Key Takeaways:

• Safety over Speed: Achieving aspirational goals with ethics, reliability, and accountability.
• Democratizing Use Cases: Using AI to widen use cases and innovate for all demographics.
• Human-Centric Design: AI development should prioritize empathy and real life complexity.
• Learning by Engaging: Women aren’t waiting for an invitation, they are proactively creating opportunity.

Women are shaping the AI age

As we celebrate Women’s History Month in 2026, Women Applying AI is exploring the strategic impact and foundational leadership of women in artificial intelligence (AI). Today we are featuring three pioneers who are asking questions, rethinking paradigms and leading teams to build ethical and scalable AI solutions. From challenging assumptions, revolutionizing workflows, and bringing inclusivity and humanity to this technology, these women are proving that the most effective way to innovate with AI is through courage and collaboration.

From Demo to Delivery: How Francesca Lazzeri is Bridging the AI Reliability Gap.

Francesa Lazzeri, Principal Group Director of Data Science and Applied AI at Microsoft, leads a global organization of AI scientists building LLM-powered applications, copilots and autonomous agents. Her team works across Microsoft business verticals, from AI for sales experiences to AI for marketplace and commerce, and support engineering. She is an expert in taking data science and AI solutions from early prototypes through rigorous validation, deployment, and operation at enterprise scale

Before Microsoft, she was a Research Fellow at Harvard University studying econometrics and technology innovation, Francesa designed and taught the first “Python for AI” course at Columbia University, and currently serves as an advisor at MIT for the Break Through Tech AI program. She likes living at the intersection of building real systems and thinking critically about where this technology is headed.

Q. What is the problem you are most passionate about solving with AI?

The problem I keep coming back to is the gap between what AI can do in a demo and what it actually does reliably in production.
Right now my team is building agentic AI experiences that serve tens of thousands of users daily, and the hardest part is not the models themselves but everything around them: evaluation, safety, trust, and making sure these systems actually help people rather than just impress them. I have led the development of AI evals and measurement frameworks that let organizations deploy AI with confidence. That work taught me that the most impactful AI isn’t the flashiest. It’s the AI that earns trust because someone took the time to measure whether it’s actually working.

Q. What are you and your team proud of creating or accomplishing with AI?

I’m proud of the work we’ve done on AutomatedML and responsible AI tooling that became industry standards. AutomatedML in Azure Machine Learning helped democratize access to sophisticated modeling techniques, making it possible for teams without deep ML expertise to build production-quality models.
Fairlearn started as an open-source project and grew into something the broader community uses to audit ML systems for bias. When your work becomes a tool other teams reach for without you having to pitch it, that’s when you know you built something that matters.

Q. What does leadership in AI mean to you?

Leadership in AI means taking responsibility for what you ship, not just what you build. It’s easy to get caught up in model benchmarks and technical novelty, but the leaders who matter are the ones asking harder questions:

• Does this system work for everyone, not just the average user?
• Are we measuring the right things?
• Are we being honest about what we don’t know?

For me, leadership has also meant building teams where researchers, engineers, and domain experts genuinely collaborate rather than just coexist. I’ve learned that the best AI products come from organizations where someone with a PhD in economics can challenge an engineer’s assumptions and vice versa, where intellectual honesty is valued over hierarchy.

And increasingly, I think leadership in AI means having the courage to slow down when speed is the default, to invest in evaluation and safety even when the pressure is to ship fast and figure it out later.

Q. What advice would you give other women who are curious about AI?

Start building. Don’t wait until you feel like an expert, because honestly nobody in this field feels like an expert for long since it moves too fast. Pick a problem you actually care about, whether it’s in healthcare, finance, education, creative work, or something entirely different, and use AI to try to solve it.

The technical skills are learnable, and there are more resources now than ever. What’s harder to develop and what the field desperately needs is the kind of judgment that comes from diverse life experience, from understanding real users, from asking “should we?” alongside “can we?”

I came into AI from econometrics and technology innovation research, not from a traditional CS path, and that perspective turned out to be one of my biggest advantages. The women I’ve seen thrive in AI are the ones who brought their full selves to the work instead of trying to fit a mold that was never designed for them.

Designing AI for the Human Soul: Kristen Beveridge Develops first AI life and career longitudinal guidance platform.

Kristen Beveridge is the CEO and Founder of phae, the Personal Holistic Advancement Engine. phae is a lifelong AI-powered coaching and guidance platform that helps people navigate life and work in a world that keeps changing faster than anyone planned for.

