How investment managers are addressing the AI skills gap
Accelerating adoption of AI is challenging financial institutions to build proficiency in both technical and practical skills at every level.
The rapid growth of artificial intelligence (AI) in finance is transforming skills needs in the investment industry. Professionals at all levels of an organization need to be familiar with the benefits that AI can bring – and many must be proficient at using it every day.
New graduates will likely be more familiar with using AI than more experienced colleagues who have already developed careers without the technology, but the need for AI skills today goes well beyond entry-level roles.
The result is a skills gap that organizations like the UK’s Financial Services Skills Commission (FSSC) say could prevent the sector from unlocking the growth opportunities of AI.
Mid-career and senior investment professionals face the challenge of adapting to AI workflows that may upend long-held ways of working and could threaten job security.
In this blog, we explore how investment firms are assessing the AI needs of their staff, and some of the hurdles they face.
AI is transforming skills needs
Employers increasingly seek graduates who can code, analyze, and apply artificial intelligence (AI) models in finance. Institutional insights gathered by CFA Institute’s business development team show that expertise across AI, machine learning, data science and coding are key skills requirements cited by employers.
This need is already reflected in the CFA® Program with the integration of practical skills modules on Python programming fundamentals. And professionals are also supported by the cutting-edge research of the CFA Institute Research Foundation, such as recent deep analysis of how AI is redefining the investment process.
The need for finance professionals to be not just familiar with but proficient in these fields could reshape the profile of those working in the industry.
In particular, a focus on AI skills will increase the opportunities available for STEM graduates looking to build a career in finance by adding financial knowledge to their existing technical expertise.
At the same time, it gives a compelling reason for existing financial professionals to upskill in AI and tech.
Multiple use cases
In today’s investment industry, AI has already gone beyond being a labor-saving productivity tool, and is moving into portfolio construction, risk analysis and decision-making. That challenges all investment professionals to understand how best to deploy the technology.
And even when firms are not yet using agentic AI in investment activities, discussions with US employers reveal that they are using it in other operational areas, such as to build research tools or agents that can help with investor relations and commentary.
Employers tell us they are seeking AI engineers that have broad experience with different types of AI models and who show an enthusiasm for the whole range of tools available, not just one preferred model.
Ways of demonstrating this include familiarity with and involvement in resources such as Hugging Face, an open-source machine learning and AI community platform that acts as a hub for models and tools that can be used for building AI applications.
CFA Institute Research and Policy Center has developed a similar initiative for the investment community called RPC Labs, which is designed to foster collaboration between finance professionals, data scientists, and developers.
Explore AI in Asset Management: Tools, Applications, and Frontiers from CFA Institute Research Foundation and CFA Institute Policy Center.
Watch out for new risks
Before the adoption of large language models (LLMs), roles that involved interaction with AI systems were typically filled by specialized data scientists. But with today’s more accessible AI tools, a new risk has emerged.
Our discussions with employers reveal concerns that investment professionals in the future may no longer have the data science skills to understand the limitations and identify the potential for bias in the outputs of AI tools.
Also, while the widespread use of AI in people’s personal lives has helped to create familiarity, this can create problems in a professional environment. Our insights indicate that familiarity may lead employees to overestimate their proficiency, potentially requesting advanced training without realizing they need more foundational instruction first.
But don’t be too cautious either
Sometimes the risk is too much caution. Many employers report that staff can be reluctant to upload proprietary data into AI tools even when they are built for this purpose.
This risk-averse attitude – often a legacy of the caution people attach to financial data in their own lives – will hamper success in a professional environment where AI tools are being used to provide greater insights, risk management, or solutions for customers.
This article is part of our series featuring insights from investment management firms on transforming L&D strategies. Discover how industry leaders are tackling talent development challenges and building capabilities for the future.
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