From AI curiosity to AI habit
I run a three-month change programme that makes AI the normal way your team works. It is built on live client and internal work, one day a week, and measured at the end.
This is a change project, not an AI course
A course gives awareness, a few impressive demos and enthusiasm that fades in three weeks. A change programme gives new habits inside real work, shared standards and a prompt library, internal champions, and measured movement from start to finish. My job is not to explain how AI works. It is to make using it the normal way your people do their work.
Who it is for
CEOs and team leads whose people have tried AI, but whose work looks the same as a year ago. It works best for knowledge-work teams such as consultants, project managers and finance.
What I do
- Confirm the why with the top. A 90-minute session with the CEO or owner on the business reason, how it is measured, and what leadership commits. The output is a one-page mandate every team sees.
- Measure where you start. A survey of under ten minutes per person on usage, confidence and blockers. The same survey runs again at month three, so the result is evidence, not opinion.
- Agree the rules with IT and Legal. Which tools are allowed, what data may be uploaded, and what client confidentiality requires. IT and Legal co-own the rules instead of guarding the gate.
- Kick off together. One session for everyone: why the organisation is doing this, the roadmap, the rules in plain language, and the first module delivered on the spot.
- Run a steady rhythm. Every session has the same shape: one hour of training, one hour building together on real work, and one small assignment in between.
What the six modules cover
- Claude, honestly. How the model behaves, what it is strong at, where it fails, and why output is only as good as the framing.
- Prompting as a craft. From one-line questions to reusable structures. Shared standards, not personal tricks.
- The full toolkit. Projects, Artifacts, document analysis, web search, extended thinking, and Claude inside the tools your people already use.
- Connectors and IT. What connectors and plugins make possible, and how to request tools and access the right way.
- Data discipline. What may be uploaded and what may not, and how to keep client material separate from internal material.
- Applied to your work. Research and synthesis, client deliverables, analysis, status reporting, meeting notes and risk logs, on live tasks.
How it works
One day a week for three months, in your organisation, with your people. Each participant spends two hours a week in sessions, plus one short assignment on work they already have. I stay until the habits hold, then your teams carry it. Extending to more teams or roles is scoped separately.
What stays when I leave
- A one-page CEO mandate every team has seen.
- A shared library of prompts and Claude Projects that your teams own and extend.
- Two or three champions per team who answer the first question.
- A short written readout to leadership, led by hours saved.
- Data-use rules signed off by IT and Legal.
What this is not
Not an IT rollout: there are no new systems to implement. Not bespoke software: there is nothing to build or maintain. Not ongoing dependency: your teams carry it from month three.
Questions I often get
How do you know the programme worked?
Four measures: hours saved on two or three live deliverables, programme cost against time recovered, the baseline survey re-run at month three by role, and prompts and projects in shared use. You get a short written readout led by the hours saved.
Why not a one-day AI training?
One day lifts awareness and fades within weeks. Habits change when people use AI on their own live work every week, when leaders go first, and when someone in each team is the person you ask. That takes a rhythm, not an event.
What about client confidentiality and data?
That is agreed with IT and Legal before the first module: which tools and licence tiers are allowed, what may and may not be uploaded, and how client material stays separate from internal material. The rules come from inside your organisation.
Why me
I work with these tools every day as an operator, so I know where they change the work and where they waste time. At Billetto, together with the team, I upskilled the whole organisation and made AI a daily habit rather than an experiment.