Q. What was the moment that inspired you to take a leadership role in AI?

It wasn’t one moment — it was a slow accumulation of conversations I kept having with people I loved. A friend worried about guiding her teenager toward a future that made sense. Another was debating a return to work with no idea where to start. My own son at 14 refusing the traditional path entirely, and asking why we were still preparing kids for a world that was disappearing.

I’m a designer and technologist by training — Carnegie Mellon, Harvard Design School, MIT, 25 years building digital products. I kept waiting for someone else to solve this. Then I moved to Montana to get my family out of the rat race, watched the layoffs accelerate, felt the full weight of what AI was doing to people’s sense of identity and direction, and realized I had been carrying this problem for years because I was supposed to build the answer.

Q. What is the problem you are most passionate about solving?

The fact that only 13% of people are happy in life and work. That number is only getting worse due to uncertainty and shifts due to AI and automation.

Personalized guidance, the kind that helps you figure out who you are, what you’re built for, and how to navigate a world that keeps shifting, has always been reserved for the elite few. The people with the right coach, the right mentor, the right network, the right zip code. Everyone else gets a personality quiz and a list of job titles – if they are lucky.

Meanwhile AI is eliminating roles faster than people can retrain. The old playbook is gone. And there is no trusted, private, human-centered place for people to go and ask the real question: who am I becoming and what kind of life do I want to life, and how does work fit into that equation?

That’s what phae exists to answer. For everyone. Not just the lucky ones.

Q. What are you and your team proud of creating or accomplishing with AI?

The thing I’m most proud of has nothing to do with the technology itself. It’s what the technology makes people feel.
During our beta, a mid-career professional told us she cried reading her Career DNA report. Not because anything was wrong — because for the first time, something had put into words what she hadn’t been able to say about herself to anyone. That’s not a chatbot. That’s a platform that actually sees a person.

From a technical standpoint, we didn’t put a chat interface on top of an LLM and call it a coach. We built a relational guidance engine with persistent memory, proprietary frameworks, and a living Career DNA profile that evolves with you over time. And we built privacy in from day one — your data is yours, full stop.

95% of beta users said they’d continue using phae. 94% would recommend it. But the number I keep coming back to is one. The one person who cried. That’s the one that tells me we built something real.

Q. What was a challenge (expected or unexpected) that you had to overcome?

The unexpected one: myself.

I spent most of my career as a builder and executor for other people’s visions. I was good at it. It felt safer. Taking the leap to build something of my own, something this personal, this mission-driven, this big, required a kind of surrender I wasn’t used to.

I moved to Montana in 2023, partly for my kids and partly because life made the decision for me. And in that stillness (no network, no noise, no familiar scaffolding) I had to let go of the version of myself that bulldozed her way through problems and learn to listen instead. What came back was clarity. And phae.

The expected challenge was timing: building an AI company during a moment when the technology, the market, and the human need are all moving at the same time. But I’ve learned to see that as the point, not the obstacle.

Q. What advice would you give other women curious about AI?

Stop waiting to feel ready. The people building AI right now are not smarter than you. Many of them are just less afraid of looking like they don’t know something.

You don’t need to be an engineer. You need to be curious, opinionated, and clear on the problem you care about solving. The most important thing AI needs right now is people who understand humans who can ask better questions, build with empathy, and design for the full complexity of a real life. That has always been a strength women bring.

And honestly, the world cannot afford for women to sit this one out. AI is going to shape everything about how people work, learn, and live. If we’re not in the room designing it, it will be designed without us. Again.

Get in the room.

Q. What does leadership in AI mean to you?

It’s simple. You put the human at the center. It should never be about the tech. It should always be about solving human problems, creating beauty, solving problems, fostering joy.

Q. What do people misunderstand about AI that you wish they knew?

If you have a creative brain, are inspired, and want to build something or solve a problem, the tools are here to make that happen and they are beyond powerful.

AI can be used for great good, but we need to be loud about it being used in ways that don’t human flourishing. People at all levels of society need to rally around this concept if we are going to survive and thrive.

Frances West On Becoming an Architect of Trust and Inclusion

Frances West, Founder of FrancesWest & Co, and author of “Authentic Inclusion™ Drives Disruptive Innovation,” focuses on human-first AI, inclusive innovation, and helping senior leaders translate inclusion into measurable business value.

Q. What inspired you to take a leadership role in AI?

I spent my entire career at IBM, with my last role as IBM’s first Chief Accessibility Officer based in IBM Research. IBM had been working on AI technologies for decades, long before AI became a household term. Think Watson and the Jeopardy milestone.

What inspired me was seeing how AI could be used to support designers, programmers, and testers in building accessible products and experiences. I led efforts to apply AI to embed accessibility into the design, development and deployment lifecycle. That experience made it clear to me that AI could either scale inclusion or scale exclusion, depending on how intentionally we build it.

