Last week, Brian Lutz, VP of agricultural solutions at Corteva, one of the world’s largest agribusinesses, testified to the US Congress about the role of AI in shaping the future of the agrichemicals industry.
Lutz described the three key roles for AI in the business: accelerating the discovery of new pesticides; increasing production efficiencies; and assisting on-farm decisions on when to use these products in the field.
“It is, without doubt, one of the most profound technologies to ever be invented. While we are still just getting started, the promise that AI has to transform the agricultural landscape is already very clear,” he said.
After speaking with various innovation and tech leads from a wide range of agrifood corporates at last week’s F&A Next conference, it’s clear Corteva is not alone in using AI for commercial endeavours: novel ingredients, new crop traits, new recipes, trend predictions, farmer advisory, and so on.
Using AI to accelerate the discovery and sale of new commercial products is an obvious place to start, and interest in these solutions abounded among the corporate venturing teams I met.
It was a different story when I asked if they’re looking for AI solutions to increase the operational efficiency of their supply chains; these innovations appear to fall outside their specific innovation mandates. While these were individual conversations, my colleague Sofia Ramirez had a similar experience when speaking with corporate innovation reps at the Synbiobeta conference earlier this month.
A decent example of such an AI solution, and one that highlights this internal opportunity, is AgFunder portfolio company Lumi AI, which has been top of mind since they spoke at our recent AGM. Its conversational business analytics platform gives users visibility across the supply chain, and because it uses conversational analytics, people at every level of an organization can “chat” with it-–as you might to ChatGPT—to get immediate answers about the company’s operations and business statistics.
The potential to unlock inefficiencies across a business seems endless, with examples including preventing stock-outs before they occur, identifying supplier delays before they disrupt production, and reducing procurement costs and waste. Lumi can also help adjust forecasts early, optimize order quantities, and surface cross-sell opportunities. It can even diagnose root causes (e.g., “Why are sales dropping in one area?”), and recommend actions, all without having to wait days for a data analyst to produce a new dashboard via Python or SQL scripts.
The ultimate vision for Lumi is to act as an intelligent layer over businesses, detecting what’s off, reporting what matters, suggesting what to do, and taking action. That sounds a lot like digital transformation to me!
Yet when Sofia and I have queried agrifood corporate innovation and CVC personnel about their interest in this category of AI products–those that democratize data access and drive internal operational intelligence-–some of them looked us somewhat blankly and others referred us to their digital transformation teams (if they even have one), many of which seem to be relatively new entities and often distinct from their open innovation, startup, tech, or CVC departments.
It wasn’t that they didn’t find it an interesting prospect, but more that the main focus for them was novel ingredients, new crop traits, digital farmtech, and climate tech; more obvious “agrifoodtech” innovations, and mostly revolving around external, commercial endeavours for the business.
Of course, AI will be embedded in all of these, but my impression from these conversations is that while AI’s potential is recognized, its integration into core internal operational strategies is either not yet fully formalized or the innovation teams are not yet fully aligned with these efforts. This structural disconnect points to a significant missed opportunity.
And it made me wonder: What are the biggest challenges these companies are facing? Is it that they need a new ingredient, or is it the need to make their business operations radically better?
Of course, new blockbuster products are always a goal, but for an industry where net profit margins often hover around 6-8%, every percentage point of efficiency gained translates directly to significant financial health.
Beyond this, the agrifood sector faces persistent challenges, including supply chain volatility and significant internal waste. Estimates suggest that waste from warehousing and transportation alone can account for 0.2% to 0.5% of the net revenue of food consumer packaged goods companies. This operational leakage, coupled with thin margins, means that overlooked internal inefficiencies are not merely an administrative issue—they are a direct threat to profitability and resilience.
If AI is going to truly transform food and agriculture in the profound ways Lutz indicated, it can’t be confined to external and commercial innovation.
There are decent examples of consumer businesses making strides to adopt AI meaningfully across their operations, such as Procter & Gamble, whose chief information officer, Seth Cohen, recently waxed lyrical to Forbes about the transformational impact of AI, specifically referencing agentic AI.
While there will be others, there’s still some way to go to shift agrifood’s “least digitized industry” moniker. Agrifood corporates would do well to formalize their internal AI strategies and better engage their innovation teams on this mission; teams who interface with tech entrepreneurs every day and could help ensure they’re not left behind – again.
I want to continue exploring this topic. If you are part of a digital transformation or internal operations team within an agrifood corporation and would like to share your insights on AI strategy, please feel free to reach out, on or off the record, at louisa@agfunder.com.
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