Ag tech gathered and produced a lot of data, which many struggled to understand how to analyze…up till now. The industry is entering a new phase. Karen Hildebrand, Global Head of Industry and Partner Solutions for Amazon Web Services (AWS), says artificial intelligence can help transform these large data storages into productive ag business recommendations.
Hildebrand covered this important topic during keynote presentations at the recent Tech Hub LIVE Conference in Des Moines, IA.
“Your agronomy data lives in one system. Your equipment data is really in another. Your grain contracts are generally in a third,” she says. “An AI that can see across all of that can give us a more correct answer.”
The move from precision agriculture to agentic agriculture — a future where AI-powered systems can reason through complex information, provide recommendations and support human decision-making is at hand.
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“AI has really changed from describing the field to actually reasoning, and in some cases, being able to actually recommend what the action is — and doing that responsibly,” she says.
Turning AI Into Business Value
While much of the conversation around artificial intelligence focuses on capabilities, Hildebrand believes the true opportunity lies in solving practical business challenges for farmers and agricultural professionals.
With commodity markets facing continued uncertainty and producers managing tighter margins, every decision has greater financial impact.
“When margins are thin, the value of every decision being right goes up, not down,” Hildebrand says. “That’s the business case for the technology.”
One area where she sees AI making an impact is helping growers better evaluate return on investment for field decisions.
For example, rather than simply recommending a spray application, AI can help determine whether the potential yield benefit justifies the cost.
“If that spray application is costing you $14 per acre, how many more bushels or yield do you have to protect for that decision to make sense?” she says. “That’s the type of conversation that we’re seeing AI being enabled in 2026 in the field.”
Building the Next Generation of Agriculture’s Workforce
Beyond improving decision-making, Hildebrand believes technology will play a major role in shaping agriculture’s future workforce.
She recalls an early conversation at AWS where a customer described a vision for a future where operators could remotely manage equipment from anywhere in the world, regardless of language barriers.
“I thought to myself, I’ve come to the right place if I’m working with people with a vision that big,” she says.
While that vision continues to evolve, Hildebrand believes the goal of technology should not be replacing people — it should be amplifying human expertise.
“Our job is to make the most intelligent tools that they’ve ever used be the ones that are waiting for them in the field,” she says. “We want them to take over an operation that removes drudgery but really amplifies their judgment.”
For the next generation entering agriculture, those tools could help create more accessible and attractive career opportunities.
“Agriculture is an ecosystem of careers,” Hildebrand says. “We need to be thinking about whether we are leaving a legacy and a generation that is going to make that something that sustains itself.”
Recognizing Women’s Role in Leadership
As a part of the workforce, women play an important role.
“I think women have always been in leadership positions on the farm,” she says. “Maybe they didn’t get recognized for it, but they certainly have always been there.”
Hildebrand believes emerging technologies can create new opportunities by allowing people to contribute based on their individual strengths and expertise.
“I think that’s where women are finding a seam right now — being able to understand how to take data and make decisions more easily with a better corpus of data,” she says.
She also points to low-code and no-code technology platforms as tools that can help expand participation by making advanced technology more accessible across the agricultural ecosystem.
Editor’s Note: This article was originally published on CropLife.com, a sister brand of the Global Ag Tech Initiative.
Facts Only
* Karen Hildebrand is the Global Head of Industry and Partner Solutions for Amazon Web Services (AWS).
* AI can transform large data storages into productive agricultural business recommendations.
* Agronomy data, equipment data, and grain contracts generally reside in separate systems.
* The move is from precision agriculture to agentic agriculture.
* Agentic agriculture involves AI-powered systems reasoning through complex information and providing recommendations.
* AI has evolved from describing the field to reasoning and recommending actions responsibly.
* The business case for this technology increases when margins are thin, as the value of correct decisions rises.
* AI can help growers evaluate the return on investment for field decisions by assessing yield benefits versus costs.
* A specific example is determining how many more bushels a spray application must protect to justify its cost.
* The goal of technology is to amplify human expertise rather than replace it.
* Women have historically held leadership positions on the farm.
* Emerging technologies can create new opportunities for women by allowing contributions based on individual strengths and expertise.
* Low-code and no-code technology platforms can help expand participation in the agricultural ecosystem.
Executive Summary
Artificial intelligence has the potential to transform agricultural data by analyzing disparate sources, such as agronomy, equipment, and grain contracts, into actionable business recommendations for the industry. Karen Hildebrand of AWS suggests that an AI capable of synthesizing this information can provide more accurate answers across these data silos. The shift is moving from precision agriculture toward agentic agriculture, a future where AI systems can reason through complex information to provide decisions and support human judgment.
The value proposition for this technology centers on addressing financial pressures in agribusiness; when margins are tight, the accuracy of every decision increases its financial impact. AI can specifically assist growers in evaluating the return on investment for field decisions by quantifying yield benefits against costs, such as determining how much yield protection is justified by an application cost. Furthermore, technology aims to augment human expertise rather than replace it, by serving as intelligent tools that amplify judgment and remove drudgery from operations.
Finally, the article touches on workforce implications, suggesting that these technologies could open up new career opportunities. It notes that emerging technologies can allow individuals, including women, to contribute based on their unique strengths and expertise, and points to low-code/no-code platforms as methods to increase accessibility.
Full Take
The narrative frames the transition to agentic agriculture as a necessary evolution driven by data complexity and financial necessity, positioning AI not merely as an analytical tool but as a decision-support partner capable of complex reasoning. The central tension lies between technological capability—the ability to process vast, disparate datasets—and human agency—the need for responsible, context-aware application in high-stakes environments like farming.
The emphasis on ROI links the abstract concept of AI reasoning directly to tangible economic pressures, suggesting that utility is measured by financial outcome rather than mere capability. The move toward agentic systems implies a shift from simple feedback loops (precision agriculture) to complex strategic navigation, where the value resides in the quality of the recommendation and the resulting human decision. This challenges a potential assumption that advanced technology will inherently lead to automated control; instead, it suggests augmentation rooted in assessing risk and value.
The inclusion of themes around workforce and gender leadership introduces a secondary layer: how this technological evolution redistributes roles and opportunities within an established ecosystem. The claim that AI will amplify judgment, rather than replace expertise, is crucial. The implication for human agency depends entirely on whether the tools are designed to surface obscured information or simply execute pre-programmed directives. A critical question arising is: if AI manages the drudgery, what specific forms of non-automatable, complex judgment become the most valuable human contributions? Furthermore, how are accessibility tools like low-code platforms truly bridging the gap between advanced capability and on-the-ground application for all stakeholders?