Experts of Experience - #34 How John Deere is Making Technology Accessible to All Farmers

Episode Date: June 12, 2024

On this episode, Justin Rose, the President, Lifecycle Solutions, Supply Management, and Customer Success, John Deere, discusses the implementation of AI in customer success. He highlights the challenge of determining when and how to intervene with customers based on AI insights, without being overly prescriptive or intrusive. He emphasizes the importance of customer feedback and engagement in shaping AI solutions. Justin also addresses ethical considerations, such as safety and data security, and emphasizes the need for transparency and trust. He shares resources for AI implementation and innovation, and advises customer experience leaders to be bold and incremental in driving innovation. Key Takeaways:John Deere is investing in new technology to enhance the customer experience in agriculture.Making technology accessible to all farmers through a subscription-based pricing model called Solutions as a Service.John Deere is building a customer success function that leverages AI to deliver personalized and proactive support.Developing agentic AI systems that can triage data, customize communications, and drive interventions for each individual customer.The human touch is still important in areas such as initial sales, renewals, and escalations. Determining when and how to intervene with customers based on AI insights is a challenge in customer success.Customer feedback and engagement are crucial in shaping AI solutions.Ethical considerations, such as safety and data security, must be addressed in AI implementation.Transparency and trust are key in building strong customer relationships.Being bold and incremental is important in driving innovation in customer experience. 

We've always driven innovation, but when you're 187 years old and you're highly successful, you do amazing things, there can be some degree of complacency. We are playing a long game. You are servicing people whose life's work is using your technology. Think about a model where we don't have just one agent, but we have many agents, each of which are highly tuned to some specific task. And you start to see a world where it's not just linear, it's actually reconfigurable. But we really need to think about where is the human touch going to help us build trust?

AI can build trust, but not the way a human can. We're creating highly customized content for each unique individual. And our goal is to be able to do that every day. How do you avoid being overly prescriptive, maybe even creepy? What are you going to do that's going to be bold, that's going to challenge your team to improve, and it's going to help the organization make one of those step change leaps? Hello, everyone, and welcome to Experts of Experience. I'm your host, Lauren Wood. Today, I'm honored to host Justin Rose, President of Lifecycle Solutions Supply Management and Customer Success at John Deere. Justin, so wonderful to have you.

Thank you. Title's a mouthful, huh? You got a lot going on over there. Yes, apparently. Apparently so. Thanks for having me. Well, we're so excited. I've actually been really looking forward to this conversation for a while because, I mean, one, I grew up as a big John Deere fan, grew up on a farm, and it was the coolest toy to play with. And you've also progressed a lot over the years. And I wanted to first kick it off by unpacking a little bit about the investments in new technology that John Deere has been making and how that's related to the customer experience that John Deere really envisions. I know that you've been investing in technologies like AI to weed with precision, autonomous vehicles,

Starlink to connect farmers. There's lots of really cool innovations that have been coming out of the John Deere sphere. So tell us a little bit about what it is that John Deere has been up to and how that's really impacting your customer experience as a whole. Yeah, absolutely. I'd love to. You know, agriculture is a super fascinating industry where technology is really core to what farmers do every day. And it kind of struck me as we prepared for this that many of your subscribers probably don't know a lot about ag. So I thought I would maybe just lay out a few of the basics of how that works. You know, let me give you an example. So there's a job, the first job you do when you're doing agriculture is planting. And,

you know, we've, maybe some of us have gardens and such, and you have an idea what that means. But on a large scale farm, you can imagine a planter that might be 100 feet wide running through bumpy, hilly fields. They could be dry, they could be wet. They're moving at something like 10 miles per hour. And they're actually putting down 720 seeds per second at exactly the right depth into the ground, being pulled by a tractor that's using GPS satellite correction to steer within less than one inch of accuracy. Because you can't run over the plants that you put in a different part of the field, right? So you got to be really, really precise. And this all gets uploaded, documented, created into a data profile for each customer,

put into the cloud, accessible, and over time they can use that to optimize their operations. And so it's a pretty cool overall ecosystem of technology. And Deere has been really at the forefront of accelerating that. And I say accelerating because in a lot of ways, we're bringing technology more quickly, more broadly to the market than we ever have before. You talked about some of the innovations. We have autonomous tractors running in fields right now as we speak. We have computer vision and machine learning algorithms helping us determine in a field what's a plant versus what's a weed and only putting chemicals on the things that are weeds. We have Starlink, as you mentioned, a partnership with SpaceX that we just launched that will help in rural areas create persistent connectivity of that technology.

