From AI Pilot to P&L Impact: Scaling AI Agents into Strategic Financial Assets

September 8 |  3 - 4:00 PM ET       

Credits: CTP/CCM: 1.2 | CPE 1.2 | CPE Field of Study: Information Technology

Learning Objectives

• Convert AR activity and variance insights into measurable improvements in forecast accuracy, liquidity visibility, and working capital.
• Assess AI capabilities and determine where to retain control of decision logic that drives financial outcomes.
• Structure AI initiatives across data, processing, and reasoning layers to validate value and enable expansion.

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Webinar Description

Many AI pilots improve individual tasks but fail to deliver measurable financial impact. This webinar bridges that gap through a real-world automotive case study where an AI agent analyzes daily AR activity to refine short-term cash forecasts and surface near-term liquidity risks. This case illustrates how a tightly scoped pilot—using manual data inputs, a single customer, and minimal integration—can validate value, strengthen decision logic, and build trust before scaling. Building on this foundation, a Layered Financial Intelligence framework is introduced to guide treasury teams in designing AI initiatives that move beyond isolated use cases to enterprise impact. This approach expands from targeted pilots to multi-agent environments that improve forecast accuracy, reduce cash leakage, and unlock working capital.

Speakers