Finance organisations accelerate AI adoption amid data and talent challenges

For many finance teams, artificial intelligence has moved beyond proof-of-concept and into the routines that underpin planning, reporting and risk oversight. Over the past 18 months vendors and in-house projects have delivered tangible reductions in manual work, shorter close cycles, faster variance analysis and automated pattern detection, that have persuaded some parts of the function to adopt AI as business‑as‑usual.

FP&A was among the earliest areas to embrace automation because its processes were so heavily manual. Platforms that can pull data from disparate systems, surface anomalies and produce rolling forecasts in hours rather than days have been particularly persuasive. According to a 2025 report by the FP&A Trends Group, a portion of teams now use AI to automate reporting and workflow tasks, while others apply it to forecasting and trend detection. Universities and training programmes are beginning to inject data literacy into finance curricula, addressing a skills shortfall that has hindered earlier adoption.

Nevertheless, surveys show adoption remains uneven. A 2025 Association for Financial Professionals survey reported that only 23% of FP&A practitioners use AI in their daily work, though 40% were trialling tools with plans to deploy within a year. Similarly, FP&A Trends’ wider polling found that more than half of FP&A teams had not yet implemented AI, highlighting resistance to change, capability gaps and competing investment priorities as persistent barriers.

The spread of AI beyond FP&A is already apparent in risk and compliance, where the combination of large datasets and pattern recognition is a natural fit. Cloud-based risk platforms lower the entry threshold for mid-market firms, enabling continuous monitoring, anomaly detection and audit-ready outputs that previously required specialist quantitative teams. McKinsey’s 2025 survey of chief financial officers found a sharp rise in generative AI use for multiple finance use cases, yet cautioned that many organisations have struggled to scale pilots into reliable, integrated processes.

Data quality and integration remain central constraints. EY’s and other industry studies show that inadequate data infrastructure is a leading obstacle to effective AI deployment; a majority of executives say strengthening data foundations would accelerate progress. Where organisational metrics are defined inconsistently across departments, models cannot deliver trustworthy results, so investment in data governance and consolidation is often a precondition for success.

Trust in model outputs and the availability of hybrid skillsets, people who understand both finance and machine learning, are other common friction points. Implementation missteps can produce poor outcomes when pilots are disconnected from core workflows, and organisations that underinvest in change management risk low uptake despite promising technology. Industry analysis identifies leadership, targeted training and vendor partnerships as critical factors for translating pilots into scaled capability.

There are signs the returns can justify the effort. KPMG’s research reported that a large majority of US companies believe their finance AI initiatives are meeting or exceeding return-on-investment expectations, and many plan to increase budgets for enterprise AI activities. At the same time, consultancy and practitioner surveys warn that disappointments often stem from unrealistic expectations, weak integration and fragmented data, not from flaws in the underlying algorithms themselves.

For finance leaders the choice is increasingly strategic: continue to treat AI as an optional efficiency, or make the organisational changes required to embed it across forecasting, treasury, compliance and risk. The evidence suggests early movers are broadening use cases and capturing operational benefits, but widespread value will depend on disciplined data work, credible change programmes and the recruitment or development of cross-disciplinary talent.