AI is no longer in the experimentation phase for finance teams, instead, the focus is now on delivering tangible results. The numbers speak for themselves. Our recent research highlights over three-quarters (85%) of UK finance leaders have already begun implementing the technology and nearly all UK finance leaders (99%) believe AI is essential to their operations.
Although AI is recognised for its efficiency and productivity in automating routine tasks and streamlining processes, the impact witnessed by finance teams is dependent on how the solutions are implemented. In fact, around 8 in 10 companies report no material impact on earnings following AI integration. Not due to the technology deployed, rather the difficulties aligning integration with complex regulations and rapidly evolving economic conditions.
With adoption success stories not always straightforward, it’s no wonder that 78% of finance leaders have worries about the potential risks of AI. However, this growing focus on risk and trust does not slow progress, but demonstrates a market evolving toward responsible, well-governed AI adoption.
Striking the correct balance for AI’s place in business has never been more important, especially as AI is transforming customer expectations. As requirements rise for speed, personalisation, and quality, finance teams are under increasing pressure to perform. AI systems built on trust, transparency, and effective human oversight are essential to achieving these gains while managing risk in an evolving landscape.
Redefining finance with AI
AI’s role in finance is evolving from completing simple, repetitive tasks into a strategic partner which guides decision-making using data-driven insights and analytics. Only when embedded into core processes, AI enables finance leaders to respond to real-time changes and market shifts with greater agility. It can also reassure finance leaders that long-term planning and resilience can be built through scenario modelling, enhanced forecasting, supply chain analytics, and optimised investments.
Unleashing AI’s value in finance through trust and visibility
Gaining value from AI is a balancing act. Finance teams that onboard AI too quickly risk introducing new vulnerabilities, while those that delay adoption altogether may find hesitation becomes a liability. According to OnPhase, manual processes, such as lagging approvals, unpredictable payments, and time-consuming repetitive tasks continue to consume multiple hours each day. However, AI is perfectly positioned to eliminate such inefficient ways of working.
At the same time, inadequately regulated AI introduces risks. A lack of oversight can leave systems exposed to malware and data integrity issues, undermining trust and creating challenges for auditors. This concern is highlighted in recent research, finding that over three quarters (78%) of UK finance leaders express some level of concern about AI risk, including 11% who are extremely concerned, 30% who are somewhat concerned, and 37% who are a little concerned.
The key to unlocking AI’s true value lies in visibility and control in finding the right balance between autonomy and oversight. Teams can thrive by building trust into every AI-related decision without compromising on the essential human intervention. While AI is widely used across UK finance teams for forecasting (60%), financial analysis (58%), reporting and insights (59%), and fraud detection (57%), over half of respondents say they need to review AI actions, and 48% say retaining decision-making control is essential.
Achieving long-term value requires political and regulatory resilience
Once trust and visibility are established, AI must also be resilient to global political and regulatory change. Organisations need confidence that AI systems will adapt to regulatory shifts and geopolitical tensions, without compromising speed, accuracy, and control. For finance teams to rely on AI long– term and for companies to integrate it more widely, it will need to guarantee tangible, measurable value.
This is particularly important as AI maturity varies widely across organisations. When it comes to AI agents, many businesses are taking a deliberate phased approach – initially deploying them within one or two functions to build confidence. At present, with 10% report scaling AI agents organisation wide, highlighting that most organisations are still in an evaluation and optimisation phase.
Distinguishing between different AI use cases is therefore critical. Each application brings its own considerations around value, risk and control, and organisations that take time to define these parameters early are better positioned to scale responsibly and unlock measureable, long-term impact.
Global policy shifts, such as tariffs and other policy changes, directly affect profit margins, cost structures, operational adjustments, and liquidity pressures. AI systems training on static assumptions may require retraining when conditions change, especially with many models having a knowledge cutoff point. Policy changes, like Tariffs, can also introduce new financial reporting risks, increasing scrutiny around revenue recognition.
In high-pressure environments moulded by political and economic uncertainty, AI’s effectiveness hinges on how well it has been implemented and how efficiently it has been onboarded. When finance teams are prepared to adapt processes in real time, AI can support more informed decision making; without this readiness, it risks becoming a constraint rather than an advantage.
Enabling AI to drive real impact
To ensure AI delivers real value to businesses, employees must be able to trust, guide, and oversee its outputs, particularly, if AI is to enhance efficiency and generate deeper insights in finance functions. Ensuring effective AI requires striking a balance between AI autonomy and human oversight, while keeping systems robust against regulatory shifts and geopolitical uncertainty.
AI’s full capabilities will only be met when finance teams can confidently rely on it to accelerate decision making, uncover deeper insights, and enhance long-term resilience, all without losing the need for human intervention.
