Accelerate the protein transition with data & AI
Over the past years, I have become more and more involved in the protein transition. I hear you ask ‘The protein what..?”. The protein transition is a movement in which we all start consuming less animal-based protein and replace it with plant-based alternatives for the sake of ethics, sustainability, and health. In this blog post I will explain why this is important and how data & AI can play an important role.
Why the Protein Transition Matters
Our current reliance on animal protein is a heavy burden on our planet's limited resources. It demands large amounts of land, water, and feed, and is also a significant contributor to greenhouse gas emissions. Moreover, with the population growing towards an estimated 10 billion by 2050, the sustainability of our food systems is in question. Besides these issues, the most important driver for me personally are the millions of animals that suffer in this system. It feels like a no-brainer to aim for a food-system that can feed more people with healthy and tasty meals with a lower impact on the world and animals.
A Challenge With Roots in Many Industries
Tackling the transition to alternative protein sources presents multiple challenges:
Societal change: Adjusting eating habits to embrace plant-based proteins is a gradual process, influenced by cultural norms and dietary preferences. Organisations like Fork Ranger inform people and help them to make diet adjustments.
True cost comparisons: There's an imbalance in true costs of animal products over plant-based alternatives that is not yet shown in the supermarket prices. Organisations like TAPPC are doing great work on this challenge.
Provide superior alternatives: To win over consumers, alternative protein products must meet or exceed the qualities of animal-based proteins in taste and nutritive value, while also being competitive on costs. While the Dutch food industry seeded many innovative food start-ups, their innovative power can benefit greatly from modern data & AI technologies.
The latter challenge may sound like a techno-fix, but it is a concrete challenge that I am compelled to work on. So then the question remains: How can we use data & AI to produce superior plant-based protein products?
Opportunities with Data & AI
If you look at other industries, for instance manufacturing, you will notice that data from the full value chain is available to R&D, support, and operational teams. ERP data, manufacturing data and customer data can easily be combined in dashboards that aim to improve products and processes.
Recent roundtable discussions with plant-based food frontrunners, including Revyve, Cosun, and Food Valley, have highlighted the importance of data and AI in accelerating the protein transition. Two main opportunities arose in our discussions.
Operational data collection and analysis are key to product and process improvement. The food industry needs to catch up in this area by establishing a central data platform to convert data into practical insights. Such a data platform often has a standard architecture that ensures that data is retrieved, cleaned up, enriched and made available to analyst, BI tooling or other consumers.
This is the first step towards company automated dashboards that contain cross-division performance indicators.
It will also triple the effectiveness of your data analysts, now that his/her time is no longer spent on manually retrieving and cleaning up data sets.
Production outliers can now be connected to your supply chain, working towards a more predictable and stable operation.
One of the struggles I hear, is that not enough operational data exists to start training models. In these cases it might be worthwhile to take at synthetic data, since this field has improved a lot over the last years with companies like Gretel.
An AI-powered internal knowledge assistant can speed up research. Functionality-wise, you can see it as a Generative AI assistant that knows the context of your organisation. It supports you during tasks that involve vast amounts of qualitative data, for example:
Analyse scientific, customer and market research data to steer new product development
Writing novel food requests for your products
Draft marketing campaigns based on your products
Under the hood, it works like a central data platform that is enhanced with the power of Generative AI. If you are curious how to setup such an AI Assistant, you can take a look at one of our other blog posts. We are developing Ama, with this use case in mind, to provide this technology to organisations that lack AI and data engineering experience.
An AI knowledge assist can accelerate product development by using Generative AI in the context of your organisation.
Conclusion
Society is waiting for superior plant-based protein sources. Embracing a data-driven approach and investing in AI technologies enables the industry to improve products and processes. Other industries already showcased the value of this approach, so let’s be smart followers!
Do you want to discuss something related to this story? Or do you want to make your first steps with data & AI? I am always open for a discussion on how we can use data & AI to accelerate sustainable innovation.
Feel free to connect, or schedule a call to discuss what’s on your mind.