The Many Ways AI & ChatGPT Will Disrupt Food Tech
5 Mins Read
AI and ChatGPT offer a wide range of use cases across the food tech supply chain- we explore the most exciting.
You may be part of the more than 100 million people that have given ChatGPT, Open AI’s optimized language model, a whirl. It launched in December and has since created a media maelstrom with thousands predicting the model, and others like it would transform our lives. I didn’t give much thought to it as I didn’t see many applications. However, I have since received many questions from agribusinesses and food companies. It seems ChatGPT has become a symbol for all the potential productivity increases and many concerns created by Artificial Intelligence (AI).
The number of use cases for AI across the food tech supply chain is impressive and below I consider all the ways AI and ChatGPT, in particular, could disrupt the industry, some of which are already happening.
Upstream uses cases for AI
I see many upstream applications in farming, notably in precision farming, where AI can be used in the following ways:
- Crop monitoring: for example, to analyze satellite/drone imagery and sensor data to monitor crop health and predict yields (eg. Gamaya).
- Sales prediction: helping farmers sell their output at the optimal time, depending on how markets evolve.
- Input management: to increase or decrease the amount of water or fertilizer (and which to use) needed by crops.
- Livestock monitoring to monitor the health and behaviour of animals, notably to identify and prevent diseases (e.g. Connecterra).
- Farm automation: AI can be used to pilot robots in the fields (eg Farmwise), mostly to harvest crops and control weeds.
There’s a great variety of tools in these three areas, all with their own interface. That’s where the idea of conversational AI comes into play. It can potentially add a lot of value with the farmer is able to manage all this information through a “conversation” and where only the most important elements are presented to her in an easy and actionable way.
AI is being used to create “new crops” either through genetic engineering or via traditional breeding techniques. In this application, it can help to “guess” which crossing will have the desired traits.
Midstream uses cases for AI
Midstream, AI is can be used for transformation (food science) for many applications, such as recipe optimization. It can gather consumer preference data and create new recipes, as well as create meal planners. This is still a very young space, but it is quite promising, notably in an industry where balancing cost, processing and taste is notoriously complicated.
One of the food tech verticals where AI has found applications is in the growing space of alternative proteins:
- Identifying interesting properties in nature: many plants have not yet been “explored”. Some may have interesting properties we would like to use to create cleaner labels. For example, The Live Green Co, uses it to look for alternatives to methylcellulose for plant-based meats, while NotCo’s now patented tool, dubbed Giuseppe, uses AI and machine learning algorithms to find the best plant-based replacements for animal proteins.
- Synthetic biology, more precisely, in precision fermentation: it can take years to recreate the desired protein from a bacteria and then scale the process. Multiple companies are designing faster processes to go from the idea to a semi-industrial scale. At scale this would be a true game-changer, enabling any food company to identify a protein with a desired property (let’s say a binding agent or an egg protein) and “order it” to then experiment with it in a few weeks.
- Managing bioreactors, notably for cellular agriculture: many companies in this space have demo products, but none have the ability to scale their production yet. Startups are trying to solve this issue by creating smart bioreactors to speed up this process.
Downstream use cases for AI
Downstream, and closer to the consumer, the use cases for AI are abundant. Most are invisible to the consumer, such as:
- Quality and food safety controls
- Data sharing (a major pain point) between suppliers and retailers
- B2B marketplaces (a key area of interest for investors and founders in 2023)
- Supply chain optimization
- Food waste, both in retail stores (to create some kind of yield management to adjust prices in real-time when fresh products are going to be wasted, such as Smartway), and in restaurants (through image recognition to detect what is thrown away and then to better order)
However, in some instances, AI can be used in consumer-facing applications:
- For transparency: to bring the most relevant data to the consumer about the food items she buys (or should buy)
- Personalization: to analyse consumers’ health and dietary preferences and then create customized nutrition plans. The Spoon tested ChaGPT for this purpose, and the results were impressive.
- In both of the above instances, the use of conversational AI tools could greatly improve efficiency and even adherence to the recommendations by the user/consumer.
Many other use cases exist. AI is both everywhere and nowhere at the same time. This technology is only a means to an end, one that all players can use to have a greater impact, reduce costs and/or improve the efficiency of the solution they are working on. In all instances where technology is facing its users, it has to be invisible to succeed. That’s where a conversational tool such as Chat GPT (through text now, and soon, through audio) becomes a game changer.
My bet is that the impact of AI in food tech will be massive but quite slow to materialize as it requires adaptation and often coordinated change all across the supply chain. That being said, established players have considerable opportunities to differentiate themselves right now. A first step would be to start with figuring out all the ways their current business could be disrupted or improved through A today, and in the future.
A version of this article first appeared in Zapping Foodtech by DigitalFoodLab newsletter on February 21st.