Adoption rarely stalls on the model. It stalls on three building blocks around it: the knowledge AI can use, the skill of the people, and the workflow that ties them together.
Discuss your situationWe see the same pattern over and over. An organization buys licenses, runs training, and six months later daily work has barely changed. The tool isn't the problem. The work around it was never designed.
01
Your AI is only as good as the context it can use.
Read moreAI can only work with what it can read. The knowledge that makes your organization effective sits in people's heads, old PDFs, messy SharePoints, and unwritten habits. Without structure, output stays generic.
02
Fluency comes from practice on real work.
Read moreTraining is the start. Becoming fluent takes repetition, feedback, and permission to make imperfect attempts. Organizations often confuse exposure with fluency.
03
AI creates value when the workflow changes.
Read moreMost organizations start with the tool and work backwards looking for use cases. We start with the work: where does the task begin, which decisions happen along the way, where does human judgment matter, and what can AI support?
Every engagement starts with a one-hour conversation. We listen to your situation, goals and what you've tried so far. From there we decide together whether and how to continue. No standard playbook, no fixed duration: what you need differs per organization.
Block 1, House in order
Prompt libraries, context documents and best practices per role. So best practices spread across the organization instead of staying siloed.
Block 2, Skilled people
We build skill where the work happens. Training champions, setting up peer-review moments, protecting practice time, and teaching managers how to review AI-supported work.
Block 3, Workflow design
For each team, we look at which tasks change with AI, what agreements are needed and what the new way of working looks like. Tested with the team itself, in the workplace.
One-off pilots fade. Lasting adoption needs rhythm. Each quarter we evaluate what's working, refine workflows, and grow the knowledge base.
Each quarter: experiment plan, hands-on guidance, evaluation, and follow-up advice.
Every week we work with groups of professionals trying to use AI in their daily work. We know where adoption stalls: working practices, roles and responsibilities, and knowledge sharing. That's where our recommendations come from. Read how our clients experience this.
At BNG Bank, 94 use cases that surfaced in our trainings became the roadmap for the AI policy team.
For questions outside our direct expertise we work with specialists from our network. Partners handle technical implementation (Macaw, Archipel) and adoption programs, without you needing to coordinate across multiple parties.
3.500+
conversations with professionals
100+
organizations guided
Training + consulting from one team
Where does your organization stand with AI? What's working, what isn't? Fill in the form and we'll get back to you with ideas.
