UK finance leaders struggle to measure AI’s business impact, study shows

By Divya Shah

Key Takeaways

• AI adoption is strong, but ROI remains unclear: 90% of UK finance leaders see a business case for AI in cash and liquidity management, yet 29% cannot clearly measure its value. 
• Poor cash visibility is a growing concern: 65% say investment decisions are sometimes made without a complete view of available liquidity, while finance teams spend 16.2 hours weekly reconciling cash data. 
• Interoperability is critical for AI success: Finance leaders prioritise automation, real-time cash visibility and better system integration to create trusted data foundations for scalable AI. 

Artificial intelligence has become a strategic priority for finance teams across the UK, yet many organisations continue to face challenges in demonstrating its measurable impact. New research from FIS reveals that while AI adoption in cash and liquidity management is gaining momentum, gaps in data quality, system connectivity and performance measurement are preventing many firms from fully realising its benefits. 

The study, conducted by Opinium among 100 senior finance directors at UK companies with revenues exceeding £250 million, found that 90% of finance leaders have a clear business case for using AI in cash and liquidity management. Additionally, 89% understand where AI can deliver the greatest value, while 91% believe their teams possess the necessary skills to use the technology effectively. 

Despite this enthusiasm, only 71% of respondents said they can clearly measure the business value generated by their AI initiatives. Nearly three in 10 finance leaders remain unable to accurately quantify AI’s return on investment, creating a significant hurdle for broader deployment and long-term adoption. 

The research highlights a deeper issue surrounding financial visibility and cash management. Around 65% of respondents admitted that investment decisions are sometimes made without a complete picture of available liquidity. At the same time, 97% said finance functions are increasingly being held accountable for cash and liquidity outcomes, even when the underlying systems and data sources remain outside their direct control. 

Operational inefficiencies continue to consume significant resources. Finance teams spend an average of 16.2 hours each week gathering, reconciling and validating cash-position data before sharing insights with senior management. This manual effort limits their capacity to focus on strategic activities such as forecasting, risk management and capital allocation. 

According to James Mitchell, Global Head of Office of the CFO at FIS, organisations are encountering a disconnect between AI deployment and value measurement. 

“Finance leaders are investing in AI with a clear sense of where it can add value; however, what we are observing is that the ability to measure that value is not keeping pace with deployment. If you cannot clearly demonstrate what value AI is delivering, it becomes very difficult to build the internal confidence needed to support a move beyond pilots and truly scale,” said Mitchell. 

For FinTech providers, the findings underscore the growing importance of connected treasury and liquidity management platforms. The study found that UK finance leaders manage an average of 14 legal entities, 11 banking relationships and 10 core finance, treasury, ERP or payments systems. Such complexity makes it difficult to maintain a real-time, consolidated view of cash positions across the organisation. 

Only 78% of respondents believe their systems are sufficiently connected to support AI use cases, while 79% said they can access the data required for AI initiatives without significant manual intervention. These figures suggest that many organisations still lack the digital foundations necessary for large-scale AI adoption. 

Finance leaders identified automation and interoperability as top priorities. Nearly half (46%) cited automated reporting and reconciliation as the most impactful improvement, while 43% highlighted the need for a single real-time view of cash and stronger system integration. Another 42% pointed to improved interoperability between platforms as critical for future progress. 

Mitchell added, “Finance leaders don’t lack conviction in AI. They often lack the evidence to prove it, and you cannot safely scale what you cannot trust and measure. That measurement gap is really a data quality gap.” 

For the FinTech industry, the research presents a significant opportunity. As enterprises seek to improve liquidity visibility, reduce manual processes and create reliable data foundations for AI, demand is expected to grow for treasury management, cash forecasting, data integration and interoperability solutions.  

FinTech firms that can connect fragmented financial ecosystems and provide real-time, trusted insights are likely to play a central role in helping finance leaders move AI from experimentation to measurable business value.