Catherine McDonald  00:04

A lean Yes, is about people and problem solving, but people create chaos and noise. So AI really is just helping us to take the chaos, chaos and noise out of it, but retain the people

Randy Kesterson  00:17

power. AI is not going to fix a broken process. So I think we need to start there. AI can make some suggestions and probably do so faster than than a human being can do. But it all starts with having a process before we apply automation, before we try to systematize it.

Shayne Daughenbaugh  00:36

One of the mindsets that I think is important is to see AI actually as a teammate like we’re working together. What does my teammate need to be able to do this?

Catherine McDonald  00:58

Hello and welcome to this episode of the lean solutions podcast. My name is Catherine MacDonald, and I’m joined by my co host, Shane duffenbaugh. Shane, how’s it going today?

Shayne Daughenbaugh  01:09

It’s going well, Catherine, thank you so much for asking. Thanks for having I am excited about today, as always. I’m sure this is kind of a, kind of a repeat every time we do this, I always say I’m excited, because I am. I love having the guests. I love learning new things, and I love talking about the the things that we’re passionate about in life, and how we can share that with other people. So it’s a good day. It’s a

Catherine McDonald  01:34

good we just record that and just play that every time. Just That’s brilliant. Shane, same here, same here. I absolutely am excited every single time, and today is no different. So, Shane, we have a great topic, a really a topic that’s just really hot right now. So just to let our listeners know, yep, the topic is smarter, leaner. Ai meets lean. Thinking, Okay, I’ve been dying for an episode on lean and AI, another one. I know we’ve touched on it, but I just I want more. So Shane, are you, I suppose, familiar with AI? How familiar are you? And what are your experiences with Lean?

Shayne Daughenbaugh  02:13

I am I am still learning. I am desperately trying to catch up with where AI is, because I think, as people say, and as our guest will probably mention as well, it’s a huge disruptor in every industry across the way. Currently, I use it in probably a pretty minimal way, like just, just recently, I used it to pull all of my sticky notes that I had this two day workshop, and I pulled the sticky notes, write it through AI, had aI transcribe it from the sticky notes into a digital format, into a list that actually put it in spreadsheet. And then I asked it to, like, summarize some of the themes that are found in those sticky notes. So that’s what I was able to share with my with the sponsors, after after the workshop, but I know that that’s just a small portion of what AI can do, especially for those of us that are used to whiteboards and stickies. You know, we’re hot on this. I love these,

Catherine McDonald  03:14

and I still wouldn’t skip this, the stickies part, it’s a people activity and lean and AI isn’t going to cut out that either, but the way that you use it then to do your follow up is brilliant, and we need, obviously, more of that. And our guest today will hopefully share his insights with us. Shane, would you like to introduce our guest?

Shayne Daughenbaugh  03:36

I’d love to All right, so today’s guest, ladies and gentlemen, is Randy Kesterson. He is a seasoned operations ex executive consultant and thought leader with over three decades of experience in manufacturing, supply chain, Lean transformation. Randy’s career includes executive roles at companies like Eastman Chemical and JM Huber Corporation, where he led significant enterprise wide process improvement initiatives. As a certified Lean Six Sigma Black Belt and skilled coach. Randy helps organizations achieve operational excellence through practical strategies grounded in real world experience. He currently serves as a trusted advisor and consultant, guiding leaders across industries in standard or, I’m sorry, in strategy, deployment, leadership, development and continuous improvement. Randy is passionate about empowering teams to drive sustainable change, and is a frequent speaker trainer and contributor in the lean and leadership communities. And we’re excited to have him here to share his insights and experience on today’s episode, which, as Catherine said, smarter and leaner. Hey, let’s talk about where AI meets Lean thinking. Welcome to the show, Randy.

Randy Kesterson  04:49

Thank you. Thank you. Shane, thanks. Thanks for that. Sounds like my mother wrote that introduction. So most of it is true, and I’ll try to live up to it.

Catherine McDonald  04:59

Oh no, it’s Eric. So fantastic. It’s fantastic. And one of the things that actually Randy we didn’t mention there is that you’re writing your third book. So you have to you two books already. And this third book, if I’m correct, is on AI, which is obviously you know why you’re here as well. So it’s called integrating AI into lean, driving, smarter, continuous improvements. Is that right?

