The technology exists. The use cases are proven. So why are most finance teams still running AI with the handbrake on?
Join Lucanet’s finance AI experts, Kevin Smith, CTO, Sören Labrenz, Head of Product AI, and Janis Steinmann, Head of XBRL, for a forward-looking discussion on the real barrier holding finance back: trust.
This isn't about whether AI works, it's about when CFOs will feel confident letting it work unsupervised.
What we'll explore together:
- The anatomy of trust in finance AI: Why accuracy, governance, and data quality form just one part of the equation—and what emotional and organizational factors complete it
- The 2026 inflection point: As unified data systems mature and oversight mechanisms strengthen, what shifts from theoretical to practical?
- Deterministic logic meets intelligent agents: Where should finance apply traditional algorithms versus AI? How do we build transparency into reasoning that wasn't designed to be auditable?
- The adoption paradox: Finance remains in early-stage AI adoption despite widespread experimentation. What separates genuine progress from surface-level engagement?
This session will be an interactive discussion on AI in finance in 2026. We'll examine the technical foundations being built today—context management, supervised autonomy, trust mechanisms—and debate what readiness actually requires.
Whether you're already implementing AI or still evaluating your approach, join us for an honest look at where finance AI is headed and what the transition from potential to practice truly demands.