How will generative AI change the food tech industry?

My area of interest can be expressed in the following sentence.

"Taisho Foodtech Ventures is launching a $5M Pre-Seed fund in the U.S. to invest in North American B2B food tech startups, leveraging my expertise from a 1525x B2B tech exit."

So, since everyone is paying attention these days and everyone is talking about AI in Silicon Valley pitches, in this article, I would like to summarize four points, as well as introduce some actual use cases.

1. impact on product development and innovation

1-1. Flavor and recipe generation

Generative AI has the potential to analyze vast data sets of flavor profiles, ingredients, and consumer preferences to create innovative recipes and foods and discover novel flavor combinations and food pairings.

As a specific example of its use, IBM is working with companies in the flavor and fragrance industry to develop new flavors and fragrances using AI. In this project, IBM's AI technology is accelerating product development by analyzing a database of ingredients and suggesting new combinations. As a fact-based outcome, the collaboration has accelerated the specific flavor development process by up to 50%, significantly reducing the time to market for new products. It has also improved the success rate of products that meet consumer preferences and reduced the risk of introducing new flavors to the market.

1-2. Texture and composition modeling

AI models can simulate the physical and chemical interactions of different ingredients to help develop new foods with desirable textures and nutritional profiles.

A specific example of its use is NotCo's development of plant-based products. NotCo is a Chilean startup that uses AI to develop plant-based food products. Their AI system, Giuseppe, analyzes plant-based ingredients and generates new food recipes that mimic animal-based products. In addition, as a fact-based accomplishment, NotCo has achieved commercial success in several countries with plant-based mayonnaise, milk, ice cream, and other products developed using AI. As of 2020, NotCo has generated millions of dollars in revenue, and its products are sold in thousands of retail stores.

2. impact on supply chain optimization

Generative AI can simulate various scenarios and find the most efficient routes and methods of food delivery, taking into account factors such as traffic, weather, and delivery windows.

As an example of its use, FedEx uses AutoRoute, a delivery route optimization solution. The system generates optimal delivery routes in real-time, taking into account multiple factors such as delivery priorities, traffic conditions, and weather.

With the implementation of this AutoRoute, FedEx has been able to significantly improve delivery efficiency, shorten delivery times, and reduce costs. Although specific figures are not disclosed, the system has resulted in shorter delivery times to customers and lower operating costs.

3. personalized nutrition and its impact on the diet

By analyzing an individual's health data, dietary preferences, and restrictions, AI can generate personalized meal plans that address specific nutritional needs to support health management and wellness.

Zoe, as a specific use case, is a program that combines microbiome analysis, blood glucose response testing, and fat response testing to provide customized meal plans based on an individual's biological responses.

The program uses AI to analyze vast amounts of data and make dietary recommendations to help optimize individual health. In terms of fact-based outcomes, Zoe's initial research showed that participants lost weight, improved blood sugar levels, and improved overall health as a result of following the personalized meal plans provided.

In terms of specific numbers, some program participants lost an average of 5% of their body weight in a few weeks. This demonstrates how generative AI is being used to provide customized meal plans and the extent to which its use is contributing to improved health and satisfaction among users.

4. sustainable practices and their impact on waste reduction

By predicting spoilage and optimizing the food supply chain, AI can significantly reduce food waste at the retail and consumer level.

As an example of this use case, TOMRA is a Norwegian company that provides AI-based food sorting solutions. Their technology uses AI to assess food quality and sort out defective products, improving efficiency and product quality in the food processing industry. As a fact-based outcome, TOMRA's food sorting technology has been used by customers to significantly reduce food waste, with some case studies reporting waste reductions of up to 5%.

If anyone is interested in talking directly about this subject, "How will generative AI change the food tech industry? " Please feel free to contact me directly via comments or DM!