Generative AI is driving efficiency and innovation across countless industries, but there are still unanswered questions about where, how, and when AI can play a role for food sustainability.
Whether you’re looking for decarbonization roadmaps, recipe development support, marketing copywriting, or automated supplier survey responses, it is increasingly clear that AI can transform the way you work and create more efficient pathways to impact reduction.
But how do you know where it can bring the greatest ROI?
HowGood’s Chief Innovation Officer, Ethan Soloviev, recently presented a framework for leveraging AI to drive the decarbonization and systems transformation for the food industry. This article summarizes highlights from the recent webinar, “Harnessing the Power of AI for Food Industry Transformation.”
Current status of AI in the food industry
To date, most advances in AI for the food industry have been driven by applications of Machine Learning (ML).
Platforms like HowGood’s Latis rely on advanced ML and data science to aggregate and analyze large data sets and map complex data points. As an example, ML has made it possible for HowGood to go from analyzing the sustainability impact of small sets of products to automating the generation of a million product carbon footprints upon request, which we currently do for food giants like Sysco, Ingredion, Danone and others.
Generative AI will drive what comes next for the industry, bringing data science and ML into the hands of large masses of the population, rather than being accessible only to select experts.
For now, AI is applicable to the food industry on three levels. At the most common level, people are currently using it to create and find efficiencies in daily work. A second, more limited subset of food industry employees are using it for recipe innovation to create new products or find ways to reduce the climate impact of existing products. A third, very limited group, is just beginning to experiment with using AI to bring about radical transformation in the food industry.
AI is poised to play an especially pivotal role in regard to food supply chains, improving inventory management, predicting fluctuations in supply and demand, and streamlining supply chain logistics.
Advances and opportunities for AI in the food supply chain
The two areas in which we’re closest to AI improvements in addressing food and agricultural supply chain challenges is with rapid increases in efficiency and productivity.
All eight stages of the value chain provide opportunity for this: agricultural production, sourcing and procurement, product conception and formulation, manufacturing and packaging, marketing and sales, logistics and supply chain management, retailing, and waste management.
However, some of the most exciting current opportunities may be in product innovation and impact abatement recommendations. Improving the climate impact of a product is a complex path. It takes into account an enormous web of information across countless variables. Using AI to do the math for this — generating the most efficient pathways to reduce carbon footprints, improve soil health, enhance biodiversity, and uplift human rights — will truly harness these new technologies for good.
Although AI is currently heavily used by extractive industries to further their operations and cause negative impacts on the planet, getting AI into the hands of people working to improve the food system’s impact can reverse those negative effects. Bringing AI into product innovation will improve the speed and accuracy with which the industry develops responsible, climate friendly products – and that is something that will benefit everyone.
Access the full webinar here, starting with a discussion of the AI landscape for the food and beverage sector, and including solutions for:
- Product Carbon Footprinting
- Supply Chain Risk
- Consumer Trends
- Product Innovation
- Contract Manufacturing
- Marketing & Sales
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