As AI adoption accelerates across sectors, many organizations are discovering a persistent gap: technically strong models often fail to translate into measurable real-world value. In practice, AI initiatives frequently stall not because the models underperform, but because economic impact, operational fit, and long-term sustainability were never rigorously evaluated.

This executive-focused talk introduces a practical framework for embedding economic evaluation throughout the AI lifecycle, from business problem definition and data acquisition through deployment, monitoring, and scale decisions. Moving beyond traditional model metrics, the session outlines how organizations can systematically assess costs, benefits, risks, and time horizons to determine whether an AI system is truly worth building and sustaining.

Drawing on applied experience supporting AI deployments across healthcare, finance, and public sector contexts, the talk presents:

• A structured definition of economic evaluation for AI
• The “evaluation stack” for multi-dimensional value assessment
• Lifecycle checkpoints where value is created, eroded, or misestimated
• Practical tools including ROI, cost-benefit, cost-effectiveness, and budget impact analysis
• A real-world triage AI case illustrating how value must be validated continuously—not assumed

Designed for executive and senior technical leaders, this session provides a clear, actionable lens to move AI initiatives beyond experimentation toward accountable, economically defensible deployment. Attendees will leave with concrete strategies to forecast value earlier, avoid common failure patterns, and make more confident scale-or-retire decisions for AI investments.