Luisa Herrmann

Despite AI reshaping virtually every industry, 82% of female executives report feeling like they’re watching a foreign film without subtitles whenever technical teams start explaining artificial intelligence. This refreshingly jargon-free session turns AI from mystifying tech wizardry into practical tools leaders can confidently deploy—even if the biggest tech win so far is connecting to airport Wi-Fi on the first try. Attendees will leave with clear AI fundamentals, real-wor

Entrepreneurs take different paths when it comes to finding ideas to start a business and how they go about funding it.   AINovva was born out of need. After working in AI for several years and learning how to code, she built the product to solve her own content-switching exhaustion called AIFred. It handles email prioritization, meeting prep, and relationship management so busy professionals can focus on what matters. And to fund the company, she has bootstrapped it throug

Closing the Gap: Recruiting and Upskilling for AI Success AI is reshaping how products are built and businesses operate — but many startups hit a wall w hen it comes to building the right team to bring their AI ambitions to life. In this panel, engineering and talent leaders will explore how to navigate the AI talent crunch: from knowing which skills to prioritize, to deciding when to hire vs. train, to building an engineering culture that’s ready for AI integration. Whether

Forging Changemakers: How founders develop innovative ideas into innovative companies

Luisa lays out the ramifications of working with AI on a daily basis, why it’s a tool that shouldn't be utilised just because you can, the bias built into every AI system, as well as the ever expanding AI bubble and what the final outcome of its rapid growth could look like down the road.

AI isn’t the future: it’s already here. It’s being used across genomics, biotech, and nearly every area of science and technology, often without a clear understanding of the risks. In this keynote, we explore the promises of AI, the harms already emerging from biased datasets and poorly understood implementations, and the urgent need for frameworks that respect data privacy. Most importantly, we’ll talk about how to build a path forward, rooted in equity, scientific integrity