Since July 2026 the AI Act asks providers and deployers to take measures that support their staff's AI literacy, without requiring any specific level from any individual. I learned how that kind of duty plays out from analytics adoption at JD Sports Group, where getting people trained was the easy part and keeping the habit was the real work.
What changed in Article 4
Article 4 has applied since 2 February 2025. The original text asked providers and deployers to take measures to ensure, to their best extent, a sufficient level of AI literacy among their staff. The Digital Omnibus on AI (Regulation (EU) 2026/1744, in force since 27 July 2026) rewrote it. Providers and deployers now have to take measures to support the development of AI literacy of their staff, the text adds that this does not require any specific level of AI literacy of any individual, and the Commission has to publish practical examples of how to comply (AI Act Explorer).
The Commission's AI literacy Q&A says there is no need for a certificate, that relying on the instructions for use is not sufficient, and that organisations can keep an internal record of their training and guidance.
Read quickly, the new wording sounds lighter. In practice it leaves each organisation to decide what support means for its people and how it will know whether the support is working. I had to answer the same question for analytics.
What analytics adoption taught me
At JD Sports Group I was product owner of a programme of 8 measurement projects. The result I value most from it is a measure of behaviour: adoption of analytics grew 70% year on year. Stakeholder satisfaction was 90%, which was good to hear, but adoption told us whether people used the data when they made decisions.
Two practices were part of the programme from the start. Each implementation came with an operational dashboard, so people learned on the tool they would use. When an implementation moved into business as usual, a short survey checked whether it met the need it was built for.
The checkout project taught me the other half. Customer Care logged the reason for each live chat in Salesforce, and the weekly report built on those reasons was still used in their meetings long after the project closed. As the team hired new people, the logging discipline dropped and the reasons became less reliable, while the report kept arriving every week. The plan had been a one-off handover, so I made periodic retraining of Customer Care part of how the report was maintained.
Why AI tools behave the same way
An AI tool launched with one training session is in the same position as that report. People who join after the session never had it, the tool changes with each model update, and the habits that make output safe to use fade quietly: checking a claim against its source, or knowing which customer data must never go into a prompt. Nothing breaks on the day the habit goes, which is why a course completion rate says little about it.
Day to day I use AI to automate repetitive tasks, and my own time goes on verifying the output before I sign it off. That verification step is the first thing I would teach.
What I would do today
- Start with an inventory of who uses which AI tools and for what. The role decides what literacy means: someone drafting customer replies with an assistant needs different knowledge from an analyst building a feature on a large language model.
- Put AI guidance into onboarding, and repeat it on a date in the calendar and whenever a tool or its use changes.
- Measure what people do with the tools. Two useful signals are the share of AI-assisted outputs that someone reviewed before they went out, and the incidents and near misses that people report.
- Keep a dated record of the support each group received. The Q&A asks for no certificate, and a record is the simplest way to show what was done and when.
