Your reporting queue is the requirements document
When I ran enterprise data platforms, the analyst inbox, help desk queue and Monday operations agenda told me a great deal about what people needed. The same questions returned because reports were unclear, definitions differed or a useful view was missing. I use those requests as the starting point for a question library. They preserve the language people actually use and the circumstances in which they need an answer.
I give the same advice to every organization preparing a Claude or governed analytics rollout: build your question library from those requests, not from a brainstorm. Collect recurring themes, tie each one to a business decision, and make it specific. "Explain the monthly variance" is too broad. "Which cost categories explain the difference between approved plan and posted actuals for this period?" is a question you can evaluate.
Strip the sensitive details, keep the shape
Separate the reusable question from customer names, employee identifiers and commercially sensitive amounts. Source records stay in their authorized systems. A library entry describes the shape of the task without widening access to the data underneath it. For a bank, that means no account numbers or BSA/AML case details in a shared repository. For healthcare and life sciences, it means protected health information stays where HIPAA controls already apply.
Use clearly labeled synthetic examples where data is needed. This pass also reveals questions that do not belong in a shared library at all, because their meaning depends on a specific investigation or privileged context. Recognizing those early is a governance win.
Group by decision and write an answer contract
Two requests with different wording often support the same decision. A sales manager asking about stalled deals and an operations lead asking about opportunities with no recent activity need related evidence. The reverse is more dangerous: identical words that hide different definitions. "Active customer" can mean a recent purchase, an open subscription or a support entitlement. Group around the decision and its owner, and keep meaningful variants visible.
Then give every entry an answer contract: audience, business definition, source, time boundary, expected output and review method, plus the conditions that should trigger clarification or refusal. A cash collection question might require a specific accounting date and an agreed treatment of unapplied payments. The contract does not need code. It needs to be readable by the business reviewer and the implementation team before anyone scores a model response.
Include the hard cases on purpose
A library of easy, fully specified questions makes every demonstration look better than everyday use. Add a version with a missing date, a version where the source is unavailable, and a version asked by someone with narrower access. Ask the business owner what should happen in each case. Sometimes the right outcome is a partial answer that explains what is missing. Sometimes it is a refusal.
Keep a small public set of synthetic questions for teaching and a separate, stable evaluation set for checking changes. Teams that practice only the demonstration examples learn the script, not the workflow. When the library grows, say what changed. A new score on a different set is not comparable to the old one, and I have seen steering committees misled by exactly that.
Make maintenance part of the business process
The owner of a process should help maintain its questions. A change to the finance close calendar should trigger review of period-dependent entries. A territory redesign should prompt a check of regional groupings. In a bank moving from regional to national supervision, new regulatory reporting expectations will quietly change what several "standard" questions mean. Record a last-reviewed date and a retirement reason for every entry.
Use the library during procurement and retain it for later regression testing. When a definition changes, update the affected cases with the business owner and preserve the reason for the change. The library should become easier to maintain as the team learns which questions matter. A few well-documented examples are a useful starting point for a product demonstration, including one with Nexatron.
Start with ten questions across two workflows
For the first iteration, choose roughly ten questions spanning two workflows and review them with the people who ask and the people who answer. Resolve missing definitions in a working session before any product demonstration. Then use the same library to compare the current process with the proposed one, including review time and exceptions.
The result serves adoption planning, procurement due diligence and later regression testing all at once. It is the cheapest high-value artifact in an enterprise AI program. Bring three representative questions from it to a Nexatron demonstration and you will learn quickly which assumptions need clarifying before a pilot begins.