Overall enterprise AI maturity
Your AI maturity measured, benchmarked across business units and turned into a costed roadmap.
10 themes, a 5-level scale. And the action that moves each level to the next.
The framework’s 10 themes, already written from L1 to L5. One company, one business unit, or 300 at once.
Overall enterprise AI maturity
10 themes, 5-level scale.
Nordhavn Industries
53 / 100
They measure their maturity with Datamensio
An example
This could be your situation.
Take one company as an example: three sites, three spreadsheets, no shared answer.
Nobody can consolidate.
Nordhavn Industries, 2,400 people in Hamburg, Lyon and Porto. A client asks where the group stands. Each site answers in its own spreadsheet, with its own scales.
Three weeks, a single base.
One Datamensio AI maturity framework, aligned with the CMMI method and AI reference frameworks (OECD, ISO/IEC 42001, NIST AI RMF) assessment launched across all three sites at once, from the managers’ interview notes. The framework was already written, its 10 themes and levels L1 to L5 too.
Two costs avoided before being committed.
A score of 53 out of 100, with the gap concentrated on three themes. The AI companion spotted that two actions duplicated those of another audit. The committee report took one sentence to request.
What it saved them
- 3sites measured on the same base, instead of three questionnaires to reconcile
- 2duplicate actions caught before the spend
- 1committee report, with no manual rework
These figures are an example. They could be yours.
The standard imposes processes. Datamensio says where you stand.
01
The framework is already written
Themes, questions and levels L1 to L5, all written. You do not start from an empty spreadsheet.
02
The score lands the same day
Online, by self-assessment link or in interview. Theme by theme, comparable over time.
03
The gap becomes a costed plan
Every step up carries its action. The AI prioritises on expected effect, not on the order of the standard.
04
Progress can be demonstrated
Campaign after campaign, against your target and against your own past. That is what your board asks for.
The maturity scale
One level, the next, and the action that links the two.
It is this mechanism, an observed level, a target level and the action linking the two, that turns a snapshot into a trajectory.
How do AI use cases move from prototype to production?
- N1
No use case has been put into production. Work stops at the demonstration stage.
- N2
One or two use cases run in production, carried by the people who built them, with no repeatable process.
- N3
A production deployment path exists and is applied: go live criteria, a designated application owner, defined operating conditions.
- N4
Every use case in production is monitored for performance and drift, with alert thresholds and a documented periodic review.
- N5
The path is revised based on incidents and operating feedback, and decisions to discontinue or redesign are made using value indicators.
Action to move from L2 to L3
Formalise the criteria for going into production, appoint an application owner for each use case already deployed, and add the review of these deployments to the agenda of the existing data and AI committee.
« With Datamensio, we meet our objectives far more efficiently. The ERDF inspection services and our supervising ministry particularly appreciated an approach that gives them reliable data. »
Director, CCI 94CCI Île-de-France
« We believe this is the most suitable solution to scale our transformation project and measure impact according to our needs. »
Maja SucekChief Operating Officer, Interreg Danube
Rarely on its own
Frameworks combine. Put several together to cover your business, or have the AI write yours.
Take your first measurement
What this framework covers
The AI maturity framework assesses a capability, not a stock of tools. It examines seven dimensions: ambition and how it connects to corporate strategy, data availability and quality, the technical platform and experimentation environments, skills and team organisation, governance and control of usage, the use case portfolio and its value, and finally the move from prototype to production. Each dimension is graded across five maturity levels, from isolated effort to a steered and continuously improved practice.
In practice, this capability is hard to steer because it is diffuse. AI arrives through the business lines, often bypassing the IT department, and initiatives pile up without being compared. How many of your use cases have moved past the demonstration stage and are now running in production, with an identified owner? Is the data you need accessible within a timeframe that fits a project, or does it require negotiation every time? Who decides that a use is acceptable, and against what criteria? Without a shared answer to these three questions, an AI programme produces experiments, not transformation.
