AI Maturity Assessment in Organizations
A rigorous, research-based method to diagnose and accelerate AI maturity
Most organisations do not struggle with giving access to AI tools. They struggle with how AI is actually used — and what happens after it is implemented.
The AI Maturity Assessment is a structured, research-grounded method to help leadership teams understand:
– Where your organisation truly stands in AI maturity
– What is blocking real impact
– How leadership behaviour must change to unlock AI potential
This is not a technology assessment. It is a leadership and organisational capabilities diagnosis.
Sequential mixed-method AI maturity assessment
The method combines:
- Expert interviews to surface real use cases, tensions, and governance gaps
- A tailored AI maturity survey grounded in validated research frameworks
- A structured diagnosis across three core pillars:
- Grow AI Capabilities
- Build Trust in AI
- Create an AI-Ready Culture
- An executive debrief translating insights into concrete 90-day leadership actions
The approach integrates established research. This ensures methodological rigor – not a generic consulting template.
The Technology Acceptance Model explains technology adoption by proposing that perceived usefulness and perceived ease of use shape users’ attitudes toward a system and predict their technology usage behavior.1
The Unified Theory of Acceptance and Use of Technology states that technology adoption is influenced by factors including social influence from important others and facilitating conditions such as organizational or technical support.2
The AI Literacy Framework defines AI literacy as the ability to understand AI strengths and limitations interpret system outputs and recognize bias and data dependence particularly in media contexts.3
The concept of psychological safety describes a team climate where individuals feel safe to question decisions and raise concerns which supports responsible AI use.4
Find more information about the AI Maturity assessment
Project Team
Natalia Vuori
Assistant Professor
Department of Industrial Engineering and Management (TUTA)
Aalto University
Get Involved
We invite organizations, leaders, and practitioners interested in AI‑augmented work to collaborate with us for discussions, case studies, workshops, or joint events.
1Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319 to 340. https://doi.org/10.2307/249008
2Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology Toward a unified view. MIS Quarterly, 27(3), 425 to 478. https://doi.org/10.2307/30036540; Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157 to 178. https://doi.org/10.2307/41410412
3Long, D., & Magerko, B. (2020). What is AI literacy Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1 to 16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
4Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350 to 383. https://doi.org/10.2307/2666999