Written by Move from opinion-based approval to a standardised scoring model, and you solve two problems simultaneously: slow decisions and inconsistent risk judgment.
What Is AI Use Case Triage
AI use case triage is the rapid, standardised assessment of newly submitted ideas to filter duplicates, reject out-of-scope requests, and rank the remainder before deeper review. A realistic service level target is five business days from submission to a triage outcome.
The Scoring Model
Score every use case across four dimensions — business value, technical feasibility, risk exposure, and strategic alignment — each weighted and combined into a single composite number that ranks ideas objectively rather than by internal politics. Real deployments show risk needs its own sub-dimensions rather than one generic score: separate out PHI/PII exposure, whether the use case touches a critical business process, and whether access requirements exceed what a role should normally have, since combining departments in a single workflow can quietly expose financial or health data more broadly than intended.
The Discovery That Changes Everything
The most valuable outcome of rigorous triage isn’t ranking real AI opportunities — it’s discovering how many submissions aren’t AI projects at all. After scoring 40 proposed use cases, one governance team found a large share simply wanted “a nice report pulled from two different things,” with no AI required whatsoever. Building this filter into triage single-handedly reduces committee overload, because you’re not sending simple integration or reporting requests through a governance process built for genuine AI risk.
Why This Phase Builds Organisational Learning
Every score generates a data point about where the organisation’s actual AI risk appetite and technical maturity sit, and aggregating these scores over a quarter reveals patterns — for example, discovering that most rejected ideas cluster around unavailable or poor-quality source data. This is where triage stops being a filter and starts being a diagnostic tool for the health of the organisation’s whole data and AI ecosystem.
Maturity Stages In Triage Practice
| Stage | Behaviour |
|---|---|
| Reactive | Shadow AI, duplicate spend, no consistent criteria |
| Controlled | Centralised intake with consistent scoring criteria |
| Strategic | Scoring data proactively mentors future submitters and shapes intake design |
FAQ
What happens to rejected use cases?
They are logged with a rejection reason and revisited automatically if data or risk conditions change.
Should business sponsors see the scoring criteria?
Yes, publishing the criteria openly increases trust and improves the quality of future submissions.
How often should the scoring model be reviewed?
Quarterly, using outcomes from completed use cases to recalibrate weights.
Should risk be a single score or multiple dimensions?
Multiple dimensions — separate PHI/PII, process criticality, and access scope — capture risk more accurately than one composite number.
If you’d like assistance or advice with your Data Governance implementation, or any other topic (Privacy, Cybersecurity, Ethics, AI and Product Management) please feel free to drop me an email here and I will endeavour to get back to you as soon as possible. Alternatively, you can reach out to me on LinkedIn and I will get back to you within the same day!
Nigel D'Souza 