Founders
What customer data is needed for a pilot of AI coding tools
AI coding tools: Request the minimum data needed to answer the pilot's learning question.
Request the minimum data needed to answer the pilot's learning question. More access can increase implementation work and exposure without improving the experiment. Map each requested field to an actual product or evaluation need.
Start with the decision
List data sources, fields, access owners, storage locations, and deletion expectations. Start with a permissioned sample where feasible. Ask the customer to confirm the allowed use and involve the appropriate specialists for sensitive information.
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. A data request is clearer when a buyer can trace every item to a defined purpose. A missing field may require a smaller experiment rather than a broader request for all available customer data. 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.
| Item | Illustration |
|---|---|
| Decision question | Which field is necessary? |
| Case-specific constraint | A patch that compiles can still violate the intended behavior. Repository permissions, test coverage, and rollback procedures matter alongside developer enthusiasm. |
| Record to inspect | Track accepted and rejected patches, tests run, review comments, and downstream fixes. Use representative repositories that the team is authorized to process. |
| Comparison to preserve | Compare time to a reviewed, accepted change with the existing workflow. Count investigation and rework rather than treating generated lines as delivered value. |
| Unresolved case question | Who owns the repository permission? |
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.
- Which field is necessary?
- Who can authorize access?
- Where will it be stored?
- What is excluded?
- When is access removed?
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
A data request is clearer when a buyer can trace every item to a defined purpose. A missing field may require a smaller experiment rather than a broader request for all available customer data.
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.