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    Interactive assessment

    AI Adoption Leadership Diagnostic

    A structured assessment of how well you lead AI adoption in your team across five dimensions: adoption mandate and narrative, visible role modelling, psychological safety for experimentation, support conversations and team norms, and embedding and sustaining adoption.

    The line leader is the decisive variable in AI adoption. Organisational factors such as culture and manager support account for roughly twice the AI impact of individual effort, yet most employees do not describe their manager as an AI champion (Microsoft, 2026). While the large majority of organisations are experimenting with AI, most see no meaningful bottom-line impact because adoption stalls at individual experimentation rather than embedded team practice (McKinsey & Company, 2026). This diagnostic, which underpins the Leading AI Adoption module, helps you assess the leadership behaviours that convert tool availability into sustained team adoption, so you can move your team from ad hoc use towards integrated working practice.

    Questions or items
    25 questions or items
    Completion time
    Around 10 minutes
    Dimensions
    Adoption Mandate & Narrative, Visible Role Modelling, Making Experimentation Safe, Support Conversations & Team Norms, Embedding & Sustaining Adoption
    What you receive
    Personalised results with configured interpretation bands, section scores and development guidance.

    What it covers

    • Adoption Mandate & Narrative
    • Visible Role Modelling
    • Making Experimentation Safe
    • Support Conversations & Team Norms
    • Embedding & Sustaining Adoption

    Answer as an honest reflection of how you lead your team today, not how you would like to lead. Rate each statement from 1 (Strongly Disagree) to 5 (Strongly Agree). The diagnostic is most valuable when it reflects reality, not aspiration.

    Results: Personalised results with configured interpretation bands, section scores and development guidance.

    How this interactive diagnostic works

    This page provides the documented overview before the interactive experience is loaded. It lets you read the purpose, the material covered and the way the result is presented without entering answers or sending a request. When you choose to continue, the interactive version uses the same material shown here. It records your selections while you work and only calculates its result when its required items are complete.

    The interactive experience remains responsible for its own input, score and lead-capture step. Where the route requests a first name and work email, its existing form handles validation and submission. This overview does not replace those steps or submit a lead.

    Statements in this diagnostic

    The interactive version groups the statements below by section. Read them in relation to the situation named in the instructions, then use the response scale shown by the diagnostic. The section descriptions are part of the existing configuration and explain the aspect each group is intended to examine.

    Adoption Mandate & Narrative

    Adoption starts with a credible, honest account of why AI matters for the team and what will and will not change. Narratives that acknowledge uncertainty about roles survive sceptical challenge better than those that overpromise (DDI, 2026).

    1. I have a clear, honest narrative for my team about why we are adopting AI and what it means for our work
    2. My narrative acknowledges uncertainty about how roles may change rather than overpromising
    3. I can connect our AI adoption to real business outcomes my team cares about
    4. I have tested my adoption narrative against sceptical questions and it holds up
    5. My team can explain, in their own words, why AI adoption matters for us

    Visible Role Modelling

    When managers visibly model AI use, employees report marked lifts in perceived AI value, critical thinking about their own use, and trust in AI (Microsoft, 2026). The gap between a leader's private use and visible modelling is where credibility is won or lost.

    1. I use AI openly in my own leadership work every week where it adds value
    2. I share my own AI successes and failures with my team, including my errors of judgement
    3. My team can see specific examples of how I use AI, not just hear that I support it
    4. I treat my own AI learning as visible and ongoing rather than something I have finished
    5. The gap between my private AI use and what my team sees me do is small

    Making Experimentation Safe

    Psychological safety is the foundation for experimentation: teams whose managers make it safe to try and to fail adopt faster and use AI more often (Edmondson, 1999; Microsoft, 2026).

    1. My team has explicit permission to experiment with AI and to fail without blame
    2. We run structured team experiments with a clear hypothesis, success signal and debrief
    3. When an experiment fails, I respond to it as learning data rather than a performance problem
    4. People on my team feel safe to say when AI has not worked or when they distrust an output
    5. I actively watch for and interrupt quiet pressure on colleagues who adopt AI more slowly

    Support Conversations & Team Norms

    Adoption is uneven across a team, from enthusiast to fearful to resistant (World Economic Forum, 2026). Tailored one-to-one support and co-created norms for responsible use address the fear of obsolescence that avoidance leaves untouched (DDI, 2026).

