CIOs are under pressure to prove AI's ROI, yet for many, the first tangible result is a warehouse bill growing faster than the business value behind it. The problem isn't the sophistication of the models themselves—it's that agents and copilots operating without structured context make more calls, run more queries, and consume more tokens just to produce reliable answers. Left unchecked, every new model, agent, or use case adds another non-linear jump to a compute curve that's increasingly difficult to forecast, optimize, or explain to the board. Join an exclusive group of data, analytics, and technology executives for a candid peer discussion on what it actually takes to control AI compute costs without slowing innovation. Together, we'll examine where spend is really coming from, how leading organizations are reducing unnecessary compute, and what separates teams turning AI cost into a predictable advantage from those still defending an ever-growing line item. We'll explore the hidden risks of scaling AI without addressing the cost structure underneath it, hear practical lessons from organizations already making measurable progress, and leave with actionable strategies to optimize AI economics, improve cost predictability, and scale AI with confidence.