Useful product discovery identifies a user, a problem and a first test. Compare a non-AI solution with the assisted approach, then agree the evidence needed to continue, adjust or stop.
AI product
AI product discovery playbook
Binov · 2 min ·
In this guide
At Binov, we use a discovery sequence that aligns business outcomes, user workflows, and technical feasibility before the first sprint.
A workshop canvas for the first experiment
Fictional example: a case worker needs to find a procedure across several folders. The workshop compares improved conventional search with a sourced AI answer. It does not assume AI is necessary.
| Deliverable | Example to discuss | Your answer |
|---|---|---|
| User and problem | Case worker; procedure is difficult to find | … |
| Non-AI baseline | Search by title, version and category | … |
| Hypothesis | A natural-language question helps find the right passage | … |
| Test | Compare both workflows on the same permitted questions | … |
| Continue criterion | Verifiable answers, respected permissions and acceptable search effort against agreed criteria | … |
| Decision and owner | Continue, adjust or stop; named product owner | … |
Counterexample: if the only problem is poor folder organisation and a title filter resolves the observed cases, start with that correction. An AI feature would add a dependency without a demonstrated benefit.
Keep the hypotheses to test and missing evidence at the end of the workshop. Resolve disagreement about the expected answer with the business team before evaluating a model.
1) Clarify the outcome before selecting technology
Start by defining one measurable change in operations, customer value, or revenue impact.
A strong objective prevents teams from optimizing model quality while missing the real business bottleneck.
2) Frame the workflow, not only the feature
AI features sit inside existing decisions, approvals, and exception paths.
Map the current workflow first, then decide where automation or augmentation creates the most leverage.
3) Set constraints early
Define data access rules, latency expectations, observability requirements, and fallback behavior before architecture choices are locked.
This reduces rework during integration and production hardening.
4) Commit to delivery milestones
A practical roadmap should include:
- validation milestone for assumptions
- pilot milestone with real usage signals
- production milestone with reliability and governance criteria
From an idea to a usable AI product.
Define the use case, design the experience and build a product ready to evaluate in real conditions.
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