17d • Events
AI is forcing PMs to rethink “requirements” from scratch.
Traditional software behaves predictably.
AI doesn’t, and that changes everything.
In non-AI software:
Requirements = clarity + control.
Same input → same output.
In AI systems:
Requirements = guardrails + boundaries.
Same input → multiple valid outputs.
You define what the system can’t do, and what “good enough” means.
Most PMs miss this mindset shift because deterministic thinking breaks in probabilistic systems.
For example:
Wrong password → show error. (Predictable.)
“Recommend the best song right now” → no single right answer.
Only probabilities.
That’s why AI PMs ask different questions:
- What accuracy range is acceptable?
- What failures must never happen?
- How should the product behave when it’s uncertain?
- Where are fallbacks needed?
- What does “safe” actually mean here?
Requirements shift from fixed outputs → flexible constraints.
And this impacts everything:
- Roadmaps: milestones = model improvement
- UX: UIs must support uncertainty, editing, and explanations
- Testing: evals > test cases; quality becomes continuous
If you are building AI products, this mindset isn’t optional anymore.
I’m breaking this down further in the upcoming AI PM masterclass.
And oh! Do not forget to join the WhatsApp group for updates and engaging conversations.
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AI is forcing PMs to rethink “requirements” from scratch.
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