Randy Kesterson  05:24

Exactly? Yeah, not too long ago, I couldn’t spell AI, but I’m very intrigued by the subject. I see it, as Shane mentioned. I see it as a tremendous disruptor, and I think we either incorporate it in our lives, much like the computer and the automobile and the email and all the other things that came before it similar disruptors or or we risk being less competitive with our, you know, fellow companies in our niche. So I think we need to incorporate it. We just need to incorporate it carefully. And the book I’m writing, what I’m trying to do is gather a spectrum of thought from those who are opposed to the subject of of AI incorporation and lean, and those who are the early adopters,

Catherine McDonald  06:08

yeah, great stuff. And just before we get into any of the questions, I you present just such a great, simplified understanding of the definitions of AI and lean, and you shared them with us earlier. I just thought, just, these are really good. Um, maybe can you just talk us through what your definition definitions are, just to set the scene for us, since we’re going to be talking a lot about AI and lean,

Randy Kesterson  06:32

absolutely. So I see, and this is, these are working definitions. I’m still testing them out with my audience and those experts that I’m that I’m interviewing, but I see AI as a set of tools that really, that that use data, increase the learning and help us with decision making. But it’s, it’s an enabler. It’s not a tool that, you know, that we’re going to be using exclusively to improve lean processes. With regard to lean, I kind of have a broader definition than some where there’s the narrow definition, but I think of it in terms of including tools and approaches like theory of constraints and agile and six stigma and other CI OPEX tools. I’ve spent a lot of time using organizational change management Incorporated. So I’m looking at lean from a broad perspective, but AI fairly narrowly with regard to how it can help us as CI op lean leaders.

Shayne Daughenbaugh  07:35

So how do you see these actually complementing like because we’re talking about machine learning and lean talks about people and focuses on that people so, so where do you see these guys actually complementing things?

Randy Kesterson  07:52

Yeah, I think your example you mentioned earlier is one. But what I found, personally, and I’m gaining other examples from those that I interview, some of the laborious tasks within a lean problem solving episode. For example, have you ever used a tool called interrelationship digraph?

Shayne Daughenbaugh  08:13

No. So it sounds way too complicated,

Randy Kesterson  08:18

very complicated, and much like things like Hoshin Connery. It has an unfortunate name, but it really works. So with a with a client in the last few days, well, let me drop back a bit. So a few years ago, if I used this tool, I would enter an organization, and you might have a group of leaders who were disagreeing on the problem to be solved, and each one of the 10, let’s say leaders would come into the room, and they all had their own idea. Was wrong, what we needed to attack. So you start with with chaos. We gathered data, tried to just put up on the wall all the problems that they were faced with. And then we’d use the tool you probably heard about it the affinitive affinity diagram, right? Just group those sticky notes into groups, right? So just envision it as like the positions on an an old analog clock from one to 12. And then we would draw arrows from those, from the from the cause to the effect. And by the end of that episode, it might take a few hours to complete that, depending on the complexity of the problem, we would have just a spider web of arrows, and then we would have to stop and try to count all the ins and outs. And after all of that, we would finally come up with a conclusion, which of those were drivers, potential root causes, which of those were effects? But the whole team had to sit there the entire time. Wasn’t much value gained in doing so, and we tended to lose the audience as a result. So recently, what I’ve been doing is you can incorporate AI either in helping to create that affinity diagram, or you. Stay with the older method of just having people group the post it notes themselves, but then the interrelationship digraph part, all the arrow drawing and the counting of the arrows can be done automatically. So something that might take two hours earlier this week took me about 15 minutes. And so you keep the audience in the room. You leave there within a very short period of time, understanding, okay, we came in with 10 or 12 different problems at a high level that we needed to solve when we’re leaving with right one or two drivers that are potential root causes. And so it really helps drive alignment, whether that’s a strategy session or whether it’s a session at the tactical level. That’s just one example. I found that where AI can be an incredible enabler to the process, improvement process.

Catherine McDonald  10:50

So lean yes is about people and problem solving, but people create chaos and noise. So AI really is just helping us to take the chaos, chaos and noise out of it, but retain the people part.

