Europe’s tech sector has a numbers problem moving in the wrong direction. In 2025, women made up just 19% of employees in core tech roles throughout the region, down by three points from the year before.
The consulting firm’s own analysis is blunt: as AI reshapes what tech jobs look like, existing gender gaps could widen further without deliberate intervention.
Such is a rare case of a well documented, well funded diversity effort losing ground rather than gaining it, and it raíces obvious questions for European tech leaders. What does deliberate intervention actually look like in practice?
One answer, unexpectedly, comes from Latin America. Laura Peña heads technical success at Fracttal, a maintenance management platform that has become critical infrastructure across the region, the software layer keeping hospitals, industrial plants, and cold chains running without interruption.
Peña’s day to day, however, isn’t about wrenches or factory floors. It’s databases, IoT sensors, and one question that never really goes away: what happens if a piece of critical equipment fails.
In the practical sense, it looks like a vaccine cold chain that drifts even slightly out of range, an operating room that loses power for even a second, provoking catastrophic consequences for both consumers and companies alike.
That responsibility has shaped how she thinks about leadership itself. It’s a kind of leadership, she told 150sec, that is no longer measured in physical strength but in analytical capacity and strategic vision, a shift that becomes obvious the moment you’re the person accountable for infrastructure that hospitals and industrial operators can’t afford to have going dark.
It’s also exactly the kind of technical authority Europe’s numbers show women losing ground in, not gaining.
From doer to “cloner” of knowledge
What makes Peña’s case relevant to that European problem isn’t just that she holds the role, but how she’s using AI to multiply it. Rather than treating the technology as a threat to jobs, she’s adopted it as a way to scale her expertise: she trained AI agents on her own technical knowledge so the team could resolve first-level operational questions without needing her for every call.
The result, she says, has been a major shift in how much she can manage. “I feel like I became superpowered,” Peña said of the change.
Automating routine tasks and basic checks didn’t replace her leadership role. It freed her up for strategic decisions, while the knowledge that used to live only in her head is now available to the whole team through the agent.
The pattern tracks with what’s emerging in Europe’s own AI data. A European Commission survey conducted in early 2026 found just over half of Europeans now use AI, with one-in-four using it on the job, concentrated among managers and highly skilled professionals rather than displacing them.
Meanwhile, a European Investment Bank paper of more than 12,000 EU and U.S. firms reached a similar conclusion: AI adoption raised labor productivity by 4% driven by making existing workers more capable rather than cutting jobs.
Peña’s experience is a specific, human-scale version of the same effect, and a direct answer to one of the failure points McKinsey’s report identifies: senior women’s expertise too often stays siloed with them instead of compounding across a team.
A shift moving faster than the global numbers suggest
The global picture is still uneven. The World Economic Forum puts women’s share of the global tech workforce at around 30%, and Europe’s own trajectory is currently worse than the average.
But Peña points to narrower shifts. In certain niches, predictive technology and critical infrastructure in particular, the change feels faster than those averages suggest. At a recent industry gathering, she stressed, close to 40% of attendees were women.
The claim has some backing in European contexts as well. A CEPR study on AI, automation and female employment in Europe found that roles with higher AI exposure, including predictive and pattern-detection work much like Peña’s own, are associated with a rising share of female unemployment; moving up ten percentiles in AI exposure corresponded to a 2.2-2.9% increase in women’s share of employment in those cells.
It’s a narrow, occupation-specific counterpoint to the broader 19% figure, but it lines up with what Peña is describing on the ground.
For the Colombia-based executive, that number marks a turning point: it’s no longer about asking for space from the margins, but about occupying the roles that set technical direction, exactly the technical high-stakes work where authority is usually hardest won.
Part of that shift, she suggests, ties to a broader sectoral transition. As work moves from the physical field to data analysis, it demands planning and structuring skills, which she links back to more natural forms of female leadership, now central to these roles.
The obstacle no one imposed from the outside
If there’s one barrier Peña identifies as the most persistent, it isn’t external; it’s perfectionism. She cites a well-known statistic in leadership circles, popularized by Harvard Business Review: men tend to apply for opportunities meeting just 60% of the criteria, while women wait until they meet 100%.
“We’re never going to be perfect,” she says, and her advice for those starting out is to prioritize action over waiting. “You have to take on challenges, and if they don’t come to you, go find them.”
For Peña, the real competitive edge in a fast-moving sector isn’t arriving with perfect preparation, but daring to step in, learn as you go, and adjust along the way.
And, for the European tech industry trying to explain why representation is falling instead of rising, her closing message is worth sitting with. Much of what once held women back in hard tech is already being dismantled, and AI tools, used deliberately, are accelerating that opening.
Featured image: Getty Images via Unsplash+
Disclosure: This article includes a client of an Espacio portfolio company.
