New FIS research reveals that 90% of UK finance leaders hold a clear business case for AI in cash and liquidity management, yet 29% cannot measure the value their AI initiatives deliver [1][1]. The gap is not one of conviction but of infrastructure: finance teams average 14 legal entities, 11 banking relationships, and 10 core systems, spending 16.2 hours per week manually assembling cash-position data [1][1]. Fragmented data foundations, not skepticism, are the true barrier to scaling AI in treasury functions [1].
What is Covered in this Article
- AI adoption momentum in UK treasury and cash management [1][1]
- The measurement gap: why 29% of finance leaders cannot quantify AI value [1]
- Infrastructure complexity driving manual data assembly [1][1]
- Accountability without visibility: the governance and data access challenge [1][1]
- Governance and security concerns limiting AI scale-up [1]
- FIS treasury solutions as connective infrastructure for AI value realization [1]
The News: FIS published research revealing a critical measurement gap in AI adoption among UK finance leaders [1]. The study, conducted by Opinium between 25 June and 4 July 2026 among 100 senior finance directors at UK companies with £250 million or more in revenue, found that 90% have a clear business case for AI in cash and liquidity management, but only 71% can clearly measure the business value their AI initiatives deliver [1][1]. James Mitchell, Global Head of Office of the CFO at FIS, stated that the ability to measure AI value is not keeping pace with deployment, and that addressing this requires treasury and liquidity management technology that connects data across banking relationships, legal entities, and financial systems [1].
UK Finance Leaders Bet on AI but Can't Prove It's Working
Analyst Take: The FIS findings reframe the AI challenge in finance: the obstacle is not belief, it is proof. With 90% of UK finance leaders holding a business case for AI yet 29% unable to demonstrate its value, the research exposes a structural gap between deployment ambition and measurement capability [1][1]. This mirrors a broader enterprise software dynamic where, per Futurum research, 55.1% of decision makers cite faster time to value realization as a top budget-confidence driver [2].
Conviction Is High, but Measurement Lags Deployment
UK finance leaders are not short on AI confidence. Nine in 10 have a clear business case, 89% understand where AI can deliver the greatest value, and 91% believe their teams have the skills to use it effectively [1]. Yet almost three in 10 (29%) cannot clearly measure the business value being delivered by their AI initiatives [1]. This gap matters because unmeasured value is unfundable value. Futurum research shows that 51.4% of enterprise software buyers measure ROI primarily through efficiency improvements [2], precisely the gains that AI in cash management should produce. When finance leaders cannot demonstrate those efficiency gains, internal confidence stalls and deployments remain in pilot mode rather than scaling across the enterprise.
Fragmented Infrastructure Is the Root Cause
The measurement gap has a structural explanation. UK finance leaders manage an average of 14 legal entities, 11 banking relationships, and 10 core finance, treasury, ERP, or payments systems [1]. At that level of complexity, assembling a reliable cash position requires 16.2 hours of manual gathering, reconciling, and validating each week [1]. 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 [1]. When the data feeding AI models is itself manually assembled from disconnected sources, neither the accuracy nor the measurability of AI outputs can be trusted. Futurum data reinforces this: 55.2% of enterprise software decision makers say improved integration capabilities would make them more confident allocating budget to future purchases [2].
Accountability Without Visibility Compounds the Risk
The infrastructure gap creates a governance paradox. 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 [1]. Yet two-thirds (65%) admit investment decisions are sometimes made without a complete picture of available liquidity [1]. Finance leaders are being held responsible for outcomes they cannot fully see. Mitchell put it directly: decisions can slow down, opportunities can be missed, and confidence in AI models deployed before data foundations are in place can erode. The accountability-visibility mismatch is not just an operational inconvenience; it is a strategic risk for organizations operating at scale.
Governance and Security Concerns Constrain Scale-Up
Beyond infrastructure, finance leaders cite specific AI-readiness concerns that limit broader adoption. Seventy-two percent say accuracy, explainability, or governance concerns are limiting AI adoption, while 76% flag data security risks as a constraint [1]. These concerns are consistent with what Futurum observes across the enterprise software market, where generative AI ranks as the top underlying technology priority at 90.4% of organizations [2], yet buyers remain cautious about deploying it without trusted data foundations. Finance leaders have identified their priorities clearly: greater automation of reporting and reconciliation (46%), a single real-time view of cash across entities and accounts (43%), better system integration (43%), and improved interoperability between systems (42%) [1]. These are not AI feature requests; they are data infrastructure prerequisites.
FIS Positions Connected Infrastructure as the Prerequisite
FIS frames its treasury and liquidity solutions as the connective tissue that closes the data-quality gap. The argument is straightforward: 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 [1]. By consolidating near-real-time cash visibility across banking relationships, legal entities, and financial systems, FIS positions interoperability as the prerequisite for measurable, scalable AI value. This positioning aligns with the enterprise software market's growth trajectory, where the base-case 2030 market value reaches $682.9 billion at a 12.2% CAGR [3]. In a market dominated by large ERP vendors including SAP at 33.9% share and Oracle at 28.6% [3], FIS's differentiation on treasury-specific data connectivity and AI readiness targets a gap that generalist platforms are slower to close. The organizations that address data foundation and interoperability issues first are, per Mitchell, the ones expected to pull ahead.
What to Watch
- Measurement capability adoption: whether UK finance leaders above the 71% threshold expand to peer organizations in the next two quarters, signaling that data infrastructure investments are translating into provable AI value [1][1]
- Integration investment signals: how enterprise software buyers shift budget allocation toward integration capabilities in Q1 2027 procurement cycles, given that 55.2% already cite it as a top confidence driver [2]
- Competitive treasury platform response: how ERP incumbents including SAP and Oracle repackage treasury connectivity features to close the gap FIS is targeting in the UK mid-to-large enterprise segment [3]
- Governance framework development: whether UK regulatory guidance on AI explainability in finance functions emerges in early 2027, which could either accelerate or constrain the 72% of leaders currently limited by accuracy and governance concerns [1]
Sources
1. FIS Research: UK Finance Leaders Back AI in Cash …, Fisglobal
2. 2H 2026 Enterprise Applications Decision Maker Survey Report, Futurum Research, August 2026
3. 2H 2026 Enterprise Applications Market Sizing & Five-Year Forecast, Futurum Research, August 2026
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
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