Nine in 10 (90%) of UK finance leaders have a clear business case for using AI in cash and liquidity management, but only seven in 10 (71%) can clearly measure the business value their AI initiatives deliver, according to new research from FIS®, a global leader in financial technology. The study, conducted by Opinium in June–July 2026 among 100 senior finance directors at UK companies with £250 million or more in revenue, suggests what is holding AI back in finance is not belief in its value, but the ability to prove it.The same gap between accountability and visibility runs through cash itself, with two-thirds (65%) saying investment decisions are sometimes made without a complete picture of available liquidity, a finding that carries real risk, given that almost every finance leader surveyed (97%) says the function is increasingly accountable for cash and liquidity outcomes, even when the underlying data, systems or processes sit outside its direct control. Respondents report that their finance teams spend an average of 16.2 hours each week gathering, reconciling and validating cash-position data before sharing it with senior stakeholders, which reduces capacity for the higher-value analysis leaders are being asked to provide.AI deployed but value unprovenAI is firmly established on the finance agenda. Nine in 10 (90%) UK finance leaders have a clear business case for using it in cash and liquidity management, 89% understand where it can deliver the greatest value, and 91% believe their teams have the skills to use it effectively. Yet the findings point to a measurement gap, with almost three in 10 (29%) unable to clearly measure the business value being delivered by their AI initiatives. That challenge is compounded by concerns around the conditions needed to scale AI responsibly: 72% say accuracy, explainability or governance concerns are limiting adoption, while 76% cite data security risks.James Mitchell, Global Head of Office of the CFO at FIS, stated: "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. 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. For instance, 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."Accountability without visibility: the infrastructure challengeFIS research shows the infrastructure challenge runs deep: UK finance leaders manage an average of 14 legal entities, 11 banking relationships and 10 core finance, treasury, ERP or payments systems. At this level of complexity, maintaining a real-time, consolidated view of cash is inherently difficult. Only 78% say their systems are sufficiently connected to support AI use cases, and only 79% can access the data they need for AI initiatives without significant manual effort, suggesting that the foundations required to close the visibility gap are not yet fully in place for many organisations.Finance leaders are clear on what would make the biggest difference. Greater automation of reporting and reconciliation ranks highest (46%), followed by a single real-time view of cash across entities and accounts and better system integration (43% each), and improved interoperability between systems (42%). These priorities collectively point to a need for better-connected financial infrastructure as the foundation for both clearer visibility and more trustworthy AI.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. 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.”NoYesArtificial Intelligence22 Oct, 2026