Training your teams on AI: the executive's guide

Training is the most under-invested ROI multiplier in AI programmes. The data converges: organisations that dedicate more than 20% of their AI budget to upskilling capture gains three times higher than those betting everything on the tool. This guide gives executives a grid to structure that investment.

First mistake to avoid: one training for all. An executive needs to understand strategic stakes and risks; a manager, to spot use cases and drive adoption; an operator, to master the tools of their station. Three audiences, three formats, three durations — a serious AI training plan distinguishes them explicitly.

Second principle: train on the company's own use cases, not generic examples. The session where a salesperson watches AI prepare THEIR proposals, where a technician queries THEIR machine documentation, is worth ten abstract demos. It is also the best use-case detector: participants identify the deposits in their own daily work.

Third principle: institutionalise internal relays. A network of 'AI champions' — one per department, trained in more depth, animated regularly — turns a one-off training into a permanent capability. It is the mechanism most correlated with lasting adoption in the organisations we support.

Finally, measure: active usage rate at 90 days, declared time saved, use cases surfaced by teams. AI training is managed like an investment, with indicators — not like a regulatory box to tick.