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    Interactive assessment

    AI Adoption Stage Assessment (AISA)

    Identify where you genuinely sit in the four psychological stages of AI adoption — and understand what you need to progress.

    Most AI adoption programmes fail because they assume everyone is at the same stage. The AISA framework, Awareness, Investigation, Skill-building, Adoption, maps the psychological patterns most people move through when working with new AI capabilities. It is descriptive rather than predictive: in practice, adoption is recursive, role-dependent, and shaped by social context. This assessment helps you identify where you are sitting most of the time, understand the dynamics at play, and know what support or action will move you forward. Based on research in change psychology, adult learning theory, and organisational behaviour (Prochaska and DiClemente, 1983; Bandura, 1997; Rogers, 2003; BCG, 2026).

    Questions or items
    16 questions or items
    Completion time
    Around 10 minutes
    Dimensions
    Awareness, Investigation, Skill-building, Adoption
    What you receive
    Personalised results with configured interpretation bands, section scores and development guidance.

    What it covers

    • Awareness
    • Investigation
    • Skill-building
    • Adoption

    Rate each statement from 1 (Strongly Disagree) to 5 (Strongly Agree). Answer based on your actual experience — not how you think you should feel. The value of this assessment depends entirely on your honesty.

    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.

    Awareness

    The Awareness stage is characterised by relevance-testing — assessing whether AI genuinely matters for your specific role and context. People in this stage are not yet evaluating AI's quality or safety; they are asking whether it connects to their real work (BCG, 2026).

    1. I can name at least two specific ways AI tools could change the work I do in my current role
    2. I understand why my organisation is asking people in my kind of role to engage with AI
    3. I have seen a concrete demonstration of AI being used for something genuinely similar to what I do
    4. I believe AI is relevant to my professional future, even if I have not started using it

    Investigation

    The Investigation stage involves risk evaluation at both practical and psychological levels. People assess whether AI outputs can be trusted and whether the social environment makes it safe to ask questions, admit uncertainty, and learn openly. Psychological safety is the gating condition for progress through this stage (Edmondson, 2019).

    1. I feel comfortable asking basic questions about AI in my team or organisation without worrying about how I am perceived
    2. I have tested or reviewed AI outputs enough to form a view on whether they can be trusted for my kind of work
    3. I know what kinds of errors or limitations to look for in AI outputs in my area of work
    4. I feel it is safe to voice scepticism about AI tools without being seen as resistant to change

    Skill-building

    The Skill-building stage is characterised by developing self-efficacy — the belief that you can use AI competently. It requires experimentation and a willingness to interpret disappointing initial results as learning rather than evidence of failure. The framing leaders create around experimentation at this stage determines whether genuine capability develops or productivity theatre results (Bandura, 1997).

    1. I am actively experimenting with AI tools and learning from what works and what does not
    2. When an AI tool produces a poor result, I treat it as information to learn from rather than evidence that AI does not work
    3. My confidence in using AI tools has grown noticeably over the past few months through practice
    4. I can describe specific things I have learned about working with AI that have improved the quality of my outputs

    Adoption

    The Adoption stage is characterised by normalisation — AI tools have become integrated into how you work rather than a separate thing you manage. People at this stage are no longer consciously thinking about 'using AI'; they are simply doing their work with AI as invisible infrastructure (BCG, 2026).

    1. I use AI tools without thinking about them as a separate activity — they are simply part of how I work
    2. I automatically consider whether AI can help me when I start a new task rather than remembering to try it
    3. I can teach colleagues how to use AI tools effectively in our specific context because I have built genuine experience
    4. My use of AI has become sophisticated enough that I am combining capabilities in ways that were not obvious at the start

    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.

    • Awareness Stage, 16 to 33: Your responses suggest you are in the Awareness stage — you are still forming a view on whether AI is genuinely relevant to your specific work. This is the correct starting point. The most valuable thing you can do right now is not to use AI tools but to find a demonstration of AI being applied to work that looks like yours. Role-specific, concrete examples are what move people out of Awareness. Abstract communications about AI's transformative potential rarely do. Ask someone in your organisation to show you a specific AI output for a task you recognise as your own. That single act of making relevance concrete is the most direct path forward.
    • Investigation Stage, 34 to 50: Your responses suggest you are in the Investigation stage — you have accepted that AI is relevant and are now evaluating whether it is safe and reliable enough to invest in. The critical variable at this stage is not the quality of the AI tools you are testing but the quality of the psychological environment around you. Can you ask basic questions without judgement? Can you admit uncertainty without professional risk? If not, addressing the environment is more urgent than improving your AI knowledge. If the environment is safe, the most useful thing you can do is to identify one specific task in your role and evaluate two or three AI tools against it systematically, keeping a simple note of what works and what does not.
    • Skill-building Stage, 51 to 65: Your responses suggest you are in the Skill-building stage — you are developing genuine competence and your self-efficacy is growing. The risk at this stage is encountering a disappointing result and treating it as evidence that AI does not work, rather than as the normal data of learning. The most useful discipline is structured reflection: after every significant AI-assisted piece of work, take five minutes to note what worked, what did not, and what you would do differently. Sharing these reflections with colleagues accelerates the learning of everyone around you and helps build the kind of tacit knowledge that separates genuinely skilled AI users from those who are simply using the tools.
    • Adoption Stage, 66 to 80: Your responses suggest you are in the Adoption stage — AI tools have become part of how you work rather than a separate thing you are managing. Your priority now is two things: ensuring that your adoption is genuinely sophisticated rather than habitual (that you are continuing to learn and combine capabilities in new ways), and sharing what you have built with colleagues who are earlier in the journey. You are one of the most valuable resources your organisation has for making AI adoption genuine rather than performative. Teaching what you know — through peer conversations, informal demonstrations, and honest sharing of what has not worked — compounds the value of your own development significantly.

    Sources named in this diagnostic

    • Bandura, A. (1997) Self-Efficacy: The Exercise of Control. New York: W.H. Freeman.
    • BCG Henderson Institute (2026) AI at Scale: The Human Factors That Determine Success. Boston: Boston Consulting Group.
    • Deloitte (2026) Global Human Capital Trends 2026. New York: Deloitte Insights.
    • Edmondson, A.C. (2019) The Fearless Organization. Hoboken, NJ: Wiley.
    • Prochaska, J.O. and DiClemente, C.C. (1983) Stages and processes of self-change of smoking: toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51(3), pp. 390–395.

    Where to take the next step

    Explore development for helping leaders and teams build the human capabilities needed for AI-related change.

    Explore AI Leadership Readiness

    Frequently asked questions

    What does the AI Adoption Stage Assessment (AISA) measure?

    Most AI adoption programmes fail because they assume everyone is at the same stage. The AISA framework, Awareness, Investigation, Skill-building, Adoption, maps the psychological patterns most people move through when working with new AI capabilities. It is descriptive rather than predictive: in practice, adoption is recursive, role-dependent, and shaped by social context. This assessment helps you identify where you are sitting most of the time, understand the dynamics at play, and know what support or action will move you forward. Based on research in change psychology, adult learning theory, and organisational behaviour (Prochaska and DiClemente, 1983; Bandura, 1997; Rogers, 2003; BCG, 2026).

    How many questions are in the AI Adoption Stage Assessment (AISA)?

    It contains 16 questions grouped into 4 dimensions: Awareness, Investigation, Skill-building, Adoption.

    How is the AI Adoption Stage Assessment (AISA) 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 Stage Assessment (AISA)?

    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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