Engineering teaches us to understand complex systems: how individual components interact, where information comes from and how a system responds to changing conditions. But what happens when we apply the same way of thinking beyond machines?

This talk explores a journey from mechatronics engineering and industrial data science toward human-centered AI, using the concept of Local Perspectives as an experimental case study. Local Perspectives explores how data about places, interests, mobility and local knowledge could be combined to create more personalized ways of discovering a city — moving beyond generic “top 10” recommendations toward experiences shaped by context and individual interests.

Through this example, I will explore how engineering thinking can help structure an AI problem, where recommendation and personalization can add real value, and why data alone is not enough when designing systems for people. The session looks at the intersection of physical environments, data and AI — and asks how we can build intelligent systems that understand not just what is popular, but what might actually be relevant to an individual.