Leadership AI Maturity Assessment

Studies by MIT, McKinsey, and BCG consistently show that the vast majority of AI initiatives deliver no measurable impact on revenue or the bottom line. When they fall short, the reason is rarely the technology, but the organization behind it – processes, skills, and culture that are not yet built for AI.

This means the AI transformation is not an IT project, but a profound shift across the entire company. And because technology, regulation, and skills keep evolving, it is an ongoing task with no end date.

This puts particular importance on leadership when it comes to AI. It concerns not only individual top executives, but leadership teams – from the board and executive management level all the way to the operational units. The same applies to supervisory and advisory boards in their advisory and oversight roles.

Together with appliedAI, Europe’s largest initiative for the application of trustworthy AI, we support our clients on this journey. With the Leadership AI Maturity Assessment, we measure the AI maturity of leaders and leadership teams across eight dimensions and from two perspectives: the self-view and the collective view. From this, we derive concrete recommendations for action.

Scientifically grounded, refined in practice 

The assessment builds on the findings of MIT, McKinsey, and BCG on value creation with AI and on four years of joint practical experience by appliedAI and Odgers.

1. Behavior-anchored 
   Each statement describes observable behavior or verifiable artifacts – this limits social desirability bias.
2. Dual perspective
   The self-view and the collective view are assessed separately. The gap between them reveals blind spots and misalignment within the team.
3. Evidence-based
   The question set builds on the findings of MIT, McKinsey, and BCG on value creation with AI.
4. Proven in practice
   Four years of the Data & AI Leadership Circle by appliedAI and Odgers, plus serving on the jury of the Best of AI Awards with Capital.

Eight dimensions of AI leadership

1. Strategic data & AI literacy
   How well do leaders understand key AI concepts, use AI themselves, and treat data as a strategic resource?
2. Ambidextrous transformation leadership 
   How well does leadership manage to evolve the existing business while creating room for experimentation, innovation, and reorientation through AI?
3. Change & culture management
   How well do leaders manage to communicate a shared vision for AI, take concerns seriously, involve employees, and lead the cultural change by example.
4. AI governance & Responsible AI
   How well do leaders manage to establish clear responsibilities, ethical guardrails, and effective risk management for the safe use of AI?
5. Use case portfolio & scaling 
   How well do leaders manage to prioritize AI use cases by business value, move successful pilots into production, and scale them across the organization?
6. Ecosystem & technology alliances
   How well do leaders manage to build strategic technology partnerships and deliberately manage dependencies on individual vendors?
7. Talent & future skills 
   How well do leaders manage to build AI skills in a targeted way, close skill gaps, and embed the effective use of AI in roles and collaboration?
8. AI strategy & organization 
   How well do leaders manage to establish a clear AI strategy with a target vision, responsibilities, structures, and a roadmap, and to integrate AI into products and services as well?

Developed by two leading partners

Where does your leadership team stand on AI?

Finding out where you stand starts with a conversation. appliedAI and Odgers support you from the initial evaluation to the agenda and answer your questions about the process, effort, and analysis. Get in touch now and schedule a free consultation!