White Paper: AI Skills and the Future of Work – Closing the Gender Gap in AI Hiring

Artificial intelligence is changing the skills employers need. AI engineering and machine learning remain specialist areas, but AI literacy is also becoming valuable across technology, transformation and business roles.

This is creating new opportunities for people who can apply AI effectively within their existing expertise. However, women are not accessing these opportunities at the same rate as men.

The UK is already seeing strong growth in AI-related employment. LinkedIn reports that more than 95,000 AI-related roles have been added in the UK since 2023, while AI engineering hiring is growing faster than the overall workforce.

However, women remain underrepresented in the UK’s AI talent pool. LinkedIn’s international research shows that women are significantly less represented in AI roles than in the wider workforce, highlighting a potential challenge for employers as demand for AI skills grows.

For employers, this is more than a representation issue. AI is becoming an important source of business capability, and organisations that draw from a narrower talent pool risk missing skilled candidates.

Closing the gap requires employers to think differently about AI hiring. Rather than relying solely on AI job titles or traditional career paths, organisations can assess transferable experience, practical AI capability and potential alongside technical expertise.

AI Is Creating New Skills Demand

AI is no longer limited to dedicated AI teams.

Organisations are using AI across software development, data, cybersecurity, operations, product development and business transformation. As adoption expands, more employees will need to understand how AI tools work, where they can add value and where human judgement remains essential.

This is creating a distinction between AI expertise and AI literacy.

AI expertise may be essential for roles such as AI Engineer or Machine Learning Engineer. AI literacy is broader. It can include using AI tools effectively, evaluating outputs, understanding limitations and applying AI within a particular area of expertise.

For employers, this creates a much wider pool of potential AI talent.

The person with the right skills may not have “AI” in their current job title.

AI Hiring Is Growing

The demand for AI capability is already reflected in recruitment data.

The financial value of these skills is also significant. PwC’s 2026 AI Jobs Barometer found that UK workers with AI skills commanded an average wage premium of 34.2% in 2025, up from 11% the previous year. Specialist AI job postings also increased by 61%, from 112,000 to 180,000.

AI skills are therefore becoming associated with some of the fastest-growing and highest-paid opportunities in the labour market.

This makes access to AI careers an important workforce issue.

Women Are Underrepresented

Despite the growth of AI opportunities, women remain significantly underrepresented in AI hiring.

The UK AI labour market also has a significant gender gap. The UK Government’s AI Labour Market Survey found that women accounted for just 20% of AI roles in 2025, a four-percentage-point decline from 2020. The report also found that 35% of organisations were struggling to fill AI roles, with senior positions particularly difficult to recruit for.

The gap is therefore occurring alongside significant demand for AI skills. For employers already facing shortages, the underrepresentation of women represents a missed opportunity to access a broader pool of potential talent. The Government report specifically identifies workforce diversity as one factor affecting the UK’s ability to address its AI skills gap.

This creates a risk for employers as demand for AI capability continues to grow.

A smaller proportion of women entering AI roles means a smaller pool progressing into senior positions. If organisations continue recruiting from the same narrow talent pools, they may limit their access to the skills needed to support future growth.

Look Beyond AI Job Titles

One way employers can widen the talent pool is by changing how they define AI capability.

Not every AI-related position requires someone who has spent their entire career working in artificial intelligence. Some roles require strong technical expertise. Others require someone who understands how to apply AI to a particular business problem.

A data professional who has introduced AI into their organisation may bring valuable practical experience without having held an AI-specific title. A software engineer who has integrated AI tools into development processes may have relevant skills for a role that was previously defined around traditional engineering experience.

Recruitment should therefore assess what candidates can do rather than relying too heavily on previous job titles.

This does not mean lowering technical standards. It means defining those standards accurately.

Assess Transferable Skills

The rapid development of AI means employers cannot always rely on established career paths when recruiting.

Many relevant skills are emerging within existing roles. Candidates may have gained experience through AI projects, internal initiatives, professional development or practical use of AI tools without moving into a dedicated AI position.

Transferable skills can therefore become particularly valuable.

For example, an organisation recruiting for an AI transformation role may benefit from someone with strong change management and business analysis experience alongside practical AI knowledge. A technology leader may need experience implementing AI within an organisation rather than deep technical expertise in building AI models.

Understanding these distinctions can make recruitment more effective and broaden access to suitable candidates.

Build AI Capability Internally

Recruitment is only one part of closing the AI skills gap.

Organisations also need to consider how existing employees can develop AI literacy. If AI becomes embedded across different functions, relying entirely on external hiring will not be sustainable.

Training can help employees understand how to use AI effectively within their existing roles. It can also create pathways into emerging positions that did not previously exist.

For employers, this creates two complementary approaches: recruit specialist AI expertise where it is genuinely required, while developing broader AI literacy across the existing workforce.

This approach can also expand the future talent pool.

The Recruiter’s Role

Specialist technology recruiters can help employers navigate this changing market.

AI roles are evolving quickly, and organisations may not always know whether their requirements reflect the talent available. A specialist recruiter can provide market insight, challenge unrealistic expectations and identify transferable skills that may otherwise be overlooked.

This is particularly relevant for senior appointments.

An organisation searching for a Head of AI, CTO or AI Transformation Director may find that the strongest candidates have developed their expertise across several disciplines rather than following a conventional AI career path.

Understanding where these candidates sit in the market requires more than searching for a matching job title.

It requires an understanding of how AI skills are developing across the wider technology workforce.

A Wider Talent Pool

Closing the gender gap in AI hiring will require action from employers, education providers and the wider technology sector. Recruitment cannot solve the problem alone.

However, employers can ensure that their hiring practices do not unintentionally narrow the pool further.

Clearly defining the capability required, recognising transferable experience and assessing practical AI skills can help organisations identify candidates who may otherwise be overlooked.

For women in particular, this may create greater opportunities to move into AI-related roles through existing technology, business and transformation careers rather than requiring a complete career change.

That matters for employers as well as candidates.

As competition for AI capability grows, organisations that can identify strong talent beyond traditional AI career paths will have access to a broader pool of skills and experience.

Conclusion

AI is changing the labour market, but the opportunity is not limited to people with highly specialised technical backgrounds.

AI literacy is becoming a valuable complement to existing technology, business and transformation expertise. As organisations adopt AI more widely, people who understand how to apply it effectively will have an important role to play.

Yet women remain significantly underrepresented in AI hiring and leadership.

For employers, addressing this gap should not be viewed solely as a diversity objective. It is also an opportunity to strengthen access to scarce and valuable skills.

That starts with better definitions of AI capability.

Organisations that look beyond job titles, recognise transferable experience and invest in AI literacy can build broader talent pools without compromising technical standards. Specialist recruitment partners can support this by helping employers understand the market, challenge assumptions and identify candidates with the right combination of skills.

The future of AI hiring will not simply depend on finding more people who already have AI in their job title.

It will depend on recognising where AI capability already exists, developing it where it does not and ensuring talented people have a fair opportunity to build careers in one of the fastest-growing areas of the technology market.

Closing the gender gap is therefore not separate from building AI capability.

It is part of it.

Key Takeaways

• AI skills are expanding beyond dedicated AI and machine learning roles.
• AI literacy is becoming a valuable complement to existing technology and business expertise.
• Employers can broaden their talent pools by assessing capability rather than relying solely on AI job titles.
• Transferable experience can be valuable when recruiting for emerging AI roles.
• Developing AI literacy internally should complement external recruitment.
• Specialist technology recruiters can help employers identify emerging skills and overlooked talent.
• Building a broader AI workforce can improve access to the skills organisations need as adoption continues.