Q. What have you found to be the biggest challenge when working in AI?

One of the biggest challenges with AI today is that we are not sufficiently incorporating what I call “edge user” experiences into data models and system architecture. When the lived realities of people with disabilities, older adults, or non-native speakers are not reflected in the data and logic, AI systems can become narrow in their understanding of the real world. In the worst cases, bias becomes embedded, eroding long-term trust between humans and technology.

My current work focuses on educating senior leaders and decision-makers about integrating inclusion from the start. I emphasize that inclusive AI is not a cost center, it is a strategic investment that delivers tangible returns in innovation, market reach, and trust.

Q. What types of problems are you working to solve with AI?

I serve as an advisor to innovative companies that use AI to solve real human challenges.

• Aira leverages AI to help blind and low-vision individuals navigate the real world with greater independence.
• 3Play Media uses AI to caption video and learning materials, increasing access and productivity at scale.
• Personal.ai develops digital twins that extend a professional’s expertise across multiple channels.

I am proud of helping demonstrate that solutions designed with accessibility in mind can scale into enterprise-wide transformation and competitive advantage.

Q. What was a challenge (expected or unexpected) that you had to overcome?

One persistent challenge is the lack of understanding that as technology becomes more human-like, humans must become more intentional and humane in how we guide it.

There is often an assumption that AI is purely technical. In reality, it is deeply social. Aligning technical innovation with ethical responsibility, governance, and inclusion requires cross-disciplinary leadership—and that mindset shift can be difficult.

Q What does leadership in AI mean to you?

My book is titled Authentic Inclusion™ Drives Disruptive Innovation. The shorthand for Authentic Inclusion is “AI.” That is intentional.

I believe Artificial Intelligence requires Authentic Inclusion to become meaningful and sustainable. Leadership in AI means ensuring that intelligent systems reflect diverse human experiences and are designed to build long-term trust and economic resilience.

Q. Have there been other women in AI who have been role models or inspirational to your work?

Lisa Su, CEO of AMD and a former IBMer, is an example of steady, technically grounded leadership at global scale.

Fei-Fei Li, Co-Director of Stanford’s Institute for Human-Centered AI, has been instrumental in advancing a human-centered framework for AI development.

Both demonstrate that technical excellence and principled leadership can and must coexist.

Q. What do people misunderstand about AI that you wish they knew?

Many people believe AI is being developed solely by a small group of technologists and that others have little influence over its trajectory.

In reality, using AI (prompting it, questioning it, refining outputs) is a way to shape it. Influence does not happen only at the institutional level; it begins at the individual level. We do not need to wait for perfect systems. We can engage now, learn, and contribute to shaping more responsible AI from where we stand.

Q. What advice would you give other women who are curious about AI?

Start now. Begin using AI tools in your daily work. Experiment, prompt, and learn by doing.

At the same time, educate yourself on AI governance topics such as trust, security, accountability, and responsible deployment. AI is not shaped only by engineers. Every user interaction influences how these systems evolve. Women do not need to wait to be invited into the conversation. Engagement itself is a form of leadership.

Women in AI are Building Systems Where All Can Flourish

These innovative women remind us that the AI race is not a solo sprint, but rather a collective and intentional journey. By prioritizing transparency, collaboration, and inclusivity, they are setting new “rules of the road” for the next generation of AI developers and creators. Notably, these women are using AI for more than just productivity gains but for a larger mission: a thriving human experience for all.

But perhaps the biggest message from all three leaders was clear: don’t wait. Learn by experimenting – no one has all the answers right now. You don’t have to have a special degree or a massive team to have real impact. As we look beyond Women’ s History Month, Women Applying AI invites all women to explore the free training and hand-on workshops as part of our free membership. Women are shaping the AI future, and there’s no better time to be a part of it.

FAQ:

What is Women Applying AI?

Women Applying AI is an inclusive global community designed to help women navigate the transition into the “Intelligent Age” through practical, hands-on experience. Moving beyond technical theory, the organization focuses on “learning by doing,” providing a collaborative space where women—from the AI-curious to the expert—can solve real-world challenges in both their professional and personal lives.

What are the benefits of joining Women Applying AI?

The community empowers women to move from curiosity to confidence through three core pillars:

• Practical Application: Hands-on programs designed for busy women to apply AI tools immediately to work and life.
• Strategic Networking: Meaningful connections with other women leaders and innovators shaping the future of technology.
• Inclusive Leadership: A supportive environment that helps women develop the judgment and skills needed to lead AI initiatives with impact and ethical clarity.