Why the AI era needs women leaders more than ever

The age of AI is not just a technological inflection point — it is a leadership one.

In conversations with leaders across industries, I keep asking a version of the same question: What does it actually mean to lead when the technology beneath every decision is being fundamentally rewritten? The qualities defining great leadership in the AI era aren't new; they’ve just been undervalued — until now.

Empathy and the courage to ask hard questions are now what separate teams that thrive with AI from those that fall behind. Women are not just well-positioned for the AI era. We are essential to it.

Leadership in the AI Era Requires Curiosity, Not Certainty

We need to stop thinking about AI as a product category or a technology trend. It is becoming the underlying infrastructure of every industry: mitigating supply chain complexity and disruptions, redefining airline operations, and more. The organisations that win aren't the ones that integrate AI to gain efficiencies. They're the ones that use it to reimagine their operations, their customer experience, and their employee engagement entirely.

That level of transformation doesn't come from knowing the right answers. It comes from asking the right questions — and teaching the next generation to do the same.

Consider Blast Motion, a leader in sports technology. Instead of asking "How do we process more data?", they asked: "How can we translate complex biomechanics data into actionable guidance for athletes?" Partnering with Persistent Systems, they transformed raw sensor data into real-time, personalised coaching and individualised performance journeys.

Why Empathy and Trust Matter More Now

For years, the qualities that many women bring to leadership — the ability to listen deeply, to hold multiple perspectives at once, to build trust across difference — were treated as secondary to harder-edged metrics of performance. The AI era is inverting that calculus.

When AI automates the technical, human skills become the differentiator. When you're asking teams to adopt AI tools that change how they work, understanding their fears, workflows, and incentives is the difference between adoption and resistance.

There is a line I keep coming back to from a former Merck CEO: “A leader's job is to frame reality and provide hope.” In a moment when automation is reshaping entire categories of work, that is not a platitude. It’s a job description. The leaders who will guide their organisation through this transition aren't the ones with the most technical expertise. They’re the ones who can be honest about what's changing, acknowledge the anxiety that creates, and help people see what becomes possible when AI handles the routine work.

Waiting for AI Certainty Is the Riskiest Move

Early in my career, I faced a choice between two roles: one where I would have been the world expert from day one, and another that would stretch me in ways I couldn't fully predict. People far more senior than me told me to play it safe. I chose the stretch.

That decision taught me that the advice to "wait until you're ready" is usually the wrong advice. Small ambitions and big ambitions take the same amount of work. So go for the hard thing.

This same logic applies to AI adoption. Leaders who wait until they fully understand AI before engaging will already be behind. But taking risks with AI doesn’t mean being reckless. It means applying the same empathy and curiosity I described here.

We talk too much about pilots moving to production, and not enough about what we learn from experimentation. Not every pilot needs to scale. Some teach you what not to do. Others reveal opportunities you hadn't considered.

For example, Fibe, an Indian fintech working with Rapyder, recognised its chatbot couldn't handle nuanced customer questions about loans and payments. They started experimenting, learned what worked, and iterated fast. Today, their solution delivers 2–3 second response times across 400,000 conversations and 5 million users. The value wasn’t just in what made it into production — it was building the organisational muscle to test, learn, and move.

The leaders who succeed with AI combine curiosity with empathy. They ask the right questions, understand their organisation's readiness, and create psychological safety that makes experimentation possible. At AWS, we see this every day: the organisations winning with AI started early, learned from failure, and built transformation muscle while technology evolved. They didn't wait for certainty. They created it.

Ruba Borno

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Dr. Ruba Borno is Vice President - Global Specialists and Partners at AWS.