A scientifically grounded diagnosis of where your organization stands with AI. Not just the AI team, but the entire chain: strategy, value, policy, people, data, and technology.
In 4 to 6 weeks, we map out where you stand, where you want to go, and what the three to five priorities are that make the biggest difference. Substantiated, comparable, and repeatable.
We believe that AI maturity is not a single score, but the balance between capability and willingness across seven areas of the organization. Many scans only measure capability: how far along is the strategy, how good is the data, and how mature is the governance. What is then missing is whether there is actually an owner for each of those topics. An organization that scores reasonably well on all pillars on paper can get stuck in practice because no one is in charge.
The AI Maturity Scan therefore combines two measurement layers. The first layer scores maturity across seven pillars, from strategy to data. The second layer assesses whether the six operational workflows required to realize AI are actually assigned. The interplay between these two layers is what distinguishes our approach: pillars tell you how well things are going, while workflows tell you whether anyone is responsible for them at all.
The scan combines a 34-item questionnaire among 15 to 25 stakeholders with 6 to 10 in-depth interviews. The outcome is a spider web based on the seven pillars, a workflow chain that identifies the weakest link, and three to five concrete priorities for the coming period.
Lead time: 4 to 6 weeks. Result: a shared vision that enables internal dialogue and allows you to build forward in a politically responsible manner.
Strategy, value, organization, people and culture, policy and oversight, construction and management, data as a foundation. Four questions per pillar on a 5-point scale.
Trend analysis, organizational development, oversight and risk management, architecture and platform, data and solutions, execution. For each workflow, we measure whether an owner exists.
questions, 15 to 25 stakeholders. A validated questionnaire plus 6 to 10 stakeholder interviews. No standalone interviews, but a measurable basis with figures and interpretation.
From intake to feedback. Includes questionnaire, interviews, analysis, and presentation of the findings.
Organizations considering a Maturity Scan typically face one or more of these issues. One or two recognizable points are usually sufficient reason to assess where you stand.
Something is happening with AI in multiple places, but scaling up is not progressing smoothly. No one can pinpoint exactly why.
IT, business, HR, and governance operate based on different views of where you have come. A shared foundation is lacking.
Innovation, policy, architecture, portfolio. The work takes place in multiple locations on the margins of someone's actual function. As a result, it remains vulnerable.
Since February 2025, AI literacy must be demonstrably ensured. From August 2026, the provisions regarding risk classification will follow. Who within your organization keeps track of this systematically?
We measure AI maturity across seven pillars, a classification that has been widely adopted in the market and is scientifically substantiated. The seven pillars stand on the shoulders of leading research: the leading AI Maturity Model used as the market standard, peer-reviewed research by Mikalef and Gupta into AI capability, and the requirements of the EU AI Act. We do not believe that this foundation itself constitutes our added value. Good models may be widely used, and we use the best available.
What distinguishes our approach is what we add to it. In addition to the pillars, we also measure the six operational workflows required to realize AI in practice. Pillars tell you how well things are going; workflows tell you whether anyone is responsible for them at all. It is precisely the interplay between these two layers that yields the most useful insights, and in our experience, this is often where the real issue lies.
In practice, we observe a recurring pattern: organizations often score reasonably well on pillars because strategy and good intentions are documented, while the associated workflow is dispersed or unassigned. That is where the gap between ambition and execution lies. By measuring both layers simultaneously and visualizing them as a spider web plus workflow chain, we make that gap visible and open for discussion.
Does your organization have a well-thought-out, supported AI strategy, and is it being put into practice? We measure whether the AI strategy is explicitly linked to the broader business strategy, whether the ambition is realistic given your capabilities, whether there is a multi-year roadmap with milestones, and how AI trends are systematically translated into implications for your own organization.
How systematically does your organization convert AI into value? We look at the portfolio of AI use cases and how it is prioritized, whether explicit business cases are drawn up before investments, whether realized value is measured and fed back afterwards, and whether lessons learned are actually reused in new initiatives.
