How to prioritize AI use cases: a stage-gate model for operation

Start with the work problem, not the model

A good use case describes a concrete task, bottleneck or decision. “We need a chatbot” is already an assumed solution. Before evaluation, it should be clear which workflow should improve, who works with it and how a good result will be recognized.

This description does not need to be complicated. One sentence on the problem, one on the audience and one observable quality benchmark are enough for initial intake. Only then should the team assess whether AI is appropriate, or whether process simplification, better data or conventional automation would work better.

Four assessment areas create comparability

Value alone is not enough. A use case can sound valuable but fail because of data quality, missing ownership or an unsuitable risk profile. Four shared assessment areas create a better basis without turning the process into a pseudo-precise scoring exercise.

Assessment should reveal uncertainty. If nobody knows how outputs will be reviewed or who will own operation, that is not a detail for later but an open gate item.

• Value: Which work improves and how can the improvement be observed?
• Feasibility: Are data, integrations, skills and review capabilities available?
• Risk: What are the consequences of errors, misuse or changes in context?
• Ownership: Who owns the business outcome, technical operation and approval?

Four gates from intake to governed operation

The model limits investment in untested ideas without prohibiting early exploration. Each stage has a limited mandate and ends with a decision. Review depth grows with reach, risk and integration effort.

• Gate 1 – Intake: Problem, audience, expected value and accountable owner are named.
• Gate 2 – Pilot: Test data, quality benchmark, scope and stopping criteria are defined.
• Gate 3 – Controlled use: Real users test with clear rules, support and documented outcomes.
• Gate 4 – Operation: Monitoring, change process, escalation, documentation and operational ownership are established.

Define stopping criteria before the pilot

Pilots often continue because time has already been invested. Stopping criteria move this difficult decision to a calmer moment: before the test begins. They may include the absence of a reliable review basis, manual review effort exceeding the value, or a critical quality failure that cannot be controlled.

A stop does not mean the team failed. It prevents further integration and operating effort for an unsuitable use case. The lessons are documented and can be revisited if conditions change.

Example: test automation with a clear quality benchmark

In a product engagement, AI-assisted test automation was introduced to replace manual regression checks. The starting point was not the broad question “Where can we use AI?”, but a concrete recurring workflow. Quality, working practices and handover into operation could therefore be assessed together.

This principle transfers to other use cases: a bounded process with an observable outcome is more suitable for a controlled pilot than a broad initiative without a clear quality benchmark. Portfolio decisions, governance and the operating model should work together from the start.