Research and analysis

Executive summary with introduction and next steps

Published 29 October 2025

Applies to England

Introduction

Artificial intelligence (AI) is changing how we live, work, and learn, reshaping sectors in the UK. The ‘AI Opportunities Action Plan’ identifies that AI adoption could boost the UK economy by up to £400 billion by 2030 as stated in ‘Google’s Impact in the UK 2023 - Public First’ report, through enhancements in innovation and workplace productivity.

As AI becomes embedded in daily workflows, the UK workforce must develop new skills to remain economically competitive, socially inclusive, and technologically future-ready.

According to The Alan Turing Institute and McKinsey, AI can be defined as machines that can perform cognitive functions we associate with human minds such as:

• reasoning
• learning
• interacting
• problem-solving
• exercising creativity

From a skills perspective, engaging with AI means developing the technical, non-technical, and responsible competencies and abilities needed to design, use, and govern such systems effectively

Yet access to AI Skills training opportunities remains deeply uneven. Disparities risk deepening structural inequalities and hindering UK goals for productivity and global leadership in emerging technologies. Creating an inclusive, high-performing skills system will require training provision that is employer-informed, regionally responsive, and accessible, especially for communities and workforce groups at risk of exclusion.

This report responds to this challenge. It presents findings from 6 national stakeholder workshops and a senior stakeholder roundtable from 2025, as well as desk research which we have shown in the methodology annex. The aim is to inform AI upskilling pathways that match employer needs. This will build workforce readiness and tackle AI skills inequality across sectors and job levels in the UK. It supports a fair and future-ready skills system. It is the first stage of the British Academy Policy-led Innovation Fellowship, in partnership with Skills England.

The Skills England ‘Assessment of priority skills to 2030’ was published in August 2025 and identifies a cross-sector demand for digital roles and skills, with additional employment demand projected across most priority sectors. AI, digitalisation, and automation are expected to drive part of this increased need, reshaping job roles and the required skills. This Fellowship report focuses specifically on AI skills, examining inequalities in access to training, sector-specific training gaps, and inclusive workforce development models.

Outputs include insights on 10 key sectors and barriers to AI skills development. They also include an AI skills tools package with 3 tools.

The first tool is an AI skills framework that categorises skills into technical, non-technical, and responsible and ethical AI skills. Definitions of these categories are provided in the AI Skills Tools Package. These skills apply to entry-level, mid-level, and managerial roles.

The second tool is an employer adoption pathway model which sets out 9 stages of AI adoption in organisations.

The third tool is an AI adoption checklist as a self-assessment tool for organisations. This work can inform local and national policies.  This includes ‘Local Skills Improvement Plans’ (LSIPs), and the Government’s ‘AI Opportunities Action Plan’.

An important insight from this research is the high transferability of AI skills across sectors. While technical requirements often differ depending on the industry, both non-technical, and responsible and ethical AI skills are highly portable and can be applied in a wide range of organisational contexts. Prioritising these transferable competencies will not only support mobility between roles and sectors, but also strengthen interdisciplinary collaboration, reduce duplication of training efforts, and build resilience in career pathways. Ensuring that the workforce has these adaptable skills is vital to preparing for rapid changes in AI adoption and use.

This report supports Skills England’s mission to address skills gaps. It builds on previous work by the Department for Education including the report ‘The Impact of AI on UK Jobs and Training’.

Results should be interpreted with caution

This report presents key insights on AI skills from national workshops and stakeholder roundtables. As with all qualitative research, there are a few considerations to keep in mind when reading the findings. The data captures a moment during rapid changes in AI technologies and skills needs, but some regional and less-represented voices may be underrepresented. Workshop discussions provided rich and experience-based insights, but are not designed to be statistically representative. Thematic analysis was carried out rigorously, though interpretations may still carry an element of subjectivity.

Participant quotes included in this report are anonymised and, in some cases, paraphrased or constructed as composite quotations to reflect the dominant themes and repeated sentiments expressed across workshops. This approach supports anonymity and clarity while preserving the intent and voice of contributors.

Taken together, these insights offer a meaningful contribution to ongoing efforts to address AI skills inequality. The findings provide a strong basis to inform future policy development, programme design, and stakeholder engagement in a rapidly evolving landscape.

