Audit & scoping.
Before you invest, know where you stand. An outside view — short and structured — of your Data & AI maturity: the concrete opportunities, the risks and a clear roadmap. No invented figures; whatever is stated is explained.
Where do you start a Data & AI effort, and what does a scoping audit contain?
A Data & AI scoping audit is a short, structured diagnostic that establishes, from interviews and a review of what exists, an organisation's maturity level and a prioritised list of its opportunities. It avoids the most expensive trap in AI: launching proofs of concept with no direction, which end up abandoned. The deliverable includes a picture of maturity, a value × feasibility ranking, a short- and medium-term roadmap, and GDPR and AI Act points of attention. It is the recommended entry point: it turns intent into a concrete action plan.
Data & AI maturity audit — A structured assessment of how well an organisation masters its data and artificial intelligence — governance, skills, tooling, use cases and compliance — producing a prioritised, quantified roadmap.
- Short
- Structured diagnostic
- Prioritised
- Value × feasibility opportunities
- Roadmap
- Short and medium term
- GDPR · AI Act
- Points of attention
Why an audit before anything else
Most AI projects fail not for lack of technology but for lack of framing: no clear business problem, no owner, no measure of success. The audit fixes that upfront. It aligns the leadership team and the operating teams on a shared diagnostic and a list of opportunities ranked by value and feasibility — before a single euro is committed to development.
What we examine
- Data assets — sources, quality, governance, accessibility.
- Use cases — existing, planned, and unidentified opportunities.
- Skills and organisation — who does what, where the gaps are.
- Compliance — GDPR and AI Act exposure, hosting, sovereignty.
- Footprint and cost — cloud efficiency, sobriety opportunities.
No invented KPIs
Our commitment is simple: every figure in the report is sourced and reproducible. We do not inflate an ROI to sell an engagement. If data is missing to support a recommendation, we flag it rather than invent it. That is the condition of a useful audit.
How we work
- 01
Frame
Leadership and team interviews, a review of what exists, scope agreed together up front.
- 02
Diagnose
Analysis of maturity, opportunities and risks — short and concrete.
- 03
Report
A clear readout: picture of maturity, prioritised opportunities, roadmap.
What you get
- A picture of your Data and AI maturity
- Prioritised opportunities (value × feasibility)
- A short- and medium-term roadmap
- GDPR, AI Act and NIS 2 points of attention
- Sobriety and cost-control leads
- A concrete, prioritised action plan
Frequently asked
Answers to the most common questions — timelines, sectors, compliance, hosting, methodology.
How long does a scoping audit take?
It depends on the scope — the idea is to stay short and concrete. Rather than announce a fixed timeline, I agree the duration with you up front, based on your stakes and your teams’ availability.
Do I need existing AI projects to run an audit?
No — often the opposite. The audit is designed for organisations that want to know where to start. It surfaces opportunities teams had not identified and rules out the false good ideas.
What happens after the audit?
You leave with a self-contained action plan — free to run it in-house, with us, or with a third party. When you wish, we support delivery of the prioritised work, from POC to production.
Ready to unlock
your data?
Let’s discuss your transformation challenges. An initial conversation, no commitment, to frame your need and see how to move forward.