The workshop is not a one-off event but a learning journey. Before the live session, participants receive intake questions about their role, their biggest time-consuming tasks, and their previous experience with AI tools. We also send a short selection of reading material — deliberately kept brief, not a 60-page PDF.
During the session itself, we work hands-on with the team's own tools and documents. What is built by the end of the session — prompts, templates, workflows — can be used straight away as practical material. No academic slides, just practical output.
Afterwards, there is a homework assignment that participants tackle over the following few weeks, plus a follow-up session where they share their experiences and ask questions that came up during implementation. That follow-up is what makes the impact lasting — without it, the insights typically fade quickly.
It also helps that participants bring their own examples and documents to the workshop. Instead of practising on invented case studies, they work directly on something they have on their desk. The effect is that the step from "interesting to hear" to "I'm already using it on Monday" becomes much smaller. We regularly see participants showing new workflows to colleagues within a week of the workshop — often in a way that lifts the whole team, including those who didn't attend the session themselves.
For managers and team leads, we add a short management session discussing how AI adoption can be encouraged within the team, which signals point to a blockage somewhere, and which governance questions need addressing — think data use, quality control and intellectual property of AI output. This session is designed to outline the broader organisational context within which individual participants then continue working.
For organisations looking to take this further, the workshop fits naturally into a follow-on programme — a broader AI business training, an in-depth AI literacy training, or an enterprise AI implementation where AI applications genuinely land in workflows and systems.