From product discovery to a testable AI prototype, the team should narrow uncertainty rather than accumulate features. Discovery identifies a user problem, operating context, constraints, and a decision worth making. The prototype then tests the weakest assumption in that decision. It is not a small production system and it does not need to imitate one.
Start by writing the decision in plain language. For example: should a support reviewer use model-assisted evidence grouping for a defined case type? That wording identifies a user, task, proposed role for AI, and a choice. Build nothing until the team can state what observation would change that choice.
Turn discovery evidence into one hypothesis
Collect the current journey, source materials, decision owner, failure consequences, volume band, and existing workaround. Separate observation from inference. A repeated manual step is observable. The belief that AI will remove it is a hypothesis. Missing access or policy information remains an unknown.
Choose the intersection between a real user need and a capability the model can support under current constraints. Avoid starting from a model feature and searching for a problem. Name what remains human: approval, negotiation, sensitive judgement, or responsibility for an external action.
Context retrieval may be part of the proposed mechanism. The guide to business-context retrieval in agent workflows provides implementation ideas, but it cannot prove that a user needs this prototype or will adopt it.
Build the smallest credible test
Limit the prototype to one input set, one user role, one decision, and one destination. Use approved or synthetic data. Fix the model version and prompt, record every source, and define the reviewer. The purpose is to make the test repeatable enough to examine, not broad enough to impress a room.
Prototype the risky interaction as well as the model output. Show uncertainty, missing evidence, correction, rejection, and handoff. A technically accurate answer can still fail if the user cannot verify it or place it into the next step of work.
Create an acceptance table before the run. Include task completion, source support, unacceptable error, correction effort, reviewer agreement, processing time, cost boundary, and any risk stop. Use thresholds only when the team can explain their origin. Otherwise describe the decision rule qualitatively and label it internal.
The release discipline described in the precision release notes is useful for reproducibility. A product prototype still needs its own user task, evidence, and acceptance owner.
End with a decision, not a demo
Run the prototype with realistic cases and observe the full task. Save inputs, outputs, corrections, reviewer decisions, failure modes, and assumptions. A successful example shows that one path worked under recorded conditions. It does not establish demand, scale, integration readiness, or compliance.
Review negative cases first. If the model misses the evidence that drives the decision, requires excessive correction, or creates unsafe confidence, narrow or stop. If users cannot understand the boundary, redesign the interaction before tuning the model.
Choose one next state: reject the hypothesis, gather missing discovery evidence, test a revised interaction, run a controlled pilot, or prepare an engineering spike. Each state needs an owner and new evidence requirement. Do not let a polished demonstration drift directly into production scope.
Legal and operational constraints can emerge from seemingly small product choices. The example of a website font licence risk audit illustrates why an attractive interface asset may carry a separate obligation; it does not prove risk in this AI prototype.
Frequently Asked Questions
It should produce a named user task, observed journey, source boundary, constraints, decision owner, risky assumption, and stopping condition.
Limit it to one input set, user role, decision, destination, reviewer, and acceptance table whenever that scope can test the hypothesis.
It proves that a recorded method worked on the tested cases under stated conditions, not that demand, scale, compliance, or production readiness exists.
The team can reject, research further, revise the interaction, run a controlled pilot, or plan an engineering spike with new evidence requirements.
When discovery has produced a clear task, evidence boundary, and decision owner, AI4SALE AI product development can define the prototype and its acceptance test. The engagement is a bounded product step, not a guarantee of production or market success.