UK finance leaders back AI but struggle to prove value

UK finance leaders are backing artificial intelligence for cash and liquidity management, according to new research from FIS. But the survey found that 29% cannot clearly measure the business value those initiatives deliver.

The findings are based on a study of 100 Senior Finance Directors at UK companies with annual revenue of at least GBP £250 million. They point to a gap between support for AI and the ability to show results, even as finance teams face growing pressure over cash and liquidity decisions.

Nine in 10 respondents said they have a clear business case for using AI in cash and liquidity management, while 89% said they understand where it can deliver the greatest value. Another 91% said their teams have the skills to use it effectively.

Yet confidence in AI appears to sit alongside weaknesses in the underlying finance infrastructure. Some 72% said concerns about accuracy, explainability or governance are limiting adoption, while 76% cited data security risks.

Visibility gap

The research also found that 65% of UK finance leaders said investment decisions are sometimes made without a complete picture of available liquidity. At the same time, 97% said the finance function is increasingly accountable for cash and liquidity outcomes, even when the underlying data, systems or processes sit outside its direct control.

That mismatch appears to be reinforced by manual work inside finance teams. Respondents said their teams spend an average of 16.2 hours a week gathering, reconciling and validating cash-position data before sharing it with senior stakeholders.

The study points to a complex operating environment for many larger businesses. On average, respondents manage 14 legal entities, 11 banking relationships and 10 core finance, treasury, ERP or payments systems.

Only 78% said their systems are sufficiently connected to support AI use cases. A similar 79% said they can access the data needed for AI initiatives without significant manual effort, suggesting a notable minority still face barriers to getting reliable information into the tools they want to use.

"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 move beyond pilots and scale. That measurement challenge is closely linked to data quality, trust and connectivity across the enterprise. AI is only as reliable as the information it works with, and in many finance functions, that information is still being assembled manually from disconnected systems. Addressing that requires treasury and liquidity management technology that connects data across banking relationships, legal entities and financial systems. This interoperability can help reduce the manual effort involved in building a trusted cash forecast and provide AI with the reliable data foundations it needs to support measurable value," said James Mitchell, Global Head of Office of the CFO at FIS.

Infrastructure priorities

When asked what would make the biggest difference, respondents put greater automation of reporting and reconciliation at the top of the list, with 46% selecting it. A single real-time view of cash across entities and accounts and better system integration followed at 43% each, while 42% pointed to improved interoperability between systems.

These responses suggest finance leaders see the challenge not simply as AI adoption, but as building cleaner, more connected data flows across treasury, banking and internal systems. Without that, organisations may struggle to produce the evidence needed to justify broader rollouts.

The survey was carried out by Opinium among UK-based Senior Directors in accounts and finance roles. All respondents worked at companies with revenue of GBP £250 million or more.

Mitchell said: "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. When a cash position is assembled from disconnected banks, entities and systems, with any manual process involved, neither the finance team nor an AI model can fully utilise it until that data has been rationalised. As a result, decisions can slow down, opportunities can be missed, and confidence in AI models deployed before that work is done can erode. We expect the organisations that pull ahead to be the ones that address their data foundation and interoperability issues first. That is where FIS can help: our treasury and liquidity solutions are designed to give finance teams a consolidated, near real-time view of liquidity by connecting data across their banking relationships, legal entities and financial systems, helping to reduce manual effort, support a more trusted cash position and forecast, and provide AI with a reliable data foundation to help deliver value leaders can measure and demonstrate."