AI is reshaping work far beyond automation. As organizations move from AI experimentation to AI at scale, the skills that matter most are shifting toward adaptability, trust-building, coaching, and organizational awareness.

That matters for women in tech. A recent Forbes article citing a study of more than 55,000 professionals across 90 countries found that women scored higher in 11 of 12 emotional intelligence competencies, including inspirational leadership, coaching and mentoring, and adaptability.

In the era of production AI, these are not “soft skills.” They are increasingly essential to driving business value. This article explores why women in tech are well positioned to help lead that shift — and redefine what leadership looks like in the era of AI.

The real ROI of AI is human leverage

AI is transforming work faster than most organizations ever anticipated. Yet the biggest changes are not happening in the lines of code or the size of the large language models; they are happening in how people work, collaborate, make decisions, and create value.

For two decades, “I (Catalina Herrera) have had a front-row seat to the evolution of data.” In the early days, the conversation was purely technical: infrastructure, algorithms, and computing power. Today, as a Field Chief Data Officer working daily with some of the world's largest enterprises, I see a radically different reality playing out. The most successful AI initiatives are not replacing humans; they are explicitly redesigning work.

The organizations achieving the greatest competitive advantage aren't necessarily those with the most advanced technology. They are the ones that understand how to combine raw human expertise with AI capabilities to scale trusted outcomes.

This shift is creating an unprecedented opportunity for women in technology and AI leadership. As AI becomes embedded in daily operations, technical depth remains essential — but its value grows when paired with human judgment, strong governance, domain expertise, and the ability to influence decisions across teams and functions. Organizations desperately need leaders who can navigate organizational change, build trust, mitigate bias, and connect technical innovation to measurable business outcomes, and research consistently validates this.

From personal reinvention to enterprise impact

When we talk about the AI revolution, we have to look past the hype of individual productivity. Making one worker 20% faster at writing emails doesn't scale an enterprise. True transformation happens when workflows are fundamentally re-engineered and roles are entirely redefined.

Currently as a Field CDO at Dataiku, as someone who has navigated the shifting tides of tech for over 20 years, I've had to reinvent my own professional identity multiple times. I know firsthand that change causes friction. But when AI changes the structural nature of jobs, it gives women a blank canvas to redefine what leadership looks like.

Let's look at how three formidable leaders within the industry view this transition.

When AI changes jobs, women are redefining the roles

“What gives me confidence is that I'm starting to see more women not just participating in AI, but leading it: shaping strategy, governance, and how organizations turn AI into real business value.

From my experience, representation matters, but access is what drives AI success: access to opportunity, sponsorship, and leadership roles. Leaders have a responsibility to create those on-ramps whilst also ensuring AI is built responsibly.

What does that mean? It means it's built on high-quality, representative data, with strong governance, active bias mitigation, and clear accountability. Ultimately, AI success isn’t just about building models; it’s about delivering trusted outcomes, measurable ROI, and impact that can scale.”

— Faye Murray, Field CDO at Dataiku

Faye hits on a critical truth: The battleground for AI success isn't the model itself, it's the data quality and the governance framework surrounding it. When women lead strategy and governance, we ensure that the infrastructure is built on accountability, the only way an enterprise can safely scale.

“AI is actually changing what organizations value.

As we move from being experts using AI to organizations that are actually working alongside AI agents, success becomes much more human. So it depends more now on our judgment, our ability to collaborate, empathy, governance, and actually understanding how people work together. So this is one of the reasons why I believe that diverse leadership matters so much.

Different perspectives help us challenge assumptions and build AI that works for more people, not just a few people. So, as leaders, our responsibility isn't simply to get more AI into the business; it's to build AI that people trust, that reflects diverse perspectives, and that helps more people succeed. So ultimately, that’s what AI success looks like to me.”

— Sarah Finchum, AVP, Sales, Northern Europe

Sarah’s perspective highlights the profound shift in the corporate definition of "value." In a world where AI can commoditize outputs, generating a flawless report or a perfect piece of code in seconds, the human signal is what cuts through the noise. Value is no longer measured by what you can produce, but by the judgment you apply to the parameters and the outputs.

“Women in tech is actually an interesting topic to discuss. And I think it's going to be interesting also to see how things will be evolving in the future.

