Measure the starting point
We review tools, habits, current use cases, risks, and confidence levels role by role.
← Adoption · Training and enablement
Responsible AI playbook
Usage rules, permitted data, review, traceability, vendors, and incidents — in language the team can actually apply.
See if it fits →Result
Less shadow AI and safer decisions, without blocking every useful experiment.
It fits if...
Companies already using AI that need practical limits for data, external content, automations, and sensitive actions.
What's included.
The final scope is set after the diagnosis, but these are the blocks that make the solution useful and operable.
- Risk inventory
- Policy by data type
- Autonomy matrix
- Incident and review protocol
How it goes from idea to real work.
Design around the work
We turn the company's real processes into exercises, decisions, and safe practices.
Learn by building
The team practices on its own context and finishes with prompts, policies, or a working flow.
Sustain adoption
We leave a playbook, owners, a recurring case review, and follow-up to turn learning into habit.
Other solutions that might fit.
AI training for companiesHands-on training per role, with safe data, the company's own cases, and real work as the exercise.→Executive AI briefingWhat's changing, where to invest, what to avoid, and which governance decisions leadership needs to make.→AI build dayThe team learns by building a real flow and ends the day with a working system.→
Let's see whether this is the right entry point.
We look at your current process, the result you're after, and the limits. If another solution fits better, we'll say so.