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The farm-to-fork cooperative uses artificial intelligence to improve agricultural yields.
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You might have seen Land O’Lakes’ dairy products on store shelves without giving much thought to how they got there, but that’s something CTO Teddy Bekele thinks about every day. While the farmers and agricultural retailers of Land O’Lakes work to produce the cooperative’s products, starting from the seeds used to grow animal feed, Teddy Bekele is focused on supporting agriculture’s “fourth revolution” — one that’s embracing technologies like artificial intelligence. On this episode of the Me, Myself, and AI podcast, Teddy explains how Land O’Lakes uses predictive analytics and AI to help farmers and other agricultural producers be more productive and make better decisions about the business of farming.
Teddy Bekele is the CTO of Land O’Lakes, leading the organization’s digital transformation by leveraging existing and emerging technologies to discover, implement, and deliver solutions and ecosystems. Previously, Bekele served as vice president of ag technology for WinField United. Bekele holds an MBA from Indiana University and a bachelor of science degree in mechanical engineering from North Carolina State University. His community leadership includes serving as chair of the Minnesota Broadband Task Force and the Federal Task Force on Precision Ag Connectivity, and as a board member for Stella Health, Genesys Works Twin Cities, and the Minnesota Technology Association.
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Sam Ransbotham: You may have used the phrase “bet the farm,” but if you don’t work in agriculture, you might not fully appreciate what that means. On today’s episode, find out how technology can support successful farm production.
Teddy Bekele: I’m Teddy Bekele from Land O’Lakes, and you’re listening to Me, Myself, and AI.
Sam Ransbotham: Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I’m Sam Ransbotham, professor of analytics at Boston College. I’m also the AI and business strategy guest editor at MIT Sloan Management Review.
Shervin Khodabandeh: And I’m Shervin Khodabandeh, senior partner with BCG, and I colead BCG’s AI practice in North America. Together, MIT SMR and BCG have been researching and publishing on AI for six years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.
Sam Ransbotham: Shervin and I are talking today with Teddy Bekele, chief technology officer at Land O’Lakes. Teddy, thanks for joining us. Welcome.
Teddy Bekele: Thank you for having me. I’m very excited to be here.
Sam Ransbotham: I think we first met back in 2018, when you were at WinField United and we did a webinar together about data and analytics. Now you’re at the parent company, Land O’Lakes. Can you tell us about your current role?
Teddy Bekele: Yeah, absolutely. I’m the chief technology officer here at Land O’Lakes, and it’s a farmer-owned cooperative — a farm-to-fork cooperative, I would say as well. We cover the whole gamut, all the way from the time the seed goes into the ground, crops come out of the ground, they turn into grain. The grain then is used for animal feed. Purina is also a Land O’Lakes brand, and so that grain is then used to create feed formulations for animals. And then, those dairy producers are part of the cooperative. We buy the milk from those producers and turn it into value-added products, such as butter and cheese, which you find at the store.
When we talk “farm to fork,” you truly cover the whole spectrum here in a very large way, and my responsibility as a chief technology officer is obviously anything that runs the internal systems for our organization — enterprise uses for planning, ERP, websites, CRM, things like that. But another exciting area is the area of data and analytics, as well as a lot of software that’s used actually in our business — working with our customers, whether it’s supporting an existing business model or helping develop a new business model that then runs on the technology, and really working with those business leaders to be able to define what that is, and then working either with the internal resources or with external third parties to be able to develop some really neat solutions.
Shervin Khodabandeh: And the composition of the coop is what?
Teddy Bekele: It’s really interesting. The coop is made up of two sides of the membership. One, I mentioned a second ago — the dairy producers — and that is how the company got started. Exactly 101 years ago, dairy producers here in Minnesota got together and said, “We want to get our product out East,” and so they created this cooperative to be able to handle more of an aggregation of all their products. The other side of the membership are ag retailers. These are independently operated businesses in rural communities, and they serve row crop farmers — predominantly row crop farmers — in those communities, so corn, soybeans, wheat, canola. And what we do for them is, as a supply cooperative, we buy the large seed crop-protection products from large manufacturers, and we sell it to them, and then they sell it to the farmers that they serve, and in a lot of cases, these ag retailers are also cooperatives themselves, so their boards are made up of the farmers they serve.
Our board is made up of half of these dairy producers, and the other half is these ag retailers. And that combination is the Land O’Lakes cooperative.
