burger menu
✕
Subscribe
✕
Subscribe

💬 AI in FoodTech

Published on February 22, 2023

You may be part of the more than 100 million people that have already toyed with ChatGPT. I didn’t give much thought to it as I didn’t see many applications. However, I 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 all along the food supply chain is impressive, notably those. Let’s review them, with a focus on those involving ChatGPT.

Upstream, many applications in farming, notably in the large area of precision farming.
There, AI can be used in the following ways:

  • Crop monitoring, notably to analyze satellite/drone imagery and sensor data to monitor crop health and predict yields (eg. Gamaya). This can notably be then used for:
    • Sales prediction: helping farmers sell their output at the best moment, 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 (e.g Farmwise), mostly to harvest crops and control weeds.

We have a great variety of tools in these three areas, all with their own interface. That’s where the idea of conversational AI comes to play and potentially adds a lot of value: we can imagine a situation where the farmer is managing all this information through a “conversation” and where only the most salient elements are presented to him in an easy and actionable way.

Additionally, AI is already used to create “new crops”, either through genetic engineering or traditional breeding techniques. Here, it can help to “guess” which crossing will have the desired traits.

Midstream, AI is already used for transformation (Foodscience) for many applications, such as recipe optimization. It can gather consumer preference data and create new recipes. This is yet a very young space, but it is quite promising, notably in the industry, where it’s always hard to balance cost, processing and taste.

One of the areas where AI is already used is in the growing space of alternative proteins, notably in:

  • 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. One example is The Live Green Co, which looks for alternatives to methylcellulose for plant-based meats.
  • Synthetic biology, and 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 semi-industrial scale. Eventually, at scale, this would be really game-changing, 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. My companies in this space have demo products, but none have yet the ability to scale their production. Startups are trying to solve this issue by creating smart bioreactors to once again speed up this process.

Downstream, and closer to the consumer, the use cases for AI are once again abundant. Most are invisible to the consumer, such as:

  • Quality and food safety controls
  • Data sharing (a great pain) between suppliers and retailers
  • B2B marketplaces (one of the hottest topics of this year)
  • 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 he buys (or should buy).
  • Personalization: to analyse consumers’ health and dietary preferences and then create personalized nutrition plans.

Here, in both instances, the use of conversational tools could greatly improve the efficiency and even the observance of the recommendations by the user.

Many other use cases exist. In a word, AI is everywhere and nowhere at the same time. This technology is only a mean that all players can use to have a greater impact, reduce cost and/or improve the efficiency of the solution they are working on. To succeed, in all instances where technology is facing its users, it has to be invisible. That’s where a conversational tool (through text and soon through audio) such as Chat GPT makes a big change.

My bet is that the impact of AI in food will be massive but quite slow to materialize as it requires adaptation and often coordinated change all along the supply chain. However, established players have considerable opportunities to differentiate themselves right now. A first step would be at least to list all the areas where their current business could be disrupted or improved through AI, now and in the future.

Have a great week,
Matthieu

Subscribe to our weekly newsletter

Abonnez vous à notre NL mensuelle en Français

Related posts

You're in a good company

Join the 60+ clients of Digital FoodLab: leading agrifood companies, retailers, banks, investors, startups, and public organisations.

✕

Use case: project for a global F&B company looking to map its AgTech innovation ecosystem and the best startups to partner with

What we did:

  • Mapping of the AgTech ecosystem: startups, research regulators, and other leading companies.
  • Discussion to select areas to focus on.
  • Analysis of the information to reveal the trends and a model to analyse eventual partners.
  • A workshop to validate the opportunities based on our recommendations.
  • Scouting of relevant partners followed by introductions.

Results:

  • Mapping the different categories of innovations in AgTech that should be considered now to create long-term benefits for the business.
  • Identification of key partners (an incubator and a couple of startups).
✕

Use case: project for a CPG company on the healthy ageing ecosystem

What we did:

  • Education of the board through a couple of workshops to define the perimeter
  • Identification of key opportunities and threats created by long-term evolutions (technologies, business models, behavioural changes).
  • Deep dives on each of the priority categories.
  • Co-construction of a vision on how the company should address these challenges.
  • Identification of partners (startups, incubators, funds) to move forward.

Results:

  • Creating a consensus on which categories to prioritise and how to address them.
  • Implementation of an open innovation strategy through the development of partnerships.
✕

Use case: project for a global CPG company to develop a strategy on the healthy ageing ecosystem

What we do (ongoing mission on a subscription model):

  • Kick-off where we present an overview of the AgriFoodTech ecosystem to select with the client the categories to cover and for each, the level of information required.
  • Monthly newsletter: each month we send a newsletter with the articles that we have gathered ranked by relevance, their summaries, and a layer of analysis.
  • Database: we set up a personalised database that will be filled month after month with the information gathered on the companies identified for the watch.
  • Workshops: twice a year with the client’s innovation team and other “innovation curious” team members, we present an overview of the evolutions, key trends and a dashboard of the topics followed by the watch.

Results:

  • A clear, regular and evolutive tool to follow what is happening in terms of innovation on key topics.
  • A forum (through the workshops) to discuss innovation trends and new opportunities.
✕

Use case: opportunity screening for an ingredient company

What we did:

  • Kick-off to define the perimeter of the ecosystem studied.
  • Mapping of the different trends shaping the innovation ecosystem of the client.
  • Analysis of the trends on DigitalFoodLab’s trend curve and other relevant frameworks.
  • Workshop to discuss DigitalFoodLab’s recommendations on key trends to prioritise

Results:

  • Shared view of the innovation ecosystem for the client with a view of the trends to prioritize.
  • Clear document (personalised trend curve) that can be easily shared internaly to explain the company’s innovation choices and which can be then updated each year.
✕

Use case: scouting for an agriculture coop

What we did:

  • Kick-off to define the perimeter of the client, the goals of the scouting (partnerships) and the criteria on which startups should be evaluated.
  • Set-up scouting: we selected the first batch of 20+ key startups following the criteria of the client.
  • On-going scouting: then we set up a quarterly scouting of about ten startups.
  • For each scouted startup, we created an ID card with key information such as the business and technological maturity, funding, and corporate partnerships. We also added an explanation of why we selected this startup.

Results:

  • An ongoing and evolutive scouting are matching the client's criteria and its capabilities in terms of deal flow.
✕

Use case: working on an acquisition process for a CPG company

What we did:

  • Kick-off to define what the client is seeking, notably in terms of maturity.
  • Workshop with the client based on a mapping of the different innovation ecosystems adjacent to its activities to select some priorities and discuss inspiring examples of startup acquisition stories.
  • Identification of 20+ targets.
  • Workshop to select the most relevant to engage with.
  • DigitalFoodLab worked as a sparing partner during the acquisition process, notably to help design how the acquired startup could be integrated into the overall company’s strategy.

Results:

  • Different results from traditional M&A processes with a focus on the client’s innovation strategy.
  • Identification of a good match for an acquisition.
✕

Use case: market due diligence on sugar alternatives

What we did:

  • Kick-off with the client to discuss its interest on this category, its expectations and existing level of information (notably on the target company).
  • Mapping of the ecosystem to analyse the different existing alternatives and technologies to compare them.
  • Interview (calls) with relevant startups made by our internal biotechnology expert.
  • Recommendation on whether to invest or not.

Results:

  • Clear view of the ecosystem and of the reasons to believe (or not) in each sub-category.
  • Enforceable recommendations based on facts and expertise.
✕
Get in touch

5 rue Paul Dupuy
75016 Paris, France