The AI Daily Brief: Artificial Intelligence News and Analysis A Framework for Choosing Winning AI Use Cases [Agent Readiness Part 3]
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Nov 9, 2025 
Nufar Gaspar, an AI practitioner and advisor specializing in enterprise agent readiness, shares insights on identifying and prioritizing AI use cases. She introduces a practical framework for evaluating opportunities and explains the significance of bottom-up sourcing for ideas. Nufar highlights high-value characteristics of agent use cases and suggests managing an agent portfolio like an investment strategy. She also addresses the importance of measuring real-world ROI and the challenges of balancing efficiency with growth in AI initiatives.
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Source Ideas From The Ground
- Do source agent use cases from bottoms-up or mid-level employees who know feasibility and need to be bought in.
- Avoid top-down fiat like "build an agent" because it often yields the wrong solution.
Pick Tasks With Changing Decisions
- Focus agents on complex, highly changing decision tasks rather than fixed decision trees.
- Prioritize cases with human bottlenecks, 24/7 needs, personalization, and tolerance for some errors.
Begin With Work Employees Want To Offload
- Do start with tasks employees want to offload, especially repetitive or tedious work, to build momentum and reduce fear.
- Use those wins to justify moving to more sensitive or complex use cases later.