Shervin Khodabandeh: And as the CTO, what’s your mandate for the coop?
Teddy Bekele: We are here to serve the members, all the way down to that row crop farmer, and making sure that we make them competitive at the farm gates — whatever they need to do on a day-to-day basis to stay competitive. And particularly, as a chief technology officer, [I think about] how do we keep up with the latest trends? How do we make sure we apply those technology trends to the solutions we give them? How do we make our supply chain more efficient and, really, be here for another hundred years, to make sure that we’re successful?
Shervin Khodabandeh: And technology’s been transforming the ag business, from what I understand. Can you just give the 101 of that to our audience so they get a sense of how technology is really changing the agriculture and farming business?
Teddy Bekele: It’s one of the oldest industries in the world. And for hundreds, maybe thousands of years, it was done exactly the same way. And in the last hundred years, you’ve had some tremendous changes … starting with the tractor. All of a sudden, things get a little more automated. When crop protection was introduced, that was a big deal. You did all this work; it could all be gone because of some pestilence that occurred on the field.
The next piece was the biotech revolution. And so now there is a lot more research and a lot more technology that went into the seed that goes into the ground. And could it be drought resistant? Could it still come out of the ground when it’s too wet? Could it be resistant to certain diseases? And that was a big game changer, and so we call it kind of the third revolution.
But this fourth-revolution agriculture is now the application of technology — and particularly software technology — to ag, from simple things like being able to run descriptive analytics to the various components of artificial intelligence. And … this fourth one, though, it’s put agriculture in hyperdrive. Because all of a sudden, you’re starting to see all of this complex environment that really was managed with intuition, and now your productivity goes up; now data and the technology is really helping you through this.
You’re more and more reliant on data coming off the field and then making decisions. That really is the change in what’s happening in agriculture today.
Shervin Khodabandeh: And that data is really massive, right? I mean, many different sources, many different permutations, right?
Teddy Bekele: There are so many things that happen. … Obviously you have weather, which is a big component in agriculture. In aggregate, we probably do a better job of predicting weather, but when you think on a farm, you need it down to a little 3 meter by 3 meter pixel. Like, is it going to rain in this specific spot? Now, can you predict that with 100% accuracy, and can you tell me exactly how much rain and when is it going to happen? … We’re at that hyperlocal level. We’re not quite there yet. So you have all that variability that happens, but imagine all the massive amounts of weather data you can capture that goes into figuring this out.
Soil composition is yet another big piece. Then there’s a selection of the right type of seed. You’ve got to take that into account. And then we talked about applying nutrients and applying crop protection. When’s the right time? How should I do it? There’s 40 macro decisions that a farmer has to make on a field, and there’s 180 subdecisions, and all those could impact what happens at the end in a variety of ways, with all the unpredictable climate changes and things that could happen along the way.
Shervin Khodabandeh: Really millions and billions of possibilities, right?
Teddy Bekele: Billions, maybe even trillions, yes.
Sam Ransbotham: So Teddy, when you were describing this, you used the words “aggregation” and “cooperative.” I like those words. They’re great ideas. They’re strategic, but they’re not obvious in your context. I actually use one of your examples in class every semester when we talk about data and strategic positioning. Can you describe a bit about how this testing and experimentation works?
Teddy Bekele: Yeah, absolutely, and we get into more of the work we’re doing in artificial intelligence, which is super exciting, by the way. But part of the role of our cooperative is, how do we help that farmer be more productive on their field? How do we provide them better intelligence?
And the way we’ve done it is, we actually have said, because we’re sort of this aggregate entity, “Let’s go work with all the manufacturers and get all their seed, even in advance of when it’s commercially available.” We used to have about 200 applied research plots throughout the areas where we did business. And in a variety of climate types, soil types, different practices, we would plant all these seeds and then apply on those acres in applied research plots.
And then we want to be able to say, “Look, we’ve replicated this enough times to where we can say, ‘We know exactly how this will perform in this given climate type, etc.’” We used to have 200 of these plots. I think, Sam, the last time we talked, that was about 210 or so. We are down to actually 115 sites now versus the 250, although we cover more geography.
Primarily, a lot of the work was, we tried to figure out, like, OK, what’s the key component? And we got down to, it was three things. It was climate, it was soil — which was no surprise — and topography was the other one. And Sam, to round out your question, the idea is, we do all this research, we get this information, we get these analytics and these insights, and then that’s what we offer to our farmers, along with, “These are the products you can buy, but these are the best selections” — because we still make it the farmer’s decision at the end of the day — but “these are the best selections for the outputs you can expect at the end of the day.”
