Autonomous finance sounds compelling until the investment memo hits the CFO’s desk. When automation shifts from proof of concept to capital allocation decision, enthusiasm is replaced by scrutiny. What is the return? What changes structurally? What risks are introduced? This panel focuses on how finance leaders build defensible economic cases for AI-driven planning, workflow automation, and intelligent decision systems, grounding innovation in cost structure, productivity metrics, and governance discipline. The discussion centers on translating technical capability into board-level logic: how to model impact, how to define measurable value, and how to avoid initiatives that increase complexity without increasing leverage. Key takeaways: - Construct ROI models for autonomous finance initiatives that withstand executive and board scrutiny - Quantify structural impact across cost base, cycle time, and decision quality - Frame automation in terms of capital efficiency and operating leverage - Avoid overengineering solutions that dilute credibility and inflate complexity