CFOs are moving AI from pilot programs into production environments, with a clear focus on measurable returns, as discussions at the Gartner CFO & Finance Executive Conference in London made clear. The shift reflects growing pressure from boards and executives to demonstrate concrete ROI from AI investments.
From pilots to production
At the conference, conversations consistently pointed to a transition from early experimentation to execution. Finance leaders said AI is now expected to deliver results, not just potential. This marks a departure from treating AI as a side initiative.
The pressure is mounting even as many organizations build foundational digital capabilities. Research from the Wolters Kluwer Future Ready CFO Report shows that while 85% of finance leaders expect AI to reshape their role in the near term, only 18% consider their organizations digitally advanced. This gap between ambition and readiness is a defining challenge for the finance function. The shift from experimentation to execution requires finance leaders to understand how AI can be applied to core processes - a topic explored in depth in AI for CFOs Training.
Where AI is delivering results
Several areas stood out where finance teams are already seeing impact. In planning and forecasting, models analyze drivers, test scenarios, and update forecasts more quickly. This supports more frequent and informed planning cycles. For financial close and consolidation, AI helps identify anomalies, automate reconciliations, and provide earlier visibility into potential issues, reducing manual effort and improving consistency.
Reporting and disclosure efforts are also benefiting. Teams use AI for variance analysis and narrative reporting, giving quicker access to insights and wider use of financial data across the organization. These applications are no longer theoretical; they are built into daily operations. For professionals looking to build these skills, AI for Finance courses cover the practical applications finance teams are now deploying.
Integrating AI into core finance processes
At Severn Trent, a UK water utility, predictive planning models have moved into production. CCH Tagetik's AI improved energy cost forecasting, one of the largest P&L drivers, by incorporating external factors like rainfall data. This gave finance teams a more accurate baseline and shifted their role from reporting to business partnering. The organization now targets over 90% forecast accuracy.
Darren Fellows, Finance Systems Manager at Severn Trent, said, "We're already seeing quicker answers to key questions and faster decision-making. The models are helping teams understand what's driving costs and enabling more informed planning across the business."
As these efforts expand, organizations are embedding AI into unified platforms that bring planning, close, and reporting together. Use cases are aligned with financial metrics, ownership is assigned, and models are designed to be reviewed and explained. The approach reduces manual effort and enables more continuous scenario analysis.
Why this matters for finance, IT, and management professionals
The conversation has shifted from what AI can do to what it delivers. Teams that move beyond pilots and embed AI into core processes are holding it to the same standards as any other investment: measurable, repeatable impact. For finance and IT leaders, the immediate priority is to tie AI use cases directly to business outcomes, ensuring that the technology improves forecast accuracy, close speed, and reporting quality in ways that can be tracked and demonstrated to leadership.
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