So it's super, super, super cool stuff. Now, the problem is, is we also have to be realistic about the barriers our customers face. And so what we see is that a lot of this technology on an upfront cost basis is super expensive. It's more than many farmers can bear, especially farmers that don't sit at that really top end of the biggest operations. And so that's an issue. And second, every farmer's operation is different. If you said you grew up. You know, I would guess that your family thought about farming slightly differently than the family next door and differently than the one in the county down the road. will work in their operations, especially when they're paying so much for it. So what we're doing is one element of this is launching a new, what we call customer value delivery model. We're calling it solutions as a service, sort of riffing on SaaS in the technology world. And the key elements are number one, lower upfront cost.

So the customer will pay less than the full sort of cost of what that technology would require to give it to them one time. There will be a subscription attached to that, but they only pay for it when they use it, presumably when they get value out of it, when they need it, and it'll get better over time. Now, it's a pretty common concept probably to many of your subscribers, but at the end of the day, this is revolutionary in the agricultural space. And it's something that we see that could be a real accelerant to delivering technology to the farm. So you're rolling out these new technologies that are advancing the industry, and then you're making it actually accessible for all types of farmers

to implement that technology and make it a part of their day-to-day workflows. It's really interesting, this pricing model that you're talking about, because it's really making it possible for so many. And I'm curious to know why you decided to take that strategy instead of just focusing on the top end, the people who can buy this technology outright, and actually open it up so that it's something that everyone can access through a subscription model. John Deere has a big mission-driven component to the company. We believe very strongly that doing things that help us build a good business can also be good for our customers and good for

the world. And so I gave the example. Yeah, exactly. I gave the example of the computer vision and machine learning algorithms for our sea and spray technology. And I said that it helps put chemicals only on the weeds in the field. That reduces the total amount of herbicide deposited on the field by something like two thirds. And so that is just naturally goodness for the earth. We're putting less chemicals under the earth. The farmers are saving money because they're spending two-thirds less on the chemicals. And of course, we get the right to say, hey, we'd like a share of that value that we're helping you create. And that's a good business for us too. And for us then, the question is, how do you democratize that across as many acres, as many farms, as many customers

as possible? And that's what I think leads us to that business model that I mentioned, the pricing model that I mentioned. We can't just go to the large farmers. They could afford it. But we have an aspiration to get that technology as ubiquitously driven across North America, South America, Europe, the world, as we possibly can. I really appreciate that. And also, as someone who has been working in the environmental space for much of my career, whether it was my actual job or a passion project, when we find those win, win, win situations, that's really where we can make big change. So it's really inspiring to hear that that's something that John Deere is really committed to. And as we are talking about customer experience,

I want to dig in a little bit to how John Deere really envisions the end-to-end customer experience for your customers. Is there a guiding light or kind of a goal or a vision, a mission statement perhaps that you have? I think the first thing to say is our customer experience is a hybrid. So obviously as the manufacturer of the equipment, you know, we help steward the customer's data, build the digital platforms for them decades. And so they play a really, really significant role in helping us deploy technology, drive adoption with our customers, and then help service and support them. But it's one integrated face to the customer. That's what we're going for. That we're supporting our dealers, our dealers are supporting our customers. We're all collaborating to do that directly. When I think about technology in particular, for us, it really has to be an

end-to-end approach. And so there is a value-based sale that is much different than what we've done historically. So being able to say, not just that the equipment has more horsepower or it should be more reliable, but rather if you use this technology, it will save you money in some other part of your operations or help you get more yield out of the field. That's a different type of selling motion than we've historically been accustomed to. But it's obviously the starting point of this

because that's how we get the customers interested. We get them in the door along with the lower upfront price to reduce the friction with them actually trialing and ultimately using the technology. Then it's about, okay, let's welcome them to the community. Let's help them understand how others are using this technology to its fullest extent, how they can get set up for success in their first days of the new season as they get out in the field.

