I’m a female AI agent builder.
For a long time, AI felt like something other people did—coders, solo hackers, guys on LinkedIn declaring that “prompt engineering is the new MBA.”
It didn’t feel built for me, a Customer Success leader with no coding background.
But then I spoke to a woman using AI to solve a real problem. That changed everything. I gave it a try. And that wound up changing the trajectory of my career. I’m now an AI agent expert, building expansive agents and teaching others how to do it.
That’s why this conversation matters. Because if I’d waited for AI to look like it was for me, I’d still be on the sidelines.
As artificial intelligence reshapes the workplace, it’s not doing so equally. New research shows clear gender disparities in both AI adoption (who’s using it) and AI development (who’s building it).
If your team is investing in AI transformation, this is the context you can’t afford to ignore.
| Metric (2024 Data) | Men | Women | Source |
| Use of Generative AI Tools at Work | ~50% | ~33% | New York Fed (2024) |
| Global Share of AI-Skilled Professionals | 71% | 29% | Randstad (2024) |
These aren’t just numbers—they’re signals. Let’s break them down.
Despite the surge in generative AI tools like ChatGPT, Copilot, and Claude, women are still significantly less likely than men to use AI in professional settings. A 2024 global survey of 140,000+ professionals found women were 20–25% less likely to use generative AI tools than men. In the US, about 50% of men said they had used a generative AI tool in the past year, compared to just 33% of women. These gaps were seen across industries and countries, with the exception of a few tech-forward regions like San Francisco, where some reports found women slightly outpacing men.
The good news? Deloitte reports that women’s experimentation with gen AI doubled in 2023, and parity with men may be achieved by 2025 if trends hold.
The gender divide is even starker when it comes to who’s building AI systems—the engineers, data scientists, and product managers creating the tech.
As of 2024, only 29% of AI-skilled professionals globally are women, and women hold fewer than 15% of leadership roles in AI and advanced tech fields. However, among early-career AI professionals, women now make up around 34%, compared to just 21% of those with 30+ years of experience.
This matters because teams that build AI shape the algorithms, guardrails, and user experiences that define how the rest of us work.
Why aren’t more women using AI at work? It’s not about ability. It’s about access, relevance, and trust. Let’s break it down:
The earliest AI adopters weren’t corporate execs; they were insiders in fast-moving tech circles. Most women never got the invite. I got mine from another woman I respected, who nudged me to try Agent.ai. But that kind of peer pull is rare.
Women still make up only 12% of AI researchers, 29% of the workforce, and <20% of professors in the field according to Women’s Media Center. Women in the US are also 8–16% more likely to express concern about AI’s use in everything from medicine to automation (2022 Pew Research Center).
If you don’t see yourself in the ecosystem, it’s easy to assume AI is not for you. Visibility and representation matter, because when early adoption happens through informal networks, being outside them means getting left behind.
In the UK, a study found that older women from lower-income backgrounds had never used GenAI at work. But with just a little training? Weekly use jumped from 17% to 56%, and daily use rose from 9% to 29% in only three months.
The myth that underrepresented groups need to “catch up” is lazy. These women didn’t need years of upskilling. They just needed examples that clicked with their daily work.
Early AI tools were optimized for coders, solo hackers, and productivity junkies—all fields where women have long been underrepresented. So the default use cases didn’t resonate. But when tools match real needs? Women spike, not trickle, into adoption.
A colleague once texted me:
“I have big feelings about [AI]. While I understand it’s unfortunately inevitable, I personally want to avoid it until I can’t.”
That tension is real. Women often bring more ethical scrutiny and long-term thinking to new tools. But in a field moving this fast, caution can quietly turn into exclusion.
We’ve seen this before. In the early internet era, US women were 68% as likely as men to use the internet at home. The gap eventually closed, but the early influence window didn’t wait around.
If women wait for AI to feel safe, regulated, and made “for them,” they may find the future already built without their input.
Discernment is powerful. But so is being in the room where it happens. We’ll all need to find a way to strike a balance between the two.
A 2024 Danish study found women were 20% less likely than men to use ChatGPT in the same job and workplace. Not because of skill. But because of time, access, and the “green light” to try.
AI rollouts tend to focus on product, engineering, and analytics—all male-dominated domains. Meanwhile, women often hold down ops, admin, HR, and care roles, which get fewer incentives to innovate.
A 2025 Robert Half survey found 38% of men planned to upskill in AI vs. just 27% of women. That matters. Early adopters shape workflows and pick the tools everyone ends up using. If women aren’t part of those first waves, they inherit systems designed without them. And in AI, design is power.
Women don't need more convincing. They need visibility, relevance, trust, and access—so they can help shape the future, not just adapt to it.
This isn’t just an inclusion issue—it’s a business and innovation risk.
If women are less likely to use or shape AI, they’re less likely to benefit from productivity gains or influence AI outcomes. Fewer women in development roles increases the risk of algorithmic bias, model blind spots, and missed market opportunities. Closing these gaps is essential to building trusted, human-centered AI systems that work for everyone.
Slower AI adoption among women puts them at a structural disadvantage as AI reshapes jobs, workflows, and required skill sets. As generative AI becomes embedded across industries, from marketing to legal to customer service, those who adopt early gain productivity advantages while others risk falling behind.
This means women may be disproportionately impacted by job displacement or role reshaping if they’re not using AI tools at the same rate as men. Moreover, since many of the jobs most exposed to automation—like administrative support, retail, or content moderation—are held predominantly by women, a lower rate of AI upskilling could widen existing gender gaps in income, opportunity, and advancement.
Without intentional skilling and access efforts, AI could replicate and even amplify the workforce inequities of the past.
Despite these concerns, there is hope—because we’re still early, and there’s still time for many more women to become early adopters. Here are a few pieces of good news for women in AI:
Corporate Reskilling Programs
In Australia, 45% of organizations now offer gender-focused AI reskilling, and women account for nearly 40% of AI‑related education programs.
Small-Group Training Works
A UK study showed low-income women 55+, previously unfamiliar with AI, grew weekly usage from 17% to 56% within 3 months after targeted training.
Global Growth in Female AI Use
An International Labour Organization/UN meta-study highlights that women across dozens of countries are increasingly using AI, particularly as digital access expands.
Empowerment Through Inclusion
Experts emphasize that AI can advance equality if women are included early—not excluded bystanders.
Diverse Teams Produce Better AI
Recent academic research shows that AI codebases with gender-diverse teams have better code quality and community impact.
Structural Supports Are Growing
Programs like Code First Girls, Ada Developers Academy, and Women in Data Science are training tens of thousands of women in tech and AI.
Don’t wait for permission or a perfect use case. Start now.
That’s what I did: I picked a real problem, spun up a simple agent, and started building. It became the Customer Website Experience Audit, enabling companies to review their sites from the perspective of a customer. To date, it has 2136 runs, and won Agent.ai’s Agent of the Week.
You don’t need a Computer Science degree or a PhD in machine learning. You just need curiosity and a use case.
Choose a task you do often. Automate it with a no-code AI tool. Try out a GPT prompt for your workflows. Tinker with an agent builder (Agent.ai is free and where I build). The fastest way to understand AI is to use it, and the most powerful way to shape its future is to build with it.
Encourage your teams to do the same. Build a low-stakes sandbox. Run internal AI experiments. Celebrate early wins. Normalize curiosity over perfection. The goal isn’t just parity, or having a seat at the table. The goal is for women to have undeniable influence and a defining voice in how AI shapes our world.