Building audit ready AI agents in finance with Avalara

What should a finance leader be able to prove before giving an AI agent permission to act?

In this episode of AI at Work, I welcome Danny Fields, Chief Technology and Customer Operations Officer at Avalara, for a conversation about the distance between a convincing AI pilot and a finance workflow people can trust. We discuss the appeal of faster execution alongside the evidence, controls and business ownership needed when software begins making decisions or recommending actions.

Avalara's research provides the starting point. Its global survey of 1,505 CFOs and senior finance leaders found that 92% felt career pressure to demonstrate AI agent ROI, while half reported limited measurable returns. Respondents came from businesses already deploying, piloting or evaluating agents, so these findings describe a particular group rather than every finance department. What interested me was the tension between pressure to demonstrate progress and the work required to make that progress defensible.

Danny argues that copying an existing process into an AI workflow can preserve the problems already inside it. He describes why controlled pilots can struggle when production introduces incomplete records and unpredictable inputs. We consider what it means to redesign the workflow, measure accuracy over time and judge value through risk, audit preparation and business outcomes as well as hours saved.

We also discuss accountability. Danny's view is that responsibility stays with the team that owns the business outcome, supported by documented roles across finance, compliance, IT and legal. Permission boundaries, recorded approvals and tested incident response become part of the operating model. A CFO may not need to understand the mathematics behind a language model, but should understand what information an agent uses, what it can do and when a person takes over.

The conversation closes with a practical test for expanding autonomy: can the business demonstrate value, sustained accuracy, clear ownership, appropriate access and controls that reflect how agents actually work? These are Danny's recommendations, rather than a guarantee of compliance or financial performance. Please share your thoughts with me. What would your team need to see before trusting an AI agent with a consequential finance decision?