A Blueprint to Create Reliable AI

In this deeply practical session from the Artificial Unintelligence Conference 2025, Tim Biedenkapp and Inga Glotzbach present a clear, actionable framework for building AI systems that are reliable, explainable, and built to last.

Drawing from real-world experience, they walk through the process of engineering reliability — not as an afterthought, but as a discipline embedded throughout the entire AI lifecycle. From data versioning and model validation to documentation and collaboration across teams, this session shows what it really takes to move from proof-of-concept to production with confidence.

Key themes include:

Why reliability must be designed into the process, not added later

Cross-functional collaboration as the foundation of trustworthy AI

Managing data lineage, quality, and version control for traceability

Measuring reliability as consistency over time and context

The role of documentation and transparency in scaling trust