Randy Kesterson  11:03

Yep. Now I think that’s very well said. And I think the the people part, you know, a lot of what I try to do in my work and in my daily life don’t always live up to it, as I try to, to follow the the Shingo tenants of, you know, respect every individual and lead with humility. And so in in starting to write this book, I’ve been thinking about, are those tenants sufficient? And I think to keep the people engaged, I’m suggesting a third tenant. Don’t know exactly what it is yet, but preserve human judgment is the one I’m kind of zeroing in on now, so that we make sure that people are involved in the in the decision making, and you can see how this could, this could become an over reliance on automation if we allow it to be so. So I think it’s important to make sure that those, those tenants, or those foundational principles for Lean, are maintained as we introduce more automation.

Catherine McDonald  12:02

Yeah. And actually, in your lean definition, I picked out three words I really like. The first one is disciplined, because often lean isn’t so disciplined is really important, and the discipline part will help with proper use of AI as well. So disciplined. I like the word everyone, because that’s really important. Lean should involve everyone in the business and see people as experts in their place of work. And then consistent is the other one, because, again, that’s where Lean can fall down when we’re not making consistent efforts for continuous improvement. So yeah, really like those definitions, Randy, well done, right? Thank you.

Shayne Daughenbaugh  12:38

And you called that. I just, I just want to confirm again, because I was writing that down. Inter relational

Randy Kesterson  12:46

Yeah, interrelationship

Shayne Daughenbaugh  12:48

diagraph, interrelationship diagram,

Randy Kesterson  12:51

yeah. It’s a tool that was developed, I believe, in Japan, decades ago, and it was one of the fundamental quality tools that was in use, and it’s fallen out of favor, I think in large part because it’s just so laborious to work through, but it’s extremely effective in in identifying root causes or potential root causes.

Shayne Daughenbaugh  13:11

Yeah, yeah, other than that example you just gave with using that tool, having AI, use that tool and create that for you. Do you have any other real world examples that people might be a little more familiar with? How lean has significantly or how AI is significantly improved a lean process?

Randy Kesterson  13:31

Yeah, that’s one of the questions I asked the interviewees in my book. And I’ve got a long list that I’m I’m hoping to to summarize a bit and be able to clarify, but one that comes to mind is an example of if you’ve ever tried to plan or schedule a job, shop, manufacturing environment, AI has been a huge enabler in that regard, because people can only go so far. People have attempted to use heuristics and so forth in the past, but AI has really helped not necessarily come up with a perfect answer, because the next day enough of the variables change, the answer on Tuesday is not the same answer on Wednesday, but at least directionally moving the organization in the right and they’re on the right path, which helps reduce The whip level. It helps increase throughput in the business. It helps increase velocity, all those things we’re looking for in a in a manufacturing environment, complex manufacturing environment, where it’s not process orientation, it’s more of a job shop, small lot, batch environment, interesting.

Shayne Daughenbaugh  14:39

Do you have a you said? You have this long list of things that people are saying, Hey, this is how we have used it. Is there any out there that that really surprised you? You’re like, wait, what I can think of right now.

Randy Kesterson  14:54

There’s, there’s nothing that really surprised me. Most of the applications so far are those that. That eliminate administrative effort, you know, and so the same things that I think you and I are doing now, there’s some suggested applications that people are starting to test out, but really, most organizations are fairly early on. There are a lot of skeptics and so forth, and I’m old enough to remember some of these disruptors that have occurred in the past. So like when the computer was introduced at the at the laptop or the personal computer level, it was viewed as a a something that might be able to assist in certain applications, but people, right never envisioned, you know, where we are today. So I think the same thing will happen with AI, where some of the early adopters are just on the on the fringes. But I think 10 years from now, we’ll all be amazed at some of the uses of AI and within lean and really our entire work environment,

Shayne Daughenbaugh  15:55

right? And I love you. Sent us this slide for the listening audience that is not watching it. You know, what do these things have in common? You know, electricity, personal computer, telephone, railroad, automobile, AI, radio, aviation. And the thing they all have in common, other than being really cool, things that we’re all used to now, was most people didn’t see them coming. You know, like you mentioned about the personal computer, personal computer and and, you know, the internet is just another passing, fade and passing fad that our man Bill Gates said, like, seriously,