Shervin Khodabandeh: This is fascinating, Teddy. One of the things that Sam and I have researched quite a lot, and I see in my work all the time, is the success of an analytics or AI program is very highly proportional to the interactivity that it allows the user or the agent or the person in charge to do it, so that you can understand how it works, you could agree or disagree, and AI learns something and the human learns something, and that way they both get better. That’s what we call organizational learning. So how does this work in the farming industry? You provide the insights and the decisions, and the farmer agrees — fantastic — or says, “No, I want to override this.” Is there a feedback mechanism, or are you guys thinking about that? Is that even valuable?
Teddy Bekele: It absolutely is valuable. And there’s one other actor in this equation that’s really critical to all of this, which is this agronomist, which is almost like the personal adviser when it comes to anything related to agronomy for that farmer.
A really good agronomist is a really, really strong, trusted adviser of that farmer. And so, like I said, those 180-some subdecisions those farmers have to make, there’s everything from, not only some of the things we talked about — soil, climate, all that — but there is equipment: when to buy, [when] not to buy, how to use the equipment. And when it comes to agronomy, this agronomist is the person that would present them [with] this information. And the reason I mention that is, for us to be able to get validity as well as to make sure that what we’re proposing makes sense and is valuable, is to make sure this adviser’s bought in. They are the feedback mechanism for us.
Shervin Khodabandeh: That is fantastic. This is so fascinating, because it’s such a big data play — like, a truly big data play.
Sam Ransbotham: It’s huge.
Shervin Khodabandeh: And the power of the coop — it makes it even exponential, because every experiment actually empowers the whole coop, and I’m assuming a farmer in Minnesota isn’t really competitive with a farmer in Cleveland, so the information sharing shouldn’t be an issue like it might be in retail or some other industry, right?
Teddy Bekele: Yeah. Yeah.
Sam Ransbotham: So with all these data and artificial intelligence, machine learning models you’re doing, somehow you’ve gone in this fourth revolution or fourth … I can’t remember what you called it, fourth wave?
Teddy Bekele: Revolution. Yes.
Sam Ransbotham: What’s next?
Teddy Bekele: With some of the capabilities, both the biotechnology as well as the software technology component of it, there’s farmers that can get up to 540 bushels per acre. That’s the potential that’s up there. And so some of it is now getting into this practice and is capturing data on the field all the time, making the adjustments. Some of it is just changing a lot of legacy practices. So particularly here in the Northern Plains, one of the things is that people apply all the fertilization in the winter, because it gets so cold and it snows, and then it stays white forever, until it melts.
And then you have all that … when it melts, it goes down into the ground, and now you have the moisture to be able to grow the crop. Traditionally, all that was applied in the fall: Right after you harvest, the first thing you do after you harvest is then get into tilling — or maybe, maybe not — and then you do the fertilization.
Now, the idea is no, no, no — when you want to spoon feed, you actually should do that in the spring, right before the crop goes into the ground, as well as once the crop is in the ground. But that creates some logistical challenges, because now you have to be in the field at a given time, and it’s twice, maybe it’s three times, and there’s a scheduling aspect: Who’s going to do it? Is it the farmer? Is it the retailer that works with them? So it’s kind of getting through that, and it’s learning that, but those are the things that folks are learning now and making some of those adjustments.
Sam Ransbotham: Yeah. That’s great. You’re talking about, you provide these recommendations on average and then people adjust them, or it’s not even that they consciously maybe reject your suggestion, but they implement it slightly differently.
Teddy Bekele: I always say, when I started working here nine years ago, I realized when people say, “You bet the farm,” like, “Do you want to bet the farm on it?” people bet the farm. In this business, they actually really do, because every year, I mean, if you go badly, all the investments you make in all the crops as well as the equipment, you could totally be upside down and not be there two years later. So there’s just always this risk aspect of it. And part of it is, [farmers are] very entrepreneurial in nature. So they want to be able to be empowered to make their own decision and have their own flavor to it. And that’s totally OK. All we’re saying is, make sure you use the data to be able to help you with that, and maybe there’s an option A, where you put your foot on the gas a little bit harder, and then there’s an option B, where it’s maybe not as risky, and you want to be able to balance that out, so I think that that has to be there. And farmers, they’re built that way. They’re built to be able to do that.