How is your organization structured to do AI work? We measure whether AI roles and responsibilities are clearly assigned, whether there is a conscious choice between in-house building, co-development, and procurement, how external partnerships are strategically managed, and whether business, IT, and data truly collaborate cross-functionally rather than alongside each other.
Are the people, skills, and climate present to deploy AI widely? We measure whether there is insight into AI skills and the gaps that still need to be filled, whether a structured AI literacy program is running that reaches all employees (as required by the EU AI Act), whether employees feel comfortable expressing doubts and concerns, and whether leadership exemplifies AI in its own work and behavior.
Does your organization comply with policies, ethics, and laws and regulations regarding AI? We measure whether there is established policy for responsible AI use that is also applied in practice, whether risks regarding privacy, bias, security, and legal aspects are systematically identified before going live, whether compliance with the EU AI Act and GDPR is demonstrable, and whether it is clear who makes the critical AI choices and is accountable for them.
How mature is the way you build, deploy, and manage AI systems? We look at the standardization of design, test, and deployment, active monitoring of performance and model drift in production, the presence of scalable infrastructure (platforms, tooling, operational processes for AI systems), and the collaboration between engineering teams and the users of what they build.
Is the database upon which your AI rests in order? We measure whether the data quality (completeness, accuracy, currency) is sufficient for the applications you want to build, whether relevant data is accessible to the right people and protected where necessary, whether the origin, processing, and use of data are clear (data lineage), and whether the data infrastructure is prepared for the requirements of AI.
In addition to the pillars, we measure whether the six operational workflows required to realize AI are committed within your organization. For each workflow, you choose from four options: uncommitted, distributed, committed, or robustly committed.
In practice, it frequently occurs that a pillar scores reasonably well while the associated workflow is dispersed or unassigned. For example: the Construction and Management pillar scores 3 on a scale of 5, but the Architecture and Platform workflow lacks a clear owner. This is a pattern that this second layer is designed to draw attention to. In the report, we visualize the six workflows as a chain so that the weakest link becomes immediately visible.
| Workflow | What it entails |
|---|---|
| Trend analysis and partnerships | Following AI trends, maintaining partnerships, exploring new AI possibilities, and incubating transformational AI. |
| Organizational development | Building an AI organization, developing AI skills across the board, managing the impact of AI on roles and functions. |
| Supervision and risk management | Ensure ethical AI use, develop policy framework, manage AI risks (privacy, bias, security, legal). |
| Architecture and platform | Designing and managing AI architecture and platforms, making build-versus-buy decisions. |
| Data and solutions | Create and maintain AI-usable data, manage the AI portfolio and AI solutions in production. |
| Execution | Execute planned AI initiatives and manage scope, cost, quality, and time. |
From the initial intake to the feedback, we work in five steps. The turnaround time is 4 to 6 weeks, depending on the size of the organization and the speed at which respondents complete the questionnaire. We keep the momentum going ourselves.
In a session of one and a half to two hours, we refine the scope, target group, and respondent list. Lead time: week 1.
The 34-item questionnaire is distributed anonymously to 15 to 25 stakeholders. Completion time per person: 15 to 20 minutes. Lead time: week 1 to 2.
We combine the quantitative scores per pillar and workflow with the qualitative interview findings. Lead time: week 4.
In parallel, we conduct 6 to 10 in-depth stakeholder interviews of 45 to 60 minutes. An interview goes deeper than a questionnaire can. Lead time: weeks 2 to 3.
We present the findings to the management or steering committee in a session. The report will follow in writing. Lead time: weeks 5 to 6.
The report operates on three levels. An overall picture in a single image, a breakdown per pillar and per workflow, and three to five concrete priorities that you can start working on immediately.
We combine the quantitative scores with anonymized quotes from the interviews. A quote makes a score understandable; a score anchors a quote. Where the survey and interviews contradict each other, we explicitly highlight this. That is usually where the real issues lie.
The report is deliberately a combination of written and oral. A written report without a presentation is too often archived. A presentation without a written version loses detail. This combination provides you with the basis to continue the conversation internally, even after our involvement.
