Start with one job your team needs to do better

Fictitious sample: Cedar Studio asks for a supplier-onboarding summary. Its brief says the owner is Maya, the required evidence is identity plus insurance, and Maya approves the next step. Task: write a short discovery brief separating source facts, open questions and suggested workflow steps.

Complete evidence: expected output names Maya, both evidence requirements and the approval step, with each fact traceable to the sample. Missing evidence: remove the approver name; the output must ask who can approve instead of inventing a person. Conflicting evidence: add a note naming Lee as approver; retain both sources and ask the client to resolve the conflict.

Human approval: a client reviewer confirms the facts before the brief becomes a delivery specification. Progress from checking facts, to designing review steps, to testing exceptions, to handing over the method. Reuse the method on another fictitious brief and explain what must change. Measure unsupported statements and reviewer corrections; this is a practice exercise, not a credential.

Fictitious sample: Queue A requires a request ID, supplier name and insurance evidence. Case 101 has all three. Case 102 has no insurance evidence. Case 103 has two records with different supplier names for the same request ID. Task: prepare a review list; do not approve suppliers.

Complete evidence: expected output marks 101 ready for human review and lists its evidence. Missing evidence: mark 102 incomplete and request insurance evidence. Conflicting evidence: hold 103 for the process owner to reconcile the supplier names. Readiness is not a decision about supplier suitability.

Human approval: the named operations reviewer corrects the list and authorises any next action. Compare time to prepare the list, missed gaps and corrections with your manual baseline. Configurator is the no-code starting point; Surface fits when your delivery team needs a coding tool.

Fictitious sample: tenant-demo requests a summary for case V1 containing the note “Delivery is awaiting review” and source reference note-1. Define a sample interface with tenant, case ID and source notes as inputs; summary, source references and review status as outputs. This is a design exercise, not an EnterpriseAI API contract.

Complete evidence: expected output preserves “awaiting review”, cites note-1 and remains pending human approval. Missing evidence: with no notes, return an explicit needs-evidence result and no summary. Conflicting evidence: if a second note says “approved”, return both source references with a conflict status, not a guessed decision.

Human approval: your product reviewer accepts or corrects the result before release. Keep the prototype read-only. Test that a tenant-other request cannot receive tenant-demo material; define rejection and failure behaviour with your own interface. Name integration, tenant isolation, release and customer-support owners. Record accepted outputs, corrections and boundary failures before customer use.

AI literacy starts with understanding the task, the source material and the limits of generated output. Ask whether an answer is supported, what is missing and who can approve the next action. Practise with representative examples, including incomplete and conflicting information.

For consultants, turn those checks into a repeatable delivery method: discovery, workflow design, testing, client review and handover. Reuse the method on a second client, then reassess their data, rules and approvals. These are practical learning steps, not a credential or a promised training programme.

Compare the tested workflow with your baseline. Measure completeness, review effort and rework, and record the failures. Keep the current method if the workflow does not improve the job or an accountable reviewer cannot be assigned. These suggested exercises are illustrative, not customer results.