About Session
Here’s the paradox no one is talking about: finance teams are adopting AI tools at record speed, but most still re-check every output manually, duplicate workflows “”just in case,”” and treat AI recommendations as suggestions rather than signals. The result? More tools, more cost, and zero net productivity gain. In this session, we’ll confront the trust gap head-on—the psychological, procedural, and technical barriers that prevent finance professionals from actually relying on the AI they’ve already deployed. Drawing on behavioral research and real-world finance transformation case studies, we’ll unpack why trust breaks down, how to build validation frameworks that give teams genuine confidence, and what governance structures separate organizations that tinker with AI from those that scale it. If your team has AI but still operates like it doesn’t, this session is for you.
Learning Objectives
- Diagnose the root causes of low AI trust in finance—from black-box anxiety and past automation failures to misaligned KPIs that reward manual verification over intelligent automation.
- Learn how to design transparent validation and exception-handling frameworks that give finance professionals the confidence to let AI outputs drive decisions without redundant manual checks.
- Build a governance and change management playbook that systematically closes the trust gap, including escalation protocols, accuracy benchmarking, and feedback loops that improve AI performance over time.
