1. AI’s Biggest Challenge Is Not the Algorithm. It’s Your Data.
The hardest part of adopting AI in finance is not the technology. It is the quality of the data the technology depends on. Research from BARC shows that an unsuitable data architecture or data foundation is a barrier for 34% of organizations, and 33% struggle with data integration and data quality.
If data is not clean, consistent, and well organized, even the smartest AI models will fail. With agentic AI, the need for reliable data becomes even more serious, especially for finance teams. Bad data leads to weak insights and poor decision making.
“Governing data is a critical and far-reaching practice area for AI competency.”
This problem places the CFO at the center of the AI transformation. Finance teams work with financial data, operational metrics, and strategic governance. That makes them the natural leaders for building the trusted data base the entire company needs.
2. The Spreadsheet Hell Era Is Closing Fast
Many finance professionals still feel stuck in manual work and endless spreadsheets. But the overall trend is clear. Leading companies are moving away from these outdated processes. Dedicated, more advanced tools are becoming the norm.
According to The Planning Survey 25, 68% of companies now use specialized planning products for their main planning activities. This shift away from manual and error-prone processes frees finance teams from repetitive data tasks. It also sets the stage for more advanced capabilities like predictive planning.
3. Predictive Planning: No Longer a Niche Idea
Predictive planning and forecasting may sound futuristic, but the data shows they are already delivering real value. Organizations that have moved beyond traditional planning methods are seeing strong results.
27% of organizations already use predictive planning in a productive way.
65% are either achieving or expect to achieve significant benefits.
64% report better accuracy and less effort in everyday FP&A activities.
This technology is no longer just hype. It is a proven way to improve financial operations and strengthen strategic planning.
“The use of predictive algorithms in corporate planning, known as predictive planning and forecasting, has been an important development in finance and controlling over recent years. Its aims are to improve planning and forecasting, and to increase automation in order to reduce the workload of planners.”
Success with predictive tools is helping shift the CFO role from recording the past to guiding the future.
4. The CFO’s New Mandate: Architect of Enterprise Insight
The CFO role is changing more than ever before. Making sure financial information is accurate is still important, but it is now just the starting point. In the age of agentic AI, the CFO must become the architect of enterprise insight. This means designing how data moves through the organization, how it connects, and how it supports strategy.
Modern tools help finance leaders take on responsibilities that mix finance, data, and technology.
Integrating data from finance, operations, and HR
Creating governance frameworks that explain where data comes from and how models work
Building a culture that understands and values data
Working with the CIO or CDO on enterprise-wide standards
The CFO’s office is the only function that brings together data, governance, and strategic insight for the whole organization. This makes it the key driver of company-wide transformation.
Conclusion: Grounding Decisions in Trusted Data
The CFO of the future is not just a user of new technology. They are the creator and caretaker of the data foundation that makes AI successful. The focus is no longer only on financial results. It is about making sure every strategic decision is based on trusted, high-quality data.
As AI continues to shape business, is your finance team building the data foundation needed for success, or are you simply looking at the next tool?
All insights and data come from “The Strategic Imperative for CFOs to be AI Entrepreneurs” by Kelley Lynn Kassa, Senior Analyst at BARC.