So we have education, we have training, we have a lot of investment there. Now they're in the field, let's make sure that they know how to use it, that they can turn it clear at each step before that, that there's a ton of value. We've made it easy for you to use. And you felt great when you went into the field the first time you were well-prepared. It's an interesting journey, actually, because you have these distributors who, and tell me if I'm wrong, but they're not part of John Deere. They're a third party. They're independent. For the most part. And so how do you ensure that you have a really great customer experience end to end when someone else is owning such a key part of it? It's a really good question. And I actually think it's an advantage for us. And so,

you know, when you think about a really big corporation, what are we good at? We are good at running a global supply chain, getting parts at the right time, lots of processes to, you know, resolve issues we've had in the past and make sure that things run smoothly. What our dealers, I would say, excel at is they are just hair on fire, you know, every day serving customers. Of course, they have processes that they follow, but, you know, they do whatever they need to do to get the job done. They have personal relationships with these people. They have each other's mobile phone numbers. They see each other at church and in the community. And, you know, they are just deeply embedded. And so I actually, of course, we spend a lot of time with dealers. Of course, we have metrics that, you know, we sort of ask them to live up to, to earn incentives and things like that.

So there are ways that we drive alignment, but they are also just naturally hungry and aligned. Many of them are actually former John Deere employees. And so I think they've seen both sides of that. Many of them farm, you know, their own land or have. And so they've seen it from the customer standpoint. And so we might have this unique ecosystem where there's different parties in it, but they all truly understand each other and have a stake in each other's success. It's so key that you have people who really understand the customer. And that's the thing

that perks up for me when I hear that there's a third party actually making that sale because as someone who's worked in the traditional SaaS for a long time, that moment of sale is such a key moment of trust building. And you really need to have someone who understands the customer and can build trust with the customer on your behalf at that key stage. So it's great to hear that you have some trusted parties on your side who are really bridging the gap between you and your end customer. I wanted to talk a little bit about customer success and knowing that, I mean, John Deere and just the nature of the agricultural industry, we are playing a long game. You are servicing people whose life's

work is using your technology. And so I'm curious to know a little bit about your approach on the customer success side of things. So specifically focusing on that post-sale retention, engagement, renewals, all of that stuff. So tell me a little bit about how you approach customer success. I think, let me answer your question in two parts. One is, what are we doing today? And so, as I mentioned, the tools and the equipment, the software, the hardware that we put into the field, these are businesses. We sit at sort of a weird hybrid where it's individuals buying, but really think about them. It's more of a B2B sale than I think many people would appreciate in terms of how we go to market. And because it's B2B, the pitch is that there's value there.

The promise is that there's value there. And so our customer success motion is largely about helping them get the most value out of the technology and then being able to document to them as close as to the time that they do the job that there was value there. Put another way, if you think about farming, you might plant in April and harvest in August, September, and maybe even October in some parts of the country. Boy, that's a long time window. And so if I tell you use technology to plant really, really well, and then I back it up with a claim in September that,

hey, you got a lot of yield out of the field. There's a million literally reasons that that could have been outside of the fact he planted well. The weather was good or bad. We got the right amount of rain, all the other jobs in between, et cetera, et cetera. And so we're trying to shorten the time windows. I gave the example of our spraying, seeing spray. That's a great example of literally every pass through the field, you can conceptually do the math to say, you would have sprayed X, you did spray Y, multiply that times a cost per gallon of chemical, you know what your savings are. And so how do we drive that down? And the customer success team we're building is really working to try and connect those dots as clearly as they can for the customer. There's also things like the machines sometimes will not work. So they go into something called fallback mode and it just sprays everywhere. Well, why is that? Well, in some cases, it's because the customer is driving too fast. In some cases, it's because the boom height is too high or too low.

