Nine in 10 (90%) UK finance leaders have clear business case for using AI in cash and liquidity management, yet only 71% can clearly measure business value being delivered by AI initiatives. Two-thirds (65%) say investment decisions sometimes made without complete picture of available liquidity, according to new FIS research. Almost every UK finance leader surveyed (97%) says function increasingly accountable for cash and liquidity outcomes, even when underlying data, systems or processes sit outside direct control.
AI firmly established on finance agenda. Nine in 10 (90%) UK finance leaders have clear business case for using it in cash and liquidity management, 89% understand where it can deliver greatest value, and 91% believe teams have skills to use it effectively. Yet findings point to measurement gap, with almost three in 10 (29%) unable to clearly measure business value being delivered by AI initiatives. That challenge compounded by concerns around conditions needed to scale AI responsibly: 72% say accuracy, explainability or governance concerns limiting adoption, while 76% cite data security risks.
Finance leaders investing in AI with clear sense of where it can add value, however ability to measure that value not keeping pace with deployment. If cannot clearly demonstrate what value AI delivering, becomes very difficult to build internal confidence needed to support move beyond pilots and truly scale. That measurement challenge closely linked to data quality, trust and connectivity across enterprise. AI only as reliable as information it works with, and in many finance functions, that information still being assembled manually from disconnected systems. Addressing requires treasury and liquidity management technology that connects data across banking relationships, legal entities and financial systems. Interoperability can help reduce manual effort involved in building trusted cash forecast and provide AI with reliable data foundations needed to support measurable value.
FIS research shows infrastructure challenge runs deep: UK finance leaders manage average of 14 legal entities, 11 banking relationships and 10 core finance, treasury, ERP or payments systems. At this level of complexity, maintaining real-time, consolidated view of cash inherently difficult. Only 78% say systems sufficiently connected to support AI use cases, and only 79% can access data they need for AI initiatives without significant manual effort, suggesting foundations required to close visibility gap not yet fully in place for many organisations.
Finance leaders clear on what would make biggest difference. Greater automation of reporting and reconciliation ranks highest (46%), followed by single real-time view of cash across entities and accounts and better system integration (43% each), and improved interoperability between systems (42%). Priorities collectively point to need for better-connected financial infrastructure as foundation for both clearer visibility and more trustworthy AI.
Finance leaders don't lack conviction in AI. They often lack evidence to prove it, and cannot safely scale what cannot trust and measure. That measurement gap really data quality gap. When cash position assembled from disconnected banks, entities and systems, with manual process involved, neither finance team nor AI model can fully utilise it until data rationalised. As result, decisions can slow down, opportunities missed, and confidence in AI models deployed before that work erode. Organisations that pull ahead be ones that address data foundation and interoperability issues first.
About Research
Fieldwork conducted by Opinium between 25 June and 4 July 2026. Sample: 100 UK-based senior directors in accounts/finance roles at companies with £250m+ revenue.
About FIS
FIS is financial technology company providing solutions to financial institutions and businesses. Unlock financial technology to world across money lifecycle underpinning world's financial system. Headquartered in Jacksonville, Florida, FIS member of Fortune 500 and S&P 500 Index.
