Build an AI skills policy by setting a practical literacy baseline for everyone, adding competencies according to each person’s work and decision-making responsibility, and checking capability through role-relevant practice—not attendance alone. The policy should also explain how employees learn, where they can raise concerns, and when requirements are reviewed. There is no universal company curriculum or passing score: adapt the policy to your tools, tasks, location, and sector.
What an AI skills policy should cover
An AI skills policy defines what people need to understand and do when AI intersects with their work, and how the organization will help them develop those capabilities. It is more than a prompting guide: employees may need to select appropriate tasks, evaluate outputs, protect information, recognize risks, and know when to ask for human or specialist review.
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Start by stating which teams, work activities, and AI systems are in scope. Connect the policy to actual work—for example, drafting, analysis, decision support, system development, or tool acquisition. These boundaries are an organizational design choice, not a ready-made scope supplied by the cited frameworks.
Assign expectations by role, not by one universal checklist
The OECD AI Skills for Business Competency Framework, developed by The Alan Turing Institute for businesses and training providers, distinguishes four audiences. Treat them as adaptable categories rather than mandatory job titles: a small company may combine them, while a larger one may need more detailed job-family profiles. The framework entry was updated on 25 December 2025 (OECD AI Skills for Business Competency Framework).
| Audience | Who it describes | Policy emphasis |
|---|---|---|
| AI Citizens | People who encounter organizations using AI. | A realistic understanding of AI capabilities, opportunities, and risks. |
| AI Workers | Employees whose main role is not data-focused but whose work may be affected by AI. | Appropriate use for relevant tasks, sound judgment, and review of outputs. |
| AI Professionals | Staff whose primary responsibilities involve data and AI, such as data analysts, machine-learning engineers, and data ethicists. | Technical and cross-disciplinary capabilities for building, implementing, analyzing, or evaluating AI systems. |
| AI Leaders | Senior staff responsible for acquiring and governing AI solutions. | Governance, procurement literacy, workforce implications, and responsible implementation. |
People can belong to more than one audience: a manager might use AI in everyday work and also approve a tool. Assign expectations according to what someone does and decides, rather than relying only on their title.
Set a shared baseline for all staff
Every employee in scope should receive guidance relevant to the AI systems and work they encounter. A practical baseline can ask staff to:
- Understand what AI can and cannot reliably do in their work context.
- Use approved systems for suitable tasks and follow the organization’s tool rules.
- Check outputs, recognize uncertainty or error, and seek human review when needed.
- Handle personal, confidential, or otherwise sensitive information according to organizational rules.
- Notice potential harms, risks, or misuse and raise concerns through established channels.
The OECD’s general AI-literacy guidance supports a baseline for broad audiences alongside advanced expertise (OECD, Bridging the AI skills gap: Is training keeping up?, 24 April 2025). The organization must specify which systems are approved, what data may be entered, what review is required, and how issues are escalated; those operational rules are not settled by a general skills framework.
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Add competencies for specialist and leadership work
For AI Workers, identify tasks where AI is appropriate and specify how much checking or human judgment those tasks require. For AI Professionals, set requirements tied to their actual responsibilities—such as data, system design, analysis, implementation, or evaluation—and include the ability to work across disciplines. For AI Leaders, include oversight of acquisition and governance, an understanding of workforce effects, and the ability to enable responsible implementation.
The business competency framework organizes its coverage into five dimensions. Use them to identify gaps in role profiles rather than treating every dimension as equally necessary for every employee:
- Privacy and stewardship.
- Specification, acquisition, engineering, architecture, storage, and curation.
- Problem definition and communication.
- Problem solving, analysis, modeling, and visualization.
- Evaluation and reflection.
Include human skills that make AI use safer and more effective
AI capability is not just tool operation. OECD’s 2026 report identifies critical thinking, creativity, collaboration, and continued learning as complementary skills as work and technology change (OECD, Skills in the AI age, 8 July 2026). Turn these into observable expectations: employees might check assumptions behind an output, explain a decision, collaborate with the people affected by AI-supported work, and seek expertise when a task exceeds their competence.
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Choose learning that employees can access and apply
Make development continuous rather than a one-time launch course. OECD recommends employer-led training, ongoing upskilling, flexible modular pathways, and alignment with changing workplace needs. Possible implementation formats include short modules, practice in approved work settings, peer learning, and expert-led instruction; these are options for your organization, not formats mandated by the cited guidance.
When selecting training or a framework, compare its audience coverage, practical-use and evaluation content, privacy and stewardship coverage, fit to actual work, accessibility and modularity, assessment approach, and update process. These are decision criteria drawn from the framework and OECD recommendations, not a published ranking. Set review points for the policy and learning materials when tools, tasks, or applicable requirements change.
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Define role-relevant evidence that employees can use AI appropriately and meet their responsibilities. A work scenario or demonstration might ask a person to choose whether AI is suitable for a task, evaluate an output, protect information, and follow the required review or escalation process. Participation records can show who received training, but they do not by themselves establish competence.
This assessment approach is a policy-design implication of competency frameworks. The sources do not prescribe one universal employer assessment, benchmark, or passing score. Set expectations proportionate to the role and risk, and use results to identify where training or policy guidance needs improvement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Adapt the policy to jurisdiction and work context
Requirements vary with location, sector, data, and how AI is used. The U.S. Department of Labor’s AI literacy framework, announced on 13 February 2026, is a national resource intended to guide program design while allowing adaptation across industries, roles, education sectors, and workforce contexts (U.S. Department of Labor announcement). Secretary of Labor Lori Chavez-DeRemer said: “Our new AI Literacy Framework provides guidance that will help accelerate effective AI skill development across the country.” The announcement is not evidence of a universal private-employer legal mandate.
The OECD AI Principles offer broader policy context: they call on governments to equip people with skills to use and interact with AI, support fair worker transitions through training, and promote responsible AI use at work (OECD AI Principles). Before adopting a policy, check the rules and sector obligations applicable to your organization and the specific use cases it handles.
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Use workforce statistics as context, not a company forecast
OECD reported that AI adoption among firms in OECD countries rose from around 7% to 20% between 2021 and 2025, with generative AI contributing to the increase. It also reported that around one-quarter of workers were exposed to generative AI in 2022–2024 and that workers with advanced AI skills represented around 1% of the workforce (OECD, Skills in the AI age, 8 July 2026). Exposure does not mean job loss, and these aggregate figures do not predict what an individual employee or company will experience.
The same report discusses OECD 2024 data from 21 member countries: 14 had invested in AI-specific publicly funded training programmes, nine targeting AI professionals and seven aiming to build AI literacy among the general public. These figures describe public programmes, not employer obligations or proof of a particular training programme’s effectiveness.
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