Maybe it's too dark for it to work. And so our customer success team can be able to pull information off in near real time from those machines, understand the exceptions that are happening, and then be able to drive interventions back to the customers. One thing that's different for us in customer success is, you know, at least as I started to build this function at John Deere and talk to many, I guess, of my peers in the technology industry, I would ask them, well, how do you start? How do you know where to focus? And they'd say, well, you know, you use the Pareto principle. You think about your 20% of customers probably make up 80% of your revenue. You dedicate customer success managers with them. They might even sit on site with those customers, et cetera, et cetera. And you really teach them how to use it. That doesn't work in ACK. Every machine is

an individual unit of value. And the differentiation between one machine's value and the next, it's pretty minimal. And so we have to serve this super flat, super long tail, hundreds of thousands of customers and build a customer success motion that actually allows us to do that in a scalable way. Now, let me, if you don't mind, let me bring up my second half of this answer, which is, so how do we do that in the future? Because we're just getting started. We have like hundreds of machines today that are doing this. We're going to have tens of thousands in the future. And because of that, we know that we can't build a traditional customer success function that staffs customer success managers,

that has monthly reports, all that type of stuff. We're using AI and we have the luxury of building this from basically scratch. So there's no tech debt, no process that's been established that we have to overcome. What we're doing is we're working to say, let's embed AI from the beginning. And right now, what we're thinking about doing is we're building out what's called agentic AI systems, where there's multiple handoffs between different AI agents in the system. One can take data off of the machine and triage it to say, here's where the customer is getting the most value and here's where they weren't. The next one can say, okay, the ones that weren't getting value, what's the rational and cause for why they're not getting value?

Maybe the next one in the network of agents then says, okay, let me customize a communication back to that customer through email, text messaging, maybe pushing it to their displays in their cab of their machine. And we're creating highly customized content for each unique individual. And our goal is to be able to do that every day. And so imagine if you're trying to build out a workforce to do that across hundreds of thousands of machines, it would be enormous and expensive. We have a vision where we can do that for a fraction of the cost, but with the granularity and resolution that is going to be world-class. You just touched on the hottest topic and I have so many questions to ask. So what I'm hearing from you, you recently started this customer success function.

When did you start it? I actually joined John Deere only about 18 months ago as a part of the team to drive this customer value transformation, the SaaS transformation I talked about. And so, you know, before that, of course, we've done things to help our customers be successful. But I would generally describe it more in a more traditional customer support realm where it's reactive. They come to us, et cetera, et cetera. This proactive customer success where we have a stake in them using the technology and getting the most value out of it, that's new in the last, call it 12 months. Wow. So you really have a blank slate to build this customer success function on, which is having done it myself in startup companies more so than very established

companies. It's kind of nice when you get to just start it from scratch. And like you said, you don't have those legacy technologies and processes that you need to rebuild. So you had said agentic AI systems? Yeah, agentic. So think like agent, a mix of agents that are individually tuned to comprise sort of a bigger whole, if you will. Got it. But those are AIs. They're not real people. Correct. Not real people. And is this something you're building on your own? Are you using other technology?

We, right now, we're pretty open. It changes every day. But as we speak here and going into summer of 2024, we're working with all the leading providers and the leading models to try to figure out what works best in our context and how we actually build that. I think what's different about what others have done to this point is this, what I'm calling an agentic model. Because instead of just saying we're going to have one AI function that maybe you query as a chatbot or whatever. This is building a flexible set of tools that you can reconfigure. Imagine in this agentic model, if there's one agent that is highly tuned, maybe using a large language model type technology to look at the context of all of the technology this customer has used, what they've purchased from John Deere, how long they've been a customer, heck, maybe even where

their farm is and what college football team they're chair for. I don't know yet, right? But it takes all of that context and then it creates a really personalized, crafted message to that customer based on whatever a different agent told it it needs to communicate. So it doesn't have to be the master of all domains individually, but it's really good at one specific thing and really well-tuned to that. Now, think about a model where we don't have just one agent, but we have many agents, each of which are highly tuned to some specific task. And you start to see a world where it's not just linear, it's actually reconfigurable. And so in some cases, we may want to publish highly personalized information,

