Maturity of data and AI driven strategy
Your data and AI ambition turned into a score, identified gaps and 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.
Maturity of data and AI driven strategy
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 maturity framework · Data and AI strategy (5-level CMMI scale) 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, one level, the level above, and the action that connects them, that turns a finding into a trajectory.
How is the decision made to move a data or AI use case into production or to retire it?
- N1
No decision process exists. Initiatives start and stop depending on team availability, with no identified decision point.
- N2
Decisions are made project by project, driven by whoever is sponsoring it at the time. Criteria are not written down and vary from case to case.
- N3
Documented criteria for moving to production exist (business owner, value indicator, available data foundations) and are applied at an identified decision point.
- N4
Each use case is reviewed on a fixed schedule against its measured value. Decisions to continue, scale up or retire are logged and communicated.
- N5
Criteria and thresholds are revised in light of observed results and lessons learned, with a reviewable history of revisions and decisions.
Action to move from L2 to L3
Write a four-criteria production readiness checklist (named business owner, defined value indicator, data available and documented, operational responsibility assigned), put it on the agenda of the existing data committee and apply it to all ongoing use cases from the next session.
« 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
This framework assesses the maturity of an organisation’s data and AI strategy, from intent through to observed value. It covers the link between ambition and business objectives, governance of investment decisions, selection and arbitration of use cases, the quality and accessibility of the data feeding them, platforms and architecture, skills and culture, and finally the measurement of value. Each theme is scored on a progressive five-level scale, built on CMMI logic: from absent practice to practice that is steered and reviewed.
In practice, the difficulty is not producing a strategy but sustaining it. The questions boards actually ask are concrete. Who decides that a use case moves from the lab to production, and on what criteria? How many experiments are currently running with no business owner and no value indicator? Are the datasets needed for models documented, traced and accessible, or rebuilt by each team for every project? The answers often vary from one business unit to another, which makes group-level steering difficult.
One point of context is worth making: the generative AI wave has shifted the centre of gravity of these programmes. Many organisations launched assistants and copilots before consolidating their data foundations, access management and usage rules. The common result is uneven maturity, strong on experimentation, weak on industrialisation and measurement. One confusion also comes up repeatedly: data and AI strategy is not data governance. Governance is the foundation, strategy decides where to invest and why.
This framework is not certifying and does not prepare you for any third-party audit. It does not conclude with compliant or non-compliant. It places each practice at an observable level, then names the action that moves it to the next level. It is this logic that allows two business units to be compared, a transformation programme to be tracked from one round to the next, and a budget decision to be defended with tangible evidence rather than conviction.
The framework is ready to use and remains yours. You adjust the themes, questions and level wording; the platform’s AI drafts the variants, refines the tiers and can build a sector-specific version from your own strategy documents. After the assessment, it groups the actions arising from the gaps into a prioritised roadmap, ready to present to committee.
Reference standard: Datamensio maturity framework · Data and AI strategy (5-level CMMI scale)
The themes assessed
Ambition and strategic alignment
Articulation of the data and AI ambition, link to business objectives and the strategic plan, time horizon, sponsorship by executive leadership.
Governance and decision-making bodies
Arbitration bodies, roles and mandates (CDO, domain owners, business sponsors), investment approval process, alignment with IT governance.
Use case portfolio
Identification and qualification of use cases, prioritisation criteria, move from experimentation to production, retirement of initiatives with no value.
Data foundations
Availability and quality of the data needed for use cases, documentation and cataloguing, traceability of sources, management of access and sharing between entities.
Platforms and industrialisation
Data and AI architecture, development and production environments, automation of processing pipelines, monitoring of models in service, cost control.
Skills and organisation of work
Skills mapping, organisational model (centralised, federated, hybrid), recruitment and retention, upskilling of business teams, role of external partners.
Culture and adoption
Effective use of data in decision-making, manager upskilling, change management, handling resistance, internal communication on results.
Responsible use and oversight
Rules for AI use, review of sensitive uses, transparency towards affected individuals, alignment with applicable obligations and internal commitments.
Measuring value
Indicators per use case, measurement before and after go-live, consolidation at portfolio level, reporting to the executive committee.
Improvement and learning
Lessons learned from discontinued initiatives, periodic review of the strategy, comparison over time and between business units, capitalising on what has been learned.
A short version of the framework is available for the online self-assessment.
Frequently asked questions
Does this framework lead to certification?
No. There is no body that certifies a data and AI strategy. The assessment measures the maturity of your practices and produces a progression path. If what you need is a certifiable AI management system, look at ISO/IEC 42001 instead.
How does this differ from an audit?
An audit checks whether requirements are met and concludes with a gap. Here, each practice is placed on a five-level scale and linked to the action that moves it up one tier. The output is not a finding, it is a costed roadmap.
How long does the assessment take?
The short version can be completed in a single working session. In collaborative mode, with several contributors per theme, allow one to two weeks generally: most of the time goes into gathering input from data, IT and business teams.
Can the themes and questions be adapted?
Yes. You can edit questions, reword levels, add your own themes or start from a blank slate. The AI drafts the variants and refines the tiers, and can build a version from your own strategy documents. The framework remains your property.
How do you compare several business units?
The same framework is rolled out to each entity, then scores are compared by theme. You see where gaps are real and where they are only apparent. A cross-entity roadmap consolidates several assessments into a single plan, without duplicating shared actions.
Do you need a technical profile to answer?
The questions relate to steering practices, not technology. A data lead, a transformation director or a finance controller can answer them. Questions relating to platforms can be assigned to a technical contact in collaborative mode.
What happens after the assessment?
The gap between your score and your target generates the action plan. The AI groups it into a prioritised roadmap. Each item can be matched against a service from the catalogue, with its cost, timeline and expected effect on the score.
Where is the data hosted?
In France, with OVH, backed up with Scaleway. No transfer outside the European Union. The AI models used can be selected, including from European providers.