AI adoption could boost the UK economy by up to £400 billion by 2030, but a lack of skills in how to harness AI in the best way is holding employers and employees back, according to a new report.
The ‘AI skills for the UK workforce’ study was commissioned by Skills England and said all the UK’s key sectors have opportunities to benefit from AI but there are skills-related barriers.
In construction, for example, solutions include drone-assisted surveying for land assessments, and augmented reality for on-site safety simulations, but adoption is hindered by a lack of basic digital literacy.
In the creative industries, AI is available for content creation and digital storytelling, but the report said too many freelancers and smaller employers are using the technology without training, sparking worries over quality control and originality.
Finally, in the advanced manufacturing sector, AI is being used in areas such as automation, predictive maintenance and robotics, but it faces a growing skills gap exacerbated by an ageing workforce.
When it comes to training, the report found persistent barriers to adopting AI and improving skills across all sectors.
Among them were an inconsistent use of the term ‘AI skills’ which creates confusion, a fragmented training ecosystem with limited coordination and progression pathways, high training costs, especially for small businesses and community-based providers, and limited employer understanding of workforce AI skills requirements, particularly among smaller firms.
To support wider and more responsible AI adoption, the report introduced three new tools:
- AI Skills Framework: Identifies relevant technical, responsible, and non-technical skills needed for different job roles and at different levels.
- AI Skills Adoption Pathway Model: Shows how organisations typically progress through stages of AI adoption, from initial awareness to strategic scaling.
- Employer AI Adoption Checklist: Structured prompts to help employers assess their AI skills readiness, identify workforce gaps, and upskill. There’s a downloadable version of the checklist here.
Jacqui Smith, minister for skills, said:
“AI has the power to transform our economy – but only if people have the right skills to utilise it effectively. This report makes clear that too many employers are still unsure how to begin their AI journey.
“That’s why, through Skills England, we’re working hand-in-hand with industry to equip the workforce with the tools they need for the future. By doing so, we’re not just preparing our economy for the jobs of tomorrow – we’re raising living standards and putting more money in people’s pockets.”
Dr Nisreen Ameen, from Royal Holloway, University of London and author of the report, said:
“AI is reshaping the world of work across sectors, but without the right skills, too many people and businesses risk being left behind. This report provides a clear, evidence-based foundation to help employers, educators, and policymakers build more responsive upskilling pathways.
“By investing in practical, accessible AI skills development, we can support workforce readiness, boost economic productivity, and ensure the benefits of AI are widely shared across the UK.”
Jarmila Yu, founder of YUnique Marketing Ltd, said:
“Whilst AI undoubtedly offers potentially huge business benefits, it also presents significant challenges; especially for SMEs who are typically resource light, time poor, and budget constrained.
“Speaking as a small business owner, and one who’s business focuses on providing support to other small businesses, I’m acutely aware of the needs of a growing SME and I warmly welcome this report as it provides a valuable framework to supports SMEs with AI planning and AI skills development leading ultimately to AI adoption and the benefits it can bring to support business growth.”
Earlier this year, the government announced a partnership with large technology firms including Amazon, BT, Google, IBM, Microsoft and Sage to train 7.5 million UK workers in AI skills.
Research commissioned by the Department for Scinence, Innovation and Technology shows that by 2035, around 10 million workers will be in roles where AI will be part of their role or responsibilities.
Sector-specific AI skills needs and barriers
The report summarised sector-specific AI skills needs and barriers as follows:
| Sector | AI adoption patterns | AI skills gaps areas | Main barriers |
|---|---|---|---|
| Digital and technology | Automation, coding help, predictive analytics, content checks, personalised user experience (UX) | Using low-code tools, explaining AI outputs, designing for inclusion, using AI responsibly in products | Training 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 | Triage, diagnostics, admin tasks, early warning systems; NHS aims to be the most AI-enabled health system in the world. | Ethics, interpreting AI outputs, teamwork across clinical, admin, and care roles | Poor digital infrastructure, system problems, lack of training, digital exclusion |
| Financial services | Fraud checks, monitoring, trading, credit scoring, compliance | Governance, ethics, interpreting AI outputs, especially in compliance and legal teams | Time pressure, limited tailored continuing professional development (CPD), ignoring non-technical risks, siloed teams |
| Advanced manufacturing | Predictive maintenance, process control, robotics, real-time analytics | Model training, predictive maintenance, interpreting AI outputs, ethical use and implications of automation, inclusive design | Entry-level shortages, ageing workforce, small and medium-sized enterprises (SMEs) lacking funds, digital tools, and training |
| Construction | Drone surveys, planning, retrofit, virtual reality (VR) and augmented reality (AR) safety tools, Building Information Modelling (BIM) for green design | Drones, BIM, using AI on site, ethical use of surveillance, inclusive design | Low digital skills, limited CPD, digital exclusion, limited SME capacity |
| Professional and business services | Human resource recruitment, workforce management, legal reviews, contract checks | Auditing bias, compliance, communicating AI outputs in legal work | Limited continuing professional development, limited SME support, fewer training options for smaller local firms than large city firms |
| Creative industries | Generative AI for content, campaigns and storytelling | Prompt writing, copyright, originality, ethical storytelling | Limited formal continuing professional development, copyright uncertainty, poor training access for freelancers and small firms |
| Clean energy industries | Predictive maintenance, energy efficiency, grid forecasting, storage, trading, carbon capture | Optimisation, fault detection, dashboard interpretation, bias checks, identify bias in algorithms | High training costs, lack of role-specific training, regional gaps; large utilities move faster than SMEs and local groups |
| Defence | Logistics, intelligence, threat detection, simulation, battlefield support, predictive maintenance, cyber defence | Interpreting AI outputs, risk-based skills, ethics, transparency, accountability | Few staff trained in AI, gaps between civilian and defence training, hard to bring in outside experts |
| Life sciences | Drug discovery, genomics, diagnostics, pharma production | Bioinformatics, diagnostics, reading outputs, teamwork, data transparency, fairness, compliance | Training too focused on long degrees, poor SME access, few AI trainers, unclear standards |