Generative AI is producing transformative results for businesses, unlocking productivity and recasting job roles to be more strategic and rewarding. Many enterprises worldwide, however, continue to lack effective approaches to developing AI literacy. Faced with high education costs, misaligned incentives and a lack of proven learning tools, they’re still working out how to unlock AI’s benefits while simultaneously ensuring its responsible use.
AI Literacy in Regulator Crosshairs
The EU AI Act is a pacesetter for global AI regulation. As of February 2nd, 2025, all businesses developing, integrating, or deploying AI systems in the EU are obliged to take measures that ensure staff have a sufficient level of AI literacy. The Act defines AI literacy as the skills, knowledge and understanding required to facilitate the informed deployment of AI systems.
At time of writing, both the obligations themselves and how they might be enforced are somewhat loose. But a conversation has been sparked. Enterprises based in the EU, or global organisations with EU-based staff, now know that AI literacy will be factored into the decision-making behind potential penalty setting for any breach of the EU AI Act. Developing a strong base of AI literacy in such organisations is the sensible response. More than that, AI literacy is an essential foundation of responsible AI practice. Choosing an approach with appropriate nuances and effective balances is critical to the success of such efforts.
Building AI Literacy on the Right Foundations
As enterprises embark on efforts to develop AI literacy, they should have a “North Star” to build towards. I’ve seen firsthand how effective a layered approach can be. This entails providing access to educational and training programmes to the entire workforce but supplementing this with tailored training linked to key, practical use cases of AI internally.
Enterprises should keep in mind that AI literacy starts with foundational data literacy. If they’re not already, enterprises need to appreciate the benefits that soft skills bring in scaling data literacy across the workforce. Creativity allows employees to identify more innovative ways to use data. Critical thinking is essential in evaluating AI answers to overcome teething issues, including AI hallucinations and misinformation. Collaboration skills enable team members to work with AI with empathy. I could go on. The bottom line is that technical skills aren’t a prerequisite to working with data and AI today and that’s an important mindset shift enterprises need to enact.
To improve foundational data skills, employers must address the varying needs of their workforce and adapt training to technical capabilities. At the root of all, however, organisations should offer hands-on training opportunities, provide, on-demand resources for continuous learning and give access to low and no-code applications to work with data. Drag-and-drop interfaces paired with AI-guided assistance are particularly effective in allowing any employee to solve data problems and automate repetitive work.
Ai’s Success Hangs in the Balance
By defining clear AI use cases and establishing the foundations for AI literacy, employees are more likely to use AI responsibly, avoiding the negative scenarios that the EU AI Act intends to prevent.
However, for enterprises rolling out AI, prioritising AI literacy is much more than a compliance exercise. It should be a wake-up call. AI literacy democratises working with data and analytics for a wider set of workers. This is an aspect of AI success that can’t be overlooked. Business and IT leaders need to consider what improving AI literacy looks like for their own organisation. While it’s especially true for companies that fall under the EU AI Act’s remit, it’s applicable for any organisation that intends to achieve true value from their AI investment.