AI, Trust, and the Expanding Role of Finance: A Sage Future Special

The episode features three interviews conducted at the Sage Future Conference in San Francisco, each offering a different vantage point on AI's role in reshaping finance.

Aaron Harris, Sage's CTO, opens with a macro-level view of where AI is headed. He argues that CFOs face an 'emotional leap' in moving from AI as an assistive tool to AI as an autonomous agent—and that this leap is gated entirely by trust. Harris introduces the concept of 'black box versus glass box' AI, insisting that transparency and explainability are prerequisites for adoption. He predicts that within five years, the conversation will shift from AI automating routine tasks like invoicing and reconciliation to AI enabling consequential strategic decisions—such as market entry, acquisitions, and product launches. He also warns that unlike features or speed, trust cannot be hyped or accelerated, putting established vendors like Sage at an inherent advantage over newer entrants. On governance, Harris argues that AI agents must operate within the same permission and access frameworks used for human workers, but with additional layers of traceability—including capturing the prompt, the initiating actor, and which agents were involved. He distinguishes between areas where AI should recommend versus act autonomously, suggesting that for consequential decisions, AI's role is to empower the decision-maker with full context rather than to render a verdict.

John Fasoli, a senior leader focused on finance automation, brings the discussion to ground level. He emphasizes that in finance, '99% right is still wrong,' and argues that AI tools must be built with inspection and validation mechanisms that allow users to verify outputs before trusting them. He describes how high-performing finance teams are distinguished not by recklessness or passivity, but by aggressive experimentation in controlled environments—starting small, validating against historical records, and scaling gradually. Fasoli frames the human-in-the-loop model as essential, particularly for gray-area decisions that require accounting expertise and business judgment. He also argues that as AI eliminates administrative work, finance professionals will increasingly be called upon to handle exceptional situations—either edge cases the AI cannot resolve or high-stakes strategic scenarios requiring human expertise. He sees the finance function becoming more strategic as real-time data access improves decision-making speed and quality.

Julie Adams, who leads Sage's construction and real estate segment, applies these themes to a notoriously technology-laggard industry. She outlines the current challenges facing construction finance leaders: labor shortages, tariff-driven material cost uncertainty, and the critical need for real-time project visibility across disconnected systems. Adams argues that AI has the potential to transform the entire construction value chain—from pre-construction bid optimization and anomaly detection in estimates, to AI-powered drawing management in the field, to labor cost tracking tied directly to project financials. She highlights that Sage is embedding AI across its end-to-end suite rather than offering it as a bolt-on, and is making AI capabilities available even in its on-premises products to meet customers where they are in their digital maturity journey.

Across all three conversations, a consistent theme emerges: the opportunity AI presents is not merely about automation efficiency, but about connecting data, improving real-time visibility, and elevating the strategic role of finance leadership within the enterprise.