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    Interactive assessment

    AI Readiness Diagnostic

    A structured assessment to evaluate your organisation's human readiness for AI adoption across five critical dimensions.

    This diagnostic helps leadership teams identify where human capability gaps are limiting AI adoption success. Research consistently shows that 95% of AI pilots fail to scale, and the primary cause is human readiness, not technology (RAND Corporation, 2024; McKinsey, 2025). Use this tool to pinpoint your development priorities.

    Questions or items
    20 questions or items
    Completion time
    Around 10 minutes
    Dimensions
    Leadership Mindset & Clarity, Psychological Safety, Decision-Making Frameworks, Manager Capability, Learning & Measurement
    What you receive
    Personalised results with result bands, section scores and development guidance.

    What it covers

    • Leadership Mindset & Clarity
    • Psychological Safety
    • Decision-Making Frameworks
    • Manager Capability
    • Learning & Measurement

    Rate each statement from 1 (Strongly Disagree) to 5 (Strongly Agree). Be honest, the diagnostic is most valuable when it reflects reality, not aspiration.

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

    How this interactive diagnostic works

    Read what it covers here first. Nothing is saved or sent until you continue. Your result is calculated once every required item is answered.

    If it asks for your first name and work email, you enter them in the diagnostic itself. This page sends nothing.

    Statements in this diagnostic

    Statements are grouped by section. Answer each one for the situation named in the instructions, using the scale shown.

    Leadership Mindset & Clarity

    Leaders with an adaptive mindset approach AI as a capability to develop, not a fixed trait (Dweck, 2006).

    1. Our senior leaders are genuinely aligned on why AI matters for our organisation
    2. Leaders approach AI with curiosity rather than anxiety or dismissiveness
    3. There is a clear, communicated vision for how AI will enhance (not replace) our work
    4. Leaders model learning behaviour, openly sharing what they don't know about AI

    Psychological Safety

    Psychological safety, the single strongest predictor of team performance, is essential for AI experimentation (Edmondson, 1999).

    1. Team members feel safe experimenting with AI tools without fear of blame if experiments fail
    2. People openly voice concerns about AI adoption without fear of being seen as resistant
    3. Managers acknowledge uncertainty about AI honestly rather than pretending to have all the answers
    4. When AI-related mistakes happen, the focus is on learning rather than blame

    Decision-Making Frameworks

    Clear decision rights reduce paralysis when humans and AI work together (Raisch and Krakowski, 2021).

    1. We have clear frameworks defining where AI augments and where human judgment is required
    2. Teams know when to trust AI recommendations and when to override them
    3. Decision protocols for human-AI collaboration are documented and understood
    4. There is a clear escalation process when AI outputs are questioned or seem wrong

    Manager Capability

    Middle managers are the translators of AI strategy into practice (DDI, 2025; Gallup, 2025).

    1. Managers have received specific development in leading teams through AI-driven change
    2. Managers feel confident coaching their teams through AI experimentation and adoption
    3. Managers have adequate time and support to develop their own AI understanding
    4. Manager spans of control allow for genuine people development (fewer than 10 direct reports)

    Learning & Measurement

    Organisations that succeed with AI measure by behaviour change and performance impact (Bersin, 2026).

    1. We measure AI adoption by performance impact, not just by how many people have access to tools
    2. Learning is embedded in daily workflows rather than delivered only through training events
    3. Teams regularly reflect on what they've learned from AI experiments
    4. We iterate on our AI approaches based on evidence rather than assumptions

    How the result is read

    Your answers are totalled for each section and overall. Each total falls into one of the bands below.

    • Critical Gap, 20 to 40: Significant human readiness investment needed before AI can scale.
    • Developing, 41 to 60: Some foundations in place but key gaps remain.
    • Capable, 61 to 80: Good human readiness with specific areas to strengthen.
    • Advanced, 81 to 100: Strong human readiness for AI adoption.

    Sources named in this diagnostic

    • Bersin, J. (2026) AI in Corporate Learning: The definitive analysis. The Josh Bersin Company.
    • DDI (2025) Global Leadership Forecast 2025. Pittsburgh, PA: Development Dimensions International.
    • Dweck, C.S. (2006) Mindset: The New Psychology of Success. New York: Random House.
    • Edmondson, A.C. (1999) 'Psychological safety and learning behavior in work teams', Administrative Science Quarterly, 44(2), pp. 350-383.
    • Gallup (2025) State of the Global Workplace Report. Washington, DC: Gallup Press.
    • McKinsey & Company (2025) The State of AI: How organisations are rewiring to capture value. McKinsey Global Institute.
    • Raisch, S. and Krakowski, S. (2021) 'Artificial intelligence and management: The automation-augmentation paradox', Academy of Management Review, 46(1), pp. 192-210.
    • RAND Corporation (2024) Factors that influence the success or failure of AI projects. Santa Monica, CA: RAND Corporation.

    Where to take the next step

    We help leadership teams build the human capabilities that make AI adoption succeed, from strategy to frontline behaviour change.

    Explore AI Leadership Readiness

    Frequently asked questions

    What does the AI Readiness Diagnostic measure?

    This diagnostic helps leadership teams identify where human capability gaps are limiting AI adoption success. Research consistently shows that 95% of AI pilots fail to scale, and the primary cause is human readiness, not technology (RAND Corporation, 2024; McKinsey, 2025). Use this tool to pinpoint your development priorities.

    How many questions are in the AI Readiness Diagnostic?

    It contains 20 questions grouped into 5 dimensions: Leadership Mindset & Clarity, Psychological Safety, Decision-Making Frameworks, Manager Capability, Learning & Measurement.

    How is the AI Readiness Diagnostic scored?

    Responses are totalled within each section and across the diagnostic, and each total falls into a result band.

    What happens after I complete the AI Readiness Diagnostic?

    Once the required items are answered, you enter your first name and email to see your results: an overall interpretation, section scores and development guidance.

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