The AI Gender Adoption Gap – Why It Exists And What You Can Do About It

Understanding the barriers and opportunities for women in workplace AI adoption.

(Panel at everywoman in Tech Forum, source: Computing)

A panel at everywoman in Tech explored the gender gap in workplace AI adoption, examining the barriers women face and offering strategies for IT leaders to foster inclusive, effective AI integration.

At the recent everywoman in Tech Forum, a panel chaired by Cheryl Razzell, Head of Specialist Solutions Architecture at AWS, on how AI will impact tech careers, one statistic stood out: AI adoption sits at around 80% among men in the workplace, compared to just 59% among women.

For IT leaders, that gap signals not just a diversity issue, but a strategic challenge that could limit how effectively organizations scale AI. Fortunately, the panel provided some great insights into how IT leaders can encourage greater enthusiasm for AI from employees.

AI Is A Tool For The Skilled

The first task leaders must tackle is the perception that AI is replacing human expertise. This is no easy feat, given how readily some tech businesses have attributed sweeping redundancies to AI. The panel pushed back on this narrative, arguing that AI enhances expertise rather than eliminating it.

As Tina Diamond, Director of Cloud and AI Platforms at Microsoft UK, put it: "AI to data scientists is like a scalpel for surgeons. It is a tool." She emphasized that experience amplifies AI's value: "If you're already good and experienced at what you do, you will continue to do it better."

For IT leaders, this reinforces the importance of embedding AI into workflows in ways that complement human capability. Organizations seeing real returns are not treating AI as a bolt-on. They are redesigning how work gets done so that people can operate, as Diamond described it, "better, faster and smarter."

Productivity Gains With Humans In The Driving Seat

Joanna Haslam, Design Director at games studio Snap Finger Click, described how AI had transformed output within her creative team. "We've always got a big list of projects," she said, "and productivity has really increased so much in the last year."

Tasks that once took days, such as researching design ideas, can now be completed more quickly. "I write design documents a lot of the time, and I spend a lot of time working through different scenarios. If I've got a brainstorming partner in AI, I can speed that process up considerably. Crucially, I'm still the creative mind behind it."

This is LLMs at their most useful; not replacing human thinking but acting as a collaborator that frees teams to focus on higher-value work.

AI Workforce Strategy

The panel represented organizations at very different stages of AI deployment. Isha Jain, Technology Director at waste management company Biffa, explained that her technology team was still working to demonstrate AI's value to business executives - a process already beginning to shape hiring decisions. "Do we make the ability to work with AI part of our job descriptions by default?" she asked.

Organizations are also looking to educators to supply AI-ready talent. Jain noted the growing importance of hiring graduates already equipped with AI skills, rather than building capability from scratch. Corporate employers are increasingly likely to embed AI literacy into roles, recruitment criteria, and leadership expectations.

Marine Rabeyrin, EMEA Public Sector and Education Segment Lead at Lenovo, stressed the importance of preparing future talent early, whilst also acknowledging that was tricky. Encouraging children and young adults to use large language models sits uneasily alongside growing momentum to restrict young people's access to social media. "How do we accept the fact that AI is already there?" she asked. "How do we support teachers who may not be comfortable with these technologies, so they can prepare students for the future?"

These are important questions without easy answers. What does seem likely is that the next generation of employees will expect AI to be part of their everyday toolkit, much as millennials arrived in the workplace expecting greater digitization and flexible working.

Governance: Moving Beyond Experimentation

As adoption grows, so do risks — particularly around data security and accuracy. Haslam described how early experimentation at Snap Finger Click with free-to-use LLMs raised immediate concerns. "If we're discussing concepts for a new client under NDA, we can't have people putting that into ChatGPT," she said.

Her organization responded with stricter controls and internal policies. But governance, she argued, is not just about restricting access, it’s also about understanding AI's limitations. She shared one telling example: an AI tool began altering quiz content because it couldn't verify the answers. "When I asked the AI why it did this, it confirmed it had decided to fact-check the questions."

Haslam was clear that this wasn't a failure of the AI. It was a failure of the prompt. "You have to tell it that it shouldn't fact-check. You have to be clear and keep building those parameters in. If you don't, it can go wrong because what comes out looks correct."

Why Are Women More Reluctant Than Men To Use AI?

When considering this question, Marine Rabeyrin pointed to a tendency in women to feel that they must prove their competency at any given task. This mindset can discourage experimentation, even when AI could enhance performance. At the same time, she noted that greater caution around issues like data privacy may lead women to use AI more thoughtfully.

Isha Jain framed this tendency as a strength rather than a potential weakness. “Women generally ask more questions. We analyse everything.”

Her advice was to channel that mindset into exploration.

Takeaway For IT Leaders

Building a culture where experimentation is encouraged and where using AI is seen as a mark of skill rather than a shortcut, requires actively challenging the idea that relying on AI undermines expertise.

The gender gap in AI adoption is a reminder that successful AI strategies are as much about culture as technology. The organizations that get this right will combine robust governance, inclusive practices, and a genuine commitment to bringing everyone along.

This article originally appeared on MES Computing’s sister site Computing.