Knowledge / Frameworks
AI product management frameworks
This category explains the frameworks AI product managers use to decide: established models like RICE and Jobs to be Done, and AI-native ones built for evaluation and readiness, each with a worked example.
Pillar guides
4 guides in Frameworks
Cluster pages in this category
What this category covers
Frameworks get quoted more than they get understood. A framework applied without knowing what it assumes produces confident, wrong decisions, and AI work has enough uncertainty already.
How to use these guides
These guides explain each framework in terms of the decision it serves, walk a worked example, and state where it stops being useful. The explanation sits here; the blank canvas to apply it sits in Resources.
Best practice
Do this. Learn what each framework assumes before you apply it. A framework is a set of assumptions in disguise, and the assumptions are usually where your situation differs from the textbook one.
The common mistake
Reaching for the most cited framework rather than the one that fits the decision. RICE is popular and wrong for plenty of choices. Fit beats familiarity.
Common questions
Frameworks, answered
What is the difference between the Frameworks knowledge category and the Frameworks resource category?
This category explains how each framework works and when to use it. The resource category gives you the blank canvas to apply it. Explanation here, artefact there.
Which prioritisation framework is best for AI products?
None universally. The right one depends on whether your constraint is reach, confidence or feasibility. The prioritisation guide covers matching the framework to the constraint rather than defaulting to RICE.
Do traditional frameworks still work for AI products?
Mostly, with adaptation. RICE and JTBD carry over if you treat feasibility as a live variable. The guides cover the specific adjustments each one needs for AI work.
What is an AI-native framework?
One built specifically for AI product decisions, like a readiness assessment or an evaluation matrix, with no traditional-PM equivalent. The AI-native guide covers the ones worth knowing.
What to read next
Related across the platform
Glossary terms
Resources and tools
Supporting research
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