SFIA 9 - a framework for AI skills

The SFIA Foundation's approach to integrating AI-related skills into SFIA 9 is driven by practical considerations and a focus on the enduring professional skills and competencies for the workplace.

Contents

1. Challenges in defining AI Skills
2. SFIA 9: Supporting AI capabilities
3. Core principles
4. AI and data literacy
5. Building AI/ML solutions
6. Operationalising AI
7. Productivity enhancement
8. Workforce transformation
9. Practical application
10. Implementation guidance
11. Looking forward

Challenges in defining AI Skills

There are a number of challenges in defining AI-related skills due to the rapidly evolving nature of AI technologies. These challenges include:

• Pace of change: AI technologies are advancing rapidly, making it difficult to create a static framework that remains relevant over time. New tools, platforms, and methodologies are constantly emerging.
• Breadth and depth: SFIA is a broadly used framework. AI encompasses a wide range of subfields, from machine learning and natural language processing to robotics and ethical considerations. Balancing the depth of coverage without becoming overly specific is a challenge.
• Integration with existing skills: AI skills need to be integrated with existing professional skills in a way that complements and enhances them, rather than creating siloed specialisations. Employers may need to localise the SFIA skills to their specific tools, platforms, and methodologies as these are constantly emerging.
• Industry variation: Different industries adopt and apply AI in various ways. Employers may need to localise the SFIA skills to their specific tools, platforms, and methodologies are constantly emerging.
• Ethical and legal considerations: As AI technologies raise significant ethical and legal issues, the framework must address these aspects comprehensively.
• SFIA User needs: Ensuring that the framework remains practical and useful for current SFIA users, while also being adaptable for future advancements in AI.

SFIA 9: Supporting AI capabilities

SFIA provides a flexible framework that describes the professional skills needed for working with AI, without prescribing specific methodologies or technologies.

• This approach ensures organisations can adapt SFIA to their chosen AI platforms, tools and approaches while maintaining focus on enduring professional capabilities.
• Rather than attempting to predict or mandate how AI should be implemented, SFIA describes the fundamental skills needed to work effectively with AI technologies, allowing organisations to build sustainable capabilities that evolve with technological advancement.
• SFIA is not an academic or theoretical framework. It has a long and successful track record of use in industry and this practical approach has proven valuable across previous technological transformations and continues to support organisations navigating the opportunities and challenges of AI adoption.

SFIA works effectively alongside specialist frameworks and standards for AI and data literacy, providing the professional skills context while other frameworks deliver detailed technical guidance.

• For example, SFIA can complement AI capability frameworks, data literacy standards, and specific AI governance models.
• This complementary approach allows organisations to combine SFIA's proven skills framework with targeted methodologies, tools, and standards that match their specific AI implementation needs.
• Organisations can use SFIA to understand and develop the professional skills needed, while drawing on other frameworks for detailed technical implementation guidance, creating a comprehensive approach to building AI capabilities.

The integration of AI skills within SFIA's established and proven seven-level framework demonstrates how emerging technologies can be incorporated without creating unhelpful boundaries between specialisms.

• This approach enables organisations to develop AI capabilities while maintaining consistency with other professional skills.
• The result is greater workforce mobility and flexibility, clearer career pathways, and easier skills-based deployment of talent.
• Rather than treating AI skills as a separate specialty, SFIA's structure helps organisations understand how AI-related capabilities connect with and enhance existing professional skills, supporting both specialist development and broader professional mobility.

Core principles

• SFIA 9 focuses on enduring professional skills needed for AI adoption and development
• Emphasises capabilities that remain relevant despite rapid technological change
• Integrates AI considerations across relevant existing skills
• Introduces new skills where specific AI expertise is required
• Supports organisations in building sustainable AI capabilities

Structure and components

AI and data literacy

• Fundamental knowledge requirements for working with AI
• Understanding AI capabilities and limitations
• Data literacy as a foundation for AI adoption
• Ethical considerations in AI application

Note that the SFIA framework does not include data, digital or AI literacy measurement scales although some of the elements of these can be found in the generic attributes.

• These are not specific to SFIA and other sources are available.
• It's important to note that the measurement scales used for literacy and fluency are different from SFIA's levels of responsibility, even if they sometimes appear similar (e.g., using a numeric scale).
• Literacy and fluency measurement scales can be used effectively alongside SFIA, but the distinction between the two should be made clear.

