Explore a transformation roadmap approach organizations are advised to follow to bring AI to life.
Service leaders traditionally have focused on managing service efficiently, including parts management, dispatching technicians etc. This has created a good deal of useful historical data, but much data about how machines are being used is not captured. Furthermore, the questions a technician needs answers to once they get to a customer site are inherently different. Service events offer an opportunity to completely transform customer relationships and better capture expert knowledge for a smart long-term AI strategy. Chris Joynt, AI & Analytics Translator gives his perspective to Chris MacDonald.
Welcome to speaking of service, the podcast that uncovers practical ways to grow service revenue control costs and improve customer satisfaction. If you're looking to innovate, gain a competitive edge, or just learn about the latest service trends, you've come to the right place. In today's episode, Chris McDonald, head of AI and analytics sits down with Chris joint, AI and analytics translator to discuss how to completely transform customer relat. And better capture expert knowledge for a smart long-term AIChris MacDonald:
strategy. Welcome to the show today. We're gonna talk about how a smart, connected product strategy can enable a company to rethink their relationships to their customers and how their customers interact with the physical devices they design, manufacture and often provide service to in the field. Fundamentally. A smart, connected product strategy is an opportunity to data fly the entire process by which we interact with our customers and how we service those products. It's an opportunity for us to say, how can I capitalize on this data? How can I better manage it? How can I better collect that data? And at PTC. We are enabling the digital thread for our customers, fundamentally bringing together how our customers design, manage the product life cycle, how they manufacture and how they bring a product into the field and service that. And by bringing those things together, there is tremendous value to be unlocked from data and AI and thinking about it strategically and practic. So today I'm very excited to welcome Chris joint, an AI and analytics translator. Let me start by, um, letting Chris introduceChris Joynt:
him. Thanks. Happy to be here. Um, my primary skill is as a communicator. So what I do is I work with customers and partners to translate those technical requirements and data needs and data requirements into, uh, coherent business value. I've been in AI and machine learning for my entire career. And like you said, uh, I'm really passionate about helping our customers not only unlock the value of their AI use cases, but you know, to get on that journey. Right. And, and get on that virtuous cycle, uh, and, uh, you know, take the next step in their digital transformation journey. Awesome. Well, thanksChris MacDonald:
for joining Chris. Let me start things off by asking you. About what we hear in market about getting closer to customers as part of a smart, connected product strategy or, or developing that intimacy often tied to, you know, moving up the value stack, so to speak, they're kind of, you know, lofty business terms, but what does it really mean in a smart, connected, uh, product strategy to create that customer intimacy, uh, when it comes to the kind of data that we have access to. as well as really being able to, to unlock that better customer relationship as aChris Joynt:
result. Sure. Great question. I think it's, it's really all about strategically embedding yourself deeper into your customer's operations, right? So that you know, their processes and flows, which are dependent upon, uh, you know, the, the equipment that, that you are providing them, uh, that you are, uh, you know, understanding. You know what they need to utilize your machinery most efficiently and, and ultimately drive their own bottom line. Right. Um, so embedding deeper means, you know, we're no longer at a point where we say, oh, look how much data we can collect and we can dump it over here. If you wanna go sort through it and do something with it, it's understanding those, those flows. And how the equipment is used and providing insights that are really useful for, you know, an operator, uh, or, you know, somebody who is, uh, you know, trying to make process improvements, uh, and, and providing those insights.Chris MacDonald:
If we take the example of say, moving from reactive to predictive maintenance, a common thing we, we hear in market, um, all the time, often, not with a lot of definition or understanding of feasibility of what that means. If I think about starting a smart, connected product strategy, I think about the data that I have available today, the data I may have available tomorrow and driving after that strategy. What's some advice you can give, um, to our customers, um, and listeners that are beginning thisChris Joynt:
