According to a study conducted by EXL, approximately 89 percent of insurance and banking companies in the United Kingdom have implemented artificial intelligence (AI) solutions within the last year. However, challenges related to data optimization could impede the effectiveness of these implementations.
The research involved surveys with senior executives from leading insurers and lenders in the UK regarding their AI strategies. The findings revealed that 44 percent of these organizations have integrated AI into eight or more business functions, particularly in areas such as marketing, business development, and regulatory compliance.
A significant number of financial services executives, nearly 90 percent, disclosed investing a minimum of £7.9 million in AI initiatives during the previous fiscal year. A substantial portion, more than a third, allocated £39 million or more towards AI, underscoring the industry’s substantial financial commitment to AI adoption.
Despite the advancements made in integrating AI, the study indicated a potential oversight in the prioritization of data operations within organizations. Almost half (47%) of the respondents acknowledged that their organizations exhibit only a minimal level of data-driven decision-making. This raises concerns about the efficacy of AI implementations without a robust data infrastructure.
Kshitij Jain, EMEA Practice Head at EXL, highlighted the dilemma faced by industry leaders, emphasizing the risk associated with hasty AI investments that may sideline the crucial aspect of establishing a data-driven operational framework. Neglecting this foundation could lead to significant financial repercussions.
The study also identified a segment labeled as “Strivers,” constituting 45 percent of the participants, who have adopted a more focused approach by implementing AI across approximately four functions. This targeted strategy has enabled them to effectively utilize AI for cost-saving measures, surpassing early AI adopters by a notable margin.
Furthermore, more than half of the respondents indicated an increased investment in AI, particularly driven by advancements in generative AI technologies. However, a considerable 70 percent expressed substantial apprehensions regarding the risks associated with generative AI, including concerns about potential harm to brand reputation and the accuracy of data outputs.
Jain emphasized the importance of a methodical and strategic implementation process for AI initiatives, underscoring the significance of establishing a solid data architecture, conducting thorough solution testing, and providing adequate training for employees. He concluded that for successful enterprise-wide AI adoption, board members must fully embrace the capabilities of AI and ensure that investments are utilized efficiently.
