T-Shaped Teams: A New Roadmap for AI and Big Data Adoption

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CFA Institute, the global association of investment professionals, released new industry research that identifies a new organizational approach for enabling financial institutions to develop and successfully execute artificial intelligence and big data strategies.

The report “T-Shaped Teams, Organizing to Adopt AI and Big Data at Investment Firms draws on extensive CFA Institute field research to highlight how investment organizations can put themselves on a path to successful AI and big data adoption. The research highlights successful experiences through the T-shaped team concept, a transformational organization-wide approach that enables the technology (data science) and investment functions to collaborate to improve investment processes and outcomes.

Critically, the report identifies the role of the innovation function, which sits at the intersection of the technology and investment functions to provide the communication bridge and evaluate proposed projects for their ability to deliver meaningful results to the investment teams. The report offers a how-to menu for investment organizations seeking to build their own T-shaped teams and includes case studies from three asset managers: UOB Asset Management, NN Investment Partners and Man Group.

Key findings:

  • Leadership vision is the single-most critical factor for successful AI and big data adoption in investment organizations: organizational structure and culture must underpin collaboration, transparency and accountability;
  • AI adoption in financial institutions is far more complicated than any one individual can handle: a comprehensive strategy to introduce AI and big data into organizational processes is necessary;
  • Successful organizations have built their AI and big data capabilities by evolving firm structures from individuals with T-shaped skills toward cross-functional T-shaped teams that enable better collaboration between the investment and technology functions;
  • AI and data science are sufficiently distinct from investing that it takes an additional function — the innovation function — to join them and form the cohesive AI-age investment team;
  • The role of the different functions in a T-shaped team evolves through the early, intermediate and advanced stages of AI and big data adoption, requiring different focuses in execution;
  • T-shaped teams are not a one-size-fits-all process: firms will develop their own T-shaped team structures that best meet their needs and capabilities; this is particularly so for firms approaching intermediate and advanced stages of AI adoption;
  • The investment industry is behind the curve in appreciating the value of the innovation function, and particularly the innovation leader.

“Many believe that insufficient AI talent within finance is the bottleneck for AI adoption in the industry, but this is not what we have found,” commented Larry Cao, CFA, Senior Director, Industry Research. “We delved deep into the investment industry and found that a cohesive organizational framework is often the missing ingredient that can put firms at risk of being left behind. Collaboration between investment and data science functions is mission critical, yet investors and programmers often have little in common in terms of skills and culture and need much more coordination. Our report identifies the uncommon but transforming nature of the T-shaped team in AI and big data adoption at investment firms, and — critically — the need for the innovation function. The implication is that investment firms cannot wait. Those organizations that invest in building their T-shaped teams now will have a far better chance of success on the AI and big data adoption journey.”

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