Decision inputs
Facts that change the policy answer
The request sits in customer service and sales and connects order history, refund rules and the customer request with a customer-facing decision explanation. That context distinguishes it from a generic permission to use AI.
- 1Task and owner
- Billing support specialist wants to draft an explanation for a refund decision. Name who owns the finished a customer-facing decision explanation; ownership should not disappear because AI helped produce it.
- 2Information involved
- Order history, refund rules and the customer request. Account for every route by which the tool receives the material, including plug-ins and linked storage.
- 3Tool and account
- An approved company account. Confirm the approved account, retention setting and any connected service before the request begins.
- 4Intended result
- The expected result is a customer-facing decision explanation. Record the audience and the next system in the chain, rather than describing the output only as a draft.
- 5Consequence if it is wrong
- The explanation can hide a discretionary rule or state a result that was never approved. A familiar task still needs escalation when this consequence becomes plausible.
- 6Human review
- billing policy owner should inspect, change, reject or stop the result. The reviewer needs the source material and must be able to reject the output before it takes effect.
Possible policy routes
The task name alone cannot decide it.
A published workplace policy can return different answers for the same task. These are the practical branches worth encoding.
A routine policy route may be possible
The lower-friction route begins when the exact account is approved, only the minimum customer account information is used, a customer-facing decision explanation remains within the stated purpose, and billing policy owner reviews it before use.
Approval may be required
Specialist approval becomes relevant if the account or data handling is uncertain, the explanation can hide a discretionary rule or state a result that was never approved, or a customer-facing decision explanation reaches people or systems beyond the requester’s authority.
The request may need to stop or change
The company may need a safer design when restricted information would enter an unapproved service, the output would act before billing policy owner can intervene, or separate the decision from the draft and cite the actual rule applied cannot be maintained. Consider less information, a controlled account or a non-AI process.
Request checklist
Questions to ask before using the tool
- 01
Which approved account will perform draft an explanation for a refund decision, and what external connections can it reach?
- 02
Can any personal, sensitive, confidential or secret part of order history, refund rules and the customer request be removed?
- 03
Who receives a customer-facing decision explanation, and what will they do with it?
- 04
Can billing policy owner inspect the complete result and its source before reliance?
- 05
Which change in tool, data, purpose or impact would require a fresh request?
Worked request
What the employee should submit
This example supplies decision facts without pasting the underlying material into the approval record.
- requester
- billing support specialist
- task
- Use AI to draft an explanation for a refund decision.
- information
- order history, refund rules and the customer request
- tool
- An approved company account
- frequency
- Recurring work
- region
- Where the work and affected people are located
- purpose
- Draft or analyse
- impact
- Customer entitlement
- review
- Complete human review
- owner
- billing policy owner
Useful safeguards
Controls that fit this request
- ✓
Separate the decision from the draft and cite the actual rule applied
- ✓
Start with a de-identified sample of order history, refund rules and the customer request before considering broader access.
- ✓
Reassess the request whenever its tool, information classification, frequency or consequence changes.
- ✓
Preserve who accepted a customer-facing decision explanation, when they did so and which rule version they applied.
Questions people ask
About this AI use
Is using AI to draft an explanation for a refund decision automatically allowed?
Even an ordinary draft an explanation for a refund decision request can change route when it involves restricted information, an external audience or weak review.
What does the policy need to know about this use?
Describe a customer-facing decision explanation, identify order history, refund rules and the customer request, name the exact tool and account, explain who will receive or rely on the output, and state how billing policy owner will review it.
What should remain after the decision?
Preserve who accepted a customer-facing decision explanation, when they did so and which rule version they applied. A classification and controlled reference may be enough when copying order history, refund rules and the customer request would create unnecessary risk.