From AI to Skills: Transforming Future Talent in Financial Services

Introduction

This report is based on:

• 12 in-depth interviews with senior learning and talent leaders
• A quantitative survey of 51 financial services L&D professionals
• Insights from Fitch Learning's L&D industry events in London and Toronto: Evolution of Learning- Exploring the Future of Skills, AI and Workforce Development
  Across global and regional banks, investment banks, asset managers, and insurers.

Executive Summary

The Learning Function at a Crossroads

Across financial services, L&D is shifting from a support function to a strategic driver of competitiveness. The question is no longer whether to adopt AI and skills-first approaches, but how to prioritize them.

Through our research, one paradox underpins this shift the more AI automates, the more human capabilities matter. 57% identify AI as their single largest capability gap - yet the #1 human skill to strengthen is critical thinking and decision-making. Both are essential. Neither alone is sufficient.

5 Implications for L&D Leaders

• AI impacts roles in different ways-automation, augmentation, and creation-requiring differentiated approaches.
• Knowledge delivery is no longer the primary value; L&D’s role is to design environments where judgment develops through practice
• Managers are critical to adoption; without them, frameworks and tools fail to translate into impact.
• Skills-first approaches are effective but selective, particularly for disrupted roles, talent marketplaces, and early careers-not yet as an enterprise-wide model.
• L&D functions must transform themselves: 88% cite AI and learning technology expertise as critical, yet confidence in building these capabilities remains limited.

5 Actions to Take Now

1. Map which roles are being augmented, automated, or created - and differentiate your investment accordingly.
2. Start small with targeted AI use cases, addressing specific workflow friction rather than pursuing enterprise-wide transformation.
3. Activate managers as enablers, including models such as embedded AI champions.
4. Shift at least one program from knowledge to practice, using simulation, scenario-based learning, and applied experience
5. Build AI fluency within L&D teams first, establishing credibility to guide broader organizational transformation.