You don’t need to wait for AI to become “for everyone.” You need to make sure you’re part of the group shaping what “everyone” means.

What the Article Actually Says

Young et al. (2021) found that women make up only 22% of AI and data science professionals globally and 20% in the UK, with the gap widening in more technical and senior roles. They also found that women are more likely to work in lower-status data roles, leave the field at higher rates, and appear less often on major online platforms and research networks that shape influence in AI.

Their briefing argues that the problem is not just representation; it’s also data transparency, pay equity, job quality, and governance. The recommendations call for disaggregated reporting, intersectional data collection, gender-inclusive labor policies, and intentional inclusion of women in technical and leadership roles.

Visibility in AI Is Career Capital

For first-generation female professionals (FGPs), AI can seem like a future you’re expected to adapt to quickly, even when the people designing it don’t reflect you. Young et al.’s 2021 policy briefing shows that women are still underrepresented across AI and data science, making up just 22% of the global workforce in those fields and 20% in the UK (Young et al., 2021). That’s not a small gap. It’s a structural warning sign.

The gender gap in AI isn’t just about numbers. Women are more likely than men to hold lower-status roles in analytics, data preparation, and exploration, while men are more concentrated in engineering and machine learning—the roles more likely to shape product direction, compensation, and visibility (Young et al., 2021). In other words, women are present, but often not where the leverage is.

That matters for FGPs, because visibility in AI is career capital. If you’re not seen as technical, strategic, or future-facing, it becomes harder to earn trust, transition into leadership, or shape the systems your organization relies on. The briefing also found that women in AI and data science have higher turnover and attrition rates in tech, which suggests the issue isn’t just access. It’s retention, climate, and advancement.

The most telling part of the briefing may be the mismatch between education and role. Women in AI and data science often have higher formal qualifications than men, especially in senior ranks, yet they remain underrepresented in C-suite positions. That should land hard for any FGP who’s ever felt over-prepared and under-recognized. It means credentials alone don’t close the gap. We also need sponsorship, networks, and systems that translate expertise into opportunity.

Young et al. (2021) also found that men self-report more skills on LinkedIn than women, across industries and countries, while women are underrepresented on major platforms like Kaggle, Stack Overflow, and Google Scholar. That’s not just a confidence issue. It’s also a signal problem. If you don’t name your skills, publish your work, or show up where your field’s credibility is being built, someone else will define the story for you.

What This Means for You, Right Now

It means AI literacy is no longer optional, but neither is self-advocacy. Learn the tools. Name your skills. Put your work where it can be seen. If you’re in a nontechnical role, build fluency so you can speak with confidence about how AI affects strategy, operations, and ethics. If you’re already technical, make sure your contributions are visible beyond the back end.

The policy briefing also makes a broader case that governments and employers need to collect better data, enforce reporting standards, and include women in the design of AI systems themselves. That aligns closely with Executive Insider’s theory of change: we can’t fix what we won’t measure, and we can’t democratize opportunity if women keep being left out of the rooms where the rules are written.

For FGPs, the practical lesson is both sobering and empowering. Sobering, because the gap is real. Empowering, because once you can see the pattern, you can navigate it more deliberately. You don’t need to wait for AI to become “for everyone.” You need to make sure you’re part of the group shaping what “everyone” means.

Your Next Step

Choose one AI skill, one visible platform, and one relationship that can increase your credibility in the field this quarter.

Sources

Young, E., Wajcman, J., & Sprejer, L. (2021). Where are the women? Mapping the gender job gap in AI. The Alan Turing Institute. https://www.turing.ac.uk/news/publications/report-where-are-women-mapping-gender-job-gap-ai