At this year’s Women@Data Expo, 27 speakers took the stage – and three of them are members of the AI4ALL community. Here’s a look at the talks that make it especially clear why women’s voices in AI matter – not as a box to tick, but because they bring perspectives and solutions the field needs.
Written by Irena Tischenko
Melissa Ablett-Jordan (founder of Sibyl) and Oliviana Bailey (Co-Founder and Co-CEO of AI4ALL) sat down for a “virtual fireside chat” about a difficult but timely topic. They discussed how data on women’s health has been underfunded and under-researched for years, and how that gap flows straight into the AI systems that are supposed to help. But there’s a second, less obvious gap: even where the data already exists, almost no one is building solutions.
Melissa knows both problems discussed in the talk firsthand. After experiencing pregnancy loss herself, she created Sibyl – an AI-enabled digital care platform for miscarriage and infertility, built without a technical co-founder and without a ready-made plan. In the conversation, she explained where the need for such a product actually comes from: from a medical standpoint, pregnancy loss is often seen as predictable and relatively low-risk, and in an emergency department that triages by threat to life, a woman in this situation can end up at the bottom of the list. The system works exactly as designed – measuring danger, not pain. But the consequences add up: one in three women meets the criteria for PTSD three months after a loss, and for one in six women, that state persists much longer.
“I built Sibyl because I went looking for support after my own losses and found that nobody owned that moment. Not the hospital, not the GP, not anyone. If AI is going to be built for women’s health, I believe it’s going to be best built by people who have lived the problems they are trying to solve.”
Before Sibyl, Melissa built a career in scaling: COO of Cambridge Innovation Center, where she helped the company expand to locations across three continents, and later worked with Masters of Scale International. Sibyl went through AI4ALL’s Entrepreneur Program. The main takeaway from the conversation: don’t wait for the market to notice the problem – build the solution yourself.
Immediately after the fireside chat, Oliviana Bailey took the stage again – this time solo – to tackle a problem she sees often: many tasks get labeled as “not right for AI” when they simply haven’t been structured for it yet.
In the session, she shared a practical framework for evaluating whether a task is actually ready for an AI workflow, and how to redesign it when it isn’t – covering task fit, data flow, and security.
“So many times, I hear from nontechnical people that they would like to work with AI and have it automate their work, but they get stumped with what to build. Many times they think the work needs to fit in a narrow box of is frequently recurring and straightforward. For many, the hardest part about using AI is figuring out what to use AI for, and the first task isn’t actually building an AI system, but restructuring your process.”
Her perspective comes from building AI4ALL itself – a Netherlands-based nonprofit she co-founded, with a mission to bridge the knowledge gap between society and AI through continuous education, confidence-building, and the amplification of diverse leadership. AI4ALL grew out of her earlier role as Women in AI Benelux Ambassador. Before that, she built ecosystem and partnership programs at Rockstart, ING, and EscherCloud (where she led the launch of Hyperion Lab and an NVIDIA partnership), and ran content strategy at The Next Web, a Financial Times company.
Irina Popovikj, AI Consultant at Dynamic People, broke down what an AI agent actually needs to work in a company – through the metaphor of a “balanced diet” made up of three courses.
The first course – data quality, without which an agent simply can’t work reliably. The second – permissions and security: the boundaries within which the agent is even allowed to act. The third – “homemade” tools, which you have to build yourself when a ready-made solution for the task doesn’t exist yet.
The talk is grounded in real-world experience building and testing enterprise agents, not theory. The goal is to give participants a concrete understanding of what’s worth planning for before starting an agent project – not after something’s already gone wrong.
“Everyone talks about what AI agents can do. I wanted people to leave knowing what it actually takes to get one into production, and how to handle the challenges along the way.”
Three different talks – one about health, one about making tasks AI-ready, and one about AI agent infrastructure – but all about the same thing: when a solution doesn’t exist, women in AI don’t wait – they build it. That’s exactly why there should be more voices like theirs.
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