Webinar
The Finance AI Gap: Why the office of finance is behind, and what closing it takes
John Van Decker
Distinguished Analyst
Susan Phan
CFO
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Keyana Corliss
Head of Global Communications
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Pigment is a leading alternative for strategic planning
Anaplan
Pigment natively integrates with over 30 business applications (ERP, CRM, HRIS, BI solutions and more).
Native integrations are limited to fewer than 10 business applications using the Anaplan Data Orchestrator.
Pigment is a natively sparse engine and workspaces are not limited in capacity.
Standard Workspace size is limited, and additional products may be purchased to manage larger volumes of data up to a limit of 720GB.
Data is shared across business functions without needing to manage imports between models.
Data imports must be set up and maintained to copy data across multiple models.
Data updates happen automatically, without end users needing to trigger a data refresh for real-time planning and visibility of changes.
End users must refresh their pages to see the effect of data updates by other users while working in the same model.
Data inputs can be made by end users while imports and data recalculations are in progress.
End users are prevented from making changes to the model while data imports, exports and susbtantial recalculations are in progress.
About the event
AI is reshaping finance, but not every team is moving at the same pace.
Join John Van Decker from Dresner Advisory Services, and Susan Phan, Pigment’s CFO for a data-driven discussion on the state of the Office of the CFO.
Drawing on findings from Dresner’s The Office of Finance in 2026 report and Pigment’s CFO Index, the panel will explore what separates finance teams leading on AI from those falling behind, and what it takes to close the gap.
You’ll hear perspectives from across the market, the CFO seat, and the front lines of finance on the barriers holding AI adoption back, why data readiness matters more than ever, and how leading teams are building a stronger foundation for more agile, strategic finance.
What you’ll hear:
- Why AI maturity is becoming a dividing line between finance leaders and laggards
- Why data quality and readiness remain the biggest barriers to realizing value from AI
- Where finance leaders’ perceptions of AI readiness can differ from the reality on the ground
- How leading teams are responding to uncertainty with more frequent forecasting and scenario planning
- Why finance’s need for rigor, governance, and auditable outcomes shouldn’t stand in the way of AI adoption
- Practical lessons for moving beyond pilots, strengthening the data foundation, and demonstrating meaningful ROI from AI
