For ag retailers, the next wave of AI may matter less as a technology story than as a workflow story.
Ever.Ag is expanding its Everett AI engine into FieldAlytics and Merchant Ag with a pitch aimed squarely at one of retail ag’s persistent frustrations: Teams already have the data, but they often do not have the time to connect it, interpret it and act on it before an opportunity is missed.
Agronomists juggle soil tests, scouting notes, planting history, imagery, weather data and recommendation writing. Sales teams are expected to spot product opportunities and prepare for grower meetings with more precision. Retailers and cooperatives are also managing the commercial side of the relationship through bookings, prepays, work orders and ERP-connected pricing. The data exists, but it often sits in separate places or gets used only in the moment.
Kyle Owen, vice president of digital agronomy solutions at Ever.Ag, says that is the gap Everett is meant to close.
“If we look at all the data we have in these platforms, the data is just getting immense,” Owen says. “If we look at our users, they’re only looking at maybe the data that’s relevant at that current conversation.”
For ag retail, that can mean the system contains a grower’s broader history, but the agronomist is only looking at the latest soil sample or the immediate recommendation in front of them. Owen says Everett is designed to pull those threads together and surface what a retailer should be seeing before the next conversation with a grower.
“With Everett and AI, we’re now looking at all of that data that we have in FieldAlytics and even data we’re pulling in from Merchant Ag,” Owen says. “We are prepping that sales agronomist or agronomist to go out and say, ‘Hey, here’s your trend on your soil samples. These are outdated. You need to pull soil samples.’”
That could include something agronomic, such as fields that are overdue for soil samples or nutrient issues that warrant closer attention. It could also include something commercial, such as a grower planting varieties the retailer does not sell, applying a chemistry the retailer is not capturing, or following a fertilizer booking pattern that creates a sales opportunity if the retailer gets in front of it sooner.
Owen boils down the pain points simply: “missed opportunities in the data and time.”
AI Moves Closer To The Retail Decision
In that sense, Ever.Ag is trying to place AI at the center of the ag retail advisory model, not at the edge of it.
The company’s strongest argument may be that Everett is tied to the systems retailers already use to run the business. Owen describes the advantage as being able to combine agronomic and spatial data with transactional and ERP-connected data, rather than forcing a user to export information into a generic AI tool and manually build context around it.
“The way it’s better than just your normal frontier models like a Claude or a ChatGPT or insert any name is that it is embedded into the tool,” Owen says. “You’re not having to go in and feed data.”
That matters in ag retail because the value of a recommendation is not just whether it is agronomically sound. It is also whether the recommendation connects to what the retailer can price, sell, book and execute.
Owen describes a workflow in which Everett could prepare a sales agronomist before a farm call by summarizing the fields due for soil sampling, identifying booking patterns, flagging product opportunities and quickly estimating recommendations on the fly. Rather than spending hours printing reports or flipping through multiple screens before a grower visit, the agronomist could ask Everett to “prep” them for a specific farm and get a concise list of what needs attention.
Instead of “prepping for a grower visit for half a day printing reports,” Owen says, a user could “sit in the truck right before you pull up in and say, ‘Prep me for Owen and Russ Farms,’ and it gives me a list of, ‘Hey, these five fields are due soil samples, these are good, here’s where their booking strategy is,’ so on.”
The same logic carries into recommendation writing. Owen says a user might ask Everett for a quick recommendation on a set of fields, receive an initial average analysis or rate, and then refine the recommendation later back at the office. The idea is not to replace the agronomist’s judgment, but to reduce the time it takes to get to a usable starting point.
That time savings could be one of the more meaningful promises for retailers trying to stretch agronomy staff further. Owen argues that if a typical agronomist today is servicing 20 to 30 growers, better use of AI could help that same person maintain the same level of service for 40 to 50. In a market where many retailers are balancing labor constraints, customer expectations and pressure to add services, that is a more compelling pitch than simply calling Everett an AI assistant.
It is also why Owen frames Everett less as a cost center than as a revenue generator.
“The biggest message that I’m sending around Everett … is I don’t view Everett as a cost center, for a retailer,” he says. “This is a revenue generator.”
For ag retailers, the return is supposed to come from two directions. First, there is the operational gain of helping trained employees prepare faster, execute with fewer clicks and avoid missed opportunities already buried in the data. Second, there is the possibility of building additional paid services around recommendations and monitoring that become easier to deliver at scale.
One example that could produce relatively fast ROI is in-season variable-rate nitrogen recommendations, particularly if a retailer can wrap that into a per-acre service offering.
Back To The Adviser Seat
More broadly, the company is betting that AI-supported advising helps move retailers away from being viewed as order takers and back toward being trusted advisers armed with better context.
“I started this conversation probably a year ago with ag retail, of … the more data you have, the more insight you can give your grower, which gives you that competitive advantage,” Owen says. “You no longer become an order taker, but you’re back into that advisor seat.”
That may be especially important in areas where retailers already believe they have the ingredients for a stronger advisory business, but not always the time or consistency to fully deliver on it.
Scouting is one example. Everett, Owen says, could help identify broader patterns by recognizing that disease pressure or pest pressure seen in one geography may be moving into another.
“You’ve got all these scouts, maybe you’re a larger co-op and you’ve got scouts all across the region, different regions. You can start compiling that scouting data and AI is looking at it and says, ‘Hey, we’re seeing this disease pressure or this pest down in the south. It’s probably working its way up to you in the next week,’” Owen says.
For retailers, that sort of cross-operation visibility is where AI may begin to look less like a novelty and more like infrastructure.
The ERP Foundation Underneath The AI
Still, adoption is unlikely to come without resistance. Owen says one hurdle will be the familiar fear that AI is taking over part of the employee’s job. Another will be the challenge of persuading growers to pay for new or expanded services enabled by these tools. But he suggests those concerns may ease if Everett becomes part of the normal workflow rather than one more platform employees have to remember to open.
“Everett is planned to be just part of your normal workflow,” Owen says. “It’s almost like you don’t have to take an extra step to use it.”
That point may be crucial. Ag retail has not suffered from a lack of dashboards. It has suffered from too many disconnected tools and too much reliance on people to manually connect agronomic insight with business execution.
Ever.Ag is trying to answer that by embedding Everett into a reworked FieldAlytics platform and tying it more closely to ERP-driven processes such as pricing, bookings, prepays and work orders. For retailers, that could make the AI less about generating text and more about making the existing system more actionable.
The open question is whether retailers will see that as meaningful differentiation or simply another AI promise in a crowded market.
But if Everett gains traction, it will likely be because it addresses a familiar ag retail problem: not a shortage of information, but a shortage of time to turn information into advice, service and sales before the moment passes.
