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    Interactive assessment

    Team AI Readiness Check

    A team-level assessment of how well your group is navigating augmented working across four dimensions: psychological safety, role clarity, AI capability, and ethical practice.

    Most AI readiness tools focus on individual fluency or organisational strategy. This diagnostic focuses on the team as a unit — the level where AI adoption succeeds or stalls in practice. Research from Edmondson (2019), EY (2025), and McKinsey (2026) consistently points to team-level dynamics as the key determinant of whether AI tools generate real performance improvement or just compliance metrics.

    Questions or items
    16 questions or items
    Completion time
    Around 10 minutes
    Dimensions
    Psychological Safety, Role and Workflow Clarity, AI Capability, Ethical Practice
    What you receive
    Personalised results with configured interpretation bands, section scores and development guidance.

    What it covers

    • Psychological Safety
    • Role and Workflow Clarity
    • AI Capability
    • Ethical Practice

    Rate each statement from 1 (Rarely or never true of our team) to 5 (Consistently and reliably true). Think about how your team actually behaves, not what you aspire to or what the policy says. Honest responses produce the most useful results.

    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.

    Analytics consent is required before the interactive experience is mounted. Declining consent does not start the diagnostic or its tracking. If you continue, the existing 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.

    Psychological Safety

    Edmondson's (2019) research demonstrates that psychological safety is the strongest predictor of team learning behaviour — including adoption of new tools and ways of working. Teams without it comply. Teams with it actually learn.

    1. Team members openly discuss their concerns and uncertainties about AI tools without fear of judgment or being seen as resistant
    2. When an AI-assisted experiment goes wrong, the team focuses on what to learn rather than who is to blame
    3. People feel comfortable saying 'I don't know how to use this' without it affecting how they are perceived in the team
    4. The team creates space for people at different stages of AI comfort to contribute their expertise rather than being left behind

    Role and Workflow Clarity

    Buell and Kagan (2026) found that when AI is perceived as both tool and team member, traditional accountability frameworks break down. Clear role definition is what prevents the invisible gaps — review steps nobody is doing, accountability nobody is holding.

    1. The team has a shared, explicit understanding of which tasks are automated and which require human judgement
    2. It is clear who is accountable for outputs produced with AI assistance — there are no invisible gaps where nobody is reviewing
    3. There is an agreed, known process for handling situations where AI outputs seem wrong, biased, or questionable
    4. Team members understand how their individual roles are evolving as more tasks become augmented

    AI Capability

    EY (2025) found that 85% of workers learn about AI outside of work and 59% cite inadequate organisational support as a barrier. Genuine team capability grows through shared practice and peer learning — not just individual self-study.

    1. Most team members can use at least one AI tool relevant to the team's work with reasonable confidence
    2. AI knowledge spreads informally within the team — people share what they have learned rather than keeping it to themselves
    3. The team regularly experiments with AI for new tasks, not just the ones where tools have been established for a while
    4. Team members know when to apply AI assistance and when human-only work or judgement is more appropriate

    Ethical Practice

    Xu et al. (2025) found that ethical leadership is the primary moderator of AI adoption's negative psychological impact on teams. Ethical practice at team level means critical review, openness about concerns, and maintaining clear human accountability.

    1. The team reviews AI-generated outputs critically rather than accepting them at face value — applying professional judgement to what the tool produces
    2. Concerns about the fairness, accuracy, or appropriateness of AI tools in use are raised and discussed openly within the team
    3. Team members whose roles are most affected by AI changes are actively supported and consulted, not just informed about decisions already made
    4. The team maintains clear human accountability for all outcomes, even where AI has contributed significantly to the process

    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.

    • Foundation, 16 to 35: Your team's readiness for augmented working is at an early stage. There are likely significant gaps in at least two of the four dimensions, and the risk is that AI tools are being adopted (or avoided) without the relational and structural foundations that make adoption actually work. Review your section scores to find the lowest-scoring area. For most teams at this level, the starting point is psychological safety: hold one honest team conversation about what augmented working means for the team, naming both what is working and what is uncertain. This conversation, held well, creates the conditions for progress in every other dimension.
    • Developing, 36 to 55: Your team has genuine strengths in some dimensions and significant gaps in others. Progress is happening, but unevenly. The most valuable next step is to look at the section that scored lowest and treat it as a team development focus for the next four to six weeks. The four dimensions are interconnected — improving psychological safety usually unlocks progress in capability and ethical practice. Improving clarity in roles and workflows usually reduces the anxiety that blocks psychological safety. Choose the lowest-scoring area and go there first.
    • Progressing, 56 to 68: Your team is navigating augmented working well in most areas. There are genuine strengths to build on and likely one or two dimensions where more consistency would make a significant difference. Teams at this level often find that their weakest area is ethical practice — not because of bad intent, but because critical review and governance conversations tend to be the last thing teams formalise. If that is your pattern, making AI output review a regular team ritual is the highest-leverage next step.
    • Leading, 69 to 80: Your team is operating at a high level across all four dimensions of AI readiness. Psychological safety supports experimentation. Roles and accountability are clear. Capability is developing through peer learning. Ethical practice is embedded in how the team works. The risk at this stage is complacency: the landscape is changing rapidly, and what constitutes good practice in augmented working in 2026 will look different in 2027. The leading practice at this level is to share what your team has learned — with peer teams, with your manager, and with the wider organisation.

    Sources named in this diagnostic

    • Buell, R. and Kagan, J. (2026) What Leadership Looks Like in an Agentic AI World. Harvard Business School Working Knowledge.
    • DDI (2025) Global Leadership Forecast 2025. Development Dimensions International.
    • Edmondson, A. (2019) The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth. Hoboken: Wiley.
    • EY (2025) EY Survey Reveals Majority of Workers Are Enthusiastic About Agentic AI. EY Newsroom.
    • McKinsey & Company (2026) Six Shifts to Build the Agentic Organization of the Future. McKinsey Organization Blog.
    • Xu, G. et al. (2025) The dark side of artificial intelligence adoption: Linking AI adoption to employee depression via psychological safety and ethical leadership. Humanities and Social Sciences Communications, 12(1).

    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 Team AI Readiness Check measure?

    Most AI readiness tools focus on individual fluency or organisational strategy. This diagnostic focuses on the team as a unit — the level where AI adoption succeeds or stalls in practice. Research from Edmondson (2019), EY (2025), and McKinsey (2026) consistently points to team-level dynamics as the key determinant of whether AI tools generate real performance improvement or just compliance metrics.

    How many questions are in the Team AI Readiness Check?

    It contains 16 questions grouped into 4 dimensions: Psychological Safety, Role and Workflow Clarity, AI Capability, Ethical Practice.

    How is the Team AI Readiness Check 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 Team AI Readiness Check?

    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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