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Why better data could be the key to AI-driven food R&D

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Why better data could be the key to AI-driven food R&D

Picture a product developer creating a plant-based protein drink for three different markets. The product concept is clear; the information needed to build it is not. Supplier specifications are locked inside PDFs. Trial results sit in spreadsheets. Regulatory requirements arrive by email. Even a single ingredient can carry a different name in each document: “haldi” in one file, “turmeric” in another and “Curcuma longa” in a regulatory note. None of these files is wrong. The problem is that the information is scattered.

As a result, the developer spends more time searching, matching, and verifying data than working on the product itself. This has become common across food, beverage, and wellness R&D. Creating a product is no longer only about getting the taste and texture right. It is also about managing and connecting an ever-growing volume of data.

Food product development is becoming more complex, and food safety sits at the centre of that complexity. The World Health Organization’s 2026 estimates, which cover 42 foodborne hazards, found that unsafe food caused an estimated 866 million illnesses and 1.5 million deaths worldwide in 2021. Chemical hazards accounted for 73% of these deaths, with inorganic arsenic and lead alone responsible for 42% and 31% of chemical-related deaths, respectively. The economic toll is just as stark: WHO puts the productivity lost to foodborne disease at $310 billion in 2021.

The challenge multiplies when companies develop products for several markets at once. Each market brings its own allergen rules, contaminant limits, permitted claims and labelling norms, and the ingredients themselves vary.


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