Start with a question you can check
A pilot is easier to evaluate when its requirement describes an observable result. Instead of asking AI to handle email, start with: for each enquiry, suggest a category, list missing information and prepare a draft for review. In this example, a colleague still decides whether to send the message or commit to an offer.
The sheet below is an AURENIQ working template for a fictional scenario, not a client outcome or an offer promising a delivery date or savings. It follows our guides on choosing a process and measuring time saved; here the focus is on what to test and what evidence to keep before connecting the workflow to everyday operations.
A sheet you can adapt for your business
Complete the sheet with the person who performs the task today. Begin with fictional examples; before using real data, establish authorised access, the information actually needed and where it is processed. Record missing information explicitly rather than turning it into an assumption to fill an empty field.
| Field | Fictional example | Check |
|---|---|---|
| Task | Preparing quotation enquiries for review | One defined workflow |
| Permitted input | Fictional text and an approved internal catalogue, without real contact details | Approved sources and access |
| Expected output | Suggested category, missing fields, draft and source used | Compare with expected output |
| What AI does not decide | Final pricing, discounts, contractual deadlines or sending | No out-of-scope action |
| Reviewer | The quotation owner, appointed before testing | Named owner |
| Unclear cases | Flag for human review without inventing an answer | Observable referral |
| Measurement | Total time, corrections, omissions and unauthorised actions | A record for each case |
| Stop condition | Unsupported commitment, data disclosure or exceeding permissions | Return to manual handling |
Test enquiries that differ from one another
One well-written enquiry does not show how a workflow behaves in difficult situations. Build a separate final evaluation set, distinct from the examples used during setup. Include incomplete messages, requests for unavailable products, contradictory information and text asking the system to ignore its rules. This small set starts the evaluation; it does not prove that every risk has been covered.
For a request asking for a product quote without dimensions, useful output lists what is missing and drafts a clarification. For a request to confirm delivery tomorrow with no approved deadline in the source, the correct result is referral to a colleague. Sender-provided text is information to analyse; it cannot grant the system new permissions or change company rules.
Compare the whole process, including corrections
Measure the same activity manually and with assistance, through to a checked result. Record preparation, waiting, review, corrections and exception handling. Count accepted, corrected and referred cases separately; producing a quick draft does not automatically mean resolving an enquiry.
Set acceptance criteria before seeing the results. In this example, an invented price or an unauthorised send blocks operational use even if average time looks favourable. Quality thresholds and review effort depend on the specific process. If checking removes the benefit or errors remain hard to detect, narrow the task or retain the manual process.
Move gradually from testing to daily work
After the initial evaluation, consider a limited stage where the workflow prepares output and a colleague reviews every case. Define who monitors errors, how to return to the manual process and when to repeat tests after changes to sources, rules or the model. Connecting a CRM or email account is a separate step requiring explicit permissions and checks before activation.
As general background, the NIST AI RMF Playbook offers voluntary suggestions for managing risks while developing and using AI. The sheet in this article is our practical proposal, not a NIST requirement or certification. To start a discussion with AURENIQ, describe the task, provide a fictional example and explain the result your team needs to check; do not send passwords, statements or confidential documents through the initial enquiry form.
Common questions
Do I need to buy an AI tool before creating the test sheet?
No. Define the task, fictional examples and expected output before selecting technology or setting an implementation budget.
Can the pilot reply directly to customers?
In this scenario it only prepares drafts. Automatic sending would require a separate decision, limits, permissions and checks appropriate to the process.
How do I decide whether to continue?
Compare total time and quality on the same cases, include corrections and check stop conditions. Speed alone does not justify unreliable output.
Is this a free service or a promise of savings?
No. The article provides a preparation template. Project scope, pricing and terms are agreed separately after clarifying the process.
Sources and further reading
An AURENIQ editorial guide. Examples are illustrative, not client results. Vendor documentation is checked for the specific implementation.
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