Episode 34 — Evaluate AI Business Benefits Without Confusing Demos With Production Reality
This episode teaches how to evaluate AI initiatives with disciplined criteria so you can separate real business value from impressive demonstrations, aligning with exam themes of governance, risk management, and vendor evaluation. You will learn to define benefits as measurable improvements to cost, speed, quality, or risk reduction, then assess whether the required data exists, who owns it, and how it will be protected throughout the AI lifecycle. We explore best practices for pilots with clear success metrics, acceptance tests for outputs, and monitoring plans that detect accuracy degradation and unintended harm after deployment. A scenario examines a vendor pitch that promises broad transformation, showing how to ask for evidence, clarify assumptions, and identify hidden costs such as data preparation, integration, governance overhead, and ongoing tuning. Troubleshooting guidance includes managing stakeholder expectations, preventing premature scaling, and ensuring AI outputs are validated in workflows where mistakes carry operational or security consequences. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
