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    Interactive assessment

    Decision Trace Readiness Diagnostic

    Evaluate how well your organisation captures the reasoning behind decisions — and whether your human wisdom is machine-readable.

    As AI agents begin executing autonomously, the institutional reasoning behind human judgment calls becomes the most valuable and most endangered asset in most organisations. A decision trace is the captured record of the reasoning behind a decision — not just the outcome. This diagnostic measures four dimensions of decision trace readiness: institutional memory, decision documentation, exception handling, and the AI-readiness of your human wisdom. Based on McKinsey's 2026 research on the knowledge organisation and Deloitte's 2026 Global Human Capital Trends report.

    Questions or items
    16 questions or items
    Completion time
    Around 10 minutes
    Dimensions
    Institutional Memory, Decision Documentation, Exception Handling, AI-Readiness of Human Wisdom
    What you receive
    Personalised results with configured interpretation bands, section scores and development guidance.

    What it covers

    • Institutional Memory
    • Decision Documentation
    • Exception Handling
    • AI-Readiness of Human Wisdom

    Rate each statement from 1 (Strongly Disagree) to 5 (Strongly Agree). Answer for the organisation or team you are assessing, based on what is consistently true rather than what applies to individual exceptions.

    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.

    Institutional Memory

    Institutional memory is the accumulated contextual knowledge that experienced people carry about why things work the way they do. When this knowledge lives only in people's heads, it is at constant risk of loss through attrition, retirement, or restructuring — and it is inaccessible to the AI agents that increasingly need it (McKinsey, 2026).

    1. When key people leave, the reasoning behind their major decisions typically leaves with them rather than being captured anywhere accessible
    2. New team members understand not just what decisions have been made but why they were made and what context shaped them
    3. There are structured mechanisms for capturing the tacit knowledge of experienced professionals before they transition out of roles
    4. The organisation can reconstruct the reasoning behind significant decisions made two years ago without relying on the memory of individuals who were present

    Decision Documentation

    Decision documentation is distinct from process documentation. Process documentation records how things are supposed to happen. Decision documentation records how experienced professionals actually think when they encounter situations that formal processes do not fully anticipate — the judgment calls, the contextual factors, and the reasoning applied (Nonaka and Takeuchi, 1995).

    1. Significant decisions in this organisation are documented with their reasoning — not just the outcome but the context, options considered, and judgment applied
    2. Decision documentation is stored in a form that is retrievable and accessible to people who were not present when the decision was made
    3. There is a consistent format or structure for capturing decision reasoning — it does not depend on individual initiative or style
    4. Decision records are updated when outcomes become apparent, so they reflect not just initial reasoning but the feedback loop that validated or corrected it

    Exception Handling

    Exceptions — the moments where context required deviation from standard procedure — are the richest sources of institutional wisdom. They are also the most consistently underdocumented. An AI agent operating autonomously in a domain that lacks captured exception reasoning is operating with a systematically incomplete model of how that domain actually functions (Deloitte, 2026).

    1. When the organisation deviates from standard procedure, the reasoning for that deviation is captured somewhere accessible
    2. The most experienced people in the organisation could articulate why the most common exceptions to standard process occur and what context drives them
    3. Exception patterns are reviewed and incorporated into updated guidelines or process documentation rather than remaining as individual workarounds
    4. AI systems operating in this organisation's workflows have access to the reasoning behind common exceptions, not just the standard procedures

    AI-Readiness of Human Wisdom

    For human wisdom to be useful to AI agents, it must be machine-readable: documented in a form that is structured, retrievable, and interpretable — not held in conversation, email threads, or individual memory. This dimension measures whether the organisation's human knowledge is in a form that AI systems can actually use (McKinsey, 2026).

    1. The decision knowledge captured in this organisation is stored in systems that AI tools can search and retrieve from, not only in documents that require a human to locate and interpret
    2. The organisation has mapped which categories of decisions most rely on undocumented human judgment and has a plan to capture that reasoning
    3. There is genuine awareness among leaders that AI agents operating in complex workflows need access to human decision context, not just process documentation
    4. The organisation treats its accumulated decision reasoning as a strategic asset that requires active management and protection, not as a by-product of how knowledgeable people happen to work

    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.

