The mood at the World Agri-Tech Innovation Summit in San Francisco was unmistakable: agentic AI has arrived, and the global ag-food sector is bracing for a consequential shift.
Across panel discussions, private roundtables, and corridor conversations, anticipation and enthusiasm ran high, as speakers framed agentic AI as a transformative force capable of unlocking new frontiers of agricultural innovation and reshaping decision-making across the value chain.
Beneath the excitement, however, was a clear recognition that agentic AI may shift power dynamics in agriculture, from how decisions are made to who makes them, prompting serious discussions about governance, data stewardship, and responsible deployment.
Decision intelligence in agri-food
Speaking to AgTechNavigator, Shail Khiyara, CEO of SWARM Engineering, a platform that uses advanced agentic AI to automate agricultural decision-making, said the industry’s AI conversation is still too narrow.
“Everyone wants to talk about AI,” he said. ”But not enough people are talking about agentic AI or decision intelligence, which is where the real transformation is beginning.”
He pointed out that most discussions remain anchored in data, data quality, autonomy, and cleaning, rather than the next leap: how AI agents turn that data into decisions and automated actions.
“Decision intelligence is not well understood,” he added. “We are still seeing critical systems and decisions being made from and on Excel spreadsheets, making avoidable errors.”
From months to minutes: Decision intelligence in action
To illustrate the impact of agentic automation, Khiyara pointed to the largest berry producer in Peru.
The company must coordinate the hiring and movement of around 10,000 seasonal workers daily across a region vulnerable to natural disasters and logistical disruptions, an operational planning problem that previously took months.
With AI agents and decision intelligence,” he said, “they are able to do that within minutes.”
Why volatility is exposing a decision problem, not a data problem
Khiyara argued that the rapid rise in global agricultural volatility, from climate disruptions to tariffs, to price fluctuations, freight uncertainty and geopolitical shocks, is exposing a deeper issue. The constraint is not just access to data. It’s the ability to turn that data into decisions fast enough to matter – grounded in the realities of the ag food system and ontology.
“If you’re operating in volatility, trying to do more with less, and facing the kind of margin pressure we are seeing across fertiliser and grain companies, the issue isn’t whether you have data. It’s whether you can act on it in time.” he said.
He added that this is why AI is shifting from optional to essential, not as another layer of analytics, but as a core capability that protects margins by enabling faster, more precise decisions in constantly changing conditions.
The three barriers holding AI back
Despite the momentum, three themes keep surfacing in the conversation around AI adoption. ,
- Data quality remains top of mind.
- Trust and governance are critical, with organizations demanding transparency in how decisions are made.
- But the third barrier is less discussed and more limiting. Imagination.
“The ability to think about what AI can actually do for me is still a challenge,” Khiyara said.
Many agri-food firms continue to rely on deeply embedded, experience-driven knowledge held by long-tenured operators. That knowledge drives decisions, but it is rarely structured, codified, or scalable.
As a result, AI remains underutilized, not because the data isn’t there, but because organizations have not yet translated their operations into a form AI can act on.
“The more data you have,” he added, “the more value emerges in places you weren’t even looking.”
