Context as Infrastructure: How SIXT Wired Its Enterprise for AI Productivity at Scale

The bottleneck for AI productivity in the enterprise isn't the models - it's context. Who has access to what data, when, and in what form determines whether AI becomes a genuine productivity multiplier or stays a collection of isolated experiments.

At SIXT, we built a platform layer designed to solve exactly this: shared context and data access as first-class infrastructure, available to both engineering teams and business users. In this session, we will walk through the building blocks - how we made proprietary data accessible, how we created shared context that scales across teams, and what the platform capabilities look like that let non-engineers ship real solutions alongside engineers writing production code.

This is not a talk about choosing models or managing GPU costs. It's about the connective tissue that makes an AI-native enterprise actually function at scale.