Unlocking AI: MIT Technology Review Insights report flags how industry adoption lags

AI is making headway in the agri-food industry, but its adoption lags as companies grapple with this technological wave. This is the assertion from the latest MIT Technology Review Insights report, which examines the “huge” potential for AI in the food sector.

AI is set to revolutionize science, operations, and business across the international F&B industry, bolstering R&D, enhancing supply chain management, and helping to develop innovative products.

It already helps farmers monitor crop health to improve agricultural practices by making harvesting more efficient. AI is used in gene-editing experiments to enhance crop resilience and improve the nutritional value of raw ingredients. Some firms in the processed space use it to improve the texture and flavor of products like alternative proteins and healthier snacks, while AI is also leveraged to strengthen food safety processes. 

As AI technology develops, so will the possibilities and opportunities for F&B. 

Enhancing production and innovation

Adopting smart AI strategies is emerging throughout various F&B sectors to gain a competitive edge as AI technology continues to develop. It’s poised to enhance production and food innovation, but mass adoption of the technology is still far off.

The MIT Technology Review Insights report — “Powering the food industry with AI” — was produced in partnership with Revvity Signals. It is based on interviews with senior executives and experts from organizations including Syngenta Crop Protection, Ayana Bio, PIPA, Pairwise, Rivalz, Syngenta Group, the University of California, and Revvity Signals.

The report says that AI currently has few data governance protocols in place. One reason some companies lag behind is a gap in the talent and skills needed to keep pace with AI advancements, specifically how they can be leveraged for the food industry’s digital transformation. 

Additionally, many companies have not yet created comprehensive AI strategies. 

Data-driven insights 

Some of MIT’s key findings center on predictive analytics, accelerating R&D cycles in crop and food science because AI reduces the time and resources needed to experiment with new food products.

Laurel Ruma, global director of custom content for MIT Technology Review, says, “By harnessing predictive analytics, we can accelerate discovery, optimize supply chains, and bridge critical knowledge gaps across the industry.” 

Scientists can explore natural ingredients and processes by simulating thousands of conditions, configurations, and genetic variations “until they crack the right combination.” 

Cultivating data

Another finding is that AI can help with fragmented, complex supply chains by translating vast streams of data into actionable intelligence. For example, chatbots can serve as digital interpreters, helping farmers and growers with data analysis and supporting food companies’ decisions. 

However, the report also finds that better data strategies and industry standards are needed. 

“Current fragmentation in data practices is blocking AI implementation at scale. The industry must develop comprehensive data strategies that balance multiple priorities: secure information sharing, rigorous privacy protection, and standardized data formats,” says the report.