As work is reshaped by artificial intelligence, demographic pressure, skills shortages and increasingly complex regulation, the gender pay gap should also be understood as a warning signal that tells employers something about how opportunity is distributed inside an organisation: who gets access to future-facing work, who is trained, who is sponsored, who progresses, and who is trusted to lead through change.
Pay gaps, however, rarely begin with pay decisions alone. They are shaped by wider labour market and societal structures, from gendered caring responsibilities and occupational segregation to unequal access to STEM education, professional networks and progression opportunities. Employers operate within these structures, but they can also reinforce or disrupt them through workforce design choices extending to recruitment, work allocation, flexibility, career development, promotion, sponsorship and reward. In 2026, AI is making those factors more consequential: as generative tools reshape tasks, roles and career pathways, they are also redesigning the routes through which employees acquire skills, gain visibility, and build career value. Access to AI tools, training and experimentation increasingly determine who is able to participate in higher-value, future-facing work.
The risk is that AI-enabled opportunity may follow existing patterns of workplace inequality, unless employers actively design it differently. If women are less likely to be encouraged to use AI, trained in its application, or moved into the roles where new technology creates new forms of value, existing gender gaps risk becoming embedded in new ways. In that sense, the gender pay gap may increasingly be shaped by an emerging AI opportunity gap, layered on top of the structural inequalities that already affect women’s pay and progression.
For employers, the task is to understand whether these patterns are emerging within their own workforce, and to intervene before they become harder to reverse
From pay gap to opportunity gap
The economic case for gender equality is simple. Research by Ius Laboris has estimated that closing gender employment gaps could boost global long-term GDP per capita by nearly 20%. In the EU, achieving gender equality could increase GDP per capita by between 6.1% and 9.6% by 2050, adding between €1.95 trillion and €3.15 trillion to the economy. The OECD has similarly found that doing so could also increase annual GDP per capita growth by 0.2% across OECD countries, offsetting around half of the decline projected from demographic ageing.
These figures position gender equality firmly within the wider future-of-work agenda: labour markets are tightening, populations are ageing, and organisations are seeking new sources of productivity and growth. Against that backdrop, the underuse, underdevelopment or premature loss of women’s skills is a structural challenge to workforce resilience.
Recent data suggests, however, that progress remains fragile. The World Economic Forum estimates that, at the current pace, closing the global gender gap will take 123 years. PwC’s Women in Work Index 2026 shows that improvement across the OECD has slowed, with the average score increasing by only 0.6 points between 2023 and 2024, around half the average annual improvement since 2011, and the smallest increase since 2020.
The UK picture is particularly concerning. According to PwC, the UK now ranks 17th out of 33 OECD countries in its Women in Work Index, having fallen from 10th place since 2020. It also ranks 32nd out of 33 for improvement since the pandemic. The UK gender pay gap stands at 13.1%, above the OECD average of 12.4%, and has risen from 12.0% since the pandemic. At the same time, female unemployment rose from 3.5% in 2023 to 4.2% in 2024, while the UK continues to perform poorly on women’s full-time employment, with a rate of 67.7%, 9.1 percentage points below the OECD average.
This predicament is especially challenging for younger women, for whom PwC found the rise in unemployment to be more than three times greater than elsewhere. Reducing young female NEET (Not in Education, Employment or Training) rates to 2021 levels would represent a £3 billion opportunity for the UK economy, while reducing them to the Netherlands’ levels, the lowest in Europe, could add up to £11 billion.
For employers, these figures are a sobering reminder of the risk of women being lost from the workforce, or prevented from building momentum within it, at precisely the moment when future skills pipelines are being reshaped. As AI changes the work through which employees build skills, visibility and career value, the issue is no longer simply whether women are present in the workforce, but whether they have equal access to the opportunities that will shape its future.
AI and the new architecture of opportunity
The Future of Work Hub has previously explored how women’s participation in waged work has shaped the history of the gender pay gap, and how the pandemic and automation could reshape women’s working lives. The challenge now is how that same question evolves in the age of generative AI. Widespread adoption is beginning to reshape the architecture of opportunity at work, and it increasingly influences how work is designed, how productivity is measured, how decisions are made and how career value is created. This turns its adoption into a workforce, leadership and governance challenge, rather than a question of technology deployment alone.
Our Future@Work 2026 report suggests that many employers are still at an early stage of this transition, While more than 80% of leaders say they feel ‘well’ or ‘very well’ prepared for major future-of-work challenges, including technology and AI, 52% still prioritise or lean towards short-term planning. At the same time, investment is heavily skewed towards technology itself: 74% of employers are prioritising technology, data or platforms over the next 12 months, compared with only around 5% prioritising workforce development.
This creates a significant risk: if organisations invest in AI tools without a comparable investment in people, skills, job design and leadership capability, AI adoption may amplify existing inequalities, rather than reduce them.
The gender dimension of this challenge is already visible. The World Economic Forum’s Gender Parity in the Intelligent Age report shows that women are more likely to hold roles disrupted by GenAI, at 57% compared with 43% for men, and less likely to experience augmentation, at 46% compared with 54% for men. Women also remain less than one-third of the STEM workforce, at 28.2% in 2024, and are still underrepresented at the top, holding only 24.4% of STEM managerial positions and 12.2% of STEM C-suite roles.
