Anton Roe, CEO, MHR
AI is no longer an experimental tool on the horizon. For finance teams, it has become an essential driver of efficiency, accuracy, and insight. Under growing pressure to deliver cost savings and operational improvements, finance leaders have stepped up to embrace AI for everything from process automation to predictive forecasting. In fact, finance now ranks just behind IT and telecoms for adoption, placing the function at the heart of organisational change.
The real opportunity now lies in how these early successes can be scaled responsibly, creating a blueprint for AI adoption across the wider business, and ensuring AI enhances human expertise rather than replaces it.
AI in action
Forecasting and budgeting have become the most common applications of AI in finance. MHR’s research report, Empowering People with AI, reveals 62% of organisations are already using predictive analytics to guide decisions. This isn’t simply about crunching numbers faster. The research looks at finance, payroll and HR teams and has found that high-performing finance teams report that advanced analytics now underpin 85% of their strategic decisions, allowing leaders to act with greater confidence against economic headwinds.
Beyond forecasting, automation is streamlining day-to-day processes – anomaly detection, regulatory compliance, and creating efficiencies across all financial processes. Finance teams are recognising tangible benefits from AI use. Around 72% of organisations say automating core finance activities has improved efficiency, and 68% of finance leaders say aligning technology with business goals is a top priority. From anomaly detection to compliance monitoring, AI is starting to ease the burden of manual processes that have long slowed down finance departments, however 35% of organisations admit human error in data entry is still a challenge AI could help reduce.
From early wins to long-term value
Early adoption alone does not guarantee long-term impact. Many organisations, despite this momentum, admit they are still only scratching the surface of AI’s potential. Concerns about data security (41%) and ethics (35%) loom large, while more than a third of organisations say they lack the internal skills or expertise to take AI further. A further 13% confess they simply don’t know where to start, and 29% are struggling with legacy systems not built for AI integration.
To sustain progress, finance leaders must focus on upskilling teams. Success will depend on embedding AI within clear governance structures and focusing on people as much as the technology. Refining change management and aligning AI projects to business priorities all help to ensure adoption is sustainable. The cultural shift should emphasise augmentation, not replacement, so professionals feel empowered to work alongside AI rather than compete with it.
Finance as a blueprint for AI adoption
Finance is uniquely positioned to show other functions what responsible, effective AI adoption looks like. The function’s work with balancing complex datasets of potentially sensitive information with innovation and regulation means they have a detailed understanding the nuances of where AI can have the most impact. By approaching AI through the lens of empathetic innovation, starting with real human pain points like reducing payroll errors or easing reporting bottlenecks, finance teams can make sure their AI roll-out is technically sound and meaningful to employees and customers, and aligned with strategic business objectives.
As finance continues to champion AI, collaboration with HR, payroll and cybersecurity will be critical. This isn’t about siloed success stories, but about building an organisational framework where AI supports innovation, real-time insight, and operational adaptability across every function.
Leading the next wave
The opportunity is clear: finance teams can move beyond isolated successes and show the enterprise what responsible, effective AI adoption looks like. Strong governance, targeted upskilling, and a strategic vision will turn early wins into long-term advantage. When finance gets this right, the rest of the business follows, and AI becomes a driver of lasting value, not just a series of pilot projects.