and we pull that agent off the shelf and embed it into that logic chain of customer success. In some areas, that one may not be relevant, and it just doesn't get kind of built into the toolkit. So in some ways, we're building out a foundational set of tools using AI that we will be able to configure in a way that actually will give us a really dynamic ability to drive customer success. When you're using Salesforce to tackle your company's most important goals, failure is simply not an option. That's why their most highly skilled advisors, Salesforce CTOs, are available to help you succeed with expert guidance and implementation support at every step of the way. Learn more about Salesforce CTOs at sfdc.co slash professional services. Such an interesting concept, like AI specialists,

just as if you were to structure a team. I mean, I always think about this when I'm building customer success teams and I am a customer experience, customer success consultant. So I do this with companies a lot. And there's always this conversation of, do we go the specialist route or do we go the generalist route when it comes to onboarding and account management and tool training and things like this? And it's a really interesting concept to focus on having a bunch of AI specialists that are quite malleable in a way in terms of where you put them. There's less culture change that happens if you decide, okay, we went the wrong route. We want to change this. We want to put someone else into this flow. I can imagine it allows you to be a lot more versatile in terms of

how you're actually going about supporting your customers. That's the goal. And then getting scale. Right. So it's it's it's reusable across the enterprise. You don't have to to deploy that agent into the next product. We don't need another human agent. We just say, let's draw on that agent's capabilities into the next version of the customer success function or next product or geography we want to target. And by the way, you probably know this, but all of this is instantly doable in every language. And so, you know, there's all kinds, again, of value to being able to lead with technology. I joke, by the way, that we're trying to build a zero person customer success organization. And I don't mean that literally. I recognize we'll never get to that. Nor do I think we want to get to that. But that's the mentality that the team's trying to take. How do we not cut a traditional organization or outperform from a efficiency standpoint by 10% or even 50%, it's a dramatic, like a step change of what we're trying to build. Well, that was my next question is where do the people come into this? Because I think it's, I mean, this is a huge topic of conversation at the moment, especially in the customer experience space, because we see what AI can do. And we also know that the human touch goes so far in terms of building trust with a customer and can really tailor exactly what we need to that customer. And I also know that one bad interaction

with a chatbot, a customer and a chatbot can really sour that relationship. And so how are you approaching that? Like, what's the breakdown or if you can shed a little light on like where the human's role is in this AI success system? It's still important. The good news for all of us is that, you know, we're not totally useless yet, but we're not getting replaced. Not today. So upfront, clearly in the design, there's a key role. So how do we actually train these models, train these agents and configure the agents in a way that leads to a really good customer success experience for our end customers? customers. Then I think in the middle, you've got, when you're delivering that experience, there's obviously a, you know, role of escalations where, you know, if there's an issue that's at a certain level or some indication that, you know, we're not getting through or the customer's not

satisfied with what they're getting, you'll always have a backstop, whether that's our dealer personnel or John Deere, you knowere corporation employees that will be customer success managers and agents that will be able to step in and help the customer. And then I think at the end of the day, also at the front and the end of the overall customer lifecycle experience, the initial sale and the sort of end of year renewal or end of period renewal, those are places where I think human touch is really critical. You need to be able to dynamically understand the customer's concerns, explain how this technology is going to support them and why it's going to be good.

And then the human touch of sitting down to say, hey, here's the value, here's what we can do going forward, et cetera, et cetera. Perhaps some cross sell of what the next technologies that could be used are or what others are doing. Those places, I think, are really critical places for humans to continue to be not just in the lead, but probably the lion's share of the work for some time. We have to think about what are the moments that really require a human. And I like to think of AI as kind of the bionic arm

of us humans. How can AI be used to allow us to focus on the things that matter most? And AI can take care of a lot of things simultaneously really quickly. And so I always think about when I'm speaking to people about this topic, because it comes up a lot. I have founders being like, we have a no human customer experience team. And I'm like, okay, maybe, but let's actually think about that. Because there is some damage that can be done if we lean too far on the AI. And it's really about finding the balance between where does that human touch really take us to the next step? And where can the AI touch actually help everyone get the job done faster? At the end of the day, this is the beauty of AI and where we should really be using it,

