6 Ways I Would Use GenAI If I Were Still an Agronomist in Ag Retail

GenAI has the potential to enhance how agronomists operate, but often discussions focus on enterprise-level solutions with substantial budgets. Many wanting to take advantage of these tools do not have direct influence on those investments.

There is ample room to improve utilization of GenAi tools by individuals working in agriculture. We can even see this come through in utilization, as shown by Anthropic:

If I were still working as an agronomist in ag retail, I would focus on leveraging readily available, cost-effective tools to maximize efficiency, deepen agronomic knowledge, and improve customer engagement.

Here is how I would integrate GenAI into my workflow with relatively limited budget.

1. Leverage Deep Research Tools for Agronomic Learning

When I worked as an agronomist, I constantly read journals and research studies to get a better understanding of new active ingredient performance, fertilizer formulation understanding, knowledge of soil, agronomic practices, crop management strategies and more. GenAI would provide a way to streamline this process.

• Deep Research — Agronomy is complex. ChatGPT recently released Deep Research (albeit with a relatively large fee), Perplexity AI and Google did the same for a much more reasonable fee. Deep Research functionality aggregates, summarizes, and analyzes information from multiple sources to provide structured reports. It can help individuals quickly digest complex topics by pulling key takeaways, comparing studies, and contextualizing findings for specific use cases. The features can streamline research, making it especially useful for professionals who need to process large amounts of technical data efficiently. For agronomists, or sales people, it can pull insights from research papers, contrast findings across multiple journal sources, and synthesize key takeaways in minutes. Instead of manually scanning dozens of papers, I would prompt AI to generate research report comparisons in detail on specific active ingredients, fertilizer formulations, background on specific agronomic practices and crops. There is also ability to consider how regionally diverse data could be applied to the specific context an agronomist or sales person finds them selves in.

One thing I would not do: I wouldn’t stop reading journal articles and research altogether. GenAI can speed up the time to understanding of key points, but I find it “smooths” out the finer details which is often where the biggest upside in reading these materials lies— the biggest opportunities still lie in the details.

2. Build GPTs for Tailored Agronomic Data & Questions

While I remain cautious about using AI for direct product recommendations (I have yet to use one that’s even “decent”), I would develop a GPT model to house structured agronomic data, crop protection documents and aid in quick reference information.

• Compile Crop Protection Label Information — Two years ago when I first wrote about the implications of GenAI on agriculture, I highlighted the opportunity in rapidly finding rate information, surfactant rate information, or tank mix order needs. That opportunity remains. I would compile state/provincial crop protection guides, then augment with historical efficacy data on weed/disease control percentages to help act as a guide in considering different product recommendations, or getting quick bits of information on my phone when I didn’t have a crop protection guide readily available.

• Trial Data Integration — By feeding my GPT all trial data I could find—from government studies, extension, industry reports, supplier trial data, and internal company trials—I would create a database that helps generate insights on product performance in different environments— primarily focused on seed varieties, but for crop protection or fertilizer products, too. This would allow me to play with different recommendation concepts more in-depth. Companies like Syngenta and Cropwise AI have functionality like this available for their internal teams surrounding seed already.

3. Create a Weekly Customer Communication with AI Assistance

Customer touch points are a crucial component of agronomy and selling. These touch points do not always have to be in-person— but they need to be value added and work to build the relationship, build trust and stay top of mind to a farmer.

• AI-Generated Agronomic Updates/Newsletter — When I started in the industry, I built an email list and sent weekly agronomic updates to farmers. Writing these weekly’s can be time-consuming for an agronomist with various other time demands, but AI could cut down the effort significantly by inputting a few bullet points into a GenAi system (eg: ChatGPT) to then draft a newsletter or update that I would refine before sending. Tailoring updates to specific regions, or customers specifically (depending how many you have) could also be possible to improve relevance, something that was challenging to do previously.

4. Leverage an AI-Driven CRM Voice System

Like most agronomists and sales people, I hated entering customer call notes into CRM systems. However, I recognized the value. AI can now handle the tedious parts while improving data quality.

• AI-Powered Call Summaries — Tools like Patrick Walther’s “CRM Caller Buddy” System allow agronomists to have a follow-up AI conversation after a farmer interaction, automatically collecting, structuring key discussion points and logging them into a CRM/database. Instead of manually inputting conversation details, you simply call the number on your way to the next farmers, or back to the office, and AI can ask the necessary questions, and then transcribe, organize, and store it in a structured way:

• AI Agents — Building on the last point, I would experiment with an AI agent like Lindy.ai to integrate with this “Caller Buddy” system, allowing it to generate crop plans, to-do lists, or send an email to the location manager about inputting a product order, for example. This might be more difficult given disparate software systems in use for most businesses, but it is something I would explore.

5. Develop Personalized Sales & Marketing Assets

The future is precision marketing. Rather than generic product sheets, AI enables agronomists and sales people to create highly customized materials for each farmer. I think this would be advantageous as an agronomist.

• Personalized Materials — I would put specific prompts into a GenAI system, then extract customer crop plan data, previous cropping information (plus more) and tailor specifically how a new biostimulant (for example) could be integrated into the customers operation. Companies like Bayer and Syngenta (eg: CropWise AI) offer recommendation engines, I would take the AI-generated insights, plug them into my company’s branded template, and walk through the details with farmers in a way that aligns with their unique challenges. This is related to point #2, but unique in that it would set up the materials for use in a crop planning conversation or product discussion with the farmer. Today, most marketing and product awareness materials are broad and not customized. GenAi presents an opportunity to tailor.

6. Template Building: Fertility Recommendations

Many agronomic recommendations are built from the same base-line. Consider fertilizer recommendations. The process includes taking soil samples, sending them away, awaiting for the results, and then manually assessing each one to look for outliers/calculate needs etc. Many soil labs today have a base layer recommendation they offer, however, fertility recommendations are more art than science. Each agronomist has their unique view on how to weight specific soil ratio’s, how much N they value from different depths, mineralization rates, how they consider by crop etc.

There is an opportunity to build an AI filter that each soil test result runs through, then based on YOUR specific approach, build a base layer fertility recommendation for the farmer.

The goal would not be to give this to the farmer, but to save hours of time to get the baseline recommendations built out and then tweak with a keen eye from there— specific to unique application methods, fertilizer sources etc.

This example is the most complex, as it would entail more of an expense and some resourcefulness. However, what I would do in this instance is go to Upwork and post a request for an AI developer to build the specific system.

Final Thoughts

GenAI isn’t replacing agronomists— it’s enhancing them. Just like Google made the most resourceful people even more more effective, GenAi is the same.

In an agronomic and ag retail setting, I fundamentally believe in finding ways to achieve an information advantage— unique and novel information to:

1. take to a customer to inform a decision, augment a relationship or act as a value added touch point.

2. Or have better information than competitors surrounding agronomic practices, or tools to deliver better service.

GenAI is a point of leverage that can deliver on both of those.

While budgets for enterprise AI solutions in ag retail and for input manufacturers may be limited or not evolve at a preferred rate, many valuable tools are already accessible for minimal cost. By strategically using AI for research, customer engagement, CRM management, and personalized sales, agronomists can become more efficient, knowledgeable, and impactful in their roles.

The key is not just adopting AI, but applying it in a way that solves specific constraints and augments real agronomic expertise.