How AI is Empowering Agriculture Retailers

Jacqui Fatka

June 18, 2025

Key points

• Companies in agriculture are using artificial intelligence in their back offices, front offices and within agronomy and supply chain operation divisions to achieve predictive modeling and operational efficiencies.
• Key Cooperative in Iowa is finding that AI’s ability to automate report generation, develop agronomy prescriptions and summarize customer sentiment is helping it better serve its farmer customers.
• AI can offer many benefits but consider the caveats. Users should protect sensitive information and confidential customer information.
• AI is a tool. It won’t replace agronomists but will instead augment their work. Augmented intelligence requires ongoing feedback and insights from humans to ensure greatest outcomes.
• Companies with agriculture knowledge may be the best AI partners for co-ops. Early adoption and reliable partnerships will provide an advantage for those willing to test the AI landscape.

The modern era of agriculture generates billions of data points, which makes it a ripe landscape for using artificial intelligence tools. New advancements in artificial intelligence tools are empowering agricultural retailers to lean into their role as trusted advisors and use AI tools to enhance customer relationships and improve operational efficiencies.

Microsoft’s CoPilot, Grok, and ChatGPT have made advanced technology accessible to the public once only imagined in science-fiction films. Rapid innovations in the AI technology space have quickly advanced beyond “traditional AI” that analyzes or classifies data. The next iteration of AI is “generative AI” which creates new data based on patterns learned from existing data. Agricultural retailers integrating new AI technology can optimize workflows, improve workforce augmentation, and experience higher returns on investments.

Unlike drama-driven movie plots, agricultural companies can look to incorporate AI into several different business functions without fear of robots taking over the world or losing the crucial personal relationship with their farmer customers. An easy starting place with AI applications could include crafting job descriptions and providing weekly summaries of customer interactions. Internal efforts can be tailored and perfected before tackling more complex tasks. Testing out different pathways of acceptance can lead to greater success for early adopters.

Spotlight: Key Cooperative leaning into AI

At Key Cooperative, located in central Iowa, Chief Operating Officer and Agronomy Division Manager Brent Deppe sees the company as being at the forefront of integrating AI into daily operations in the front and back office, out in the field with the precision agriculture and agronomists teams, and sharing insights across all divisions. This strategic approach not only enhances service delivery to farmer customers but also drives operational efficiencies.

Testing out processes on a small scale and rolling out new technology to willing farmers has helped put the company on the cutting edge of AI adoption and execution. “We’ve been collecting data for years and years, and now I feel like it’s just starting to get unlocked,” he said.

In the world of agriculture, the amount of data available on everything from yields to soil analytics creates the perfect testing ground for putting that data to work for a farmer. Deppe said over the last four years, alongside the acceleration of precision technological capabilities on equipment, the company has increased its seed prescription-based agronomic advice offered from just 5% of its customers to now upwards of 15%-20%.

Key’s agronomists and precision technology team use AI to generate prescription-based seed and fertility recommendations. Driven by adoption of technology available on a planter, Key is helping growers manage each field by the square inch. As that team collects more data over the years, AI allows Key to provide a more predictive outcome of what could be achieved with a particular hybrid planted in a soil type, taking into account varying soil moisture conditions and fertility levels. Key is also using drone technology to scan for weed pressure and size and pulling data to help target spraying recommendations.

Working closely with its manufacturer partners – Bayer, Corteva, Syngenta – and their respective AI-based platforms, Key then couples AI’s knowledge with that of its agronomists’ team. “Our agronomist is on the backside making those recommendations and really utilizing some of the AI tools to help lay out the predictive analysis,” Deppe shared. Even after entering in data and allowing the AI system to generate its recommendations, there is still a “ground truth” that must take place with the agronomists and precision agriculture teams, Deppe said. A farmer knows their operation best, and those recommendations may not account for every nuance that may require a slight modification to the recommendation.

Deppe stresses that AI won’t replace agronomists but will instead augment their work. Agronomists must possess the expertise to evaluate AI-generated recommendations in that ground truth process. Deppe says his agronomists can then challenge conventional practices by expanding their evaluations upon reviewing the recommendations. In addition, agronomists see improved time management and efficiencies.

