Women use artificial intelligence tools up to 25% less than men

The rapid expansion of artificial intelligence in the workplace is opening up new opportunities? but also creating new gaps. A global analysis of 18 studies reveals that women are adopting generative AI tools at rates approximately 25% lower than men?s, even when they have the same access. This difference, far from being technical, is largely due to cultural and perceptual factors.

According to the data, many female professionals are reluctant to use these tools out of fear of how their use might be perceived in the workplace: from appearing less thorough to the feeling that they are ?taking shortcuts.? ?There is pressure to avoid mistakes and constantly demonstrate competence,? explains Radhika Kapur, Vice President of Partners and Technology for EMEA at Confluent, who emphasizes that adopting technologies still in development can be perceived as a greater risk for certain roles.

The impact of this lower adoption rate is significant. AI is already demonstrating tangible improvements in productivity, especially in administrative tasks, data analysis, and content generation. More limited use implies, in the medium term, less operational efficiency, lower internal visibility, and fewer opportunities to take on strategic roles, which can amplify existing inequalities within organizations.

Furthermore, the gap affects not only talent but also the technology itself. AI systems learn from user interactions, so lower female participation can lead to models that reproduce biases or partial perspectives?a phenomenon already detected in other algorithmic systems.

Paradoxically, AI?s potential to balance workloads is significant. Tools capable of summarizing meetings, drafting reports, or structuring complex information can alleviate organizational tasks that, according to various studies, fall disproportionately on women. Reducing this burden would allow for more time to be devoted to higher-value functions, such as decision-making or leadership.

In this context, corporate culture plays a decisive role. The gap narrows significantly in organizations where experimentation is encouraged and the imperfect use of AI is normalized. Offering training is not enough: it is necessary to create environments where testing these tools does not pose a reputational risk.

From an ESG perspective, the issue goes beyond technology. The uneven adoption of AI can become a new driver of inequality if not properly managed, affecting both diversity in decision-making and talent development. Integrating AI in an inclusive manner is thus emerging as a key challenge within corporate sustainability policies.

There is still room for maneuver. Recent experiments show how AI systems themselves tend to reproduce gender stereotypes in professional profiles, reinforcing the need to act now. As Kapur concludes, ?we are at a pivotal moment to steer AI toward inclusion, rather than perpetuating exclusion.?

The question is not whether artificial intelligence will transform work, but how and for whom it will do so. And that answer will depend, to a large extent, on the decisions organizations make today.