is to help the customer get an answer immediately. Help the customer find exactly what they need to find with a click of a button versus a person going and sifting through documents and information to find that just as an example. But we really need to think about where is the human touch going to help us build trust? AI can build trust, but not the way a human can, I think. I don't know. What do you think about that? Look, I agree with you. I think maybe I don't know. What do you think about that? what the total potential value creation could have been versus what was actually realized,

distilling out what the specific reasons were that we didn't capture or get more of that total potential value, and preparing a list of potential interventions. That is not something that's customer-facing, that is mission-critical to build that relationship. It's basically grinding through a ton of data and distilling it down to something that a human, again, whether it's a dear customer success manager or a dealer employee can take forward. Now, putting that aside for a second, I also think there's some really interesting use cases and we all need to be really attentive to how the world's going to evolve. There is some interesting customer support data that came out of Klarna. Klarna is a buy now, pay later company you may have heard of. And they put in, I believe they collaborated with OpenAI

and built an AI tool that took out something like 700, if my memory serves correctly, customer support agents. And what was fascinating was they saw both times to resolution and ability to actually deal with the issue. Those metrics improved dramatically and customers reporting on how happy they were with the experience also went up dramatically. And so it's true that a bad interaction with a chatbot or an AI system can be quite bad. A bad interaction with a human can be quite bad, too. And so the technology is moving forward. I think as leaders, it's incumbent on all of us just to stay attuned to what the state

of the art is, what's possible, and then figure out how to deploy it in the best way. And it's happening so fast. So I'm really appreciating this conversation because you've clearly been thinking about this a lot and trying something that is not very, it's not a very well-walked path, let's say. People are figuring this out at this moment, but we're really in the early stages of it. So it's super fascinating to hear

you share your journey here. What has been your greatest challenge in implementing this AI success solution? The biggest challenge, I think, is ultimately where to draw the line. So what I mean by that is there's lots of information we get off of the equipment or from the grower and their operations. You don't want to inundate them with dozens of minor potential interventions each day. I'll go back to my sea and spray example again. One reason that it goes into fallback mode is that the driver, the operator is driving too fast.

Sometimes the operator is just doesn't know what they're doing and they're making a mistake, right? Sometimes they're making a conscious decision to say it's gonna rain soon and I gotta get this job done before it rains or I'm gonna have to unload the tank and reload it the next day and so on. So every time that happens, you don't want to pound them with another text message or cab intervention that says, you're driving too fast, you're driving too fast, you're driving too fast. That's just annoying, right? That detracts from a customer's satisfaction. And so where do you

draw the line about when you actually approach the customer with an intervention versus you don't? When you flag or make a recommendation, when you don't? How do you avoid being overly prescriptive, maybe even creepy, that we're always watching and kind of trying to guide you? How we're dealing with that is we're trying to create, again, a bit of a tiered model. So there are things that we're able to come up with today, interventions or recommendations to a customer that we're not confident to share with them at all. But we're trying to test how that system works, get some confidence internally. Next up is our dealers. And so there's things that we're somewhat confident in, but we might send our dealers a batch of, hey, here's 13 issues we

saw today. Put your own eyes on them. Think about the context, like, you know, maybe give, you know, X, Y, and Z a call if you think it's appropriate. And then there's things that we would go directly to customers with where we're highly confident that what we're going to tell them is valuable, contextually relevant, et cetera. And so, you know, as you work through that, then you think about all the permutations of how that could work. That's probably the most challenging thing overall. And constantly reiterating on it, I would imagine. Do you have a system in place of like how you test, assess, revisit all of these? We get a lot of support from both some of our lead customers and from our lead dealers.