Summary

This report explores artificial intelligence (AI) skills in the UK. It aims to guide AI upskilling, workforce planning, productivity, and economic participation.

Based on 6 national workshops, a senior policy roundtable, and desk research, the report:

• analyses AI adoption, upskilling needs, barriers and opportunities in ten key growth sectors
• identifies common barriers to AI skills development that impact organisations of all sizes
• focuses on challenges for small and medium-sized businesses (SMEs), marginalised groups, and areas with less AI training and use
• introduces a set of AI skills tools

These sectors were identified by Skills England and the ‘UK’s Modern Industrial Strategy’.

The tools help employers and trainers to assess skills needs, plan inclusive training, and promote responsible AI practices.

The AI tools package is comprised of:

• the AI Skills Framework
• the AI Skills Adoption Pathway Model
• the Employer AI Adoption Checklist

The report uses insights from expert stakeholders and desk research. It offers a practical, evidence-based foundation to assess AI skills readiness in various sectors.

The goal is to develop inclusive, employer-responsive, and regionally balanced AI upskilling pathways. These pathways include training delivered via the workplace, formal education settings and community-based or informal routes. These findings support the UK Government’s ‘AI Opportunities Action Plan’ and align with efforts to create a more agile, equitable, and future-ready skills system.

Findings

Sectoral overview

AI adoption is accelerating across all 10 sectors. However, the extent of AI adoption and the AI skills each sector workforce possess varies. Sector-specific highlights include:

Digital and Technology

AI adoption patterns

Automation, coding help, predictive analytics, content checks, and personalised user experience (UX).

AI skills gaps areas

Using low-code tools, explaining AI outputs, designing for inclusion, using AI responsibly in products.

Main barriers

Training is too technical, poor support for women and non-technical staff, limited options for older workers, career returners, and people outside main hubs.

Health and Social Care

AI adoption patterns

Triage, diagnostics, admin tasks, early warning systems, and NHS aims to be the most AI-enabled health system in the world.

AI skills gaps areas

Ethics, interpreting AI outputs, teamwork across clinical, admin, and care roles.

Main barriers

Poor digital infrastructure, system problems, lack of training, and digital exclusion.

Financial Services

AI adoption patterns

Fraud checks, monitoring, trading, credit scoring, and compliance.

AI skills gaps areas

Governance, ethics, and interpreting AI outputs, especially in compliance and legal teams.

Main barriers

Time pressure, limited tailored continuing professional development (CPD), ignoring non-technical risks, and siloed teams.

Advanced Manufacturing

AI adoption patterns

Predictive maintenance, process control, robotics, and real-time analytics.

AI skills gaps areas

Model training, predictive maintenance, interpreting AI outputs, ethical use and implications of automation, and inclusive design.

Main barriers

Entry-level shortages, ageing workforce, SMEs lacking funds, digital tools, and training.

Construction

AI adoption patterns

Drone surveys, planning, retrofit, virtual reality (VR) or augmented reality (AR) safety tools, and building information modelling (BIM) for green design.

AI skills gaps areas

Drones, BIM, using AI on site, ethical use of surveillance, and inclusive design.

Main barriers

Low digital skills, limited CPD, digital exclusion, and limited SME capacity.

Professional and Business Services

AI adoption patterns

Human resource recruitment, workforce management, legal reviews, and contract checks.

AI skills gaps areas

Auditing bias, compliance, and communicating AI outputs in legal work.

Main barriers

Human resource recruitment, workforce management, legal reviews, and contract checks.

Creative Industries

AI adoption patterns

Generative AI for content, campaigns, and storytelling.

AI skills gaps areas

Prompt writing, copyright, originality, and ethical storytelling.

Main barriers

Limited formal continuing professional development, copyright uncertainty, and poor training access for freelancers and small firms.

Clean Energy Industries

AI adoption patterns

Predictive maintenance, energy efficiency, grid forecasting, storage, trading, and carbon capture.

AI skills gaps areas

Optimisation, fault detection, dashboard interpretation, bias checks, and identify bias in algorithms.

