Executive summary: What works for AI upskilling in the UK
Updated 27 July 2026
Applies to England
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Acknowledgements
This report received support from The British Academy Policy-led Innovation Fellowship award IF24RBDS\240034 (referred to as the ‘fellowship’ within the report), in partnership with Skills England. Input from different areas and sectors helped shape the workshop designs and findings.
We thank all the workshop experts for sharing their knowledge, insights, and experiences. Their input shaped the findings. It connected this work to the real experiences of learners, employers, educators, and community leaders in the UK. We thank organisations of all sizes. Their insights have been instrumental in shaping the narrative of this report.
The opinions expressed in this report are those of the author and do not necessarily reflect the views of the department.
Purpose and evidence base
The Skills for Artificial Intelligence (SKAI) programme: What Works for AI Upskilling in the UK turns national evidence on AI skills into practical insight for policy, employers and training providers. The programme brings together 4 linked outputs:
- an employer guide
- supporting case studies
- an insight briefing based on research
- a research evidence, analysis and methodology report
The evidence base is strong and mixed. It draws on 23 workshops with around 150 organisations, 10 case studies and a UK employer survey of 536 responses. It covers formal training, employer-led training and informal learning.
Main findings
AI use is rising faster than workforce capability. Over 44% of organisations we surveyed report using AI tools daily, yet many organisations are still at early stages of adoption. Staff often learn through trial and error, peer support, online videos or built-in tool prompts. This can help people get started, but it also creates uneven and risky practice.
The main issue is not lack of interest. It is capacity. Employers point to limited time, staff pressure, cost, unclear provision and fear of failing in technical areas. Training systems also struggle to keep pace with fast-changing AI tools. Many courses are generic, hard to judge, or not linked to real roles.
What works for AI skills training
Good AI training must be practical and task based. It must build technical, non-technical and responsible AI skills together. It must also help staff know when AI should, and should not, be used.
The evidence points to 6 design principles for effective AI skills training. Together, these form the PRIMES framework. AI skills training should be:
- practical, linked to real tasks and decisions
- reachable, easy to access in time, format and language
- integrated, built into work, systems, standards and governance
- modular, short, flexible and suitable for different starting points
- expandable, able to grow across roles, teams and organisations
- sustainable and kept up to date as AI tools and work practices change
Across sectors, the same principles apply, but the design must fit the setting. Regulated sectors need stronger links to safety, oversight and accountability. Fragmented sectors, including SMEs, construction, social care and parts of the creative industries, need simple and flexible formats. Operational sectors need training that supports real-world use, quality and safety.
Evidence from the case studies
Large firms such as Airbus, KPMG and Roche use structured pathways, governance and leadership support.
Small and medium enterprises (SMEs) and creative organisations use applied learning and workflow-based training.
Providers such as LinkedIn Learning and 100 School show how frameworks, challenges and peer learning can drive use.
NHS primary care and Good Things Foundation show the value of local, inclusive and confidence-building approaches.
Strengthening AI skills and capability
Developing AI skills should not be approached as a one-off course or a simple tool demonstration. Instead, it works best as a shared approach to building safe, confident and effective use of AI. PRIMES can be used as a practical guide to help organisations and providers align their efforts, supporting consistent quality, smarter use of resources, clearer progression routes and broader access to AI skills.