AI adoption in risk has reached a pivotal stage. According to our new Jaywing research, 65% of UK financial institutions have moved beyond exploration to active AI implementation. However, 36% still have no AI models in risk, leaving them increasingly vulnerable
as the competitive gap widens.
Early adopters are already reaping the rewards, reporting enhanced credit risk predictions, improved fraud detection, and operational efficiency gains. For those yet to act, the message is clear: the cost of waiting now outweighs the cost of action.
Here are some of the other key findings…
Barriers to AI adoption
Despite its potential, AI adoption is not without its challenges. Our research highlights governance, explainability, and validation frameworks as the most significant barriers, cited by 40% of respondents. These frameworks are critical to ensuring that
AI models are scalable, effective, and compliant with regulatory requirements. Without them, organisations risk losing trust in their systems and stalling their progress.
Other challenges include:
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Staff capability gaps (27%): A lack of in-house expertise is a major hurdle, requiring targeted hiring and upskilling to build internal capacity.
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Resource constraints (21%): Competing priorities and limited budgets often delay or deprioritise AI initiatives.
For organisations able to overcome these challenges, AI offers significant opportunities to transform risk management and drive measurable results.
Where AI is delivering results
Our research identifies credit risk and fraud detection as the most common applications for AI, each accounting for 39% of current use cases. These areas deliver immediate and measurable impact, enabling firms to improve decision-making and mitigate risk
more effectively.
However, an untapped opportunity lies in operational efficiency. While 46% of respondents see this as AI’s greatest potential benefit, only 11% have prioritised it. Unlocking this area could streamline workflows, automate processes, and reduce manual interventions,
accelerating decision-making across the organisation.
Firms achieving the greatest success with AI adoption are building scalable governance frameworks, addressing capability gaps, and integrating AI across multiple business areas. These efforts enable organisations to not only to optimise risk management but
also to capitalise on AI’s broader potential.
Key priorities for risk leaders in 2025
Our research outlines three critical priorities for firms looking to accelerate their AI adoption:
#1. Build robust governance frameworks
Governance is not a barrier but an enabler. Frameworks for explainability, validation, and compliance ensure AI models are trusted, scalable, and aligned with regulations. However, only 13% of organisations currently report having robust governance frameworks
in place—a gap that presents a significant opportunity for improvement.
#2. Invest in skills and resources
AI success depends as much on people as on technology. 27% of firms cite capability gaps as a primary barrier to adoption, highlighting the need to invest in both technical expertise and cross-functional collaboration.
#3. Scale AI beyond pilots
Moving from pilots to enterprise-level AI integration is critical. While 65% of firms are actively implementing AI, only 7% have achieved an advanced AI status. Scaling AI amplifies its impact, creating opportunities for broader efficiencies and improved decision-making.
Why AI adoption is urgent
With 74% of organisations already at proof-of-concept stage or beyond, the window for competitive advantage is rapidly closing. Early adopters are setting new benchmarks in risk management, leaving those who delay at risk of being left behind.
As our report concludes: “The question is no longer whether to adopt AI, but how quickly firms can build the capabilities needed to thrive.”
