CFA Society Sri Lanka discusses new roadmap for AI and big data adoption for financial institutions

CFA Society Sri Lanka President Dinesh Warusavitharana CFA

CFA Institute Senior Director, Industry Research Larry Cao CFA

CFA Society Sri Lanka Director/Acuity Knowledge Partners Director Specialised Solutions Chamara Gunetileke CFA

CFA Society Sri Lanka will lead a virtual discussion under the theme ‘T-Shaped Teams: Organising to Adopt AI and Big Data at Financial Institutions’ Wednesday, 13 October at 3:30 p.m. onwards SL time. 

CFA Institute Senior Director, Industry Research Larry Cao, CFA will be the main speaker who will present key findings of the CFA Institute research report of the above topic whilst CFA Society Sri Lanka Director and Acuity Knowledge Partners Director Specialised Solutions Chamara Gunetileke CFA, will moderate the session. 

CFA Society Sri Lanka President Dinesh Warusavitharana CFA said that the report mainly identifies a new organisational approach for enabling financial institutions to develop and successfully execute artificial intelligence and big data strategies. Further, it highlights the leadership vision and organisational change will be crucial for successful Artificial Intelligence and big data adoption.

The report draws on extensive CFA Institute field research to highlight how investment organisations 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 organisation-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 organisations 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 organisations: organisational 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 organisational processes is necessary;

Successful organisations 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.

Senior Director, Industry Research Larry Cao, CFA comments: “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. We delved deep into the investment industry and found that a cohesive organisational 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 organisations 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.”