Thursday, June 27, 2024
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Evident’s report underscores the growing importance of AI research capabilities as a key determinant of future AI success. Banks are leveraging published research to demonstrate their AI expertise to potential talent, partners, and shareholders. Over the past year, the largest banks globally have produced more than 330 AI-specific research papers, representing a sixfold increase in industry output over five years.

Annabel Ayles, co-founder and co-CEO of Evident, pointed out the strong correlation between banks that invested in AI research five years ago and their current leadership positions. She noted that banks not making similar investments now face constraints in their AI capabilities.

HSBC, BBVA, and Goldman Sachs are identified as significant contenders to watch in 2024, given their rapid expansion of AI research teams in 2023. Ayles emphasized that although these banks might not immediately challenge the current AI leaders, their increased efforts could keep them competitive in the long run.

The top 10 banks for AI research have accounted for over 80% of the industry’s total output between 2018 and 2023. Notably, US banks dominate the rankings, producing nearly 70% of the output each year. Only two European banks, BBVA and Intesa Sanpaolo, feature in the top 10, with no Asian banks making the list.

A key strategy shared by these leading banks is the centralization of AI capabilities within dedicated research labs or centers of excellence. This approach is crucial for attracting and retaining top talent, according to Alexandra Mousavizadeh, co-founder and CEO of Evident. She emphasized that producing cutting-edge AI research is essential for banks to position themselves as AI innovators and leaders. Without robust research output, banks will struggle to recruit the best talent and convince stakeholders of their commitment to AI.

Mousavizadeh also noted that the advent of generative AI is prompting many banks to reassess the organizational placement of their research teams. Ensuring both central and federated approaches to AI use-case identification is critical for scaling AI across the organization quickly.

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