Most organizations that roll out AI tools soon face a similar picture: some teams create a great deal of value with the tools, while others barely use them. The licenses are the same, the training is the same; the difference lies in how ready the organization and its teams are. In this post, we explain how to measure the AI maturity level of your organization and culture with an employee survey, which dimensions to look at and how to interpret the results.
- AI maturity is an organization's capacity to create value from AI on an ongoing basis. It is measured not by the number of tools, but by how ready strategy, governance, skills, culture and processes are together.
- Maturity is measured across seven dimensions: strategy and leadership, guidelines and governance, access to data and tools, skills and learning, culture of experimentation, collaboration and knowledge sharing, and process integration.
- The maturity level is not determined by a single average score. The weakest dimension limits the level the organization is really at.
- The gap between leadership's assessment and employees' perception is one of the most important findings. Measuring maturity through leadership statements alone usually overstates it.
AI maturity and AI effectiveness are not the same thing
These two concepts are often confused, but they answer different questions:
| Comparison | AI effectiveness | AI maturity |
|---|---|---|
| Core question | Is the AI we use working? | How ready are we, as an organization, to create value from AI? |
| Focus | Outcome: usage, time saved, work quality | Capacity: strategy, guidelines, skills, culture, processes |
| Level of analysis | Employee and team | Organization and culture |
| When it matters most | After the tools have been rolled out | Before an AI program starts and when setting the roadmap |
Maturity is a precondition for effectiveness. If guidelines are unclear, managers are distant and experimenting is seen as risky, even the best tools have limited impact. We explain how to measure effectiveness in measuring AI effectiveness with employee surveys; this post focuses on the conditions that make that impact possible.
Why ask employees about maturity?
AI maturity assessments are often based on interviews with senior leadership or the IT team. These assessments show strategy and infrastructure well, but they miss two important things:
- Has the strategy reached the front line? An AI vision that is clear to leadership may never have been heard of by employees.
- Does the culture allow it? Employees know best whether it is safe to experiment, make mistakes and share what they learn.
This is why maturity should be measured not only by what the organization states, but by what employees observe in their daily work. This is the same approach we use when measuring whether company values are really being lived.
The seven dimensions of AI maturity
| Dimension | What it shows | Sample statement |
|---|---|---|
| Strategy and leadership | Whether it is clear why and for what purpose AI is used in the organization, and how much senior leadership owns it | "I know what our organization wants to use AI for." |
| Guidelines and governance | Whether the guidelines for safe and responsible use of AI are clear, known and workable | "I know which data I can and cannot share with AI tools." |
| Access to data and tools | Whether employees can reach the tools and data suited to their work | "I can access the AI tools I need for my work through the organization." |
| Skills and learning | Employees' and managers' ability to use AI and the opportunities to learn it | "Time and support are provided for learning to use AI in my work." |
| Culture of experimentation | Whether it is safe to try new approaches, make mistakes and ask questions | "If an approach I try with AI does not work, I can share that openly." |
| Collaboration and knowledge sharing | Whether good use cases are shared across teams | "Good examples of AI use are shared regularly in our team." |
| Process integration | Whether AI has moved beyond individual experiments to become part of work processes | "Our team's work processes have been redesigned to take AI use into account." |
Three to five statements per dimension are enough. The first three dimensions show the organization's structure, the next three its culture and the last one how far AI has become embedded in the work. We explain the general design principles of culture surveys in organizational culture survey.
Why the cultural dimensions are decisive
In maturity models, strategy and infrastructure often take center stage. In practice, however, it is the cultural dimensions that hold organizations back the most:
- Culture of experimentation: Working productively with AI requires trial and error. Where mistakes are punished, employees stick to the safest uses only.
- Knowledge sharing: Employees who create value with AI often do not share their methods. Sometimes the reason is lack of time, sometimes that using AI is seen as "taking a shortcut."
- The manager's attitude: In teams whose manager does not use AI or views it with suspicion, maturity stays low whatever the organization's strategy.
- Perceived job security: Employees who worry that AI will make their jobs redundant may avoid using it and sharing what they learn.
This is why adding statements about the manager's attitude and perceived job security to the survey is important for interpreting the maturity result correctly.
Five maturity levels
Dimension scores can be used to describe which of five maturity levels the organization is at:
| Level | What you see in the organization | Typical survey pattern |
|---|---|---|
| AI is talked about, but there is no organizational approach; use is individual and invisible. | Strategy and guidelines dimensions are low; usage is low and varies greatly from person to person. |
| There are pilot projects and volunteer users; guidelines are being written. | Access is increasing; skills and guidelines are low; large differences between units. |
| Tools have been opened up organization-wide; training is under way; guidelines are known. | Strategy, guidelines and access are improving; culture of experimentation and process integration lag behind. |
| AI is embedded in work processes; good examples are shared systematically. | Cultural dimensions and process integration are rising; leadership and employee perceptions converge. |
| AI is changing the business model and roles; continuous learning is part of the culture. | All dimensions are high and balanced; differences between units are small. |
Most organizations today are between the second and third levels. What decides the move from the third to the fourth level is usually not technology but culture and processes.