    • Critical Gap, 16 to 33: Your organisation's decision trace readiness is at a critical gap level. The reasoning behind significant decisions is not being captured, is not accessible, and is not in a form that could support AI agents operating in your workflows. The strategic risk is significant: as AI deployment expands, you are deploying intelligent systems into a domain where the intelligence accumulated by experienced humans is not available to them. The most urgent action is to identify the three to five decision categories in your organisation that most rely on tacit human judgment and treat capturing the reasoning behind those decisions as an immediate priority. Start with exit interviews for senior people leaving — this is the most time-sensitive intervention. A decision that walks out the door with a departing colleague is a decision that cannot be recovered.
    • Early Development, 34 to 50: Some elements of decision capture are in place in your organisation, but they are insufficient, inconsistent, or inaccessible. The most common pattern at this level is that documentation exists but is not retrievable — stored in individual folders, email threads, or departmental systems with no consistent structure or search capability. The highest-leverage next step is to establish a consistent decision documentation format and apply it to one category of high-value decisions for ninety days. The goal is not to document everything but to establish the discipline and demonstrate the value. Once teams have experienced finding a captured decision trace useful, adoption typically accelerates.
    • Developing, 51 to 65: Your organisation has meaningful decision trace capability in some dimensions, with specific gaps in others. Look at your section scores to identify where the gaps are concentrated. Exception handling is the most common weakness at this level — organisations document standard decisions reasonably well but rarely capture the reasoning behind deviations. If that is your pattern, a targeted exception documentation initiative — asking experienced professionals to document the five most common exceptions in their domain and the reasoning behind them — is a high-value, low-cost intervention. AI-readiness of human wisdom is the second most common gap, and it requires a more structural intervention: reviewing where captured knowledge is stored and whether it is in a form that AI tools can actually retrieve and use.
    • Advanced, 66 to 80: Your organisation has a strong foundation of decision trace capability across all four dimensions. Human wisdom is being captured, documented, and stored in accessible forms. The priority at this level is to ensure that your decision trace practice keeps pace with two accelerating pressures: the expansion of AI deployment into more complex workflows, which increases the demand for accessible decision context; and the increasing pace of workforce change, which increases the risk of knowledge loss through attrition. Two governance practices are worth establishing: a regular review of which decision categories most need updated documentation given changes in the operating environment, and an explicit step in all AI deployment decisions that asks what decision reasoning the AI agent will need access to and whether it is available.

    Sources named in this diagnostic

    • McKinsey & Company (2026) The State of Organizations 2026. New York: McKinsey Global Institute.
    • Deloitte (2026) Global Human Capital Trends 2026. New York: Deloitte Insights.
    • Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating Company. New York: Oxford University Press.
    • Polanyi, M. (1966) The Tacit Dimension. Chicago: University of Chicago Press.
    • Dixon, N.M. (2000) Common Knowledge: How Companies Thrive by Sharing What They Know. Boston: Harvard Business School Press.

    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 Decision Trace Readiness Diagnostic measure?

    As AI agents begin executing autonomously, the institutional reasoning behind human judgment calls becomes the most valuable and most endangered asset in most organisations. A decision trace is the captured record of the reasoning behind a decision — not just the outcome. This diagnostic measures four dimensions of decision trace readiness: institutional memory, decision documentation, exception handling, and the AI-readiness of your human wisdom. Based on McKinsey's 2026 research on the knowledge organisation and Deloitte's 2026 Global Human Capital Trends report.

    How many questions are in the Decision Trace Readiness Diagnostic?

    It contains 16 questions grouped into 4 dimensions: Institutional Memory, Decision Documentation, Exception Handling, AI-Readiness of Human Wisdom.

    How is the Decision Trace Readiness 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 Decision Trace Readiness 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.