In Part 1 of this Feed & Grain Chat with Rob Huston, co-founder of Innovative Grain, LLC, he explains how artificial intelligence (AI) is transforming grain merchandising by unifying data systems and improving real-time decision-making.
He discusses the current low adoption of AI in agribusiness due to data security concerns, noting that new enterprise-grade tools from Google and Amazon are helping accelerate use.
Huston also shares practical ways merchandisers can use AI to streamline market analysis and encourages industry professionals to stay curious and continuously explore emerging technologies.
Stay tuned for Part 2, The business case for AI in feed and grain ops, where Huston elaborates on the return on investment businesses can expect from AI-powered systems.
Transcript of interview with Rob Huston, co-founder, Innovative Grain, LLC:
Elise Schafer, editor, Feed & Grain: Hi, everyone, and welcome to Feed & Grain Chat. I'm your host, Elise Schafer, editor of Feed & Grain. This edition of Feed & Grain Chat is brought to you by WATT Global Media and FeedandGrain.com. FeedandGrain.com is your source for the latest news, product and equipment information for the grain handling and feed manufacturing industries.
Today, I'm joined by Rob Huston, co-founder of Innovative Grain. He's here to lend us his expertise on artificial intelligence and how you can use it in your day-to-day lives. Hi, Rob. Thanks for joining me.
Rob Huston, co-founder, Innovative Grain, LLC: Hey Elise, thanks for the invite. It's a pleasure to be here.
Schafer: Absolutely! Rob, you've spent more than three decades in commodities. What first drew you to artificial intelligence and how is AI beginning to change the way grain businesses make decisions?
Huston: ChatGPT, I think, was launched on November 30, 2022, and I think it got close to 2 million followers within a month, which shattered records previously that was brought by Netflix and some others as it related to adoption. So the adoption was really fast. I've always been an early adopter, so I tried it out; I thought it was pretty cool. And then along the way, I decided to take a class from the University of Texas at Austin with their Macomb School of Business. It was a postgraduate class for AI and machine learning and business applications. And Dr. Kumar [Muthuraman] was telling the class, as he was teaching us about neural networks and large language models, he said, ‘Look, guys, even the developers who program these models are shocked and blown away at how well they perform,’ and that really captured my attention.
And later on in the class, when they actually showed us how to train a large language model, I think we were using Llama at the time, you got to adjust the models for temperature and for creativity and accuracy, and the models just kept getting better and better. So that was the aha moment — when I saw the power and the capacity of AI, I was hooked.
And then so fast forward to today. What we're trying to do is enable AI and data together, because you can't have really a good AI strategy in place unless you have good quality, clean data, but leverage those two together so that elevators can become more efficient — they can create more mutual value for themselves and for their customers as well.
Schafer: Now, there's a lot of buzz around AI, but where are we really in terms of adoption across the feed and grain sector, and what's keeping some companies from taking the leap?
Huston: I would say right now, adoption is still relatively low, but there's probably good reason for that. It wasn't until just yesterday that Google announced Google Enterprise for small businesses and large businesses. Amazon, incidentally, came out with the announcement the same day that they were offering Quick Suite, which was their AI platform for businesses of all sizes. Before that, we had, of course, ChatGPT and Claude and Perplexity. But it's just been very recently that these enterprise models were launched.
And prior to those enterprise models being launched, a lot of CEOs and chief technology officers were really concerned about data being leaked out onto the internet. Once a merchandiser gets intrigued by the technology, decides to upload a customer data list, well, that could be very dangerous if they're using a free model. And so, there were good reasons to be concerned. I think now that enterprise models are more mainstream, we should start to see adoption continue at a rapid rate.
Schafer: Innovative Grain helps customers unify scattered operational data. Can you paint a picture of what a grain operation looks like before you work with them versus after, and what kind of decisions become easier faster once everything's connected?
