Build an AI skills policy by setting a practical literacy baseline for everyone, adding competencies according to each person’s role, and supporting those expectations with ongoing learning and evidence of capability. Employees do not all need to become AI specialists: the right skills depend on the work they do, the systems they use, and the decisions they influence.
What an AI skills policy should cover
An AI skills policy defines what people need to understand and do when AI touches their work, how the organization will help them develop those capabilities, and how responsible use is reinforced in routine tasks. It should connect learning to actual work—such as drafting, analysis, decision support, system development, or tool acquisition—rather than treating AI literacy as a generic software tutorial.
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Set the policy’s scope explicitly: name the teams, activities, and AI systems it covers. Define organizational rules for approved tools, sensitive data, review, and escalation; a literacy framework can inform those rules but cannot set them for your organization.
Start with a shared baseline, then map skills to roles
OECD guidance distinguishes general AI literacy from advanced AI expertise. The OECD.AI Policy Navigator’s AI Skills for Business Competency Framework, developed by The Alan Turing Institute for businesses and training providers, offers four adaptable audience categories. They are not mandatory job titles: a small company may combine them, while a larger one may define more detailed job-family profiles. The entry was updated on 25 December 2025.
| Audience | Who it describes | Policy emphasis |
|---|---|---|
| AI Citizens | People who encounter organizations using AI. | Realistic understanding of AI capabilities, opportunities, and risks. |
| AI Workers | Employees whose primary role is not data-focused but whose work may be affected by AI. | Appropriate use of AI in relevant tasks, with sound review and judgment. |
| AI Professionals | People whose main responsibilities involve data and AI, such as data analysts, machine-learning engineers, and data ethicists. | Technical and cross-disciplinary capability for designing, implementing, analyzing, or evaluating AI-related work. |
| AI Leaders | Senior staff responsible for acquiring and governing AI solutions. | Governance, procurement literacy, workforce effects, and responsible implementation. |
Use a shared baseline for all staff, then specify what changes by role. A useful baseline asks each employee to:
- Understand what AI can and cannot reliably do in the context of their work.
- Use approved systems for appropriate tasks.
- Check outputs and recognize when human review is needed.
- Handle data and sensitive information according to organizational rules.
- Recognize potential risks, harms, and misuse, and know how to raise a concern through established channels.
These expectations go beyond prompting. The framework spans privacy and stewardship; specification, acquisition, engineering, architecture, storage, and curation; problem definition and communication; problem solving, analysis, modeling, and visualization; and evaluation and reflection. Not every dimension applies equally to every role.
Define role-specific competencies
For AI Workers
Identify the tasks where AI is appropriate and state what the employee remains responsible for. For example, a policy can distinguish AI-assisted drafting from approving or sending a final communication, and set review expectations for each. The details should follow the task’s consequences and the organization’s rules, rather than assume that every AI-assisted result needs the same level of checking.
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Rank #2
For AI Professionals
Set additional expectations for the technical work people actually perform: this may include data stewardship, system design, engineering, architecture, analysis, implementation, or evaluation. Include the ability to communicate problem definitions and work across functions; AI specialists often need to coordinate with people responsible for business processes, governance, or affected users.
For AI Leaders
Require leaders who acquire or govern AI solutions to understand relevant risks and responsibilities, consider how implementation may affect the workforce, and support responsible use across teams. Their policy obligations should include decisions about acquisition and oversight, not just personal familiarity with AI tools.
Include human skills that complement AI
OECD’s Skills in the AI age, published 8 July 2026, identifies critical thinking, creativity, collaboration, and continued learning as complementary skills for effective interaction with AI and changing tasks. Translate these into observable work expectations: check assumptions, explain consequential decisions, collaborate across functions, and seek expertise when a task exceeds one’s knowledge or authority.
Rank #3
AI uptake does not make advanced expertise the right baseline for everyone. OECD reports that uptake among firms in OECD countries rose from around 7% in 2021 to 20% in 2025, while workers with advanced AI skills represented around 1% of the workforce. These OECD-wide figures describe broader context; they do not establish the needs or likely outcomes of a particular team.
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OECD recommends employer-led training, continuous upskilling, and flexible, modular learning aligned with workplace change. Build learning around varied roles and time constraints. Short modules, supervised practice with approved systems, peer learning, and expert-led instruction are possible implementation choices, not formats mandated by the cited guidance.
Set a review point for both policy and training when tools, tasks, or applicable requirements change. OECD’s 2025 brief, Bridging the AI skills gap: Is training keeping up?, notes that training supply may not be sufficient for current and future needs and calls for broad AI literacy alongside advanced expertise; it does not prescribe a universal company curriculum. OECD’s AI Principles also provide policy context by recommending skills development, fair worker transitions through training, and responsible AI use at work. They are recommendations, not a statement of a particular employer’s legal duties.
Assess practical capability, not attendance
Define role-relevant evidence that employees can use AI appropriately, question outputs, protect information, and follow governance responsibilities. Demonstrations or work scenarios can test these capabilities more directly than course completion alone. Track participation as a learning-program measure, but do not treat attendance as proof of competence.
The reviewed frameworks do not prescribe one assessment method or passing score for every employer. Choose evidence that matches the job and risk: for instance, a scenario can ask a worker to identify when an output needs review, while a specialist exercise can assess relevant evaluation or data-stewardship practice. Document how results inform coaching, access, or further training rather than treating a single score as universal.
Choose frameworks and training for your context
When selecting a framework or learning material, compare its audience coverage and fit to actual tasks. Check whether it addresses practical use, critical evaluation, privacy and stewardship, and responsible use; whether it can be delivered accessibly in modular form; how capability is assessed; and how material is updated. These are practical comparison criteria drawn from the framework dimensions and OECD training recommendations, not a published ranking.
The U.S. Department of Labor announced an AI literacy framework on 13 February 2026 with five foundational content areas and seven delivery principles. The Department says it is intended to guide program design while allowing adaptation across industries, roles, education sectors, and workforce contexts. U.S. 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.” Treat it as a national resource that may inform adaptation, not as a universal private-employer mandate or a substitute for checking local rules and sector obligations.
Jurisdiction, industry, organization size, existing systems, and regulated use cases all affect what a policy needs to address. The OECD figures and frameworks provide broad workforce context, but they do not determine jurisdiction-specific obligations or supply a universal assessment threshold.
Quick Recap
A practical sequence for putting the policy into use
- Set scope: list covered teams, work activities, and AI systems, and connect the policy to the work outcomes it is meant to support.
- Map audiences: assign roles to the AI Citizen, Worker, Professional, or Leader categories as useful, combining or subdividing them to fit the organization.
- Write the baseline: specify expected understanding, approved use, output review, data handling, risk recognition, and escalation routes for all staff.
- Add role requirements: define task-specific judgment for workers, technical and cross-disciplinary capabilities for professionals, and governance and workforce responsibilities for leaders.
- Plan learning and refreshes: provide accessible learning suited to different roles and set triggers to revisit materials as work, systems, and requirements change.
- Check evidence and improve: use role-relevant scenarios or demonstrations, identify capability gaps, and update training and policy in response.
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