AI use case scorecard for senior leaders
Prioritisation is the hidden operating model.
Many teams choose AI use cases based on enthusiasm, politics, or what looks impressive.
A simple scorecard forces clarity and makes delivery more predictable.
Score each use case from 1 to 5
1) Business value
- Is the value measurable in revenue, cost, risk, or customer outcomes?
- Will leadership care if this ships?
2) Feasibility
- Can this be delivered in a small first release?
- Is the engineering pathway clear?
3) Data readiness
- Does the data exist and is it trusted?
- Can you define labels, ground truth, or evaluation approach?
4) Risk and governance
- What is the privacy and security posture?
- Is there a clear approval route that will not stall delivery?
5) Ownership
- Is there a single accountable owner for outcomes?
- Who owns monitoring, retraining, and rollback?
If a use case scores low on ownership and data readiness, it is not a “no”.
It is a “not yet”.
The work becomes foundations and clarity, not modelling.
This is the sort of structure that helps you decide what capability to hire first.
Product leadership, data engineering, MLOps, or modelling.
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