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I. AI-ready R&D: How to close the gap between your data reality and AI ambition?
Every AI ambition in food and beverage, from faster reformulation to predictive shelf-life, rests on a strong data foundation. Yet R&D data is often fragmented across lab notebooks, spreadsheets, sensory testing and supplier records. How are you tackling this foundational challenge, and what has it taught you about making food R&D data truly AI-ready at scale?
You'll find this session particularly valuable if you're in: R&D, Data Science, AI & Machine Learning, Digital Transformation, Food Science, IT/Data Architecture
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