Determining the maturity level correctly
- Do not use a single average: The average of seven dimensions can make the organization look more mature than it is. Look at the dimension profile instead.
- The weakest dimension limits the level: If access and skills are high but guidelines are unclear, the organization cannot scale safely. The real level is often the one indicated by the weakest dimension.
- Compare leadership and employee perceptions: Put the same statements to senior leadership. If leadership scores high on strategy and employees score low, the strategy has not yet reached the front line. This gap is one of the most valuable findings of a maturity measurement.
- Look at breakdowns: Maturity can vary greatly from unit to unit and location to location. To see differences within the organization, you can use the methods in measuring organizational subcultures and heatmaps in employee survey results.
Using maturity and effectiveness measurements together
Used together, the two measurements show both where your AI investment stands and why it stands there:
| Situation | What it means | Area of focus |
|---|---|---|
| Low maturity, low effectiveness | The organization is not ready yet. | Strategy, guidelines and access |
| Low maturity, high effectiveness in some teams | Value is created through individual effort and is not institutionalized. | Knowledge sharing and scaling good examples |
| High maturity, low effectiveness | The conditions are in place, but the tools are not used for the right tasks. | Role-based use cases and process integration |
| High maturity, high effectiveness | AI is creating value. | Scaling and new use cases |
In practice, the two measurements can also be combined in one survey design: maturity statements can go into the comprehensive annual survey, and effectiveness questions into more frequent pulse surveys.
Measurement design
- Take a baseline: Take the first measurement before an organization-wide AI program starts, so you can see the program's effect on maturity.
- Base statements on behavior: Instead of general statements like "Our organization is open to AI," ask about behaviors employees can observe.
- Guarantee anonymity: Anonymity is essential for honest answers on topics such as guidelines, manager attitude and job security. You can find the details in anonymity in employee surveys.
- Add open-ended questions: The question "What is the most important thing preventing our organization from creating more value from AI?" shows the reasons behind the scores.
- Support the survey with documents: Reviewing documents on strategy, guidelines and training programs lets you compare the survey results with what the organization states.
From results to action
- If strategy and leadership are low: Explain what AI means for the organization with three to five concrete priorities and carry this message to teams through managers.
- If guidelines and governance are low: Write short, clear usage guidelines supported by examples. Uncertainty is one of the most common barriers to use.
- If skills are low: Instead of general training, create hands-on, role-based learning opportunities built on real work examples, and set aside time for learning.
- If culture of experimentation and knowledge sharing are low: Encourage managers to share their own experiments and failures; create regular meetups and channels where good examples are shared.
- If process integration is low: Identify the tasks where individual use creates the most value and redesign the processes for those tasks with AI in mind.
We discuss the role of managers in this transformation in digital leadership, and the general principles of tracking change in maturity over time in measuring culture change.
Measuring AI maturity in summary
AI maturity is measured not by how many tools an organization uses, but by whether it has the strategy, guidelines, skills, culture and processes to create value from AI on an ongoing basis. Put the seven dimensions to employees and leadership together, look at the dimension profile and the weakest dimension rather than the average, and read the results by breakdown. When you use the maturity measurement together with the effectiveness measurement, you can see clearly where your AI investment stands and what the next step should be.
Frequently asked questions
What is AI maturity?
AI maturity is the extent to which an organization has embedded AI in its strategy, guidelines, processes and culture, and its capacity to create value from AI on an ongoing basis. It is usually described in five levels, from awareness to transformation.
What is the difference between AI maturity and AI effectiveness?
AI effectiveness measures what difference the tools in use make in employees' work, that is, the outcome. AI maturity measures the organizational and cultural conditions that make this outcome possible, that is, the capacity. Effectiveness does not rise sustainably while maturity is low.
How do you measure AI maturity?
An employee survey made up of statements based on observable behaviors across seven dimensions is run, and the same statements are also put to senior leadership. Results are evaluated through the dimension profile, the gap between leadership and employee perceptions and breakdowns such as unit and level. The survey can be supported by reviewing strategy and guideline documents.
How often should AI maturity be measured?
A comprehensive maturity measurement once a year is enough. The first measurement should be taken before an organization-wide AI program is launched. In between, priority dimensions can be tracked with short pulse surveys.