Randy Kesterson  16:32

I’m old enough to remember some of these in the early phases. So, like, the internet, you know, I never would have envisioned that it would become what it has become today. Same thing with the personal computer. People did not envision everyone having a computer in their home, let alone in their in their pocket, right? And I also have seen the evolution with the telephone over time, where what it was when I was a kid, where it would phone hanging on the wall and you had one in your house to where we are today. So I think AI is a similar disruptor. And I think no one can predict where it’s going to be, you know, 1020 years from now,

Shayne Daughenbaugh  17:11

right? It’s crazy. It’s crazy. Shoot, I don’t even know if we can predict where it’s going to be in, say, six months from now. Like I keep hearing those that are, you know, doing it, saying, hey, it’s you haven’t even seen what this is capable of yet. And it’s still, if we can keep it from like eating itself and just like feeding on its own, you know, language models and continuing to spin it a little circle. But, yeah, all this is, is amazing stuff to see how we can use it. I’m really excited.

Catherine McDonald  17:43

Absolutely, yeah, definitely. Okay, so we don’t know, I suppose, is the answer to where we’re going to be, what’s going to happen with AI, what’s going to do, what’s how’s it going to affect jobs? We don’t have answers for all of that, so a lot of it is wait and see. So let’s talk a little bit more about how it’s impacting the workplace right now and again, back to lean, and how we as lean practitioners can use AI, how we can introduce it to our customers and clients, because we really do have to get up to speed with AI. I mean, we really do it. We have no choice, and a lot of the time as consultants or as business owners, we talk about waste within our processes. That’s a big part of Lean is trying to reduce the waste through trying to reduce the inefficiencies. So where can AI help us? There? Randy, do you think

Randy Kesterson  18:39

I think in my personal life, and I think many share the same perspective when I’m writing something, whether it’s a chapter in a book, or whether I’m writing a memo for the board of directors, or whether I’m writing something I want to distribute to the employees. I’ll draft it in the old olden days, I would show it to my wife, and she would edit it for me and and there would be seven or eight words preserved from the original draft. But she’s no longer willing to do that for me. So now I turn to AI, and I submit the draft, and I say, please, you know, light rewrite, and invariably, it comes back with a little bit, you know, fewer word content. So I’m getting the message across in in fewer words. I’m doing it more succinctly. And so I think I benefit because it saves me time. My wife benefits because I’m not asking her to do edits for me. And I think the receiver benefits because there’s more clarity in in the messaging. And so I think just simple things like that free up time for people and leaders to do other more important work.

Catherine McDonald  19:49

Yeah, that’s a really good example. And actually, they’re the kind of examples I hear talk people talking about most at an individual level. There’s a lot of people using AI to pop in their COVID. Content and analyze it, and, you know, cut out the thinking and the writing part. I’ve also seen teams using it for the likes of when you upload a Value Stream Map, for example, and you ask AI to actually analyze the value stream map and come up with the bottlenecks, and then not just that, but come up with the some solutions to the bottlenecks, and not just that, but come up with a once you feed it more information, a plan, you know, for the next 3060, 90 days when it comes to this process. Have you been involved in that kind of work, or seen that happen anywhere? Randy,

Randy Kesterson  20:33

yeah, just in the last two weeks, we had a fairly elaborate swim lane map for a complicated process, and we asked AI to take a look at it and make the recommendations that you listed. And what I found was it came pretty close, but without enough context, some of the recommendations were not quite on the mark. So I think at some point in time, it may be capable of doing this without as much human intervention, but I still find that that there needs to be a human reviewing the output, making some clarification. Sometimes it’s a matter of providing additional input so that the AI application understands the environment that we’re talking about, and what are some of the constraints, and so forth. But right now, there’s still a need to really review that carefully, because sometimes it’s just not, you know, it’s not correct. So it helps, it shortcuts the time and the process, but it doesn’t replace the human mind,

Shayne Daughenbaugh  21:33

which is good. I’m really glad that you brought that up, because I was going to ask that, because there are going to be some people, some of our listeners here who you know are, I don’t want to say old school, but traditional enough that, hey, AI is it does its thing. Okay, it crunches numbers and looks for patterns. But really it’s human mind and the human experience that lean really thrives in and that makes it possible. But are there some what? What kind of other misconceptions might leaders have in regard to being able to integrate? Ai, like, this isn’t something you can just like, hey guys, give me all your stuff. We’re going to throw it in this little magic machine. We’ll come back in about 30 minutes, and all our problems will be solved.

