Agentic AI Use Case Library

Curated, scored use case libraries for governed agent deployment. Each use case assessed against the Sprint Factory Use Case Selection Framework.

The Use Case Selection Framework

Every use case in the library is scored on two axes: Impact (weighted 40%) and Feasibility (weighted 60%). Feasibility is weighted more heavily because the first use case must succeed. A high-impact use case that fails damages the entire programme.

Impact Score (40%)

CriterionWeightWhat It Measures
Time Recaptured15%Hours per week of knowledge worker time consumed by the manual version
Error Reduction10%Current failure rate, rework rate, or exception rate
Cost Avoidance10%Direct savings: FTE time, vendor costs, penalty avoidance, revenue leakage
Strategic Leverage5%Does success unlock downstream use cases or create reusable components?

Feasibility Score (60%)

CriterionWeightWhat It Measures
Data Availability15%Is the required data accessible, structured, and of acceptable quality?
Process Clarity15%Is the current process well-documented with explicit decision rules?
Ease of Implementation10%Technical complexity: system integrations, API maturity, data volume
Fallback Available10%Can the organisation revert to the manual process instantly if the agent fails?
Audience (Internal/External)10%Internal use cases score higher for first deployments

Execution Pattern Categories

Selection Principles

The first use case should be non-controversial. It should be something everyone agrees is tedious, error-prone, and low-value for the humans currently doing it. Nobody should feel threatened by its automation; they should feel relieved.

Industry Libraries

Find Your First Use Case

The Sprint Factory takes you from use case selection to a governed, production agent in 6 weeks.

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