With the introduction of Agcor Xplor, which lets ag lenders query a vast set of location-based data in plain language, Growers Edge is sparking the dawn of the age of distributed intelligence in the industry.
Put simply, distributed intelligence is a type of artificial intelligence that brings algorithms to data where it exists rather than requiring all data to be in a centralized hub to feed an algorithm. One impact is that, away from centralized architecture, AI functionality can run with more agility on less bandwidth.
This means ag lenders and others with no training in data science or software development will be able to glean insights from raw data faster than ever. To understand the implications for the ag lending industry, we sat down for a Q&A with Chris Peacock, Growers Edge’s Chief Product and Strategy Officer.
Q: As of this conversation, Agcor Xplor is about to go live. What are the immediate implications for your customers?
We soft launched Agcor Xplor at Risk 360 this year, and then at our user conference we let a bunch of people play with it. So we haven’t officially launched it, but people are already using it and they’re already having aha moments.
Those moments usually happen as soon as someone does their first query.
Most ag lenders right now have used the commercial LLMs like ChatGPT or Gemini or Claude. That’s partly because they’re curious people and they’re trying to solve problems and partly because many have an AI mandate from their leadership.
So they know what LLMs are capable of, and yet they’ve all been disappointed by general-purpose LLMs. They’ve asked about this specific water district in California, maybe, and the answer is overly general or just flat-out wrong. Because the general-purpose LLMs aren’t trained on ag-specific data.
And then they query Agcor Xplor, and boom. The answer is specific and precise. And they get it right away. It’s built on top of all the data that fuels the Agcor [land and portfolio intelligence] platform, all the data we have on soil and water and crops and markets, and it’s available within seconds, in whatever configuration you want.
So that’s the immediate change. Suddenly, you don’t need an intermediary to give you access to all this rich data. You don’t need to ask a data scientist to run a SQL query and you don’t need to learn new software. You just ask.
That’s the baseline. This is the start of the next phase of AI products, which I see as distributed intelligence.
Q: What do you mean by “distributed intelligence”?
Broadly speaking, it’s a way of making sure people doing their day-to-day work have access to data in the moment they need it, via natural language interfaces. It’s not locked up in a core system, it’s where the decision-making is.
If you think of the Internet of Things (IoT), that was all about collecting data at the edges and sending it back to a central system. Distributed intelligence is all about pushing data and decisions to the edges, where people are doing the work.
So for example, an ag lender might be gathering data about various properties. As they’re driving to a farm, they might query an AI agent about the property they’re visiting so they get its history and know what questions to ask. Then, on the drive back, they can deload the information they gathered to the agent and have everything they need for next steps when they get back to their desk.
One promise of distributed intelligence is that it streamlines how information gets to and from various systems and makes it much more accessible to people in real life.
Q: How will access to distributed intelligence change the work of ag lending?
That’s the classic concern, right? “AI is coming for our jobs.” But that’s not really the case here. For one thing, young people just aren’t becoming ag lenders in the numbers we need. The workforce is contracting.
So these AI advances are essential to supporting the people who are in the industry, and by extension the farmers who rely on them.
Q: What’s your vision for the future of the industry?
There are a few ways to answer that.
From the perspective of vendors, there’s an acceleration happening. AI is the thing now, but AI is becoming the baseline. AI that makes you faster – sure. That’s becoming expected. Efficiency isn’t a moat.
What’s happening now is that industry leaders are experimenting rapidly with AI. They’re productizing functionality. They’re creating vertical agents, which can handle specific tasks – often time-intensive but important things, like transferring data from one context to another.
And because of AI, they’re building these things faster.
On the customer side, because natural language tools are so easy to use, the learning curve is almost nonexistent. People using Agcor Xplor understand the tool as soon as they make their first query.
Right away, you see the wheels turning: if I can ask it this, can I ask it that? If it can do this, could it do that?
And so we iterate and test and learn much faster than was possible in the past. And that means the people who aren’t already experimenting today – both the lenders who aren’t playing with AI and the vendors who aren’t experimenting with it as a functionality – are about to fall way behind.
Q: How soon will we get there?
Sooner than you think. There’s a snowball effect with generative AI. Because it’s so user-friendly, the process of testing and giving feedback and iterating is faster. And we’re at the very beginning of the generative AI-driven product development era.
There’s no Uber without the iPhone, right? With products like Agocr Xplor, we’re kind of just putting iPhones into people’s hands.
If you’d like to demo Agcor Xplor, we’d love to hear your feedback. Set up a demo here.