Date: tbd
Target Market: Global
AI threatens to break budgets as enterprises struggle with booming user adoption, ballooning token consumption and voracious data demand. Agentic workflows compound the problem as each reasoning step, tool call and retrieval adds costs that are tough to forecast or attribute. AI adopters now realize that their programs will lose executive support unless they get costs under control, and fast.
This webinar examines how data and AI teams can bring financial discipline to AI without slowing innovation. It explores practical levers across the architecture, from model selection and routing to context efficiency, caching and hybrid hosting. It also explores how to tie spending to business outcomes so leaders can scale what works and retire what doesn’t.
What you will learn:
- Where AI costs hide across models, data pipelines and agent workflows.
- Which architectural and operational levers cut spending without sacrificing performance.
- How to measure AI unit economics and link spending to business value.