One confusion keeps recurring: mistaking the adoption of generative tools for AI maturity. Rolling out a conversational assistant to thousands of employees is a fact of usage, it says nothing about the ability to industrialise a model, monitor its drift or document its decisions. Conversely, an experienced data science team can coexist with an absent governance framework. The framework keeps these dimensions separate to avoid a flattering overall score that hides a specific bottleneck, usually data access or production deployment.
A maturity assessment does not answer yes or no. It places each practice on a progressive scale and indicates, for every gap against the target, the action that moves it up a level. That is the difference between a snapshot and a trajectory: the score per theme gives the picture, the gap against the target level generates the action plan, and Datamensio’s AI groups these actions into a prioritised roadmap, readable by a leadership committee. Benchmarking across business units and against your own past assessments then measures the progress made.
The framework is ready to use and belongs to you. You can adjust the themes, rephrase the questions, redefine the levels to match your internal vocabulary. The AI can also build a tailored version from your own documents: master plan, AI usage charter, use case mapping. The service catalogue then opens, against each action, a documented solution, with cost, timeframe and expected effect on the score.
Reference standard: Datamensio AI maturity framework, aligned with the CMMI method and AI reference frameworks (OECD, ISO/IEC 42001, NIST AI RMF)
The themes assessed
AI strategy and ambition
Existence of a formalised ambition, connection with the strategic plan, prioritisation of priorities, dedicated budget allocation, identified executive sponsor.
Governance and usage framework
Decision-making bodies, acceptability criteria for a use case, AI usage charter, roles and responsibilities, connection with security and data protection.
Data and knowledge base
Data accessibility for projects, quality and documentation, catalogue and lineage, access rights management, preparation of corpora and document repositories.
Platform and technical environments
Experimentation environments, computing capacity, integration with the information system, model selection and hosting, application lifecycle management.
Skills and organisation
Data and AI roles filled, split between central team and business lines, upskilling plan, manager awareness, use of external partners.
Use case portfolio
Inventory of initiatives, assessment of expected value, selection criteria, portfolio monitoring, arbitration between exploration and exploitation.
Industrialisation and operation
Move from prototype to production, application ownership, performance and drift monitoring, version management, operating costs.
Adoption by the business lines
User support, integration into work processes, measurement of actual usage, feedback from the field, change management.
Risk control and transparency
Inventory of systems and their risk level, pre deployment assessment, decision traceability, information to affected persons, incident handling.
Value measurement and improvement
Outcome indicators per use case, periodic portfolio review, decision to discontinue, capitalisation of lessons learnt, comparison over time.
A short version of the framework, with 32 questions, is available for the online self-assessment.
Frequently asked questions
Does this framework lead to a certification?
No. It measures a transformation capability, not compliance with enforceable requirements. There is no certifying body behind this framework. If your need is for a certifiable management system, the ISO/IEC 42001 framework is the appropriate starting point.
What is the difference with an audit?
An audit checks for the presence of requirements and concludes with a gap. The maturity assessment places each practice on a progressive scale and indicates the action that moves it up a level. The result is not an opinion, it is a costed and prioritised action plan.
How long does the assessment take?
The short version can be completed in a single working session. The full version, run collaboratively with several contributors, typically spans one to two weeks, most of the time being spent gathering input from the business lines and technical teams.
Can the framework be adapted to our context?
Yes. The themes, questions and wording of the levels can all be changed. The platform’s AI can rephrase content, refine scales according to the CMMI method, or build a tailored version from your own documents. The framework remains your property.
Do you need technical expertise to answer?
The questions cover steering, organisational and governance practices, not model architecture. A few questions require input from a technical referent: collaborative mode allows them to be assigned to the right person.
How can several business units be compared?
Each entity runs its own assessment on the same framework. Scores per theme can be compared across entities and against previous assessments. A cross entity roadmap then consolidates the common actions without duplicating them.
How does this assessment relate to the European AI regulation?
The regulation imposes obligations based on the risk level of deployed systems. This assessment measures overall capability, of which the systems inventory and the usage framework are part. It provides a reusable baseline for a dedicated regulatory assessment.
Where is the data hosted?
In France, at OVH, with backup at Scaleway. No transfer outside the European Union. The AI models used by the platform can be selected, including from European solutions.