AI moves from pilots to production as CFOs face rising pressure to deliver ROI

In recent months, a consistent theme in discussions has been how finance leaders are moving AI beyond early experimentation into core operations, with a clear focus on measurable outcomes. 

The focus now is on bringing AI into planning, close, and reporting in a way that’s tied to clear business outcomes. This formed the core of my talk at the Gartner CFO & Finance Executive Conference in London on June 8–9, where I shared how finance teams are putting this into practice day to day. 

In my session, and echoed in conversations across the conference, a few clear themes stood out:

A shift from experimentation to execution 

At the conference, discussions around AI reflected a consistent message. What was previously explored in pilots is now expected to deliver results. 

The focus for CFOs has moved beyond adoption. Attention is now on how to turn early use cases into consistent financial outcomes. 

This is happening while many organizations are still building digital capabilities. Research from Wolters Kluwer’s 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 becoming a defining challenge for the finance function. 

Rising expectations from leadership 

Boards and executives are asking for clear outcomes, with the discussion shifting from potential to performance. 

As a result, they are moving AI into core processes such as planning, close, and reporting. These efforts are no longer treated as side initiatives. The finance function is becoming part of daily operations. 

Where finance teams are seeing success 

At the conference, leaders highlighted several areas where AI is already being applied: 

• Planning and forecasting: Teams are using models to analyze drivers, test scenarios, and update forecasts more quickly. This supports more frequent and informed planning cycles. 
• Financial close and consolidation: AI supports the identification of anomalies, automation of reconciliations, and earlier visibility into potential issues. This reduces manual effort and improves consistency. 
• Reporting and disclosure: Teams are using AI to support variance analysis and narrative reporting. This provides quicker access to insights and broader use of financial data across the organization. 

Moving into production environments 

Finance leaders are reporting gains in forecast accuracy, data quality, and operational efficiency. At Severn Trent, a leading UK water utility, they are moving predictive planning models into production.  

CCH Tagetik’s AI has transformed energy cost forecasting, one of the largest P&L drivers, by significantly improving accuracy. By incorporating external factors like rainfall data, it gives finance teams a more accurate baseline and unlocks a shift from reporting to true business partnering and drives more confident planning decisions. This approach is now scaling across all sites, and the organization is targeting over 90% forecast accuracy. 

As Darren Fellows, Finance Systems Manager at Severn Trent, told me, “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.” 

Integrating AI into finance operations 

As these efforts expand, organizations are incorporating AI into existing processes, often within unified platforms that bring planning, close, and reporting together in a single environment. This includes aligning use cases with financial metrics, assigning ownership, and ensuring models can be reviewed and explained.   

At the same time, the use of AI is changing how teams operate. With more continuous monitoring and scenario analysis, they are spending less time on manual reporting and more time supporting decisions across the business. 

For CFOs, the priority is delivering measurable outcomes as AI becomes embedded in core finance processes. Organizations are reducing manual effort and enabling more continuous analysis. At Severn Trent, predictive models are improving forecasting speed and enabling faster responses to change. 

What was previously tested in pilots is becoming part of standard operations. Organizations are now expected to demonstrate consistent results. 

And so… 

 
What is coming through clearly, both at the Gartner CFO & Finance Executive Conference in London, and in my regular conversations with customers, is that the conversation has shifted. AI in finance is no longer about what’s possible. It’s about what delivers.  

The teams making progress are doing more than pilots, they’re building AI into core processes and holding it to the same standard as any other investment: measurable, repeatable impact.