The UK government has outlined six barriers to artificial intelligence (AI) skills development based on new research and analysis.
The barriers are rooted in practical, structural, and cultural issues such as digital infrastructure, adult education, funding inequalities, and institutional responsiveness. They cut across sectors and regions and were reported by experts working directly with learners, employers, and training providers, according to a Skills England report.
The PEX Report 2025/26, based on a global survey of more than 200 professionals, found that nearly two thirds of respondents (62 percent) believe their organization needs more AI skills and capabilities to stay competitive. Meanwhile, access to required skills and talent was voted the sixth biggest business transformation challenge facing organizations, cited by 31 percent of those polled.
What are the 6 barriers to AI skills development?
This report explores AI skills in the UK and aims to guide AI upskilling, workforce planning, productivity, and economic participation. The six barriers to AI skills development it identifies are:
- Inconsistent use of the term ‘AI skills’
- Lack of foundational digital literacy
- Fragmentation across the training ecosystem
- Curriculum responsiveness to emerging AI needs
- Training costs and funding fragility
- Limited employer understanding of workforce AI skills needs
1. Inconsistent use of the term ‘AI skills’
An absence of a shared or standardized understanding of what constitutes AI skills is a major issue, the report found. In some workplace contexts, the term referred to operational fluency – the ability to use tools such as ChatGPT or Copilot to automate everyday tasks, but in others, it encompassed more conceptual knowledge.
Experts from higher education and policy noted that ‘AI skills’ is often used as shorthand for general digital upskilling, even when the training content has little to do with AI itself.
This ambiguity creates challenges throughout the learning and employment cycle. Curriculum designers struggle to define learning outcomes or assessments without an agreed framework, leading tutors to default to tool demonstrations or generic digital exercises. In the labor market, learners who complete AI training frequently lack the vocabulary or evidence to describe what they have learned.
Employers also reported uncertainty about AI terminology. Some associate it with coding or machine learning, while others link it to data dashboards or compliance modules. This confusion discourages organizations from commissioning AI training. The problem is compounded by the absence of clear distinctions between foundational, operational, and strategic AI skills.
Takeaway: The lack of consistent language and signaling around AI competencies is a significant barrier to both learner confidence and employer investment.
2. Lack of foundational digital literacy
Experts identified foundational digital literacy as the most persistent and under-addressed barrier to inclusive AI learning. Across nearly all workshops, organizations delivering AI or digital skills programs reported that many participants struggle with essential computer tasks.
Digital skills gaps are particularly common among older adults, individuals with learning differences, and low-income adults with limited access to devices, according to the report. Even in communities with a strong interest in AI training, learners frequently drop out when platforms or materials assume a baseline familiarity with digital systems. Hardware disparities further deepen the digital divide.
Takeaway: The absence of foundational digital literacy limits learner progression and undermines inclusion efforts in AI education.
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3. Fragmentation across the training ecosystem
The AI training landscape is extensive but fragmented, consisting of a mix of libraries, universities, online platforms, employer-led schemes, and other resources. This diversity lacks coordination, creating a disjointed system with limited accessibility and alignment.
The absence of shared standards, cross-provider tracking, or referral protocols means that learners can move between different providers without a clear progression pathway. In some cases, learners complete self-guided AI training online, only to find it is not recognized by local employers or connected to further qualifications.
Experts also highlighted that the fragmentation is not only horizontal (between providers in the same region), but vertical, with national initiatives, regional skills partnerships, and local delivery organizations working on separate timelines, goals, and assumptions.
Takeaway: This disjointedness has direct effects on learners.
4. Curriculum responsiveness to emerging AI needs
Slow curriculum updates throughout education pathways, rigid continuing professional development (CPD) processes, and qualification alignment issues are other notable obstacles to AI skills development, according to the report.
Tutors and program leads noted that the lengthy process of incorporating tools into course content often leads to learners encountering outdated platforms or interfaces. In employer-led training, particularly in regulated sectors, experts noted that CPD accreditation systems do not always recognize experimentation or continuous updating of digital content.
Even experienced educators face barriers in adapting their teaching to reflect new developments due to limited CPD time, absence of incentives, or lack of protected hours for updating content.
Takeaway: Broader curriculum systems should evolve to meet the demands of a fast-changing AI landscape.
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5. Training costs and funding fragility
Affordability and financial continuity are fundamental barriers to sustained engagement in AI training, with many programs limited by temporary funding cycles. Funding instability creates challenges in planning provision, hiring and retaining trained tutors, building trust with learners, and delivering learning with continuity.
In some regions, training is launched with enthusiasm only to be discontinued or redirected when funding expires. Experts working with low-income adults noted that learners frequently disengage when a program ends without follow-on support, especially where participation requires a high level of literacy-building or outreach to begin with.
Takeaway: While some learners access free training spaces they cannot always afford to attend consistently or complete the full program.
6. Limited employer understanding of workforce AI skills needs
Experts involved in training delivery and employer engagement consistently report a lack of clarity among businesses about the specific AI skills they require. Even organizations already using AI tools in areas such as recruitment, marketing, or customer service often lack awareness of what types of staff training would enable safe and effective implementation.
This absence of strategic planning leads to underinvestment in workforce development, particularly among smaller organizations and early-stage startups, the report indicated.
Some respondents cited the gap between employer enthusiasm and operational planning, suggesting that there’s an appetite but no translation into what it means for roles, teams, or training budgets. Experts also highlighted a missed opportunity to link AI adoption with broader workforce development strategies, particularly in sectors such as health and social care, where automation pressures are increasing but staff training is lagging behind.
Takeaway: This points to a clear demand for practical skills diagnostics and workforce planning tools that can guide employers from intention to action.