To audit your team’s AI skills, compare what people can demonstrate with what their roles require—not just how confident they feel. Map AI-related work, define observable expectations, assess with realistic tasks as well as knowledge checks, and use the gaps to decide whether the answer is training, better tools, clearer rules, workflow changes, or specialist hiring.
What an AI skills audit should measure
Start with the work your organization expects people to do with AI. Build a role-by-capability map that distinguishes routine use from higher-stakes decisions and specialist responsibilities. A useful map covers:
- AI literacy: Understanding what a system is intended to do, where it can fail, and how to evaluate its output.
- Practical use: Choosing an appropriate tool, framing a task, reviewing and improving output, and fitting AI into a real workflow.
- Critical judgment: Checking accuracy and relevance, recognizing uncertainty, and knowing when human review or escalation is needed.
- Responsible use: Handling data appropriately, following organizational rules, considering ethical and legal implications, and maintaining accountability.
- Role-specific capability: Technical implementation and data management for specialists; strategy, risk oversight, governance, and change leadership for leaders; and domain judgment and safe use for general employees.
OECD public-sector guidance organizes capabilities into technical, managerial, and policy/legal/ethical areas, with literacy (“know-what”), operational (“know-how”), and attitudinal (“know-why”) dimensions. It recommends assessing existing data and AI capability, identifying gaps, and using the results to guide development. Its workforce context is public service, so adapt the categories to your own sector and roles. OECD framework for digital talent and skills in the public sector.
For a practical employer starting point, the UK Government’s AI skills tools package includes an Employer AI Adoption Checklist described as an organizational self-assessment for readiness, skills gaps, and inclusive adoption planning. Use it as a prompt to adapt—not as a universal scorecard. UK Government AI skills tools package.
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How to run the audit
- Set the scope. Name the business unit, workforce groups, and intended uses. Include both current and planned AI use; abstract tool knowledge alone will not tell you whether people can do their jobs safely and effectively.
- Map tasks to roles. For each role, list AI-related tasks, decisions, data handled, and where human accountability sits. A workforce framework can provide shared language for connecting capabilities to work roles and organizational needs; NIST describes this role-based purpose for frameworks. NIST NICE Framework Resource Center.
- Define the target before testing. Write observable behaviors for each proficiency level the work requires. Examples include identifying information that must not be entered into an unapproved system, verifying an output against an authoritative source, explaining when a human must decide, or configuring and evaluating a system when the role calls for it. Set expectations according to task complexity and consequences, not a universal percentage.
- 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 escalate uncertainty. Manager observation and work evidence can also help when used appropriately and fairly. Explain what you are assessing and how results will be used.
- Compare evidence with role targets. For each role-capability pair, record the required level, demonstrated level, and gap. Note how strong the evidence is and what the consequences of a gap could be. Low confidence alone does not prove low skill, and high confidence does not establish competence.
- Prioritize consequential gaps. Address gaps first when they affect high-impact work, sensitive information, safety, compliance, quality, or frequent tasks. Separate capability needs from other barriers, such as unclear policy, lack of approved tools, access problems, workflow design, or a need for specialist hiring.
- Choose an intervention and reassess. Match the response to the gap: guided exercises for routine use, scenarios for output review and escalation, leadership sessions for strategy and governance, or deeper technical development for specialist work. Afterward, reassess performance on the relevant task and update expectations when tools or workflows change.
Assessment design matters: a test can mislead when it does not measure the capability in question. OECD assessment guidance supports choosing methods that fit the target and interpreting results in context. OECD guidance on assessing and recognising AI skills.
Set different expectations for different roles
A single questionnaire can miss important differences between the people who use AI, those who govern its use, and those who build or manage systems. OECD’s 2026 public workforce report distinguishes general employees, leaders, and digital/data experts: general employees need effective and responsible use; leaders need strategic understanding, risk awareness, governance, and change capabilities; specialists need greater technical and regulatory depth. These are public-sector examples, not a ready-made corporate standard. OECD report on AI and the future of work in the public sector.
When adapting a framework, compare options on the criteria that affect whether they will be useful in your workplace:
- Role fit: Does it reflect actual tasks and distinguish general users, managers, and specialists?
- Evidence quality: Does it test 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 competencies and levels to local tools, policies, sector, and geography?
- Inclusion and usability: Can affected workers participate accessibly, and will results support development rather than become an opaque ranking?
- Maintenance: Is there a process to revisit expectations as systems and workflows change?
Frameworks you can adapt—and their limits
UK Government AI skills tools
The Employer AI Adoption Checklist is directly aimed at employers and describes an organizational self-assessment for readiness, skills gaps, and inclusive adoption planning. Adapt its prompts to your roles and policies; it is not evidence of a universal pass mark. Review the UK Government AI skills tools package.
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Use it for workforce needs assessment and for separating capability types and employee groups. Its public-service focus means you should tailor its examples to your organization. Read the OECD digital talent and skills framework and the 2026 public workforce report.
OECD/UNESCO G7 Toolkit and OPM model
The toolkit offers examples including the US Office of Personnel Management’s AI Competency Model and an EU competency structure. It reports that OPM’s model identifies over 43 general competencies and 14 technical skills. Those figures describe that model; they are not a recommended number of competencies for every organization. The toolkit’s EU example groups competencies into technology, managerial, and policy/legal/ethical areas, alongside attitudinal, operational, and literacy dimensions. OECD/UNESCO G7 Toolkit for AI in the public sector.
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Education frameworks
UNESCO’s teacher framework sets out 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.
The OECD/European Commission AI literacy framework published in 2026 is expressly for primary and secondary education. It may offer broad literacy concepts, but it is not a direct instrument for auditing a workforce. OECD/European Commission AI literacy framework.
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How to interpret results without inventing a universal score
The cited materials do not establish a universal team AI proficiency score or pass threshold. A survey of AI confidence alone is not an audit: the useful comparison is demonstrated capability against role-specific requirements. Keep results tied to the tasks and risks you assessed, and avoid treating a single aggregate score as a substitute for evidence by role and capability.
Training is only one possible response. If people cannot perform a task because the approved tool is unavailable, the rules are unclear, or the workflow is poorly designed, training alone will not close the gap. Use the audit to identify the right remedy and to revisit the capability map when the work changes.
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