Proving value

Test the real situations, not just the happy path

AI WorkBook editorial team22 July 20263 min read

A prototype should not only show that the ideal case works. It should reveal what happens when information is missing, a recommendation is rejected, permissions change or the work reaches a judgement the AI must not make.

A polished demonstration is not operating evidence

A happy-path demonstration usually starts with complete information, follows the expected sequence and ends with an acceptable answer. It is useful for explaining an idea, but it tells us little about whether the workflow can support real work.

Operational work is untidy. Documents disagree. A required field is blank. A policy changes midway through a case. A reviewer rejects the recommendation. Someone has access to the record but not the source behind it. These moments expose the real design: whether the workflow pauses safely, makes uncertainty visible and hands responsibility to the right person.

Build a balanced scenario pack

A credible proving round should include several kinds of scenario:

• Representative cases: ordinary work in the form and volume the team expects to see.
• Incomplete cases: missing documents, unclear requests or unavailable context.
• Conflicting cases: sources or rules that point towards different conclusions.
• Authority cases: actions that require a particular role, permission or approval.
• Exception cases: uncommon but recognised situations where the standard route should change.
• Boundary cases: requests the AI should refuse, escalate or leave for human judgement.

The aim is not to predict every future event. It is to test whether the operating model behaves sensibly when its assumptions stop being convenient.

Write the expected behaviour before the test

For each scenario, state what should happen before anyone runs the prototype. What information should be requested? Which rule should apply? Where may AI assist? Who makes the consequential decision? What record should remain?

This prevents the team from declaring any plausible-looking result a success after the event. It also separates an output-quality problem from a workflow problem. A well-written answer delivered to the wrong person, without the required review, is still a failed operating scenario.

Observe the whole route

Do not evaluate only the generated text. Follow the request from entry to outcome. Look at routing, source selection, permissions, AI contribution, review, exception handling and the final retained evidence.

Useful observations include whether the user understood what to do next, whether uncertainty was visible, whether a reviewer could challenge the recommendation, and whether another person could later reconstruct what happened. The workflow should make responsibility clearer, not merely make output faster.

Treat rejection as useful evidence

A human reviewer disagreeing with the AI is not automatically a defect. The important questions are whether the reviewer could see the relevant context, record the reason for the decision and move the work forward safely.

Repeated disagreement may reveal poor instructions, weak sources or a task that should not have been assigned to AI. A single disagreement may be exactly the human judgement the workflow was designed to preserve. Testing should help the team distinguish between those cases.

Finish with a readiness decision

The output of proving should be a short evidence pack: scenarios tested, observed results, exceptions, unresolved risks, agreed changes and the owner of each next action. The decision is not always “go live”. It may be to narrow the scope, improve the context, change the authority boundary or stop.

That is still progress. The organisation has replaced assumption with evidence before the workflow became operational.