, "cost": 4, "risk": 6, "ux": 6},
    {"name": "AI Medical Diagnosis", "value": 9, "cost": 8, "risk": 9, "ux": 5},
]


def score(item):
    return round(
        item["value"] * 0.45
        + (10 - item["cost"]) * 0.2
        + (10 - item["risk"]) * 0.2
        + item["ux"] * 0.15,
        2,
    )


def decision(item):
    if item["risk"] >= 8:
        return "do_not_launch"
    return "pilot" if item["score"] >= 6 else "wait"


ranked = sorted(({**item, "score": score(item)} for item in ideas), key=lambda item: item["score"], reverse=True)

for item in ranked:
    print(item["name"], "score=", item["score"], "decision=", decision(item))

Expected output:

AI Tutor score= 7.25 decision= pilot
AI Customer Service score= 6.65 decision= pilot
AI Code Review score= 6.05 decision= pilot
AI Medical Diagnosis score= 5.4 decision= do_not_launch

The numbers are not final truth. They force you to say what you are optimizing for and where launch should be blocked.

Product Checklist​

QuestionGood Answer
Who is stuck?A specific user group and task
What improves?Completion rate, time saved, quality, or cost
What can go wrong?Risk boundary and human fallback
What proves progress?A metric or user test result

Pass Check​

You pass this elective when you can score one AI feature idea, explain the trade-off, define a success metric, and name one condition where the feature should not launch.