How to test whether a product for AI coding tools solves an urgent problem

Test urgency through behavior and consequences rather than a positive reaction to a demo. Ask about the most recent instance, the workaround, the time spent, and what happens when the problem is left unsolved. Do not turn an interview into a request for compliments.

Start with the decision

Record concrete episodes with dates and observable costs. Compare people who recently tried to solve the problem with people who only agree it sounds inconvenient. Separate the respondent's actual actions from their predictions about future adoption.

The illustrative scenario

This is an educational workbook. The company or fund and all numerical inputs below are fictional. They are not ADIN results, customer claims, market benchmarks, or a forecast.

The fictional tool proposes small code changes in an internal development environment. Engineers review the patch and run the project's checks before merging. The product's user and the person approving a purchase may be different.

A patch that compiles can still violate the intended behavior. Repository permissions, test coverage, and rollback procedures matter alongside developer enthusiasm.

Turn the scenario into an evidence decision

Consider a review of AI coding tools in which a confident explanation arrives before the supporting records. Start with the first question in the table. Write down what an answer would change: the next interview, the experiment's scope, a request for specialist review, or a decision to wait. A question without a consequence can produce a longer document while adding little understanding.

Next, inspect the record named in the table and record its date, owner, permission boundary, and limitations. Track accepted and rejected patches, tests run, review comments, and downstream fixes. Use representative repositories that the team is authorized to process. If the record contradicts the explanation, preserve both accounts and ask which definition, period, or workflow differs. Do not silently select the version that makes the opportunity look strongest.

Finish with a conditional statement: if the specified evidence supports the claim under these conditions, take the named next step; otherwise investigate the unresolved question. Evidence of urgency is contextual. A repeated painful workflow deserves a different test from an occasional annoyance. If nobody has tried a workaround, investigate whether the obstacle is low priority, poor access, or a misunderstood problem. This worked exercise is about the decision process. It does not assert that a fictional company has met a real diligence standard or that a template can authorize an investment.

Use this worksheet

Answer the questions with a source, date, owner, and next action. Mark an unknown as unknown. Use public or permissioned information and involve a qualified specialist when a question requires expertise beyond the team’s competence.

1. When did this last happen?
2. What did the team do?
3. Who was affected?
4. What workaround was funded?
5. What would happen without a solution?

Questions specific to this scenario

• Who owns the repository permission?
• Which tests represent the intended behavior?
• How is a bad change rolled back?

Track accepted and rejected patches, tests run, review comments, and downstream fixes. Use representative repositories that the team is authorized to process.

A concrete next test

Select a small accepted patch and a rejected patch from an authorized repository. Ask the maintainer to explain the intended behavior and the checks used. Record whether the tool shortened review or merely moved investigation into a different step.

Interpret the result

Evidence of urgency is contextual. A repeated painful workflow deserves a different test from an occasional annoyance. If nobody has tried a workaround, investigate whether the obstacle is low priority, poor access, or a misunderstood problem.

Compare time to a reviewed, accepted change with the existing workflow. Count investigation and rework rather than treating generated lines as delivered value.

Sources and method

These primary resources provide background for the concepts. The fictional scenario, calculations, and worksheet are ADIN educational examples; the sources do not report or validate those invented results. Reviewed October 11, 2026.

• a16z: A Framework for Finding a Design Partner
• NIST: AI Risk Management Framework