Randy Kesterson  22:17

Yeah, very good question. So I, am a get the process right, first kind of person. I’ve always been a skeptic with regard to systems and automation, because I’ve seen it go wrong, sure more times than I can count, and I think AI falls into that same category. So I think AI is not going to fix a broken process. So I think we need to start there. AI can make some suggestions and probably do so faster than than a human being can do. But it all starts with having a process before we apply automation or before we try to systematize it, right? So I think that, for me, is fundamental misconception is AI is going to replace the CI, op, ex, lean person? No, I think they will enable their work and help them do their work, you know, faster and better, but they’re not going to replace I think a misconception that some have is about AI replacing people. And I think, having seen the computer come along, AI, I think will be just like the computer, where, when I was a kid, you’d have room full, rooms full of people with adding machines, right, right, adding pages of numbers. Well, the computer replace those jobs didn’t replace the people. Those people had to find other activities and other jobs. But I think the same thing is going to happen here, where there’ll probably be elements in companies and manufacturing companies and so forth, that those jobs will be replaced, but those people will still be needed to do other activities within the organization.

Shayne Daughenbaugh  24:00

Yeah, yeah. I see, you know, I see it kind of like how I have when I worked for the state of Nebraska, or, you know, any client that I have now, when we’re looking at improving a process, and maybe it’s a process that Catherine is heavily involved in, and when we map it out, we find out that, you know, 75% of the map are tasks that Catherine does, and we’re able to cut that down to say 30% of the map is now tasked. That doesn’t mean that Catherine isn’t, you know, a valuable team member. What we just did was we just freed up space for Catherine to do other valuable things. Right? You know that that need to be had. So if AI can help us shrink down this task that that’s typically and you can correct me if I’m wrong, but I have yet to find anyone that says I only do one thing. And you know, there’s and there’s never a problem. I never have any backlog. I don’t have other jobs or other tasks that are been assigned. You. You know, it’s, it’s always, hey, we’re trying to free up time for you to get more value done than you’re able to do right now. More valuable work. Value Add work

Randy Kesterson  25:11

absolutely. But another application I’ve found with AI is that a lot of times the the continuous improvement OPEX lean person is fairly narrowly focused, and what I find is there’s there’s work going on. Let’s say it’s on the factory floor to improve a process. But when you step back and look at the organization, the problem is in the business operating system. So it might be in on the demand side, it might be that there’s not a working SIOP process. It might be that the ERP system is not working effectively. It might, you know, there’s a long list of things with with AI, if you provided enough data, of all the problems that are taking place within the organization there. There are very few, probably known human beings who have expertise in all elements of a business operating system, it at least identify, Hey, these are some places you probably need to look because back to the root causes again. If we’re working on something on the factory floor and improving process X, if that’s not really a root cause of the business problems, whether it’s capacity, quality, safety, cost, you know, on time, delivery, then we’re really not helping the business to a large degree. So I think it’s, it’s a valuable way to do a quick scan of, where should we start?

Catherine McDonald  26:34

Yeah, yeah. And I think there’s a bit of a barrier here in a lot of organizations? Well, definitely, a lot of small to medium organizations in terms of data that they can use, you know, clean data that they can, let’s say, put into AI models to actually get what they need from it. Because a lot of organizations are not actually, let’s say, collecting data in the place where work happens, they might have a process, a step by step process, to be clear on what that is. They don’t know day to day, hour to hour, what’s happening within that process. So even trying to get to the point where you have a Value Stream Map is difficult for some organizations. Now we tend to talk about Lean and these tools, like everybody’s using them, but they’re not, you know, so we do have to, I suppose, remember that AI will help us, but we still have to do the basics in terms of setting up our processes and setting up our data collection methods to get the information we need in to enable AI to use it. Is that right? Yeah,