There are, however, encouraging signs. The gender gap in AI expertise has narrowed in 74 of 75 economies, with women representing 29.4% of AI engineering skill-listers in 2025, up from 23.5% in 2018. But there is also evidence of a new usage gap. PwC’s Workforce Radar shows that only 32% of women reported using GenAI at work, compared with 57% of men. McKinsey’s Women in the Workplace 2025 report similarly found that only 21% of entry-level women are encouraged by their manager to use AI, compared with 33% of men.
For employers, these data points should raise an important question: who is actually accessing AI-enabled opportunity? As AI capability becomes a marker of labour market advantage, access to tools, training, experimentation, AI-enabled projects and future-facing roles will increasingly determine who gains visibility, builds confidence and progresses. If women are more exposed to disruption, less able to access augmentation, less encouraged to use AI and underrepresented in the roles shaping its deployment, existing gender gaps are amplified by the very technologies intended to transform work.
The early-career risk
These risks are especially acute at the beginning of working life. Many organisations are already asking how AI will reshape junior roles, administrative work, research, analysis and routine tasks. These are often the roles through which employees learn, build confidence, develop judgement and move towards more senior work. If they are redesigned too narrowly around efficiency, without equivalent attention to learning and progression, early-career pathways may become more fragile.
The recent PwC UK AI Jobs Barometer suggests that the shape of entry-level opportunity is already changing. It shows that AI-exposed junior roles are seven times more likely than the least AI-exposed junior roles to require traditionally senior skills, such as leadership and strategic thinking, and that AI-exposed ‘seniorised’ entry-level roles have grown by 35% since 2019 while other entry-level roles have declined. This suggests that AI is changing the shape of entry-level opportunity itself, as junior workers may increasingly be expected to exercise judgement, adapt quickly and contribute to higher-value work from the beginning of their careers.
For young women, this could compound existing challenges in labour market access and progression. PwC’s findings on rising unemployment among young women should therefore be read as both a current labour market concern and a future workforce warning. If young women are less likely to be placed on AI-enabled projects, less likely to receive coaching in the use of AI, less likely to be sponsored into technical or strategic work, or less likely to receive encouragement from managers, they may be excluded from the experiences that will shape future leadership pipelines.
Employers will therefore need to think carefully about how junior roles are redesigned. The issue is whether entry-level work still gives employees opportunities to practise judgement, build technical confidence, interact with clients, receive feedback and demonstrate potential. If access to those experiences is unevenly distributed, early-career employees may start from different points on the progression ladder long before promotion or pay decisions are made.
The question for employers is therefore not simply whether women are present in the workforce, but whether they are being given equal access to the work that will matter most.
Pay transparency as a governance trigger
This too is where the changing regulatory context becomes important. As employers face greater expectations around pay transparency, they will need to explain not only how pay decisions are made, but how pay, progression and opportunity connect across the workforce.
The EU Pay Transparency Directive is a significant part of this shift, and requires greater visibility over pay, pay progression and equal pay, including pay information for job applicants, employee rights to request information on individual and average pay levels for comparable work, gender pay gap reporting for employers with at least 100 employees, and pay assessments where unjustified gaps exceed relevant thresholds. Even for employers outside the EU, pay transparency is becoming a broader market expectation. The OECD’s 2026 stocktaking report notes that 55% of OECD countries currently mandate private-sector gender pay gap reporting, and that this is expected to rise to 84% by the end of 2026, largely driven by the Directive.
But transparency alone will not close the gap. Pay reporting can expose disparities and create accountability, but the deeper value lies in using pay data to understand where inequality is being produced. Are women concentrated in roles with lower pay progression? Are they underrepresented in AI-enabled or strategically important work? Are promotion criteria clear and consistently applied? Are flexible workers progressing at the same rate as others? Are managers able to explain and justify pay decisions?
This is where pay transparency, AI governance and workforce planning converge. Employers who bring these issues together will be better placed to understand how future opportunity is being distributed across the workforce, and whether today’s AI-enabled transformation is creating new routes to progression or reinforcing existing gaps.
Building a gender-inclusive future-facing workforce
The gender pay gap is increasingly a systems issue. It sits at the intersection of pay, progression, technology, care, culture and regulation, and is shaped by wider assumptions about how care is distributed, valued and accommodated in working life. In an AI-enabled economy, it will also be shaped by who is able to access the tools, training, work and sponsorship through which future career value is created.
For employers, this means looking beyond representation and asking a more demanding question: are women able to participate fully in the transformation of work itself? If AI-enabled opportunity is unevenly distributed, the consequences may not appear immediately in pay data. They may first appear in who gains confidence with new tools, who is trusted with higher-value work, who develops judgement, who is sponsored into growth roles and who is seen as leadership-ready.
Closing this gap requires more than technology deployment: employers will need to audit access to AI tools, training and experimentation by gender and career stage, treat AI literacy as part of inclusion and progression, ensure women are sponsored into future-facing work, and connect pay transparency to job architecture, promotion criteria and workforce planning.
The employers who make progress will be those who understand gender equality as part of workforce design. They will look beyond annual reporting cycles and ask deeper questions about whether women can build sustainable, future-facing careers in their organisation. They will ensure that AI adoption does not widen existing inequalities, and that investment in technology is matched by investment in people, skills and leadership capability.
In a labour market defined by uncertainty and rapid transformation, the ability to keep, develop and empower women in work is one of the clearest tests of whether employers are building a workforce that is resilient, inclusive and ready for the future. As AI reshapes work, the gender pay gap may increasingly be shaped by unequal access to AI-enabled opportunity. The task for employers is to ensure that it is not.
