AI consulting, implementation and training programmes.

Format

two to three weeks · in-person interviews · in-person validation session

What happens

• Interviews with decision makers, typically between six and ten conversations.
• Mapping the decisions that actually move the business, and those that only consume time.
• Selection of one to three indicators that already existed in the management report.
• Measurement of the current baseline from existing data.
• Validation session with the board, in person.

By the end, the organisation can

• Identify the decisions that need better information.
• Choose one to three business indicators that AI has to move.
• Measure the baseline for those indicators using the data the organisation already holds.
• Prioritise where to start, and justify that order to the board.

What you keep

Target Charter, a single page with indicators, baseline and definition of success, validated and signed.

Format

one day in person · prior assessment · risk, compliance, IT and business in the same room

What happens

• What the regulation requires, who it applies to and on what deadlines.
• Inventory of the AI systems in use across the organisation, built during the session.
• Classification of those systems by risk level.
• Prohibited practices and what has to stop immediately.
• Transparency and literacy obligations, and what they mean in practice.
• What to do over the next ninety days.

By the end, participants can

• Distinguish the obligations that fall on providers from those that fall on deployers of AI systems.
• Classify the systems in use across the organisation according to the risk levels in the regulation.
• Identify the practices the organisation has to stop immediately.
• Prioritise the actions for the next ninety days, each with a named owner.
• Justify to the board what requires investment and what does not.

What you keep

Preliminary inventory of the systems in use, risk map and ninety-day plan.

Format

quarterly cycle · individual sessions with each member and group workshops · in person

What happens

• Individual sessions, to work through the concrete decisions in each area.
• Group workshops, to align criteria among those who decide.
• Cases applying AI to team management and to running the company.
• Critical reading of supplier proposals, with real proposals on the table.
• Internal communication of the change, and what to say to those who are afraid.
• Building prompts, skills and agents for board work and for managing teams.

By the end, participants can

• Distinguish market noise from what changes the business of the organisation.
• Assess an AI supplier proposal without depending on the supplier to interpret it.
• Decide where to invest, what to postpone and what to refuse, with explicit criteria.
• Design the internal message about AI, including what is said about the effect on people.
• Use the prompts, skills and agents built during the programme in their own management work.

What you keep

Board decision agenda for the year, with priorities and investment criteria, and the set of prompts, skills and agents built over the cycle.

Format

half a day in person · up to 40 participants · teams of four to six people

What happens

• Short framing: what an agent is and where its limits are.
• Each team picks a real problem from its day-to-day work.
• Building the agent, with support in the room.
• Testing, fixing and preparing the presentation.
• Final presentations, with voting and a gamification element.

By the end, participants can

• Identify, in their own work, a process with potential for automation.
• Build a working agent from that problem.
• Test the agent and fix what does not work first time.
• Present the result and estimate the gain for the team.

What you keep

One agent built per team, and a prioritised list of ideas for the next session.

Format

five half-day sessions · one a week · 6 to 12 developers · work in a real team repository

What happens

• The anatomy of a coding agent, and the current landscape: Claude Code, Codex, GitHub Copilot, Cursor and Antigravity.
• Project context and extensibility: CLAUDE.md, skills, MCP, subagents, hooks and plugins.
• Building a team of specialist agents for the client repository, run in parallel.
• Planning from a written specification, and tests generated and validated with agents.
• Integration into CI/CD, with automated code review and cost control.
• Security and governance: permissions, secrets, prompt injection, and what always stays with the developer.

By the end, the team can

• Run Claude Code and Codex fluently, and choose between them on their own criteria.
• Build and maintain specialist agents for their repository: documentation, tests, API and security.
• Plan from a written specification and validate what the agent produced, with tests and human review.
• Integrate agents into CI/CD with permissions, secrets and audit trails under control.
• Decide what always stays with people, and leave that rule written down for the whole team.

What you keep

The specialist agent team built by the participants, the context and specification templates, and the team adoption plan.

Format

four to six weeks per function · observation, redesign, build and training · in person

What happens

• Observation of the real work of the team, on site.
• Mapping and measurement of the current process.
• Redesign of the process together with the team that runs it.
• Building the solutions and agents required.
• Training the team on the new process.
• Drawing up the list of what stays human, with justification.

By the end, the team can

• Describe and measure the current process, with numbers.
• Run the redesigned process with its own team.
• Identify what was deliberately left human, and justify why.
• Measure the effect on the indicator the organisation chose.

What you keep

New process documented, solutions in production, team trained and the list of what stays human.

Format

project · scope defined case by case · working sessions at the decision and delivery points

What happens

• Framing the problem with the people who do the work every day.
• Designing the solution and defining the success criteria, before writing code.
• Building, with intermediate validations.
• Testing with real users, in real work.
• Going into production and transferring knowledge to the internal team.

By the end, the organisation can

• Run the agent in production, inside the existing work process.
• Change and maintain the agent with the internal team.
• Measure the effect on the indicator defined at the start.

What you keep

Solution in production, technical and operating documentation, and an autonomy plan.