Strong AI use cases combine measurable value, suitable task structure, available data, manageable risk and an accountable process. Prioritisation starts with the problem, not the tool.
What the concept actually means
Assess frequency, effort, quality problem, data readiness, integration cost, impact of failure and learning value of the pilot.
Why it matters in the enterprise
A portfolio should distinguish quick assistance, medium-term process improvement and strategic capability. Even a small pilot needs stop criteria and an outline of future operations.
A controlled method
The CTPM practice framework for controllable AI applications uses seven stages: understand the task, clarify context and data, apply AI deliberately, review professionally, handle deviations, approve accountably and document transfer. It is a transparent working framework, not a certification.
- Define task and impact
- Clarify data, context and permissions
- Review against domain criteria
- Control deviations, approval and evidence
CTPM practice example
CTPM practice example: Three pilots are selected from 24 ideas: high manual effort, accessible sources, low irreversible impact and named business owners. Fully automated decisions are deferred.
Quality and test criteria
The following criteria make quality observable for this use case:
- Value has a baseline and target.
- Data and process ownership exist.
- Failure impact and reversibility are assessed.
- The pilot yields decision-ready learning.
Risks and common misconceptions
Idea lists without problem context, tool demos and inflated ROI estimates create activity without sustainable value.
Example transfer artefact
Transfer artefact: a prioritised use-case matrix with value, feasibility, risk, owner, pilot question and stop criterion.