Why the finance sector's tech adoption isn't down to talent

Report suggests AI and big data success stems from engaged leadership and the creation of T-shaped teams

Why the finance sector's tech adoption isn't down to talent

Investment organizations may hesitate to make the leap into big data or AI, despite the technologies’ potential to improve outcomes and enhance productivity throughout the organization. One often-cited stumbling block: a lack of tech talent in the industry.

But according to a new report from the CFA Institute, it’s not a scarcity of technology talent that holds back tech adoption. What’s needed, rather, is a more big-picture approach.

“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.”

The report said that while it’s important to have individuals with a T-shaped skill set – that is, having one area of deep specialization complemented by a working familiarity with other functions – the responsibility of developing AI and big data capabilities is too challenging to assign to just one person. Rather, organizations that successfully build their AI and big data capabilities have evolved their structures toward having cross-functional T-shaped teams.

The T-shaped team must include three groups: a group focused on the investment function, one focused on the technology function, and another focused on innovation. The innovation function – which the CFA Institute said the broad industry “is behind the curve in appreciating” – should act as a communication bridge between the technology and investment teams, assessing proposed projects based on their potential to meaningfully impact the way the firm’s investment teams.

And rather than having a fixed distribution of roles, the report said the members of the T-shaped team will take on different levels of importance and executional priority throughout the different phases of technology adoption.

The early stage of adoption, the report said, should focus on finding a project to showcase the value and power that AI and data science tools can bring to the investment function. That puts the spotlight on those assigned to the technology function, who must identify projects that they’re highly confident they can deliver effectively in a reasonable timeframe.

“If the technology function successfully pulls off the first AI and/or big data application to the satisfaction of the investment function, then the team can expect more input from the investment function down the road,” the report said.

At the intermediate stage, the focus will shift to have more balanced input from both the investment and technology functions. Here, the innovation function leader will take centre stage, coordinating and leveraging a small number of initial wins in select areas to gain credibility across the broader organization. Once more people from the investment function are more open to asking the technology function for support in security selection, portfolio construction, and other functions, then the T-shaped team structure will be better able to realize its potential.

Finally, while the CFA Institute researchers believe organizations’ long-term survival hinges on their ability to get to the advanced stage, only a few will be able to reach that summit. At this point, the T-shaped team will be performing at its full potential as investment and technology functions understand each other much better, share their opinions openly, and communicate more frequently as needed. As the successful implementation of AI and big data makes the investment function will become more effective, the institute estimates investment professionals will care relatively more of the burden of success during this stage.

“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,” Cao said. “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.”