Vonnie Estes is vice president of innovation at the International Fresh Produce Association and the host of the Fresh Takes on Tech podcast. The latest eight-episode season focuses on AI’s role in reshaping the global food system in a series of conversations with scientists, investors, and agtech leaders.
The views expressed in this article are the author’s own and do not necessarily represent those of AgFunderNews.
Artificial intelligence is no longer a side experiment in agriculture. It is becoming core to R&D, capital planning, supply chain strategy, and investing.
In my recent conversations on the podcast, I heard practical examples of how AI is driving innovation and value creation. For agrifood investors and executives, the takeaways go far beyond efficiency gains. They signal where capital, talent, and infrastructure need to move next.
Here are the five biggest themes that stood out to me.
1. R&D is moving from randomness to prediction
“We’re moving from screening and selection to more prediction,” said Brian Lutz, VP of R&D at Corteva, describing how AI is reshaping discovery. By modeling outcomes before products ever reach the field, teams can find “types of hits that we would never find without AI-enabled discovery.”
For investors, this means shorter time-to-market, faster validation, and stronger ROI on innovation pipelines. AI lets R&D dollars work harder and helps companies de-risk expensive discovery programs before field trials begin.
2. Data governance is becoming a core capability
AI success depends on trustworthy, shareable data. Multiple guests stressed that poor adoption often stems not from bad models but from siloed or inconsistent data. Companies like Corteva are now treating data as a managed product, with defined ownership and quality metrics. Elliot Grant, founding CEO of Mineral, put it succinctly: “If we get the data layer wrong, everything else fails. AI needs good data the way crops need good soil.”
This is a clear opportunity for infrastructure startups and enterprise platforms that enable interoperability and privacy-preserving data sharing.
3. From tools to systems: the next wave of AI
AI is evolving from point solutions into distributed systems that coordinate decisions across operations. Ranveer Chandra of Microsoft described the future as “lots of tiny models which are agents that can learn and make decisions on their own.”
These agentic systems could autonomously adjust planting schedules, reroute logistics, and even initiate compliance workflows — creating efficiencies that compound across the supply chain. I noticed how similar this is to what retail is already doing. Joe Don Zetzsche pointed to Walmart’s automated perishable distribution centers, which synchronize inventory, routing, and labor planning. These kinds of system-level approaches within the food system show what’s possible for agriculture as tools begin working together.
4. Market-paced growth and capital efficiency
AI is allowing startups to achieve milestones with far less capital. “Instead of watching these businesses burn… two million a month… now you can burn 250k a month and get a lot more runway,” said investor Chuck Templeton.
This capital efficiency enables right-sized pilots, faster iteration, and better timing relative to market readiness — allowing companies to grow at the pace of the industry instead of outrunning it.
5. Workforce enablement is critical
AI will not replace agronomists and operators, but it will change how they work. Companies that invest early in training will see faster adoption and better outcomes. Joe Don Zetzsche highlighted Walmart’s September 2025 partnership with OpenAI to deliver free AI certifications to US associates through Walmart Academy, part of a nearly $1 billion skills investment through 2026. Programs like this demonstrate how workforce training can accelerate adoption and turn AI into a competitive advantage across the food system.
Risks and watchouts
AI adoption still faces friction. Patrick Vizzone cautioned that “many projects fail not because the tech isn’t good but because teams don’t want to share data,” highlighting the need for robust governance.
Labor disruption is another pressure point, especially where AI could automate marketing or compliance work.
Leading companies are engaging early with employees and partners, setting expectations, and building retraining programs.
Regulatory uncertainty also looms. As governments begin to set rules for data privacy, model transparency, and AI accountability, agribusinesses will need to stay ahead of compliance requirements to avoid costly delays.
Reputational risk is equally important. Poorly validated AI outputs in forecasting, compliance, or operations can quickly erode trust with growers, regulators, and customers. Clear testing protocols and governance frameworks must come first.
What’s next
In the next 12 months, expect a wave of AI-native agtech startups to raise Series A and B funding as capital efficiency accelerates their path to traction. Corporate pilots will move into full-scale deployments, making AI part of standard operating procedure across R&D, logistics, and compliance. This shift will likely trigger the first significant M&A activity in ag AI, with input providers, robotics companies, and food brands competing to secure data platforms and decision-support systems.
AI teaches users how to interact with it, and companies – across the entire spectrum are looking for use cases that fit their own business models. In my own organization, we are rolling out IFPA Intelligence Engine to make very granular proprietary data and its analysis accessible to customers/members on demand in a format that is most useful to them, instead of lengthy reports that themselves required interpretation.
These conversations convinced me that AI is shifting from a buzzword to a strategic lever for growth. The companies and investors that act now will be the ones shaping how technology is embedded into every acre and every deal.
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