Founders
What customer data is needed for a pilot of AI customer support agents
AI customer support agents: 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 team drafts replies to delivery and refund questions for an online retailer. A support manager owns the queue, while finance controls refund authority. Seasonal peaks change the mix of easy and difficult tickets.
A reply that sounds fluent can still promise the wrong refund. Keep suggested language separate from permission to change an order or issue money.
Turn the scenario into an evidence decision
Consider a review of AI customer support agents 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. Use anonymized ticket histories, escalation reasons, reviewer edits, and repeat-contact counts. Record whether a customer issue was actually resolved rather than counting a generated reply as success. 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 reply that sounds fluent can still promise the wrong refund. Keep suggested language separate from permission to change an order or issue money. |
| Record to inspect | Use anonymized ticket histories, escalation reasons, reviewer edits, and repeat-contact counts. Record whether a customer issue was actually resolved rather than counting a generated reply as success. |
| Comparison to preserve | Compare the draft-and-review workflow with the current queue, including time spent fixing errors and handing cases back to people. |
| Unresolved case question | Who can approve a refund? |
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 can approve a refund?
- Which issues require a human handoff?
- Does the test include peak-season complaints?
Use anonymized ticket histories, escalation reasons, reviewer edits, and repeat-contact counts. Record whether a customer issue was actually resolved rather than counting a generated reply as success.
A concrete next test
Choose one ordinary delivery question, one refund exception, and one repeat complaint. Have the support owner trace each from intake to a usable resolution. Record where draft quality, authorization, and customer follow-up require different evidence or different people.
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 the draft-and-review workflow with the current queue, including time spent fixing errors and handing cases back to people.
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.