Food & beverage R&D wasn’t built in modern software – it was built in Excel. Legacy formulation tools and PLM/ERP systems later tried to standardize that work, but usually by capturing only the final formula, not the messy iterations, trials, and decisions behind it. As a result, teams still do the real work across spreadsheets, email, and shared drives, then re-enter the same data to produce labels, ingredient statements, regulatory checks, and manufacturing instructions. This double entry and fragmented source of truth is increasingly unsustainable as clean-label demands, claims scrutiny, ingredient volatility, and faster launch cycles raise the stakes.
In this webinar, we’ll explore why AI is finally viable in food R&D, what it can reliably handle today, where it needs guardrails, and where the hype breaks down. We’ll cover practical starting points like constraint-based ingredient substitution, capturing iteration history and rationale, and automating documentation, helping teams understand how AI fits into R&D workflows without adding risk.
Learn how non-purpose-built systems create double entry and fragmented truth, and why that model breaks under today’s R&D complexity.
Understand what AI can reliably do in food R&D today, where it needs guardrails, and where the hype falls apart.
See practical, low-risk workflows like ingredient substitution, iteration tracking, and documentation automation that teams are already using to drive real value.