UK Finance Leaders Struggle to Measure AI Value in Cash Management Despite High Adoption

UK finance leaders are struggling to quantify the return on investment for AI initiatives in cash and liquidity management, despite widespread adoption of the technology. New research highlights a significant "measurement gap" where nearly a third of senior directors cannot prove the business value of their AI deployments, potentially stalling the transition from pilot projects to enterprise-wide scaling.

What was announced

FIS released findings from a study conducted by Opinium between June and July 2026, surveying 100 senior finance directors at UK-based companies with annual revenues of £250 million or more. While 90% of these leaders have established a clear business case for AI in cash and liquidity management, 29% admit they cannot clearly measure the business value these initiatives deliver. This discrepancy exists despite 89% of respondents claiming to understand where AI delivers the greatest value and 91% believing their teams possess the necessary skills to use it.

The study reveals a broader visibility crisis within the finance function. Approximately 65% of respondents noted that investment decisions are occasionally made without a complete picture of available liquidity. This occurs even as 97% of finance leaders report increased accountability for cash outcomes, even when the underlying data sits outside their direct control. The manual burden remains high, with finance teams spending an average of 16.2 hours every week gathering, reconciling, and validating cash-position data.

Complexity in corporate structures is a primary hurdle to AI efficacy. On average, these UK finance leaders manage 14 legal entities, 11 banking relationships, and 10 core finance, treasury, or ERP systems. Only 78% believe their current systems are sufficiently connected to support AI use cases, and only 79% can access necessary data without significant manual effort. Consequently, 72% of leaders cite concerns regarding accuracy, explainability, or governance as a limit on AI adoption, while 76% point to data security risks.

"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."

James Mitchell, Global Head of Office of the CFO at FIS

The companies involved

FIS (Fidelity National Information Services) is a global provider of financial technology solutions, listed on the New York Stock Exchange (NYSE: FIS). The company operates as a major player in the treasury and liquidity management space, offering software and services designed to connect disparate financial systems and banking relationships. FIS provides the infrastructure that allows large corporations to consolidate data across multiple legal entities and financial institutions, aiming to reduce the manual effort associated with cash forecasting and reporting.

With a significant footprint in the UK market and 119 previous mentions in FF News reporting, FIS focuses on interoperability between core finance systems, ERPs, and payment networks. The company’s portfolio includes solutions for the "Office of the CFO," targeting the specific needs of senior finance directors who manage complex liquidity environments. FIS has established itself as a central figure in the fintech ecosystem, frequently addressing regulatory developments and technological shifts such as the integration of artificial intelligence into traditional banking and corporate treasury workflows.

What FF News has reported before

FF News has extensively tracked FIS's recent moves in the artificial intelligence and treasury space. In October 2026, the company expanded its AI capabilities by launching an FIS Launches Agentic AI Suite to Revolutionize Cash Flow Forecasting for CFOs, which directly addresses the forecasting challenges highlighted in the new research. This followed the commercial release of an FIS Launches AI-Powered AML Investigations Agent to Slash Bank Compliance Costs designed to reduce compliance costs for banks.

Beyond AI, FF News covered FIS's work in transaction transparency through its FIS Integrates Spade to Provide Real-Time Transaction Clarity for Debit Issuers. The publication also featured insights from FIS leadership regarding the UK’s financial landscape, specifically how an FIS Expert: UK Stablecoin Success Hinges on BoE and FCA Regulatory Coordination.

What this means

The "measurement gap" identified in this research suggests that the initial hype surrounding AI in the treasury is meeting the cold reality of legacy infrastructure. While finance leaders are eager to automate, the fragmented nature of corporate banking—spanning dozens of entities and systems—creates a "garbage in, garbage out" scenario for AI models. This puts pressure on traditional ERP and treasury management system (TMS) providers to prioritize interoperability over proprietary silos. If the industry cannot solve the data reconciliation problem, AI will remain a peripheral tool for reporting rather than a core driver of strategic liquidity decisions. The focus must shift from AI capability to data integrity.