    1. I know where each of my team members sits on the adoption readiness spectrum
    2. I hold tailored one-to-one conversations about AI, including with colleagues who are fearful or resistant
    3. I do not avoid the difficult conversations about role uncertainty and job security
    4. My team has co-created explicit norms for responsible AI use covering data, quality, disclosure and escalation
    5. Our AI norms are in visible use in the team's actual work, not just written down

    Embedding & Sustaining Adoption

    Impact materialises when a team moves from ad hoc experiments to embedded practice (Chartered Institute of Personnel and Development, 2026). Recognition, honest measurement, reinforcement and barrier escalation sustain the shift beyond the initial push.

    1. I recognise and reward adoption behaviours, not just adoption outcomes
    2. I track my team's AI usage honestly rather than assuming adoption is happening
    3. I have a reinforcement plan that keeps AI adoption visible in our routines over time
    4. I remove local blockers to adoption and escalate systemic ones to my sponsor
    5. Adoption in my team is moving from individual experiments towards embedded team practice

    How the result is read

    The interactive diagnostic totals the selected item scores by section and across the whole assessment. It then matches those totals to the configured interpretation bands and shows the section-level results alongside the overall result.

    • Getting started, 25 to 50: AI adoption in your team is still largely down to individuals. The leadership behaviours that convert tool access into embedded team practice are not yet consistently in place. Start with a credible adoption narrative and visible role modelling, the two behaviours that unlock everything downstream.
    • Emerging, 51 to 75: You are building adoption leadership habits, but role modelling, psychological safety, support conversations or reinforcement remain inconsistent. Identify your two lowest-scoring dimensions and make them deliberate practice over the next quarter.
    • Established, 76 to 100: You lead adoption deliberately across most dimensions. Focus on the areas where practice is not yet consistent, particularly the difficult support conversations and honest measurement, to move your team fully into embedded use.
    • Leading, 101 to 125: You lead AI adoption to a high standard: a credible narrative, visible modelling, psychological safety, tailored support and sustained reinforcement. You are ready to coach other leaders to champion adoption in their own teams.

    Sources named in this diagnostic

    • McKinsey & Company (2026) The State of AI. New York: McKinsey Global Institute.
    • Microsoft (2026) Work Trend Index Annual Report. Redmond, WA: Microsoft.
    • DDI (2026) Global Leadership Forecast 2026. Pittsburgh, PA: Development Dimensions International.
    • Chartered Institute of Personnel and Development (2026) People Profession 2026: AI and the world of work. London: CIPD.
    • World Economic Forum (2026) Future of Jobs Report. Geneva: World Economic Forum.
    • Edmondson, A.C. (1999) 'Psychological safety and learning behavior in work teams', Administrative Science Quarterly, 44(2), pp. 350-383.

    Where to take the next step

    Our Leading AI Adoption experience helps managers turn AI investment into embedded team practice.

    Explore AI Leadership Readiness

    Frequently asked questions

    What does the AI Adoption Leadership Diagnostic measure?

    The line leader is the decisive variable in AI adoption. Organisational factors such as culture and manager support account for roughly twice the AI impact of individual effort, yet most employees do not describe their manager as an AI champion (Microsoft, 2026). While the large majority of organisations are experimenting with AI, most see no meaningful bottom-line impact because adoption stalls at individual experimentation rather than embedded team practice (McKinsey & Company, 2026). This diagnostic, which underpins the Leading AI Adoption module, helps you assess the leadership behaviours that convert tool availability into sustained team adoption, so you can move your team from ad hoc use towards integrated working practice.

    How many questions are in the AI Adoption Leadership Diagnostic?

    It contains 25 questions grouped into 5 dimensions: Adoption Mandate & Narrative, Visible Role Modelling, Making Experimentation Safe, Support Conversations & Team Norms, Embedding & Sustaining Adoption.

    How is the AI Adoption Leadership Diagnostic scored?

    Responses are scored within each section and totalled across the diagnostic. The total is compared with the configured interpretation bands.

    What happens after I complete the AI Adoption Leadership Diagnostic?

    After the required items are answered, the existing first-name and email gate is shown before the results are displayed. The result includes an overall interpretation, section scores and development guidance.

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