Development and implementation skills

Building AI/ML solutions

• Specialist skills for developing AI models
• Data science and machine learning engineering
• Model training and validation
• AI solution architecture
• See here

Developing and operationalising AI

• Skills needed for developing/operationalising software- and data-intensive systems
• MLOps and deployment practices
• Integration with existing systems
• Performance monitoring and optimisation
• Maintenance and updating of AI systems
• See here

Organisational impact

Productivity enhancement

• Individual productivity through AI assistance
• Team collaboration using AI tools
• Process automation and augmentation
• Decision support systems
• See here

Workforce transformation

• Job role evolution with AI integration
• Skills development and transition
• New role emergence and definition
• Change management for AI adoption
• See here and here

Practical application

For organisations

• Framework for assessing current AI capabilities
• Guide for developing AI skills strategy
• Support for job design and team structure
• Basis for training and development planning

For individuals

• Career development pathways in AI
• Core competencies for AI-related roles
• Skills progression framework
• Professional development guidance

Implementation guidance based on previous use of the SFIA framework

Good practices  

• Focus on sustainable skill development
• Balance specialist and general AI skills
• Integrate AI considerations into existing roles
• Maintain ethical awareness

Common challenges

• Rapid technology evolution
• Tool and platform diversity
• Skills gap identification
• Change management

Looking forward

While SFIA 9 provides a comprehensive framework for current AI needs, it acknowledges the dynamic nature of AI technology. The framework emphasises enduring professional skills that adapt to technological change, rather than specific tools or platforms that may quickly become outdated.

Organisations are encouraged to:

• Use SFIA as a foundation for AI capability development
• Adapt the framework to their specific context
• Stay aware of emerging AI trends
• Focus on sustainable skill development
• Maintain flexibility in implementation

This approach ensures SFIA 9 provides practical value today while remaining relevant as AI technology continues to evolve.

SFIA's approach to AI evolution

Current focus vs future speculation

• SFIA 9 focuses on proven, practical AI skills needed today
• Emphasises enduring professional competencies over specific technologies
• Avoids speculation about future AI developments
• Maintains relevance through focus on fundamental capabilities

Maturity considerations

• Core SFIA skills incorporate well-established AI practices
• Emerging AI skills are carefully evaluated for inclusion
• Some developing AI capabilities may be better suited as supplementary guidance
• Framework allows for skills to mature before full integration

Learning from experience

• SFIA's evolution from v1 to v9 demonstrates successful technology integration
• Historical pattern shows progression from specialist to mainstream skills
• Previous technological shifts inform AI skill integration approach
• Regular updates allow for careful evaluation and incorporation of maturing skills

Implementation approach

For organisations

• Build on established SFIA skills that support AI adoption
• Focus on fundamental capabilities that remain relevant
• Adapt framework to specific technological choices
• Maintain flexibility for emerging AI developments

Good practices

• Balance current needs with future adaptability
• Focus on enduring professional skills
• Avoid over-dependence on describing AI skills as specific AI tools or platforms
• Regular review of skill requirements and maturity

Looking forward

SFIA benefits from the collective experience of a worldwide community of practitioners who apply the framework in diverse contexts. Regular updates incorporate real-world feedback, ensuring SFIA evolves with changing workplace needs while maintaining its practical value. The framework is freely available to individuals and most employers, reflecting SFIA's commitment to supporting skills development across the global workforce. New users - whether employers, professional bodies, or practitioners - are welcomed into this collaborative community, where they can both benefit from and contribute to SFIA's ongoing development. This open, community-driven approach has proven essential in keeping SFIA relevant and practical while making it accessible to those who need guidance on professional skills development.

SFIA 9 provides a practical framework for current AI needs while maintaining the flexibility to evolve with the technology. The framework:

• Reflects proven practices rather than speculation
• Focuses on enduring professional skills
• Allows for natural maturation of emerging capabilities
• Maintains SFIA's traditional balance of stability and adaptability

Organisations should use SFIA 9 as a foundation for building sustainable AI capabilities while remaining flexible and aware of the rapidly evolving AI landscape.