Why AI Falls Short at Ag Sales (And what to do about it)

By Chris Sprehe, Head of Data & Engineering, CamoAg
2-minute read time

Imagine two sales agronomists, let’s call them Dave and Sarah. Both are tasked with expanding their company’s market share in 2026. Both are given a high-tech "AI Advisor" to help them identify the best growth opportunities and prospects. 

Dave’s AI was trained on a large data dump, a collection of forecast spreadsheets, CRM notes, and agronomy web scrapes. To start his week, Dave queries his AI chat: "Who are the largest corn prospects in Champaign County?" It returns a list of names, but several he knows are retired and three are actually the same person listed under different LLCs. Dave then spends his afternoon fact-checking the AI. To Dave, the AI isn't a time-saver, it’s just another research project.

Because his AI lacks data cleaning and standards, it pulls from a fragmented "notebook" style database. An LLM is like a high-performance engine. But if you fuel it with muddy data, it will sputter. Without robust training on the right data, AI often can’t distinguish between a "landowner" and an "operator," leading to confident but incorrect answers that can end up hurting a sales rep's trust in the technology.

There is a Better Way

Meanwhile, Sarah opens her company’s AI service. Sarah’s AI was built on the CamoAg platform: a robust, standardized environment of USDA data, verified property records, integrated internal intelligence, first-party sales records, and more. She asks the same question as Dave: "Show me the largest corn farmer prospects in Champaign County."

Because our team at CamoAg has spent years simplifying the complexities of ag data, the AI doesn't just guess. It scans a structured universe where entity relationships are already mapped. Within seconds, Sarah has a verified list of prospects that are synced with her CRM. She follows up with, "How many soybean acres did these prospects plant last year?" and gets an instant answer.

For Sarah, the AI is a real-time sales enablement engine. She isn't wrestling for answers, she’s identifying growth opportunities.

The 2026 Standard: Intelligence Over Information

As we navigate the market in 2026, the "magic" of AI isn't in the code, it’s in the context.

The ag industry has run on scattered notebooks - both paperback and virtual - for too long. By choosing tools built on credible, large-scale data standards, ag sales and marketing teams can finally stop being data miners and start being informed strategists.

Agribusinesses that put AI to work for sales growth don’t just find a faster way to search records, they unlock a more accurate way to see the real-world marketplace. When your AI is planted in the right data, the insights grow much richer and your offerings get more competitive.

Chris Sprehe is the Head of Engineering and Data at CamoAg, Inc. He has close to 20 years of product and engineering experience working in digital media and software development across industries. Prior to CamoAg, he ran an engineering team spread across Chicago, New York, and Europe developing software for a media group of 15 brand websites, including The Onion & Gizmodo. Chris holds a B.A. in mathematics and computer science from DePauw University. Connect with him on LinkedIn.