To audit your team’s AI skills, compare what people can demonstrate against the capabilities their roles require—not just how confident they feel using AI. Map the work and risks, set observable expectations, assess with realistic tasks as well as knowledge checks, then decide whether each gap calls for training, better tools, clearer policy, workflow changes, or specialist support.
What an AI skills audit should measure
Start with work your organization does or plans to do with AI. For each role, identify the tasks involved, the decisions people make, the data they handle, and where human accountability sits. Then describe the capabilities needed to do that work safely and well.
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A useful capability map can cover:
- AI literacy: understanding what a system is intended to do, where it can fall short, and how to evaluate its output.
- Practical use: choosing an appropriate tool, framing a task, improving its output, and fitting it into a real workflow.
- Critical judgment: checking accuracy and relevance, recognizing uncertainty, and knowing when to seek human review or escalate.
- Responsible use: handling data appropriately, following organizational rules, considering relevant ethical and legal issues, and maintaining accountability.
- Role-specific capability: technical implementation and data management for specialists; strategy, risk oversight, governance, and change leadership for managers; and sound domain judgment and safe use for general employees.
OECD public-sector guidance groups capabilities into technical, managerial, and policy, legal, and ethical areas, alongside cross-cutting literacy (know-what), operational (know-how), and attitudinal (know-why) dimensions. It also recommends assessing existing data and AI capability, identifying gaps, and using the results to shape a development strategy. Those categories are a useful starting point, but their public-sector setting matters when adapting them to a company or another sector: OECD guidance on AI skills and the public workforce.
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Do not give everyone the same test by default. OECD’s 2026 public workforce report distinguishes general employees, leaders, and digital and data experts: the groups need different levels of practical, strategic, risk, governance, technical, and regulatory capability. Use that distinction as a prompt to define your own roles, not as a universal job taxonomy: OECD report on skills and training for an AI-ready public workforce.
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A seven-step audit
1. Set the scope
Name the business unit, workforce groups, and intended uses in scope. Include both current and planned AI use. An abstract quiz about tools cannot tell you whether people can handle the work your organization expects them to do.
2. Map tasks to roles
For each role, list AI-related tasks, decisions, data handled, and points of human accountability. A workforce framework can help give teams a shared vocabulary for connecting capabilities to roles and organizational needs. NIST describes workforce frameworks in those terms: NIST Workforce Framework for AI.
3. Define the target before measuring
Write a small set of observable behaviors for each capability and proficiency level the role needs. Depending on the work, examples might include identifying information that must not be entered into an unapproved system, checking an AI-generated answer against an authoritative source, explaining when a human must make the decision, or configuring and evaluating a system. Set expectations according to task complexity and consequences; there is no universal team AI score or pass mark.
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4. Gather more than self-ratings
Combine a confidence or familiarity survey with short knowledge checks and realistic work samples or scenarios. Ask people to demonstrate how they would complete a task, check an output, protect data, or respond to uncertainty. Where appropriate and fair, add manager observation or relevant work evidence. Tell participants what is being assessed and how results will be used.
Self-ratings can help surface concerns or confidence gaps, but they do not establish capability on their own. A confident employee may still miss a risk, while a cautious employee may perform well in practice.
5. Compare evidence with role requirements
For each role-capability pair, record the demonstrated level, the required level, and the difference. Note how strong the evidence is and what the consequences of a gap could be. Avoid treating a single score as a complete picture of a person or team.
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6. Prioritize consequential gaps
Address gaps first when they affect high-impact work, sensitive information, safety, compliance, quality, or frequent tasks. Then identify what kind of response fits the cause. Some issues call for skill-building; others point to unclear policy, unapproved or inadequate tools, poor workflow design, access problems, or a need for specialist hiring.
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Match the response to the gap: guided practice for routine use, scenario exercises for review and escalation, leadership sessions for strategy and governance, or deeper technical development for specialist work. Reassess performance on the relevant task after the intervention, and update the map when tools, workflows, or responsibilities change.
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Assessment design matters. OECD work on AI skills assessment cautions that a test can mislead when it measures something other than the capability it is meant to represent. Use tasks that resemble the work and interpret results in context: OECD analysis of assessing AI skills in the workforce.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose or adapt a framework
Published frameworks can provide useful categories and prompts, but none should be treated as a universal corporate scorecard. Check each option against the work, workforce, and risks you actually need to assess.
- UK Government AI skills tools: The employer AI adoption checklist is described as an organizational self-assessment for readiness, identifying skills gaps, and planning inclusive adoption. Review the package and adapt it to your roles, tools, and policies: UK Government AI skills tools for business.
- OECD public-sector guidance: Useful for thinking about workforce needs assessment, capability categories, and differences among workforce groups. Its public-service context should remain explicit when applying it elsewhere.
- OECD/UNESCO G7 Toolkit: Offers examples including the US Office of Personnel Management’s AI Competency Model and an EU competency structure. The toolkit reports that OPM’s model identifies over 43 general competencies and 14 technical skills. These are counts in that model, not a recommended number for every organization: OECD/UNESCO G7 Toolkit for AI in the public sector.
- UNESCO teacher framework: Provides an education-sector example with 15 competencies across five dimensions and three progression levels. It is designed for teachers, not as a general corporate audit standard: UNESCO AI competency framework for teachers.
- OECD/European Commission learner framework: The 2026 AI literacy framework is expressly for primary and secondary education. It may inform broad literacy concepts, but it is not a direct workforce assessment instrument: OECD/European Commission AI literacy framework for primary and secondary education.
Compare options on six practical axes
- Role fit: Does it reflect actual tasks and distinguish general users, managers, and specialists where necessary?
- Evidence quality: Does it assess demonstrated performance, or only confidence and familiarity?
- Risk coverage: Does it address data handling, human accountability, output evaluation, ethics, and applicable rules?
- Adaptability: Can you tailor capabilities and proficiency levels to local tools, policies, sector, and geography?
- Inclusion and usability: Can affected workers participate accessibly, and will results support development rather than create an opaque ranking?
- Maintenance: Is there a way to revisit expectations as systems and workflows change?
Turn findings into a useful training plan
Keep the audit’s findings tied to the role and task. A team-level statement such as “AI skills are weak” is too broad to guide action. Record the capability, required behavior, observed evidence, risk or impact, and appropriate response. For example, if staff can draft with an approved tool but cannot reliably verify factual outputs, use realistic verification exercises and assess that behavior again; if the underlying problem is that staff lack an approved tool or clear data-handling rules, training alone will not fix it.
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