Sam Ransbotham: And at least this lets them know if they’re pressing the gas or if they’re backing off. I mean, you’re what you’re doing is you’re saying, “Here’s the data that says, ‘Hey, this is an aggressive position,’ or, ‘This is a more conservative position.’” Before, they were just guessing where they were on that continuum, perhaps, and now you’re at least letting them know that.
Teddy Bekele: Exactly. And one of the things I always tell them is, “If it makes you any feel any better, call it augmented intelligence. How about that? It’s your intelligence; it’s augmented with data and some of the models we put together. It’s not doing its own thing, and you’re not just there now reacting to what the model’s telling you.”
Sam Ransbotham: Teddy, we have a segment where we ask you a series of rapid-fire questions, so just answer with the first thing that comes to your mind. What’s your proudest moment in AI?
Teddy Bekele: Proudest moment in AI … I would say we were using this computer vision to be able to take a look at cows in the field and could we, just with a snapshot, take a look at those cows and say, “Yep, it’s too thin, it’s too heavy, or just about right amount of weight.” I remember when that came to life. I was ecstatic when I when I saw the output of that model.
Sam Ransbotham: Perfect. What worries you about AI?
Teddy Bekele: What worries me about AI is unintended consequences. You run the model, and you have the best intentions in the world of reaching a certain answer, but in the background … because we don’t understand what happened behind the scenes — because the machine was learning, and it was writing its own code, and then it learned something else — and all of a sudden, we can’t get to understanding why we got to that answer, and we can’t validate it. But we keep going forward because it’s truly too embedded into what we’re doing to be able to go analyze that. The unintended consequence of something is truly something that I worry about every day.
Sam Ransbotham: What’s your favorite activity that involves no technology?
Teddy Bekele: Favorite activity that involves no technology is trying to teach my kids how to write code … but I’m trying to do it in such a way that I use no tech and they can have fun trying to think through the mental process to be able to do it and then say, “Oh, look, and now you can do this.”
Sam Ransbotham: What was the first career you wanted? What did you want to be when you grew up?
Teddy Bekele: Mechanical engineer.
Sam Ransbotham: Ah, great.
Teddy Bekele: I was 7 years old when I … and by the way, my degree is in mechanical engineering. I just realized I didn’t quite want to be a mechanical engineer a year after I graduated.
Sam Ransbotham: Yeah, well, I had the same thing with chemical engineering. I didn’t want to do that once I graduated with it. What’s your greatest wish for AI in the future?
Teddy Bekele: My greatest wish for AI in the future is truly continuing the work we’re doing now; it’s to provide help with really identifying, [taking out] the guesswork. I mean, I want to make a decision — as an individual, as a producer, as a farmer, as a retailer, as a large manufacturing, large cooperative company, I want to be able to make the decisions, but I want to be able to have everything at my fingertips so I can make the most optimal decision. So can you get me to that spot where all the information is de-codified as much as possible so I can get to the best possible answer?
And so I would love to take that out of the way and really get to a spot where I don’t have to worry about the guesswork aspect of it; now I can make the most optimal decision.
Sam Ransbotham: OK, Teddy. Great talking to you again. Thanks for taking the time.
Shervin Khodabandeh: Thanks, Teddy.
Teddy Bekele: Thank you. Take care.
Sam Ransbotham: On our next episode, Shervin and I speak Tonia Sideri, head of Novo Nordisk’s AI center of excellence. Please join us.
Allison Ryder: Thanks for listening to Me, Myself, and AI. We believe, like you, that the conversation about AI implementation doesn’t start and stop with this podcast. That’s why we’ve created a group on LinkedIn specifically for listeners like you. It’s called AI for Leaders, and if you join us, you can chat with show creators and hosts, ask your own questions, share your insights, and gain access to valuable resources about AI implementation from MIT SMR and BCG. You can access it by visiting mitsmr.com/AIforLeaders. We’ll put that link in the show notes, and we hope to see you there.
Sam Ransbotham (@ransbotham) is a professor in the information systems department at the Carroll School of Management at Boston College, as well as guest editor for MIT Sloan Management Review’s Artificial Intelligence and Business Strategy Big Ideas initiative. Shervin Khodabandeh is a senior partner and managing director at BCG and the coleader of BCG GAMMA (BCG’s AI practice) in North America. He can be contacted at shervin@bcg.com.
Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rüdinger.
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