Lakshya

Product & Delivery · Chapter 8 of 8

Scoping a feature whose core is non-deterministic

AI appears in 92% of product postings. This is the one skill that genuinely differs from ordinary product work.

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Most of what makes AI product management hard is not the technology — it is that you cannot specify the behaviour, only the acceptable distribution of behaviours. That changes scoping, acceptance, launch and pricing simultaneously.

Ordinary featureAI feature
SpecificationThis button does thisThis works acceptably across this distribution of inputs
AcceptanceMatches the specClears an evaluation threshold you had to define
Launch gateFeature completeQuality bar met — and you decided what quality means
CostRoughly fixedVariable, with a tail that can invert margin
A defectReproducibleA confident wrong answer, often not reproducible
Roadmap riskCan we build itCan the model do it well enough — frequently unknown until you try

The question that decides the feature

Not ‘how accurate is it’, which is unanswerable in isolation, but what is the cost of being wrong, and who finds out? A drafting assistant a human reviews before sending is useful at 85%. An auto-approval at 85% is a liability. Same number, opposite decision.

  • Where do the errors fall? Uniform errors are survivable; errors concentrated on your highest-value cases or one customer segment are not, and the aggregate hides it.
  • Can the user tell? A visibly wrong answer is far safer than a plausible one. How the output is presented is a product decision with safety consequences.
  • What is the human baseline? If the process it replaces is right 80% of the time, 85% with review is an improvement — and almost nobody has measured the human baseline.
  • What is the escape hatch? Every AI feature needs an obvious route to a human, and its usage rate is one of your best quality signals.

Define the launch gate as an eval threshold, in advance

“We ship when the golden set clears 80% with no regression on the safety cases” is a gate. “We ship when it feels good” is a gate that moves under commercial pressure, and it always moves in the same direction.

For the mechanics of building that evaluation, read Evaluation — how anyone knows it works; for what the model can and cannot do, What a model actually is.

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