State of data & AI: Where do we stand today in the effective use of data and AI technologies?
Value first: How can we spot AI use cases that truly move OEE, yield or GMP-compliance instead of remaining interesting data experiments?
Data readiness: What information (process data, lab results, quality records) do we actually need, and how we can access it without creating audit headaches?
People & culture: What capabilities and ways of working are required – from operators to managers – to turn AI ideas into measurable results?
From pilot to daily use: How do we turn a promising prototype into a trusted, audit-ready tool that people rely on every shift?