We know that traditional engineering courses had a tendency to attract many more men than women, and that created the biases that we have today. But GenAI is actually going to help us close that gap. With GenAI and the huge process of re-engineering opportunities that are being raised, it's going to be even more important to have diversity in the mix. I believe we are going to see much more diversification over time, and that it's going to be simpler to bring in all the right talents that we need to have in those company re-engineering and transformation motions.”

— Sophie Dionnet, SVP, Product and Business Solutions

Sophie highlights a hopeful shift: GenAI can open new paths into technical work, but organizations still need to remove the barriers technology alone won’t fix. For decades, the tech industry suffered from a pipeline problem, heavily skewed by the demographics of traditional engineering tracks.

By lowering the purely technical barrier to entry, GenAI democratizes access to complex systems. The focus expands beyond technical execution to include business transformation and systemic re-engineering — areas where multi-disciplinary talent and diverse perspectives are essential to prevent building old biases into new, automated workflows.

When women in AI redefine professional identity

For women looking to succeed and lead in this new landscape, as a Field CDO, my advice is grounded in two decades of trial, error, and triumph: Don’t try to out-compute the machine. Build technical fluency — and the judgment, governance, and accountability machines can’t own.

Early in my career, I (Catalina) thought technical perfection was the ultimate shield. It isn’t. In the AI era, your value lies in your ability to provide what I call a "Proof Map." Just as top job candidates are bypassing automated AI filters by auditing company inefficiencies and bringing upfront value to interviews, you must bring that same diagnostic mindset to your current role.

• Own the logic, not just the output: Don't just deliver a dashboard or a model. Explicitly document your "thinking logic,” the trade-offs you considered, the biases you mitigated, and the business pain points you are solving. AI can generate the answer, but only you can defend the judgment behind it.

• Pull others up as you climb: Don't climb the corporate ladder alone. Being a real leader in AI means opening the door for the women coming up behind you. Mentor them, advocate for them, and build teams where different kinds of thinkers feel safe to speak up and challenge the status quo.

Designing an AI-mature workforce: what lies ahead

Looking toward the horizon, an "AI-mature workforce" will look vastly different from the siloed structures of today. We are moving rapidly toward an environment where human professionals don't just use tools, but orchestrate teams of autonomous AI agents.

The workforce won’t be divided simply by technical skill, but by who can combine technical and domain expertise with the judgment to design workflows, evaluate outputs, manage exceptions, and own accountability. If you want to remain indispensable, you must develop a deep understanding of systemic workflows. You need to know how data flows across an organization, where decision rights sit, and how to build governed guardrails so that autonomous systems don't hallucinate or drift off course.

My futuristic advice for women in AI is simple: Become the architect of the system, not just a user within it. Focus on data orchestration, ethical governance, and the operational layers that turn raw algorithmic intelligence into stable, scaled enterprise value.

Moving beyond displacement

As AI becomes deeply embedded in everyday workflows, the conversation about jobs must move entirely beyond displacement. What matters now is how work is being restructured: which tasks are automated, where decision-making shifts, and how organizations build the human capabilities required to translate AI into business impact. Women in AI and technology play a critical role in that transition.

Across functions, women are not only adapting to changing roles themselves, but they are also helping their broader teams adopt entirely new ways of working: guiding collaboration, strengthening corporate trust, and connecting technical innovation to real operational outcomes. That holistic leadership profile is uniquely suited for an AI-shaped workplace.

The path forward with Dataiku

Ultimately, AI success is a team sport that requires a unified, governed approach. This is exactly why organizations look to platforms like Dataiku, the Platform for AI Success. By serving as an orchestration and governance layer, Dataiku brings your people, diverse teams, data assets, and AI tools together into a single, controlled environment. It allows companies to scale their data operations while maintaining full visibility and trust.

Technology alone is inert; it requires human leverage to bring it to life. As AI raises the strategic value of capabilities like judgment, collaboration, and responsible leadership, it creates an opportunity to better recognize women’s contributions and expand their influence across both technical and leadership roles, driving the empathy, collaboration, and rigorous governance that transforms isolated algorithms into lasting enterprise triumphs. The future of AI isn't just automated; it is trusted, inclusive, and led by those who understand that our greatest asset will always be human judgment.