And so these are the types of things that, you know, we'll take to the advisory groups, whether it's a customer advisory group or a dealer advisory group and say, hey, here's what we can do. Like, how would you think we would deliver that in the most useful and valuable way? And, you know, one concern that was raised a lot with our dealer channel, I would say more internal to Deere, we had this concern was the dealers would feel like,

well, if Deere is going to tell the customer something directly, does that cut them out of the picture? Does that threaten their role and their importance? And actually, I think we've been very clear with our channel that we think they are incredibly valuable to helping drive this transformation. So there's obviously a role for them. We're deeply committed to that. And then I think once we've earned that trust with them, they've then said, hey, if you've got something

you can take care of on our behalf that you're highly confident in, that we would stand behind if our name is on it as well, by all means, let's just go straight to them. We don't want to be involved in all the minutiae every single day of following up on these things. So that's, I would say, it's not a machine that we can sort of turn the crank on yet, but that's what we're working up to is how do we use that level of escalation and those different points of input to help us calibrate? I think you shared something that is just so incredibly important. And I would say this to any leader who is building a customer facing, honestly, anything. Talk to your customers and having that advisory group, especially as we're charting into new

territories with AI, right? We don't really know how it's going to go. We don't really know how our customers are going to experience these changes and bringing to them, here's what we're thinking, or can you try this? Tell us what concerns come up for you. It's just so incredibly important because just like you said, you had an assumption or a fear that they were going to take it one way. And in fact, they actually were more embracing of this technology because they saw the potential of how it could help them.

And so we can have assumptions all day, but we cannot know that they are real until we actually speak to our customers. And I think that's just, it's just such a key thing I want to underscore as we talk about this AI revolution in customer experience and customer success, talking to our customers to understand their opinions of what they're seeing and making sure that we're training the AI with really our customers in mind. Yeah, I think it's really, really well said. I'm curious about the ethics. And have you had any major ethical considerations? You know,

you had mentioned we don't want to show up creepy. What are some of the things that you've been thinking about or having concern about as you really lean into AI? First and foremost, I think is obviously safety. So some of the ways we'll use AI in our equipment, of course, are things like autonomy. And so, you know, we have really high barriers, a really active safety council to help make decisions, not just on what we send to customers, but even what we test in our own sequestering farms and test sites and things like that. I think the second layer of that has to be around customers feeling that their data with us is secure and that the products will actually deliver. I've been using the scene spray example throughout today, and so I'll continue. What we're asking them to trust is that when that machine goes through the field looking for weeds, and by the way, it's fascinating because our most advanced technology will drive 15 miles an hour, will scan several thousand square feet per second

for weeds, and will pick up weeds the size of a pencil eraser in the field and then spray it precisely, but not spray the plant that's sitting next to it. And so it's really superhuman in terms of the way it acts. It's not something that an individual could do by themselves. And so we're asking the customer to put a lot of trust into the AI that, in fact, it will work because if it doesn't, and they get weeds in the field, you know, that detracts from their ability to grow a crop that's going to be successful. And so it's real money out of their pocket as well if we don't get it right. I mentioned data. We've had a really, really clear and I think compelling policy on data. It's simple. Customers own their data. We've created an ecosystem. Our main platform is called Operations Center. Some customers joke it's

the only app they spend more time on than Twitter. And so, you know, it's pretty highly integrated into their operations. And we've created an ecosystem where if the customer wants to share their data with even one of our competitors, they can. We've built APIs and the ability to connect so that they can send their data out. People can see, and their advisors or their other people that they trust can see their data directly. And so, you know, it's theirs. We keep it pretty simple and pretty clear and straightforward. We're not going to sell it to make profit on it. Yes, we're going to use data to make our machines better, to make our services better, etc.

Like we talked about in the customer success examples. But, you know, we're not out there trying to arbitrage the amount of yield that's going to come out of Iowa on the Chicago Board of Trade. It's just not what we're going to do. So I think those are the ones where I'd say it's the most mission critical for us to really have a strong set of values that we've articulated, that we've made clear, and then live up to it. That transparency is so key. Here's the values that we're standing by, and we're going to show you those values in action. If we say we're going to do something, one, it's really powerful to say, hey, here's how we were approaching this. And then two,