journey. Yeah. Great question. I think a lot of times when we're talking about predictive maintenance, the, you know, knee jerk reaction is to. Well, we just need to predict when we're going to have a failure event, right. And then we can, then we can transition to, to proactive. And there's actually a lot more to it than that, right? If that's the way that you're thinking about it, you're missing out on a big piece of the opportunity. And a big, big piece of the opportunity is let's optimize our service visits so that when we're there, we're providing, providing maximal value to the customer. But we're also. Mindful and, and collecting the right data at those customer touchpoints. Those are customer touchpoints, uh, and, and they're very valuable for using them the right way. Um, so things like, you know, uh, maintenance machine maintenance, logs, uh, you know, we don't want to just have valuable information in kind of free form text. In maintenance logs anymore. We want to structure our applications and processes so that we're asking customers the right questions and mindful of what are we going to do with this in the future? How do we transform these interactions so that we're, we're ensuring that the customer is getting what they want out of. Out of the relationship with our company. Um, but also that we're learning something of value, right? Be that about how the machine is utilized, how the machine is, is performing, you know, what are, what are common causes of, of problems and just what are operators have to say about it? Right. Those are really great opportunities to capture information like that. If we can transform service.Chris MacDonald:
No, that's, that's uh, fantastic insight. So if you think. The process, like you said, it's not just about, you know, the, the product information or the device information. It's about how a customers interacting are those touchpoints with the product and thus with the company and add to that, the process of servicing and providing that service, um, you know, on behalf of a customer, if I think about data frying, so to speak that entire process. And in particular, I think about the knowledge that exists with an experienced personnel or service technicians. What should I think about in terms of the process, the service technician, the experience service technician and the data that I'm collecting as a service organization and what kind of business outcomes can that enable?Chris Joynt:
Sure. Uh, so that, that can do a lot of things for you that can actually get you to a point where, you know, you can really turbocharge your predictive maintenance program. You know, for example, just understanding something simple, like, you know, my experienced technicians, right? Maybe I want to look at, you know, who has the best, first time fix rates, or I want to see where do I. Pockets where I have better meantime between failure metrics that matter. Right? Take your, take your pick. Right? I'm gonna look at something like that. And I'm gonna say, okay, you know, what is unique, uh, in, in these situations, you know, and, and take a closer look at those service visits. What are the diagnostics that they're running? How do they know which diagnostics to run when they first, when they first get there? Um, those are the types of questions I might want to ask. Um, that'll give me a lot of information. That'll help me make better predictions, uh, so that I can, you know, cascade that knowledge to the rest of the organization, but also open the door that in the future, I may be able. To, uh, you know, offer greater automation and actually do some prescriptive analytics, right. Where I might, I might have enough confidence in those, in the output of those models that, you know, I can, I can put in, uh, work orders that are in automate work orders to have, you know, very specific tasks carried out. Uh, and it'll, it'll be, uh, you know, you know, a big part of that is, is making sure that I gather the right information. And so much of that is. In the technician's heads, right? We, so we have to figure out where do we wanna look, how do we capture those things, uh, and, and kind of make that a bigger part of our data strategy.Chris MacDonald:
Absolutely. And as you know, at PTC, we're enabling the digital thread, so to speak for our customers. Can you give me your perspective on if we can really enable a true digital. From the design of the product to manufacturing it to the physical device existing in the field, giving that, that product, a voice in terms of service and operation in the field. If we can really have a full sense of that digital thread or a customer can have that full digital thread. what is that unlock in terms of the opportunity for data and AIChris Joynt:
moving forward? Oh, it's, it's it's big time. Uh, but it's also a challenge, right? Mm-hmm um, so, so, so the opportunity is I think, you know, closing the loop between, uh, service and, and, and engineering, right? So, so finishing that life cycle and getting, you know, data. From the actual piece of equipment in the field, back to engineering who, who designed it, right. Who knows how these things should be, should be working together, um, that can help them in a big way to accelerate the design of version two. Right. Um, and when you're doing that, here's the challenge of it when you're doing that. The challenge of it is if I'm going to design version one, so as to teach me how to make version. I have to be mindful of what data I'm going to be collecting. Right. And I have to bake that into my process so that I can kind of build these, these fast cycles of problem solving loops. Uh, and, and I think that's really the holy grail and that's where we're going. That's where we want to take our customers with a digital threat is being able to connect all of that back, uh, and, and, you know, complete that loop. If you will complete that. SoChris MacDonald:
we at PTC have been on a journey like our customers. And as you in particular know very well about what it really means to, to adopt a mindset, a data first mindset, um, and an AI centric sort of way of thinking about how to solve problems. And, and for us, it's really an applied AI and analytic strategy. Thinking about. What our customers do, how they leverage PTC to, to design and manage the process of the life cycle and connect to their devices. So we think about it from a very, you know, solution oriented lens, but you, um, are someone that has been educating, especially PTC, uh, with an AI academy, um, and scaling that knowledge. I'm wondering if you can gimme a sense of if I'm a service. What elements of that sort of education is necessary as sort of an end customer, someone who might be leveraging solutions that take advantage of AI. And as I try to embed a more data centric, even AI first approach into my service strategy, what are some of the things that I need to be aware of and that I would want to educate myself and my organization on?Chris Joynt:
Sure. I think the, the first thing you need to know is that as far as AI is concerned, Uh, you know, data is, is data, the AI itself doesn't know anything about your equipment or your machinery. It it's reading the, the data. Right. Um, so what that means is, um, you have to be very specific. A lot of times we talk about, uh, you know, technology and we're thinking about the functionality. Of applications and the experience of applications and all that's great, but we also need to think about every click, right. That we're expecting a user to make. What, what decisions are they making and what data is that creating for us specifically? Right. Um, and it's, it's sort of a bit down in the weeds, but we have. We don't necessarily have to have executives themselves deep down in the weeds, but they need to understand that they have to be mindful about what data is being created, uh, you know, by these applications because that's, what's going to feed the AI. And when it makes a prediction, uh, if it makes a prediction about, uh, you know, in, in error code, that was, that was, uh, Thrown off by a machine, right. It's gonna make a very specific prediction about that error code. If it's going to make a prediction about a part replacement that was ordered, right. That, you know, we have to be mindful of, that's actually different, right? um, that means something different and there's space between those two things. Those are the kinds of things that service executives have to be mindful of, you know, as, uh, you know, when they're thinking data. Right. And they have to design for that and not just design for, uh, functionality and the experience. They have to think about the, the data as well. Um, and you don't always know exactly where that's going to lead you, but start with good data now about the things that matter to you, you know? Yeah, absolutely.Chris MacDonald:
As we close out our conversation, I want to ask. Um, about a concept that you and I, and in the data science and AI community, we talk about the, the virtuous cycle, so to speak. And I look at that as at the, the center of the potential value that can be unlocked between a good data strategy, um, and a solid AI strategy is all built around this concept of a virtuous cycle. Yep. Can you translate for business executives, what a virtuous cycle is, right. And what it means to a, uh, service organiz.Chris Joynt:
Sure. Uh, so a virtuous cycle in, in the AI world means, you know, if you build a useful AI application, it's making good predictions that people trust. They're going to use it more when they use it. They're going to create more data. That data is going to improve the AI, right? Because AI is thrive on, on data. So that gives you the opportunity to, to do 2, 1, 2 things. Uh, one is, you know, build AI that get more and more accurate as they learn more and make better predictions as they learn more and more about the things that you're already predicting, but also, you know, accumulate data. To make associated associated predictions. Right? So for example, in our, in our service use case, here we can, if we build the right applications, we can go from making predictions about when we're going to have error code X, Y, Z, um, to then accumulating data and getting, you know, more and more accurate about that. But also then being able to, uh, predict, you know, what a, uh, first time fix rate might be, you know, on, uh, assets, when they have failure code X. Right. Um, and that's the virtuous cycle. Uh, you have, and there's an organizational aspect to that as well. Right? When people begin using it and trusting it, and you know, they're finding efficiencies and they're, they're running their part of the business more, more efficiently. They're going to be hungry for, for more data. And they're going to, uh, you know, interact with the AI better and they're going to take their own data quality seriously. Right. And they're going to be involved in that process. Um, so it very much is a virtuous.Chris MacDonald:
Well, Chris join as always. It's awesome to hear from you. Um, and I'm sure our listeners appreciate the insights you provide. Thank you very much for joining us today.Chris Joynt:
Happy to be here. Thanks.Announcer:
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