AI also allows a better understanding of yield opportunities that can assist the grain merchandising team on the other end of the business. After the 2020 derecho that hit Iowa, Key needed to know where grain would be short and where temporary storage would be needed. The co-op has continued to collect that data and share the yield potential opportunities based on monthly surveys to guide grain merchandisers’ efforts to assist farmers in hedging and selling opportunities.

Even the semi-trucks at Key have AI integrated with driver-facing and forwarding-facing cameras. AI will alert when approaching a vehicle at an unsafe distance. But the AI-calibration can allow Key to enter its own standards for driving.

“I consider us an early adopter,” Deppe said. “It still boils down to just execution and how you can utilize it best with that grower.” Deppe sees this as a strength for Key as it leans into AI to help provide a more customizable approach to meeting growers’ needs.

5 ways agriculture operations are incorporating AI

Co-ops, retailers and AI research companies all noted that artificial intelligence is most impactful in agriculture in specific areas.

Simplify everyday business functions. Every business includes functions such as human resources, accounting, operations, and sales. These functions all operate off workflows. An agricultural retailer can start leveraging AI to optimize those workflows. This offers a high return on investment relative to the efforts required to build or implement such solutions. Test out recording your next virtual meeting with Microsoft CoPilot. Within minutes receive an AI-generated summary of the meeting, takeaways, and future tasks assigned.

Empower customer interactions. Retailers often aim for their agronomist advisor teams to consistently use a customer relationship management system. However, many CRMs are not designed to support the specific functions related to selling inputs and the routine duties of an agronomist or advisor, resulting in lower adoption and usage rates. “AI can bridge that gap,” said Lawrence King, CEO of Headstorm and founder of the ag retailer app AGPilot.

AI can help agronomists with their normal day-to-day activities, while also providing management and forecasting oversight that managers and executives want. Apps like AGPilot eliminate the need for agronomists to manually log into CRMs, track activities, and document customer notes through rigid processes. The app enables agronomists to verbally interact with AI in a natural manner while traveling between customers, allowing them to capture customer notes and potentially advance deals from one stage to another smoothly and efficiently. AI can sort through verbal customer notes and capture a unique customer profile of not only purchases made and customer preferences, but also unique details such as birthdays and relationships to enhance that personal connection.

Growmark utilizes Agvance to help bridge the gap between growers and agricultural retailers. Agvance provides customers with real-time information about field applications, fuel fill-ups, grain positions, pay-off and more, while allowing the ag retailer team to stay in constant communication with customers and each other.

Level set your workforce. Building on the reality that relationships matter, AI can enhance employees’ understanding of customers’ unique desires and personalities. Making specific customer profiles available for the future can allow for a smooth transition in managing and maintaining those relationships between new employees as staff retire or transition out of the workforce.

AI can help augment skills of mid- to low-performing employees. AI technology is not aimed at eliminating jobs, but in many cases it can help simplify tasks and reduce human error and bias. In addition, as labor continues to be a challenge for many rural enterprises, AI can help staff cover more acres, customers, or responsibilities.

Rhishi Pethe, who runs the independent agrifood tech consulting company Metal Dog Labs, said AI can also act as a “sparring partner.” For an agronomist, allowing AI to see what holes may exist in a recommendation, tweak, and improve based on ongoing interaction with the AI tool can be an important opportunity to fine-tune recommendations. AI tools can also help new employees check their recommendations against those from AI to see if they’re on the right path.

Identify sense-and-respond use cases. Imagine if a model can identify pest and disease pressures based on weather conditions or after a major weather event such as hail or extreme heat. Rather than predicting the future or making recommendations, AI can assist in sending out notifications for a response once an event has occurred. An example could be pest and disease notifications. “As an agronomist or advisor, part of my job is creating a very tight, trusted advisor relationship with my grower customer. These sense-and-respond type cases help enrich that relationship,” explained King.

