AI coding tools are moving faster than the organizations using them. Some engineers see 10x gains, most teams don’t. Why is that, and how can we translate individual AI productivity gains into team performance?
In this talk, Elisabeth shares hands-on lessons from two years of leading teams through this shift. You’ll learn how to manage expectations when a demo built over the weekend makes executives think any feature can ship in production in a week, how to set clear expectations with engineers around learning time, budgets, and sharing what works, and why AI productivity metrics are tricky – but still worth using.
She’ll also explain why introducing more speed without fixing your delivery system simply creates chaos faster, how to best leverage senior and junior engineers, and why leaders need to be hands-on with AI tools to support individual growth. Finally, she’ll share why principles and mindset matter more than rigid rules in a world where AI tools evolve every month.
Director of Engineering at dltHub (Berlin). Ex-Parloa.
Elisabeth is a Data & Engineering leader and Director of Engineering at dltHub Berlin, where she builds AI-native data tooling and infrastructure. Previously, she led engineering, AI and data teams at Parloa and ResearchGate, working across fast-scaling startups and large, complex systems. She has experience building teams from the ground up and modernizing petabyte-scale data platforms and products. With expertise in data, machine learning, agentic AI and platform engineering, Elisabeth connects deep technical execution with clear business outcomes. She is passionate about helping teams become AI-native and redesigning workflows for greater speed, quality and impact.
