How AI is powering the alternative protein industry

We explore the innovative ways that AI tools are being utilized in the alternative protein industry

In what seems like the blink of an eye, artificial intelligence has become part of everyday life, woven through nearly every aspect of work and home, from how we communicate and shop to how we design complex systems. 

In the alternative protein sector, AI is rapidly moving from an experimental tool to a practical necessity. As companies race to deliver foods that are more sustainable, affordable, and appealing than animal products, AI is enabling progress at speed and scale. From crop selection and ingredient discovery to optimisation of processes based on consumer insights, AI tools are being applied across the entire alternative protein space and supply chain. 

Below, we outline how businesses can utilize the power of AI alongside real-world examples of companies using these tools.

Faster, more reliable product development

AI is already accelerating the search and development of functional ingredients such as proteins, fats, and emulsifiers that behave like their animal-derived counterparts.

Traditionally, determining which plant proteins perform well in food systems involved lengthy trial-and-error. AI is changing this by enabling researchers to predict how proteins and other molecules will behave before they reach the lab. 

An example is Shiru, whose AI platform scans millions of natural proteins to rapidly identify ingredients that meet specific functional needs. This approach has led to commercially viable, clean-label ingredients such as protein-based texturisers and fat replacers derived from familiar crops, dramatically shortening discovery timelines from years to months.

Crop selection and agricultural optimisation

Alternative proteins rely on consistent, high-quality crops, and AI is increasingly being applied to optimize both selection and cultivation. Modern approaches combine machine learning with real-time sensor data, satellite imagery, and climate modeling to predict crop yield, resilience, and functional traits relevant for alternative protein production. 

Lightweight AI models can analyze mounds of data on soil moisture, temperature, light, and other environmental parameters to determine best practices for farmers, such as optimal irrigation schedules, reducing water use and improving both yield and crop quality.

By integrating all this data, AI-driven systems can enhance efficiency, sustainability, and reliability in the supply of raw materials for alternative protein production.

Scaling fermentation and novel proteins

Fermentation-derived proteins, mycelium-based ingredients, and cultivated inputs offer strong potential, but scaling these systems reliably has been a major challenge. Some companies are using AI to stabilize production and improve economics by monitoring and adjusting processes in real time.

One example is Pow.Bio, currently working with Buehler (a Swiss equipment manufacturer) to introduce an AI-driven continuous precision fermentation system that helps companies establish scalable fermentation with more predictable performance, higher productivity, and lower costs compared with traditional batch approaches. This aims to enable faster commercial deployment of fermentation-derived proteins and bio-ingredients.

Improving texture and consistency at scale

Texture, flavor, and mouthfeel are crucial factors in securing repeat purchases. One of the biggest challenges for alternative protein companies is ensuring that a product tastes and feels the same every time it is made, especially as production scales up.

AI helps by allowing manufacturers to test and optimize production processes digitally before running full-scale manufacturing. Bettani Farms, for example, uses machine learning and analytical models to reverse-engineer sensory attributes in plant-based dairy alternatives, ensuring consistent mouthfeel across batches. These approaches reduce guesswork, shorten scale-up timelines, and help teams avoid costly mistakes.

Smarter manufacturing and fewer disruptions

AI also helps factories run more smoothly and efficiently. Predictive maintenance tools can detect potential equipment problems before they happen, reducing downtime and missed deliveries. AI-powered quality control systems monitor moisture, texture, and appearance in real-time, catching issues early so that off-spec products don’t reach the supply chain.

For example, Tetra Pak integrates AI to deliver real-time insights in food and beverage production. These improvements benefit everyone: manufacturers save on waste and repairs, brands maintain their reputation, and retailers and foodservice operators experience fewer shortages or quality complaints.

Stronger sourcing and sustainability performance

Sustainability is no longer just a marketing angle; it’s a business imperative. AI is helping real companies make smarter sourcing decisions by analyzing supply chains for environmental impact, risk, and alternative sourcing options.

Intelligence platform Planet FWD, for example, helps brands measure and manage carbon footprints across products and suppliers. It transforms complex supply chain data into trusted insights, moving food companies from rapid compliance to strategic improvements. For retailers and foodservice operators, this means clearer sustainability reporting and stronger credibility with consumers and investors.

Using consumer insight to guide decisions

AI also helps businesses understand what consumers really want – essential for retail success. By analyzing purchasing patterns, reviews, menu feedback, and social media trends, companies can identify preferences for flavor, format, pricing, and nutrition.

The REWE Group, a large supermarket chain in Germany, is utilizing AI in this very area. The retailer uses AI to analyze anonymised purchase data and customer behavior in its markets to understand buying patterns and improve product availability. By recognizing associations between products and detecting when expected combinations are missing, the system helps optimize shelf placement and ensure the right products are in stock at the right time, improving customer satisfaction and reducing waste. 

But that’s not all. REWE has taken one step further, using AI to measure their protein balance. In the sector-leading move, the retailer has partnered with KPMG in a new pilot scheme that uses AI to assess the proportion of protein they sell that comes from animals or plants (the protein split or balance). While measuring the protein split would usually take around a year, using AI has allowed REWE to complete its assessment in just 10 weeks.

The move follows REWE’s 2025 commitment to increase the share of its plant-based offerings to 60% by 2035. At the time, REWE was not explicit about its current protein split, but this latest measurement should allow the retailer to see where it currently sits and follow through on its commitment, making sure its protein strategy includes a target to rebalance protein sales to meet health and environmental targets. 

Many supermarkets remark that they’re missing the right supplier data to assess their protein split. But, REWE has demonstrated that it’s possible to measure the protein split quickly and efficiently using AI. We urge other supermarkets to follow in REWE’s footsteps. 

Actionable insights

AI isn’t a single tool; it’s a set of capabilities that, when applied strategically, helps alternative protein businesses operate faster, scale reliably, and deliver products that meet consumer expectations. For retailers, these capabilities matter because they influence product reliability and consumer trust. For brands and manufacturers, they unlock efficiency and differentiation. For ingredient suppliers and foodservice operators, they enable consistency and resilience. 

ProVeg International makes the following recommendations for improving operations and sales through AI tools:

1. Adopt predictive tools for equipment and quality control to reduce downtime and improve consistency.
2. Integrate AI in sourcing to optimize supply reliability and sustainability reporting.
3. Leverage consumer analytics to align products with market demand and shorten iteration cycles.
4. Use digital process modeling (e.g., Bühler, Bettani Farms) to scale production while maintaining texture and sensory quality.
5. Partner with AI-enabled suppliers to accelerate innovation and reduce operational risk across the value chain.

For more support on your alternative protein strategy, get in touch with our experts at [email protected] and subscribe to our podcast and newsletter.