
Why the Biggest AI Business Case Is Often the Weakest One
The use cases with the highest value are often the hardest to deliver. Library data shows why the largest AI business case deserves the most scrutiny.
Put every AI use case on the same scale, record each decision with a status and a reason, and see which initiatives compete for the same work.
High business value and strong feasibility. The logical candidates to move forward.
Strong value, but harder to realise. They often need data, integration or skills first.
Feasible, with more limited value. Good for targeted improvement or bundling.
Limited value and limited feasibility. Reassess before allocating capacity.
Across the Library, 43% of use cases are strategic bets and 33% are ready to scale.
Use cases cannot be rejected or put on hold without a written reason, so a year later everyone can still see why a choice was made. Contributors submit, portfolio managers decide and customer admins manage access.
Every status change is kept, so the portfolio shows not only what was decided but also when and why.
What each status meansSix claims use cases in the Library each promise a share of the same claims handler's time. Added up, they claim 147.5% of it. MetroMinds shows overlap by business capability and suggests correction weights before the total reaches your CFO.
The portfolio heatmap shows business value, feasibility, risk, strategic fit and confidence for every accepted use case, so a high score on one dimension cannot hide a weak score on another.


The use cases with the highest value are often the hardest to deliver. Library data shows why the largest AI business case deserves the most scrutiny.

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