Huston: Before, which is pretty much the common state that we're at today, you'll walk into find a merchandiser with a single laptop, but at least one or two more monitors accompanying that laptop, and so he's looking at futures market data on one screen, she may be looking at basis information on another screen, accounting information on another one. And then you may have a browser open that has eight, 10, maybe 15 tabs open that you're trying to look at all this different information. And so, it really becomes difficult. A farmer will call in and ask some questions, and two things might happen. Either one, they have to transfer the farmer to someone else to answer the question, or they say, ‘Let me take your name and number and I'll call you back in a few minutes once I get the answer to these questions,’ because I've got to find it through these multiple systems we have available to us.
So, what it looks like afterwards is we start with your grain accounting data. Then we connect, perhaps your CRM, if you have one, or other data sources, third-party data, and we enable that all to be in one platform where the merchandiser has access to all of it. It is in near real time, and they can answer questions relative to that hour of the farmer’s asking questions, whereas before they couldn't. A lot of their data was 24 hours old and it became very difficult to service their customers effectively. So that's what we hope to bring is unification of the data, democratization of the data and allowing all people at the organization to see the data as well, that way whoever answers the phone can help out the farmer customers immediately.
Schafer: Rob, you're an AI productivity enthusiast. What are some practical ways individual managers or merchandisers can use AI in their daily work, even outside of the enterprise system?
Huston: Even the example that I was just speaking about, where a merchandiser has a browser opened with, say, 15 different tabs, right? And what they could be looking at, as an example, is cash bids for their location or locations, and then cash bids for competitor locations, and they're trying to get a sense of the market across a broad geography.
Well, one of the examples of leveraging AI, Perplexity came out with a browser called Comet, and within that browser, there's actually agent workflows where you can open up as many tabs as you want, and group them all into, say, a cash grain bids tab. And then you can ask the agent, okay, analyze all of the quotes within the tabs that I have open for corn prices during the month of October. And then the agent takes over and analyzes all of those screens simultaneously within seconds. And so what used to take a merchandiser 30 minutes to 45 minutes now can be done in seconds through an agent and leveraging AI tools.
Schafer: Now, AI seems to evolve daily. How do you stay current on new developments, and what's your advice for those who want to keep up with emerging applications without getting overwhelmed?
Huston: I personally use LinkedIn to follow a lot of the top voices within AI, Andrew Ng is a favorite of mine. Dharmesh Shah, who's the CTO of HubSpot. Ethan Mollick is another one. Allie Miller, just get on LinkedIn and try to follow a lot of the top voices in AI. They all always offer quick tips. And even follow me and follow Innovative Grain. I try, especially as it relates to the grain and merchandising space, to offer tips relative to our industry. And so that's one thing that I suggest.
And then, I know there's a lot of models out there. I tend to follow all of them. You don't have to do that. They're all good. So, find one that you're comfortable with and then explore. Be curious, explore the different things that are out there, try different prompting techniques and learn, maybe develop a framework to solve some problems that you have, some tasks that are mundane, that cause you some pain throughout the day, that things that has to be done but doesn't really add a lot of value. The models that I currently look at are ChatGPT, Claude, Perplexity, of course, Gemini, Microsoft Copilot, and even Alexa+ is available if you use Alexa as an assistant.
And then on top of that — I'm not trying to overwhelm anyone — but another set of tools that I look at quite a bit that are quite helpful are they used to be called vibe coding platforms. Some people call them agentic coding platforms today, but you've got tools like replit or Cursor or Lovable or Manus. These are platforms where, with just a single natural language prompt, you can ask it to build something, and it will just start building things automatically with code. So, no coding is required on your end, but just finding an idea and allowing these models to take that idea and expand onto it. And then when they come up to a roadblock, they'll ask you questions on how to get past it. And that's where your expertise as a merchandiser or a bookkeeper or a manager comes into play. If you've got 10, 20 years of experience, you can provide the context to these large language models to help them make you successful. They're really powerful, so curiosity and just a passion for continuous learning is really what will help you out today.
Schafer: That's all for Part 1 of this Feed and Grain Chat. Join us for the rest of my conversation with Rob Huston in the next episode.
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