Know where AI will make the difference.
In plain wordsNot sure where to start? We identify where AI will bring you the most, and hand you a clear roadmap.
The challenge
Most companies already use AI, but in pockets: a few tools, a few pilots, and no shared way to choose or to scale up. Budgets spread thin, and few projects reach production.
- Use cases chosen for visibility rather than value
- Tools adopted team by team, with no shared architecture or rules
- Data, security and compliance questions raised too late
- Teams unsure how to use AI in their daily work
What the diagnostic covers
Processes and use cases
Interviews with leadership and business teams, mapping of the processes and of where time and money are lost.
Data and systems
Where your data stands, what your systems allow, and what AI would require.
Target architecture
The models, tools and integrations the whole company can share, chosen once for every project.
Governance and risks
Policies, roles, risk management and validation rules, adapted to your sector and regulatory context.
A prioritised roadmap
Use cases ranked by value and feasibility, the investments they require and the decision points.
Team readiness
What your teams need to use AI every day: training, support and new ways of working.
A short, concrete diagnostic
It replaces assumptions with a plan.
Scope
Interviews with leadership and business teams, review of key processes, data and systems.
Deliverable
A prioritised portfolio of use cases, an architecture recommendation and a roadmap with estimated effort.
Format
A few weeks, on a fixed scope agreed beforehand.
Outcome
A clear decision: what to launch first, what to stop, and what it requires.
Examples of use cases
Typical situations where a diagnostic applies.
Mapping AI opportunities across business units
- Context
- A group receives AI requests from every business unit, with no common way to compare them.
- Outcome
- One prioritised portfolio, shared selection criteria and a roadmap approved by the executive committee.
Framing the use of AI tools
- Context
- Employees already use public AI tools, sometimes with company data.
- Outcome
- Clear rules, approved tools and the plan for a secure internal alternative.
Giving a pilot a path to production
- Context
- A promising pilot works on a laptop but has no route to production.
- Outcome
- Target architecture, security review and a deployment plan.
Preparing a rollout across subsidiaries
- Context
- Each subsidiary tests its own AI tools, with no shared foundation or rules.
- Outcome
- A shared foundation, approved tools and a rollout plan in waves, with the training each entity needs.
Expected results
- Investment focused on the use cases with the highest value
- Tools and rules shared across the company
- Risks identified and managed from the start
- A roadmap your teams can carry out
From the first interviews to a decision
- 01
Interviews
With leadership, business and IT teams: objectives, processes, irritants.
- 02
Analysis
Review of processes, data and systems, and assessment of each opportunity.
- 03
Roadmap
Opportunities ranked against shared criteria and presented to your leadership.
- 04
Decision
You choose what to launch, and with whom: with us, with another team or internally.
Start with a diagnostic.
A short engagement that gives you a clear, prioritised view of where AI will matter most.