Main barriers

High training costs, lack of role-specific training, regional gaps, and large utilities move faster than SMEs and local groups.

Defence

AI adoption patterns

Logistics, intelligence, threat detection, simulation, battlefield support, predictive maintenance, and cyber defence.

AI skills gaps areas

Interpreting AI outputs, risk-based skills, ethics, transparency, and accountability.

Main barriers

Few staff trained in AI, gaps between civilian and defence training, and hard to bring in outside experts.

Life Sciences

AI adoption patterns

Drug discovery, genomics, diagnostics, pharma production.

AI skills gaps areas

Bioinformatics, diagnostics, interpreting AI outputs, teamwork, data transparency, fairness, and compliance.

Main barriers

Training required by employers is too focused on long degrees, poor SME access, few AI trainers, and unclear standards.

Levels of adoption, skills needs, and workforce readiness vary depending on organisational size, access to training infrastructure and location. SMEs are disproportionately affected by capacity, cost, and awareness barriers. These limit their ability to implement structured AI training and benefit from innovation. Marginalised workforce groups, including women in underrepresented sectors, older workers, low-income adults, and those with limited digital literacy, face additional entry barriers to AI skills development. Regional variations are pronounced, urban hubs and innovation clusters benefit from more developed training ecosystems. Rural and economically disadvantaged areas report limited or no access to AI-specific provision.

Structural barriers to AI upskilling

6 persistent barriers were identified:

• inconsistent use of the term AI skills, creating confusion across employers, educators, and learners
• low foundational digital literacy in sectors with lower digital maturity
• fragmentation of the training ecosystem, with limited coordination and progression pathways
• length of mandatory education, systematic lag and complexities in curriculums adapting to emerging AI tools and sector specific needs
• training costs and funding fragility, especially for SMEs and community-based providers
• limited employer understanding of workforce AI skills requirements, particularly among smaller firms and in sectors where AI adoption is still exploratory

The AI skills tools package

The report introduces 3 practical tools for workforce planning and training. These tools are developed by the report author from the insights gathered through 6 national workshops, a British Academy expert roundtable, and desk research.

AI Skills Framework

Lists AI skills by job level across 3 types of AI skills:

• technical skills, which are the practical, applied competencies required to operate, monitor, and guide AI systems effectively in real-world settings
• responsible or ethical skills, which are the ability to uphold ethical principles, ensure transparency and accountability, assess bias, and apply legal and regulatory standards when using AI tools
• non-technical skills, which are the foundational, transferable competencies needed to understand, engage with, and critically evaluate AI tools for efficiency, even without technical expertise

These apply to entry-level, mid-level, and managerial roles. The framework aligns with UK skills classification systems and can adapt to any sector.

AI Skills Adoption Pathway Model

This model sets out 9 stages of AI adoption in organisations. Each stage connects to changing skills needs.

Employer AI Adoption Checklist

A self-assessment tool for organisations to:

• evaluate readiness to adopt AI
• identify skills gaps
• plan inclusive adoption strategies

These tools can help organisations of any size and sector to address skills gaps, plan training, and promote responsible AI use. They are particularly useful for employers and providers. This includes supporters of small businesses, advocates for underrepresented groups, and areas with few AI resources. These tools and findings support Skills England’s mission by matching employer needs with easy access to the required AI skills.

Next steps

The next stages of the fellowship will build on the findings in this report to generate deeper, sector-specific insights and inform the design of inclusive AI upskilling pathways.

This will include a detailed sectoral analysis of growth sectors identified as priorities by Skills England. These deep dives will explore:

• occupation-level effects
• training needs
• emerging use cases to guide practical workforce interventions

Mapping AI skills requirements across occupations using the Skills and Standards Classification (SSC) framework. This will support clearer alignment between job roles and the technical, ethical/responsible, and non-technical AI skills outlined in the framework.

A national survey with underrepresented groups to identify the specific barriers they face in accessing AI tools and training. Solutions emerging from each stage will be co-created with employers, training providers, and policymakers to ensure relevance and equity.

Together, these next stages will support Skills England’s priorities around inclusive economic growth, employer-responsive training, and regionally balanced workforce development.