Department-specific Applied AI
One function · your workflows · your governance
“The training was generic and nobody could see themselves in it.”
One function, its real workflows, and its real constraints. People leave knowing where AI fits into the work they do on Monday morning.
Who this is for
- Single functions: HR, finance, marketing, operations, legal
- Teams who have done a general AI session and asked what about our work
- Functions with governance or regulatory constraints that generic training ignores
Who this is not for
- Mixed groups from many departments, who should start with Spark
- Teams looking for a tool demonstration rather than applied practice
What happens
We build the session around what your function actually does, using your workflows and your governance rules as the constraints rather than as a footnote. People work on their own real tasks, then present what they built to their peers and take critique on it, because a use case that survives a colleague's questions is a use case that gets used. The explicit output is the human and AI decision boundary for your function: what AI drafts, what it never decides, and how you tell whether the output is any good.
What you leave with
- 3 to 5 validated use cases from your own work, tested against your governance rules
- A clear boundary for where AI helps and where the human decides
- One working example per person, from their own desk
- Peer critique on each use case, so the weak ones are found early
Proof
Four hours. That's all it took to shift something I've been trying to move for months.
Read the full story
“Four hours. That's all it took to shift something I've been trying to move for months. What surprised me most wasn't the content, it was watching our most skeptical team members leave the room energised. By the end, the team wasn't just more informed about AI. They had developed a real AI mindset, actively asking themselves how AI could improve the way they work weeks after the workshop. The impact didn't take weeks to show up. It was immediate.”
- +38%
- willingness to experiment with AI
- +37%
- readiness to share AI knowledge across the team
Tanja LandroveHead of Regulatory & Scientific Affairs, Nestlé
Find out whether this is the right starting point.
Thirty minutes. We go through what you have rolled out, what your numbers are really telling you, and where the gap is.