Framework Dimension 3 of 6
Use Cases & Applications
Portfolio management, ROI tracking, and the deployment discipline behind operational AI use cases.
The Use Cases & Applications dimension measures whether your GenAI investments have a portfolio structure — with ROI targets, prioritization discipline, and post-deployment measurement — or whether you are shipping demos.
Framework v2026.1 · Updated · machine-readable spec
Why this dimension matters
The pilot-to-production gap is where most enterprise GenAI programs stall. Organizations that impose portfolio discipline (kill low-value use cases, double down on high-value) ship 3-4× more production systems per year than those that let every pilot mature independently.
Signals we look for
- Prioritised use-case portfolio with ROI targets
- At least one production deployment with measurable business impact
- Post-deployment monitoring in place
What this dimension looks like at each maturity level
A short list of experimental use cases with no ROI framing.
2-3 funded pilots with rough success metrics. First business-owned use case.
At least one production deployment with measurable business outcome. Prioritized backlog with owners.
Formal portfolio with stage-gate reviews. Deprioritization of underperforming use cases is routine.
Continuous evaluation pipeline auto-flags drift and quality regressions. New use cases proposed via structured intake.
GenAI capabilities enable entirely new product lines. Use-case discovery is a product-led discipline.
Common blockers
- Every use case competes for the same central team's attention.
- No mechanism to deprioritize a use case once launched.
- ROI is calculated pre-launch but never re-measured post-launch.
How to move up a level
- Stand up a quarterly use-case portfolio review with kill/scale decisions.
- Require post-launch ROI reporting at 90 days and 12 months.
- Publish a public backlog with prioritization criteria visible to all stakeholders.