The limiting factor for AI in finance is rarely the technology. It is people who can use it well, question it and supervise it.
Three layers of literacy
- Everyone: what AI is, where it fails, what data may go where, how to check output, when to escalate.
- Power users: prompt and workflow design, building with approved tools, measuring results.
- Specialists: finance data engineers, AI product owners, controllers who design and test AI-enabled controls.
A regulatory baseline in Europe
The EU AI Act's Article 4 on AI literacy has applied since 2 February 2025. It addresses providers and deployers of AI systems and the AI literacy of their staff and others operating AI on their behalf, taking into account their knowledge, experience, education and the context of use. The Act defines AI literacy as the skills, knowledge and understanding to make an informed deployment of AI systems and to be aware of their opportunities and risks. The article has since been amended, so check the current text with counsel; the business case for literacy stands either way.
Changing roles
- Preparers become reviewers and exception handlers.
- Analysts spend less time assembling data and more on interpretation and partnering.
- Controllers add AI control design and testing.
- New roles: finance AI product owner, finance data steward, automation lead.
What works
- Short, mandatory basics for all; deeper paths for volunteers.
- Leaders using AI visibly in their own work.
- Time and recognition for building and sharing workflows.
- Clear messages about how roles will change and how people will be supported.