Gender equity is no longer just a social imperative; it’s a profound economic one. As AI rapidly and fundamentally transforms the world of work, closing the gender gap in AI skills will be critical for companies to thrive. The ability to compete, innovate and grow critically depends on whether businesses can access the full spectrum of available talent. Therefore, when women are included—through equitable access, training and confidence-building—organizations unlock the true extent of their potential.
randstad’s 2026 workmonitor research illustrates the gaps that persist today:
- career impact: One-third of women surveyed said they are worried that their job will disappear in the next five years because of AI. This mirrors predictions from the United Nations that nearly 28% of jobs held by women globally are at risk from AI vs. 21% of men's jobs.
- access to AI at work: Women are slightly less likely to report access to AI training from their employer – only 32% of women, compared to 34% of men. Indeed, 35% of women do not believe they have the skills yet to leverage AI positively for their career.
- confidence in training: 59% of women surveyed believe that AI makes them more productive at work, compared to 64% of men.
how can organizations unlock more potential?
By investing in the AI skilling of women, organizations contribute to and benefit from the broader economic growth—referred to as the “$172 trillion gender dividend”—that comes from equal participation.
Here are three actionable steps to help drive equity in AI adoption:
1. adopt more inclusive skilling.
Move beyond traditional, monolithic training programs and embrace continuous learning models that are varied, more accessible and flexible. Focus on micro-credentials, on-the-job mentorship and scenario-based learning to build practical confidence alongside technical capability.
It’s important to understand the unique barriers different groups face and tailor skilling opportunities accordingly. Recognizing that because women at different career stages face varied challenges (e.g., the gender gap is narrowing for younger talent, with 34% of women with <1 year experience having AI skills), training must be tailored across all levels of experience. Offer flexible scheduling, address confidence barriers and provide clear career pathways to ensure engagement and retention.
2. conduct an audit.
Auditing for bias shouldn’t just be a "check the box" exercise; it’s a fundamental shift in how organizations can build technical trust. Bias rarely starts in the code, but is usually inherited from data. For IT leaders, this means moving from a reactive mindset to a forensic approach. Multidisciplinary reviews involving HR and legal experts in the pre-deployment phase can be particularly effective – they may catch nuances that a technologist might miss. For example, historical scrubbing for a variable like "years of continuous service" might unfairly penalize women who took maternity leave, embedding gender bias without ever using a "gender" tag.
3. collective action & partnership.
Creating a sustainable talent pipeline requires collaboration beyond the organization. By partnering with universities, non-profits, community groups or talent partners, organizations can influence curriculum development, offer apprenticeships or outreach programs that encourage and support women in pursuing AI and tech careers from an early stage. Organizations like AnitaB.org and IWD.com are two great resources to explore how to make an impact across academia, the early-career pipeline, and through enterprise-level systemic change.
By focusing on equity now, using methods like personalized and collaborative approaches, organizations can contribute to and sustain a long-term, diverse talent pool across all levels, including specialized areas like Generative AI.