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What customer data is needed for a pilot of AI workflow automation
AI workflow automation: 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 product routes purchase requests through a small operations team. A requester supplies context, a budget owner approves spending, and an administrator maintains the workflow. Exceptions are common enough to affect adoption.
Automating a step can move work to the exception queue rather than remove it. Keep proposed actions, approvals, and completed actions separate in the event history.
Turn the scenario into an evidence decision
Consider a review of AI workflow automation 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. Inspect request histories, approval timestamps, exception reasons, manual overrides, and changes in process ownership. Include requests that stalled or were withdrawn. 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 | Automating a step can move work to the exception queue rather than remove it. Keep proposed actions, approvals, and completed actions separate in the event history. |
| Record to inspect | Inspect request histories, approval timestamps, exception reasons, manual overrides, and changes in process ownership. Include requests that stalled or were withdrawn. |
| Comparison to preserve | Compare the full request-to-decision process with the current system. A faster initial classification can hide a slower approval or correction stage. |
| Unresolved case question | Who approves a purchase? |
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 approves a purchase?
- Which exceptions stop the workflow?
- How are manual overrides recorded?
Inspect request histories, approval timestamps, exception reasons, manual overrides, and changes in process ownership. Include requests that stalled or were withdrawn.
A concrete next test
Follow one request that passes normally and one that needs an exception. Ask the requester, approver, and administrator what each can see and change. Record whether automation removes work or increases the burden on the person managing stalled requests.
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 full request-to-decision process with the current system. A faster initial classification can hide a slower approval or correction stage.
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.