Value unmeasured
Scattered experiments
Initiatives multiply, but no one can say what they deliver.
AI audit for demanding leadership teams and organisations
Solutions Decide where AI will create real value for you.
Identify high-impact uses, rule out false good ideas, control your GDPR and EU AI Act risks, then move forward with a realistic roadmap.
- Map of uses
- Quantified prioritisation
- Compliance
- Execution roadmap
The reality
The challenge is no longer access to AI. It is knowing where and how to act.
Data exposed
Shadow AI
Teams already use uncontrolled tools, sometimes with sensitive data.
Risks unchanged
Cautious inertia
Doing nothing solves neither the risks nor the existing uses.
Budget misdirected
The wrong priorities
The most spectacular cases are not necessarily the most profitable.
The point is not to produce more ideas. It is to make a decision you can defend.
What the audit lets you decide
In six weeks, turn uncertainty into an action plan.
- 01
See what really happens on the ground
Declared uses as well as the undeclared ones.
- 02
Decide with shared criteria
Every opportunity is assessed the same way, instead of depending on how persuasive each department is.
- 03
Build compliance in from the start
GDPR, EU AI Act, data, transparency, risk level.
- 04
Prioritise what can succeed
A portfolio sequenced around your capabilities, your data and your sponsors.
The method
A methodology that combines value, feasibility and compliance.
DimensionQuestionWhat it prevents
Value What concrete business gain? Gimmick projects
Feasibility Can we really build it and get it adopted? Unworkable projects
Compliance Can we deploy it within a controlled framework? Legal and reputational risks
A score alone is not enough. Cases are also analysed by business line, by the departments involved and by sector constraints. That is what makes the recommendation actionable rather than generic.
12 criteria · 1 filter
« Every selected case is assessed against 12 weighted criteria and a compliance filter. »
Deliverables
You do not leave with a report. You leave with a decision.
AI baseline
Map of uses, maturity, tools in place, gaps between leadership and the ground.
Prioritised portfolio
Qualified, costed use cases organised into deployment waves.
Compliance plan and roadmap
Identified risks, prioritised actions, owners and first milestones.
How it runs
A clear framework, a controlled commitment from your teams.
- 01
Scoping and fieldwork
Scoping, interviews, observing pain points and real uses.
- 02
Qualification and build
Analysis, scoring, costing and regulatory qualification.
- 03
Read-out and decision
Executive committee, first wave approved, owner named and milestone set.
Your teams help surface what really happens; we do the analysis and structuring.
Why Artemia
An audit designed by people who also deploy.
- We know how to go from recommendation to delivery.
- Sovereignty and confidentiality are treated as architecture choices.
- We are independent of model providers.
A good audit can recommend not automating some things.
Is the audit relevant if we have already launched AI experiments?
Yes, it is one of the most common situations. The audit takes stock of what has been launched: what each experiment really delivers, what deserves to be scaled and what should be stopped.
Your existing initiatives are assessed with the same criteria as new opportunities, so they can be compared and built into a coherent portfolio.
How do you handle undeclared uses?
Without looking for culprits. The aim is to understand which needs these tools meet and which data flows through them. Shadow AI is often the best signal of the value teams expect.
These uses are included in the map alongside declared ones, and the audit proposes a framework to secure them rather than simply ban them.
Do we need data ready or an in-house AI team?
No. The availability of your data and your internal capabilities are part of the feasibility criteria assessed during the audit. The roadmap is sequenced around what you actually have: the first selected cases are those you can carry now.
Your teams’ involvement is bounded: they surface what really happens on the ground, we do the analysis and structuring.
Can the audit conclude that a project should not be launched?
Yes, and that is one of its roles. A use case can be ruled out because the expected gain is too small, because it cannot be delivered with your current data or resources, or because its risk level cannot be controlled.
Recommending not to automate something is part of a decision you can defend in front of your executive committee.
What happens after the read-out?
You leave with a first wave approved by your executive committee, a named owner and a first milestone. You keep the scoring workbook to update the cases and rerun the prioritisation over time.
For delivery, you remain free to choose: your own teams, the provider of your choice, or Artemia, with MIA, the custom API or targeted training.
Does the audit cover GDPR and the EU AI Act?
Yes. Every use case goes through a compliance filter: personal data processed, transparency, risk level under the EU AI Act. A case that cannot be deployed within a controlled framework is not selected, whatever its value.
The compliance plan delivered at the end lists the identified risks, the actions to take and their owners. It fits with the work of your DPO.
AI audit
Give your leadership a clear AI decision
In a first call, we qualify your context, your challenges and the most useful audit scope to investigate.
- Your challenges and current AI uses
- The most useful audit scope
- A timeline and a quote, with no commitment
A question first? Read the audit FAQ · info@artemia.ai