The AI Maturity Scan is methodologically identical for all organization types. We tailor the sample and respondent circle to your size and context. The pillars and workflows remain the same, ensuring the outcome is comparable across organizations.
For SMEs that want a grip on where they stand without their own AI team, and that want to substantiate their initial investments in AI before they are made.
For municipalities, provinces, and implementing organizations that want to get their policy framework in order before the EU AI Act deadlines, and that wish to provide administrative accountability for their AI initiatives.
For healthcare institutions that want to deploy AI without stumbling over privacy, professional secrecy, and the critical gaze of patients and professionals.
For housing associations seeking efficiency in administrative processes and tenant interaction, and wanting to know where the greatest levers lie.
For educational institutions that want to translate the widespread use of ChatGPT among teachers and students into policy, and that want to ensure compliance with the literacy requirement of the EU AI Act.
For corporates that want to test their AI strategy against industry standards, and that want to systematically measure whether the execution actually follows the strategy.
Organizations considering a Maturity Scan typically face one or more of these issues. One or two recognizable points are usually sufficient reason to assess where you stand.
| AI Team Scan | AI Maturity Scan | |
|---|---|---|
| Level | Employee in a work context | Organization |
| Who fills in | Entire team or organization, anonymous | 15 to 25 stakeholders plus 6 to 10 interviews |
| Lead time | 2 to 4 weeks | 4 to 6 weeks |
| Completion time per employee | 12 to 18 minutes | Varies (by stakeholder type) |
| Output | Dashboard on 9 dimensions, optional persona segmentation | Spiderweb on 7 pillars plus workflow chain |
| When do you choose this | For the rollout of AI tools or for training strategy | At the start of the AI strategy or executive accountability |
| More information | More information |
Are you unsure which scan is right for you, or are you considering a combination? In an introductory meeting, we will clarify together what the most valuable first step is.
The turnaround time is 4 to 6 weeks, from intake to feedback. This includes the questionnaire among 15 to 25 stakeholders, 6 to 10 in-depth interviews, the analysis, and the presentation of findings to the board or steering committee.
We combine three sources. First, a leading market standard for AI maturity, which provides the seven pillars and the five-level model. Second, peer-reviewed scientific research (including Mikalef and Gupta) into what explains AI adoption at the organizational level. Third, the EU AI Act as an external standard. On these foundations, we build our own measurement layer of six workstreams plus practical insights from years of implementation in the Dutch energy, healthcare, and public sectors. We view the existing models as tools, not as a secret recipe. Our added value lies in the combination and the execution.
The price depends on the size of the organization, the number of respondents, and the number of interviews. For a guideline price and a tailored quote, it is best to schedule an introductory meeting. During that meeting, we always provide a price range before a quote is drawn up.
In Pillar 5 (Policy and Supervision), the scan explicitly assesses compliance with the EU AI Act, including the literacy requirement in Article 4 (in force since February 2025) and the provisions regarding risk classification and compliance (applicable from August 2026). The outcome is not a legal judgment, but rather a substantiated indication of where you stand in relation to the requirements, and which areas deserve priority to build compliance.
We build our own benchmark across scans, which we can provide anonymously and in aggregated form. Additionally, the pillars and the 5-level maturity model are aligned with market standards, ensuring external comparison remains possible. Furthermore, with repeat measurements after 12 to 18 months, you can statistically substantiate your own growth.
Yes. The scan is methodologically identical for all organization types. However, we adjust the sample and respondent circle based on organization size: an SME has fewer stakeholders than a municipality or a corporation. The pillars and workflows remain the same so that the outcome is comparable across different organization types.
Individual answers are not reported in a manner traceable to persons. We report at the pillar, workflow, and potentially department level, but never at the individual score. We use quotes from interviews only in an anonymized form, and if the content is potentially traceable, after explicit consent from the interviewee.
An introductory meeting lasts 30 to 45 minutes and is always without obligation. We discuss your context and the issues currently at play, and we indicate whether a Maturity Scan is appropriate, whether a Team Scan is better suited, or whether another approach would yield more value.
You don't need to prepare anything in advance. An open conversation often yields more than a tightly scheduled agenda.