AI Adoption Blueprint for Ag Retail

Artificial intelligence (AI) is quickly moving into mainstream agriculture. And according to Alan Brady, Vice President of Business Management at Ever.Ag, this pace promises to only increase.

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“AI adoption in agriculture is accelerating, but the real impact comes from how it is applied,” says Brady. “By embedding intelligence into ag retail workflows, teams can reduce manual effort, move faster, and make better decisions as part of their daily work.”

According to Jeremy Groeteke, Global Head of IT and Digital Strategy at Syngenta, AI is helping solve the data overload problem by providing analysis of metrics to create action steps for the grower.

“If you look at the last decade of technology innovations for agriculture, most everybody focused on delivering solutions to the customers,” says Groeteke. “But what you are seeing now is about systems that deliver innovations internally. This is allowing users to take the information they have and unlock its value for their own workers, who can then share this with the customers.”

Ag Retail Experiences

As ag retailers look to AI adoption, there are many important factors to consider. This includes identifying real business pain points, assessing a company’s data readiness, and establishing governance early in the process.

“Successful AI adoption in ag retail starts with alignment between business goals, data quality, and frontline workflows,” says Scott Fegenbush, Chief of Digital Agriculture and AI, Nutrien Ag Solutions. “Retailers need to think beyond the technology itself and consider change management, trust, and long-term scalability. AI is most effective when it’s embedded into day-to-day decision-making, not treated as a standalone system.”

Tracy Soper, Senior Director of Data Excellence at Keystone Cooperative, agrees.

“Don’t start with the technology — start with the problem,” says Soper. “AI only creates value when it’s solving a real business challenge. The biggest mistake is treating AI as a technology project rather than a business strategy. Companies buy a tool, hand it to IT, and expect magic — that’s a recipe for expensive shelfware.”

To prevent this from happening, Soper says Keystone Cooperative has an internal checklist for evaluating new AI systems at its locations. These include:

Considering data quality. “Our own initiatives revealed significant data gaps across multiple systems,” Soper says. “Users should budget time for data discovery and integration before expecting AI to deliver. Data quality is an ongoing discipline, not a box you check. Implementing process changes and other fixes to improve data quality when issues are found can take time and not every issue can be architected around.”

Fragmented systems and siloed data. According to Soper, many ag retailers tend to run on a patchwork of platforms that are not always designed to work together. “We addressed this by building a centralized integration ecosystem connecting operational systems, field equipment, Internet of Things sensors, and fleet telematics into a single data warehouse,” Soper says.

Cultural resistance. At Keystone, says Soper, the company started with accessible tools such as Microsoft Copilot and paired this with structured education, “to help people see AI as a helpful co-worker rather than a threat.”

Trying to do too much at once. “Evaluate use cases on business value and speed to implementation, pick the sweet spot, and maintain a pipeline,” says Soper. “This way, good ideas get sequenced, not lost.”

Future Developments

According to Soper, today’s AI systems will only improve as we approach the 2030s. Among the new advances, these four will stand out.

1. Agentic AI. These systems won’t just answer questions but take actions and orchestrate workflows semi-autonomously across a business’ system.
2. Predictive operations. These will shift ag retail from reactive to proactive across its business operations.
3. Blending internal and external data. These systems will provide transactions, weather, commodity markets, and vendor pricing, enabling smarter procurement and risk management.
4. AI-enhanced customer engagement. This ability will matter, says Soper, as digital tool adoption among growers accelerates, particularly with younger producers.

“Three to five years ago, ‘AI in agriculture’ mostly meant precision hardware and descriptive dashboards,” Soper says. “Since then, generative AI tools have brought AI into every employee’s hands, cloud data platforms have made consolidation dramatically easier, and machine learning tooling have matured so that models can be built directly where the data lives. Retailers who invest in data foundations now will be positioned to capture these advancements.”