Recent research conducted by EXL has revealed that approximately 89% of insurance and banking firms in the UK have implemented AI solutions in the past year. However, challenges related to data optimization could potentially limit the effectiveness of these AI initiatives.
The study involved surveying executives from leading UK insurers and lenders regarding their AI strategies. The findings indicated that 44% of these organizations have integrated AI across eight or more business functions, with a particular focus on areas such as marketing, business development, and regulatory compliance.
Financial services leaders in the UK have shown a strong commitment to AI adoption, with nearly 90% reporting investments of over £7.9 million in AI technology during the previous fiscal year. A significant portion of these organizations (over a third) invested £39 million or more, underscoring the industry’s willingness to allocate substantial capital towards AI implementation.
Despite the progress made in integrating AI into operations, the research suggests that organizations may be neglecting the importance of prioritizing data-driven practices. Nearly half (47%) of respondents admitted that their organizations are only “minimally data-driven,” raising concerns about the potential limitations of AI implementation without a robust data foundation.
Kshitij Jain, EMEA Practice Head at EXL, emphasized the need for a balanced and strategic approach to AI implementation. He cautioned against hasty investments driven by external pressures, highlighting the importance of ensuring that operations are truly data-driven to avoid costly pitfalls.
The study also identified a segment of respondents referred to as “Strivers,” comprising 45% of participants, who are implementing AI in a more targeted manner across approximately four functions. This focused approach has enabled them to effectively leverage AI for cost-saving initiatives, outperforming early AI adopters by a significant margin.
Furthermore, over half of the survey respondents indicated increased investments in AI, citing advancements in generative AI as a key driver. However, concerns regarding potential risks associated with generative AI, such as brand damage and inaccurate data outcomes, were expressed by 70% of participants.
Jain concluded by emphasizing the importance of a measured and strategic approach to AI deployment, including optimizing data architecture, testing solutions, and providing adequate employee training. He underscored the critical role of board buy-in and effective utilization of AI investments for successful enterprise adoption.
You can access the full research report here (registration required).
(Photo by Alev Takil on Unsplash)
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