Randy Kesterson  27:35

no, absolutely. I most of the clients I work with now are small to medium sized companies, and every one of them suffers from the lack of accurate data. And so what I think AI can help with there is, let’s say a client is struggling with on time delivery, and they don’t know why, and they don’t have the data to be able to produce the models that will allow you to understand what’s happening, what AI will do is it will suggest some some equivalence. So let’s say, if you don’t have labor hour information, it will suggest revenue as a proxy. And so then it’s okay, it’s not 100% accurate, but let’s use revenue as a proxy for supply versus demand so we can see, are we running into a mountain? Are we going to go off a cliff from a load versus capacity standpoint? And and use that data to do some high level predictions, where, if you don’t have the data to do it accurately, at least direction, it’ll help you understand is September going to be loaded, you know? So we can begin to take some actions ahead of time. So I think, I think you’re right. In most cases, the data is not available to do what we’d like to do, and so you need to find other, you know, proxies For the data that you’re you’re trying to use. I

Randy Kesterson  29:20

Yeah, right,

Speaker 1  29:39

yep, so

Randy Kesterson  30:04

Yeah, no, I agree. And I think for the leader to admit they don’t have all the answers, you know, AI with AI or not, that’s that’s a good place to start. But if you’re trying to solve a problem together, and you’re using AI as a tool to solve that problem, especially as you mentioned, AI will come up with a number of suggestions, and several you can probably dispense with right off the bat, because they don’t fit the situation, right. But invariably, there might be one, two or three that do make sense. And so the team can select from that. And I think that’s a good way to build some ownership, because they then, okay, this is something we selected. It’s got a probability of working, or the things we’ve tried in the past have not. So I think it’s a good step forward.

Shayne Daughenbaugh  30:49

And I think one of the things that people may not understand that what you guys are talking about, or what we’re talking about here, but specifically some of the things you guys talked about in regards to, hey, how do we solve this problem? It may take a team a while to figure out how to get AI to do what it wants to do, or what they want it to do, like I’ve, I’ve been working with AI this week, just trying to understand it and find better ways. And I’ve spent, I mean, I don’t want to say I’m embarrassed to say this, but I wish it wasn’t quite as long, and I it’s user error, but it’s taken me two to three hours to figure out, you know exactly, hey, this is what I want to do. How can I get you to understand what I want to do? What information, what data do you need to have to be able to do this? And it took some iteration, but one of the one of the mindsets that I think is important is to see AI actually as a teammate, like we’re working together. What does my teammate need to be able to do this? Now it’s not a person. It’s not, you know, I don’t want to anthropomorphize it or anything like that. I’m not going to take it on a date and go get ice cream. But if I think about it as a teammate. That’s different than if I think about it as a tool, because as a tool, I’m just gonna be I’m gonna throw things at it. I’m gonna get frustrated because it’s not making what I want it to make. But if I think of it more as a teammate, okay, so let’s, let’s go back and forth and what more do you need for this? Here’s what I want. Here’s an example. How can we get this that I have found, while it takes a little bit of time, but once you have that, then you know, just like the process of trying to come up with a really detailed SOP, once you have it, like it took a while to get that and to confirm that this is all the steps we need once we have it, though, now it’s So much easier for everybody moving forward. I think that’s that’s something that people need to, that I needed to, when I say people, I mean shame to be

Randy Kesterson  32:48

aware of, yeah, very good example. So I think of the AI as being similar to the internet. So when I was a kid, if I wanted to know anything, I would either look in the encyclopedia, which is always come on now or go to the library and research it right when the internet came along, it gave me a place to go for information. It wasn’t, you know, it was faster, it wasn’t always 100% accurate, but it gave me some where to go for some information that was at hand. I see AI as being just one step beyond that, another disrupter that that provides the next level of information quicker and oftentimes better. But again, it’s it needs to be tested, because just as information on the internet is not always accurate, the AI recommendations are not always going to fit the situation, at least in 2025

Catherine McDonald  33:44

very true. And also, so you asked about the misconceptions Shane like, so definitely, definitely, that’s a, I suppose, a really good list of misconceptions on what we can do. And I think we touched it in there. And another one, and that is this whole area, this whole area of AI in organizations. It’s not the responsibility of the, I suppose, the IT team. Do you know it is the responsibility? We need to get everybody involved in it. And if we, if we think that it just sits with the IT team, and we don’t start bringing people together from all of the different departments and front lines, I think we’re going to end up with a problem, so we have to make sure that, I guess, that misconception gets wiped out as well in terms of specifically how AI is implemented in organizations. And have you seen anybody running into that problem? Or I know,

