Making AI Work in Finance: Data, Process, and Operating Model Choices
June 4, 2026 | Al Biernat’s North | Dallas
In partnership with
Making AI Work in Finance: Data, Process, and Operating Model Choices
Organizations do not struggle with AI because the technology falls short, but because existing finance processes, data foundations, and operating models were never designed to support it.
Genpact’s CFO playbook emphasizes that CFOs who see real returns are not scaling AI pilots in isolation. Instead, they are redesigning core finance processes, integrating fragmented data, and putting clear business ownership around AI-enabled decisions. Value emerges when AI is embedded directly into how finance work is executed, measured, and governed—across payables, receivables, close, and forecasting—not when it sits alongside them as experimentation.
This roundtable builds on that practical CFO perspective. Moderated by Brajesh Jha, the discussion focuses on how finance and technology leaders are modernizing data foundations, reducing process debt, and reshaping operating models so AI can move from pilot activity into repeatable, controlled execution.
Rather than theory, the conversation centers on what CFOs are actually changing: how processes are standardized first, how data is unified across systems, and how accountability is defined so AI initiatives can be funded, governed, and measured like any other transformation program—against cash flow, cycle time, risk, and decision quality, not technical novelty.
Cut through AI pilots, fragmented data, and inconsistent processes to understand where finance leaders are prioritizing change first across payables, receivables, close, and forecasting—and which foundational gaps must be addressed before scaling AI into production.
Learn how peers are moving from siloed AI experiments to embedded, process-led execution, aligning Finance and Technology around shared ownership—while simplifying governance, defining decision rights, and ensuring AI runs within control frameworks, not outside them.
Leave with clear perspectives on how to transition from pilots to repeatable, controlled AI execution, including the accountability, funding models, and performance metrics required to scale confidently—measuring impact in cash flow, cycle time, risk, and decision quality.
Roundtable Discussions
The conversation centers on what CFOs are actually changing: how processes are standardized first, how data is unified across systems, and how accountability is defined so AI initiatives can be funded, governed, and measured like any other transformation program—against cash flow, cycle time, risk, and decision quality, not technical novelty.
Discussion topics:
- Why AI value depends on process and data readiness: The CFO playbook shows that most stalled AI initiatives fail not because models are weak, but because data remains fragmented and processes vary by region, system, or team. What practical steps are leaders taking to standardize finance workflows and integrate data before scaling AI?
- Moving from pilots to production without losing control: CFOs increasingly insist that AI initiatives have a named business owner, clear economic measures, and defined controls. How are Finance and Technology teams redesigning governance so AI operates within existing control environments rather than outside them?
- What a shared Finance–Technology operating model looks like in practice: Rather than “AI for Finance” or “AI owned by IT,” leading organizations define who owns decisions, data, and outcomes across functions. What operating model changes—decision rights, funding, accountability—are proving essential to turning AI into repeatable results?
Dinner Format
- Cocktails & Networking
- Welcome Address
- A fireside chat/panel session on building AI-enabled, autonomous finance operating models
- Dinner and Roundtable Discussion
- Dessert & Round-Robin
