Finance teams have largely moved beyond the experimentation phase of AI adoption and are now focused on delivering tangible results.
In fact, our recent research shows 99% of UK finance leaders view AI as essential, with 85% of UK finance teams already integrating it. Investment in the technology is also set to continue rising through 2026 as organizations double down on AI capabilities.
Finance Controller at Tipalti.
The adoption AI tools accelerates, expectations are rising in parallel. This has prompted finance leaders to take a more considered approach to AI risk, with 78% acknowledging they have some concern about the risks associated with AI.
Rather than slowing progress, this growing focus on risk and trust reflects a maturing market – one that recognizes sustainable value comes from deploying AI with the right controls, governance and oversight in place.
As AI reshapes customer expectations, finding the right balance is even more crucial.
Finance teams are increasingly under pressure to deliver faster, more personalized and higher-quality service. AI systems only built on trust, transparency, and human oversight can scale these improvements without increasing risk – whatever challenge lays ahead.
The role of AI is evolving in finance
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 optimized investments.
Trust and visibility unlock AI’s value in finance
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.
Real value requires resilience through global politics
Once trust and visibility are established, AI must also be resilient to global political and regulatory change.
Organizations 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 organizations. 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 organization wide, highlighting that most organizations are still in an evaluation and optimization phase.
Distinguishing between different AI use cases is therefore critical. Each application brings its own considerations around value, risk and control, and organizations that take time to define these parameters early are better positioned to scale responsibly and unlock measurable, 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 molded 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.
What’s expected for AI to deliver on results
Both generative AI and agentic AI have the potential to boost efficiency and improve insights for finance functions, but as AI is more widely integrated, humans need to trust, oversee, and guide its outputs to ensure it delivers real value.
Teams must balance AI autonomy with human intervention, and ensure systems remain resilient amid regulatory shifts and geopolitical tensions.
Ultimately, AI will only display its full potential when finance teams feel they can confidently rely on it to support faster decisions, drive insights, and build long-term resilience, while retaining the ability to intervene when necessary.
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