here's how we're showing you that we're actually living up to it. Because the proof is really in the pudding, right? It's like, how do we actually follow through? I'm curious, how do you lead a team to innovation? It sounds like you're doing a lot of innovating throughout John Deere. And I'm wondering if you have any tips or tricks for how you really inspire the people working with you to be thinking in an innovative mindset to really create something completely new. I might actually go back for a second to what I said about our company and its founding. So again, 187 years old. I think it's important when you think about driving innovation to honor the legacy of where you come from. Like, you know, we're all, our organizations, our teams, our products are a part of that legacy. And so it's good to start and say, look, we've always driven innovation. But when you're 187 years old and you're highly successful, you do amazing things. There can be some degree of complacency that sneaks into that. And instead of, I think, you know, looking for moonshots, you start saying, well, how do I make something 5% better or tweak it

around the edges, et cetera, et cetera. And there's a place for that for sure. But I also think there's a place to challenge the teams and say, let's make an order of magnitude 10X better, 100X better. And what I like about that is that it creates a mindset where if I asked you to think about how you're going to make, sell 5% more of something next year, your answer would probably be, you keep doing basically what you're doing. You do it a little bit harder, a little bit better, et cetera, et cetera. If I asked you to sell a hundred times more of something, you have to say, okay, well, clearly I can't just work harder. Clearly I can't just take exactly what I've been doing and try and scale it. You got to really start at the first

principles of, okay, what are my options? What am I going to do to best get to that goal? And how do I reconsider almost everything I've done? And that can be a really intimidating thing to tee up for a team. But if they understand the spirit of what you're trying to do, not that you'll be fired if you don't sell 10X more or 100X more, but the spirit of what you're trying to do, it's actually quite freeing because they get to say, okay, gosh, all these things, all these compromises I made,

all this stupid thing that I've done that I never really thought was valuable, I'm going to stop because I don't have time to do that. I'm actually going to redirect my time and effort and intention to something that's a bigger picture that gives me the opportunity

to really accomplish something that's a step change rather than incrementality. I'd love to hear about a recent experience that you had with a brand that left you impressed. Tell us a little bit about that experience. I'd probably go to Tesla, actually. So my wife drives a Tesla. And as you might know, they recently did a full self-driving trial. I was impressed. The technology had progressed a lot, you know, from what I had seen in prior years. But what impressed me so much about it wasn't just that they had advanced the state of the art of the technology. It's that I think is strategically

really brilliant as well. And so what better way to use AI to get more data for your system to continue to learn and improve than to give millions of users the ability to fully use the product, you know, for a period of time, ingest all of that, learn from that, continue to advance it, really supercharging what they learned, as well as, you know, a great experience for people where everyone I know that experienced it was like, I mean, this is pretty cool, actually. So I love things like that, that, you know, both are, again, great and really cool for the customer. But you can see there's a bigger picture for the company as well.

And you're kind of killing two birds with one stone. Amazing. And then my last question for you is, what is one piece of advice that every customer experience leader should hear? You need to be incremental. And what I mean by that is there's a lot of people that they think their role in the organization is to carry the torch of whoever came before. And I always tell people, there's a lot of people that can do that. There's a lot of people that can kind of keep the

lights on. What are you going to do that's going to be bold, that's going to challenge your team to improve, and it's going to help the organization make one of those step change leaps like we talked about. And I think if you can't point to at least some things like that, you know, you're at risk of kind of not being needed ultimately. So I like to challenge people, shoot for the stars, be incremental, try and make, you know, your company, your world a better place. Leave it better than you found it. Amen. Great advice. Thank you so much, Justin. It's been wonderful having you on the show. Thank you for sharing all of this knowledge about AI and

customer success and all the innovation that you're doing within John Deere. It's really inspiring. So thanks so much for sharing with us. Yeah. Thank you, Lauren. It's been a real pleasure. All the best. You are a business leader with vision. You've seen the future as an AI enterprise thriving with Salesforce's agent force, and it is bright. Getting there? Eh, it's a little fuzzier. Don't worry. Salesforce CTOs are here to work with you side by side and turn your agent force vision into a reality. We're talking expert guidance and implementation support from the best of the best. To learn more about Salesforce CTOs, visit sfdc.co slash professional services.