How AI-powered learning can upskill teams in the investment industry
The rapid emergence of artificial intelligence is creating new opportunities for learning and development teams to drive relevance, personalization and retention.
Artificial intelligence (AI) has the potential to transform learning and development (L&D) in the financial industry from static programs to dynamic, personalized, and data-driven experiences.
AI-powered platforms can analyze employee behavior, assess skill levels and recommend targeted training, while AI-driven analytics can provide L&D teams with granular insights into training effectiveness.
These features are quickly becoming essential components of all learning programs as L&D teams rethink their educational priorities for the AI era. Demis Hassabis, Chief Executive Officer of Google’s DeepMind, recently argued that young people need to focus on meta-skills such as “learning how to learn”.
According to Ed Monk, Chief Executive Officer and Co-Founder of The Learning and Performance Institute, one of the key challenges for chief learning officers is to move from content-heavy to performance-centered learning.
“They must also move from one-size-fits-all to adaptive personalized pathways, as well as changing the focus from knowledge acquisition to learnership, which is the meta-skill of the moment.”
These needs are also being driven by changes in the way that firms are approaching their thinking around skills today.
“Firms are changing the way they recruit, and instead of cherry-picking someone with a precise knowledge base, they are bringing people on with certain transferrable skills and then training them in the specific expertise they will need,” said Whitney Coggeshall, Director of Product Management at CFA Institute.
The problem is that many point-in-time training programs still follow the model of expecting someone to learn a skill in a discrete period.
“I can go to Mexico for the week but I’m not going to be able to speak fluent Spanish at the end of that week,” added Coggeshall. “That’s not how skill development works. You have to find a way to embed it into workflows over time.”
Why relevance drives retention
Personalization and relevance are seen as critical to successfully establishing a longer-term learning culture, helping to foster the mindset employees need to take ownership of their learning as well as seeing it as a continuous process.
“The challenge today is fundamentally one of culture, but it starts with mindset,” said Monk. “Many organizations still see learning as an event, a library or a platform, with lots of resources. They don’t see it as an environment.”
A famous quote within the L&D community comes from Charles Jennings, co-founder of the 70:20:10 Institute, who said: “We are living in a world where access trumps knowledge every time.” According to this thinking, those that know where to find dynamic information when they need it will be more successful than those relying only on what they already know.
Today’s large-language models (LLMs) can provide users with richer information than ever, and can do so in an immediate and continuous way, accompanying employees through every minute of their working lives. A recent post on CFA Institute Enterprising Investor described the ways in which AI is able to augment allocators’ workflows. Not only is this useful in itself, but it also helps to foster the curiosity that is essential for engagement with the idea of continuous learning.
Continuous learning is particularly relevant to the investment industry, given the rapid and constant evolution of trends in markets and regulation. But it is also seen as a critical way to improve information retention. A familiar concept in learning is the Ebbinghaus ‘forgetting curve’, which describes how information is lost at an exponential rate in the absence of a reinforcing effort to retain it – with about 75% of information lost within the first two days.
“You have to keep nudging what you have learned, and AI can help you to do that,” said Monk. “If you had been learning in a classroom, you could share the content that you had been using with your AI platform and then ask how it relates to your role and what you should be doing.”
Rethinking the format
Firms today are increasingly under pressure to rethink the purpose and delivery format of learning into something that is authentic and relevant both to the company and to the employee.
According to Coggeshall, AI can transform training by being seamlessly integrated into an employee’s working routine to the extent that it does not feel like training, as well as being more efficient by being more relevant.
“If you are doing training that is tied exactly to the work that you do day-to-day, then the transference time for you to learn that skill and then generalize it to your role will be decreased,” she said.
AI-powered learning can also help to give the employee more agency, making proactive suggestions for relevant learning but allowing the employee to instruct the AI to add it to a learning pipeline for later study.
“The granularity and speed of data analysis that is possible with AI means that learning can be highly personalized,” said Ina Gantcheva, Partner in the Human Capital practice at Deloitte. “It can collect data on someone’s skills and present them with possible career paths and a learning curriculum.”
Broader benefits
The contextual analysis and human-like output that LLMs and generative AI can achieve also allow for sophisticated use cases like virtual coaching assistants.
“For example, you can use transcripts of calls to make automated and personalized recommendations, such as noting where people have struggled in an interaction and which piece of upskilling might be useful to tackle it,” said Frances Symes, Senior Manager in the Human Capital practice at Deloitte.
There are broader benefits too from an increasingly personalized and relevant environment, because the knowledge gathered can be fed back into companies’ talent and skills strategy.
“We’re seeing firms make progress on tailoring learning – and this feeds back into the importance of skills assessments,” said Claire Tunley, CEO of the UK’s Financial Services Skills Commission. “If you have an idea of where people need to go and what their capabilities are and where the gaps are, then you can be more tailored.
“It makes the whole skills mapping and planning and forecasting piece all the more essential.”
Ultimately, success comes down to a commitment to learning from people at all levels of an organization. Technology alone will not be sufficient.
“If you don’t have leaders demonstrating the concept of learnership, the culture will remain static,” said Monk. “If you don’t have a learning-centered culture, even the best AI-powered tools won’t drive meaningful change.”
As artificial intelligence transforms how investment professionals analyze markets, build portfolios, and manage risk, CFA Institute is delivering cutting-edge research to support the profession.
Explore AI in Asset Management: Tools, Applications, and Frontiers from CFA Institute Research Foundation and CFA Institute Policy Center.
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