AI Skills FrameworkA framework for defining, developing, and assessing AI skills for professionals
Artificial intelligence is transforming every profession, not just technical roles. To help organisations build the capabilities needed to use, build, integrate and frame AI responsibly, we developed the AI Skills Framework: a clear, practice-based model that translates AI competence into concrete, assessable skills.
Explore the full framework
Dive deeper into skill components, domains, and projections in our full interactive version.
Why a new framework is needed
Many existing AI literacy and future-skills frameworks raise awareness but fall short when it comes to professional, role-specific skills. They rarely describe the abilities needed to identify business use cases, ensure regulatory compliance, or deploy AI systems reliably at scale.
Also, organisations face recurring questions:
- Which skills do our teams actually need?
- How do we assess current capabilities?
- How do we build coherent learning pathways?
- How do we ensure compliance with obligations such as Article 4 of the EU AI Act?
The AI Skills Framework closes this gap. It unifies technical, organisational, and governance-related skills into a coherent structure, developed through applied research, expert review and real-world testing with partner organisations.
Who the framework is for
The framework covers the four ways professionals interact with AI, the essential entry points for skill development:
Using AI
Professionals who apply AI tools in their daily work, such as knowledge workers, teachers, analysts, managers and public-sector employees, as well as anyone who integrates AI into their workflows.
Integrating AI
Leaders and practitioners who embed AI into processes, manage change, and align adoption with organisational strategy and regulation.
Building AI
Engineers, developers, and researchers who design, train, deploy, and maintain AI systems.
Framing AI
Policymakers, strategists, ethicists, and communicators who set guardrails, shape narratives, and ensure responsible governance of AI.
Core principles of the framework
Skill components: concrete, demonstrable abilities
The framework consists of skill components, which are clearly defined, practice-oriented skills such as:
- Applying design patterns in software development
- Demonstrating compliance with high-risk AI requirements
- Using version control systems, including branching workflows
- Evaluating the financial and strategic value of AI initiatives
These are the skills professionals can realistically demonstrate, develop and be assessed on.
Five levels of mastery
Each skill component is structured along a progression from awareness to mastery:
- Unknown: no familiarity
- Knows about it: conceptual awareness
- Can use it: application in familiar settings
- Can adapt it: confident, flexible application
- Lives it: mastery, autonomy, and the ability to guide others
<INSERT VISUAL: SKILL COMPONENT STRUCTURE WITH LEVELS + SUB-SKILLS>
Multiple projections for clarity and comparison
Skill components can be viewed from different angles, enabling precise assessment and tailored development:
- Purpose projection: using, Integrating, Building, Framing
- Domain projection: e.g. AI Strategy, AI Ethics, Data Competence, Programming, Machine Learning, GenAI, MLOps
- Role projections: clusters of skills typical for professional profiles
- System projections: skills needed for specific AI applications
This flexibility helps organisations surface skill patterns, compare roles, and design targeted upskilling programmes.
Transversal skills as connective tissue
Collaboration, communication, problem-solving, and critical thinking underpin all AI-related work. While not standalone AI skills in the framework, these transversal skills are essential for applying technical, organisational, and regulatory skills effectively.
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