An AI-generated identification of potential pest and disease pressures allows the agriculture retailer to reaffirm to the grower the individual or company is a trusted advisor and establish a future sales point. Outreach can also move beyond existing customers to neighboring farms. “It’s a way to start consulting and building a relationship with prospects that aren’t even your customers yet,” King added.

From a supply chain perspective, AI can provide information to determine where product inventory should be moved based on these same conditions.

Optimize inventory management. AI has advanced over the last decade to use algorithms to solve problems in minutes or even seconds. Case in point is SWARM Engineering. Using AI-driven optimization algorithms, SWARM assists agricultural businesses in organizing and addressing operational challenges effectively. As an example, SWARM’s AI-empowered insight can help agriculture retailers look objectively at network design and site efficiency to determine where to open, close or consolidate for the betterment of the company and the customer experience. SWARM’s tool improved inventory management and supply/demand allocation for several large mills and food processors by optimizing places to procure products and identifying unrecognized inefficiencies. The result of this use has shown a return on investment ranging from 7 to 10 times, and occasionally higher.

Coupled with its optimization capabilities, SWARM is adopting the use of agentic AI — a type of AI that can operate independently and perform tasks without human intervention. An agent can dynamically create reports based on natural language questions, such as, "Create a report showing the main variables that impact this optimization scenario from the benchmark?" The AI agent can also act as a consultant and interview people across the organization on their specific functions. Information is extracted and matched to algorithms specific to the company to establish levels, constraints, and the scope of a problem the AI tool is trying to solve.

Although algorithms can easily capture ROI in large agribusiness entities, Brandon Buie, sales director for SWARM, said coordination between chemical manufacturers and agricultural retailers could multiply savings even further. "If they work together on optimization, both sides are likely to save significantly more regarding inventory management and transportation costs," he said.

Things to consider with use of AI

Establish guardrails and understand privacy. With “free” AI tools, public large language models such as ChatCPT can take entered private or sensitive information about a customer or the company and make it public.

The stakes are high for agricultural entities operating in an environment where margins are tight and farmers get just one chance each year to make a profit or minimize losses. Agriculture retailers play a trusted role advising their farmer customers but also need to ensure AI costs do not outweigh the benefits for the agricultural retailer. The cost of experimentation is minimal, so delaying might result in missed opportunities to evaluate potential solutions.

Use AI to help you lead. Shane Thomas, analyst for Upstream Ag Insights, said agricultural companies should not look to meet their customers where they are, but rather lead them to where you want them to go. Identify a problem and see if AI can offer a solution. If that solution ends up not working, investing a year or two to try out different solutions will not break the bank but can also lead insights into future attempts.

AI can be a recruitment tool. If AI-integration is front and center for a particular division or overall organization, it can help attract younger, more tech-savvy workers. AI adoption must come from the top down and start with a change in management to introduce transformation within workflows that bring an overall benefit to the company. If leadership picks one thing to drive improved key performance indicators, that provides for future opportunities of AI integration and adoption.

Look for ag expertise in AI partners. The surge of AI has also brought many new AI players to the marketplace, but very few focus specifically on agriculture. External expertise can assist in guiding the effective implementation of AI for this industry. Pick partners who understand agriculture and promise value beyond just lofty ROIs. Designate a staff member who will be the AI-point person to guide and direct adoption.

Retailers should have conversations with their software providers about how they are thinking about implementing or integrating AI into the software. This will help catalyze ideation and purposeful conversations.

Protect and strengthen relationships with AI integrations. For agricultural retailers, recommendations generate value only when they result in product sales. When an agronomist advises the application of fungicides, it is essential that this recommendation integrates smoothly into inventory management systems and forecasting tools, thereby facilitating the ordering process for farmers. Agricultural cooperatives and retailers serve as a critical relationship bridge between farmers and input providers.

The erosion of the grower relationship is one of the biggest challenges that a retailer faces moving forward. AI-enabled technologies, if they aren’t delivered by agricultural retailers, could potentially erode those relationships. Adopt AI early to stay ahead of competitors and maintain and improve customer relationships. In the changing agricultural landscape, retailers and cooperatives who rise to the challenge of adopting AI could strengthen their position as a trusted advisor and essential partner in the agriculture supply chain.