In a new episode of the Operator's Cut, AI practitioner Nufar Gaspar outlines a repeatable system for evaluating and selecting AI models based on real organizational needs. The framework centers on testing new models directly against your actual business tasks, comparing outputs across multiple dimensions, and weighing critical trade-offs between quality, speed, and cost. The methodology addresses a persistent challenge for enterprises: with the AI model landscape expanding rapidly, teams often struggle to make informed decisions about which tools deserve a place in their workflow. Rather than relying on marketing claims or published benchmarks, Gaspar's system emphasizes hands-on testing against actual use cases and establishing clear evaluation criteria upfront. This approach enables organizations to move beyond guesswork and into data-driven model selection. The framework is designed for continuous iteration, allowing teams to systematically reassess model choices as new options emerge. By treating model selection as an ongoing, structured process rather than a one-time decision, organizations can ensure their AI tooling remains cost-effective, performant, and aligned with evolving business requirements.