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Consulting · Learning journey
We help teams work with AI together: on their own material, in their everyday work, and with an eye on what the organization is there for.
In most organizations, many people already work with AI, but each alone in their own chat window. Results and templates stay in individual chats. And hardly anyone asks what AI does to the work they share.
Knowledge, templates and decisions live in one place, the shared context layer. Everyone on the team, and the AI, can reach it. On top of that, everyone works with the same AI agents, instructions and guardrails, the shared harness. What one person improves, the whole team uses. Every change is traceable and can be undone.
The AI does real tasks with the team's material, such as proposals, analyses, presentations and workshop templates.
The team decides which tasks it hands to the AI and which it deliberately keeps. Its own rules and regular rituals grow out of that.
Not every task belongs equally close to AI. With the team, we sort out what AI takes over, where people and AI shape things together, and what stays with people at the core.
Routine work nobody misses once AI takes it over. The time it frees goes into work there was no time for before.
For example: copying details from emails into a spreadsheet.
People and AI take turns. The AI offers drafts and variations; people choose, discard and sharpen. In the process, people learn, also for work without AI.
For example: writing. On its own, AI still doesn't write good texts; going back and forth produces better ones.
This is where people stand, and this part matters more and more: sensing what is going on in a room, clarifying vision and purpose, holding a process of change. Otto Scharmer calls it presencing: sensing what wants to emerge.
For example: a day on which a team works out what it wants to be there for in the future.
Enable first, explain second. The team works on its own tasks first. We talk about principles and limits once there is experience to talk about.
We ask how the team feels about AI, somewhere between skepticism and excitement. Together we pick two or three real use cases and settle tools and data protection.
The first use cases are set
We set everything up together in the session, not as homework. By the end, everyone on the team can work with the AI.
Everyone can start
The team works on the chosen cases with real material. We accompany it, also between sessions.
First results in everyday work
With this experience, we sort the tasks: what does AI take over, where does the team shape things together with it, and what stays with people at the core?
Its own guardrails
The team sets responsibilities, rituals and the next cases. That includes a regular slot to look at its own work without the tools.
It continues without us
What does the organization stand for, what does AI change for it, and what does it want to achieve with it? We work through these questions before we get to tools.
We accompany the team for several weeks on its own tasks, until working with AI together continues without us.
We are walking this path with our first organizations right now.
AI changes how we work, and with it, ourselves. Organizations can only bring their potential into the world in a healthy way if the people in them grow along with it. That is why we always connect practical work with AI to the question of what the work is for.
More about our stance →We look forward to a first conversation.
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