Randy Kesterson  34:43

the the people that I’m interfacing with now are not far downstream. They’re still experimenting on an individual basis. A few are starting to talk about policies around AI, but they’ve not yet implemented them. So that’s, I’m glad you mentioned that. Because that’s another good question for me to ask the people in the interviews for the book, because they’re very, I’ve seen very few early examples of that, so, you know, excellent thought. But

Catherine McDonald  35:10

I’m just, I’m just a little bit concerned about it, because I do. I chatting with my friends at the weekends, and we’re always talking about AI in the in your company, and in our company, and what’s happening. And to be honest, they don’t really know what’s happening. They sort of hear, Oh, AI is coming in, but they’re not as involved as they should be in the discussions about how it’s affecting their work. So I just think at an early stage, just like lean, when we start talking about lean, and that that lean principle actually fits in with AI, rather than we’ve been talking about how AI fits in with Lean, but it’s also goes the other way around, that we need to bring in that principle of respect for people into our AI efforts hugely as well. I think

Randy Kesterson  35:49

exactly. I’ve been in senior roles in many companies, and what I found is that leaders oftentimes don’t want to admit they don’t know something that can have to do with Lean, that can have to do with a number of things, but I think AI fits in that same category. So I think it’s the danger is that the folks in leadership roles aren’t asking enough questions, you know, admitting, hey, I don’t understand this, so that there could be some problems within the organization as a result of that. So, you know, just as many leaders don’t really understand how Lean WORKS, and they’re afraid to ask, you know, we could be in a similar situation here.

Shayne Daughenbaugh  36:30

Yeah, yeah. So, so I have, I have spent hours just watch, watching YouTube videos about how people have used AI and and prompt crafting and trying to understand, not just how can I get better LinkedIn content, but really, how can this help me in my daily task? What? What tasks do I that I have that I can then use my teammate AI to do? But yeah, focusing on just watching all kinds of videos. But what I haven’t seen in these videos is how to maintain that, that kind of human aspect, like you were talking about Catherine, that that there is, there is that human centric aspect and values in Lean, you know, how can, how can organizations, as they’re moving into this, starting to understand, you know, The power of AI and what the possibilities and or possibilities? How can they maintain that human centric value that are so vital to to what we believe in? In regards to lean?

Randy Kesterson  37:31

Yeah, I think that’s the people I’ve talked to so far who have a fear of AI. Their concern is, rather than go see you’ll ask AI to study the data and make some recommendations based on that. So the people who are now on a GEMBA walk and the people who are actually out talking to people and seeing things firsthand, the concern is that will cease, because there’s an easier way to gather that data. And so I think that I don’t have an answer to that yet. That’s one of the things I hope to capture as a result of the interviews for the book. But just what are some of the concerns, what are some of the dangers, and then what are some of the mitigation strategies we can do to prevent those things from happening? Because that’s the that’s the easy path, and it’d be very easy to just sit at your desk and and let AI gather data and make those decisions for you.

Catherine McDonald  38:23

Yeah, I read it. I read a quote somewhere. It was went along the lines of, make AI the enabler, not the hero. And I think that’s a good mindset for leaders when we’re, you know, working with teams and people is just to talk about AI as an enabler. Would you? You know, this is you. It’s your work, you know. So we’re not romanticizing it, you know, maybe as much as we are at the moment. Um, just one last question, because we’re coming to the end. Randy, we kind of have talked all about AI without you, without mentioning AI tools or things that we can you ate like, I know a lot of people would be familiar with chat GPT, but there’s literally hundreds of them out there. And people have told me, you know, perplexity is great. Claude is great. Shane, what are yours? What are your go to

Shayne Daughenbaugh  39:13

directly Claude and chat GPT, right now, those are my

Catherine McDonald  39:17

three. So people are tending to focus on these few narrow, I think, tools. So where, how do we get beyond that and you start using a broader set of AI tools?

Randy Kesterson  39:27

So very good question. I asked my AI tools if I should be using others, and they’re telling me no.

Randy Kesterson  39:39

Honestly, I I did because I wasn’t getting the response that I was asking about a Microsoft product. And I got a, I got a, a fairly stern reply back, you know, with regards so but no, I I’m using a small set. I would like to explore more. I have. Some friends who are are exploring other tools, and so I’m going to be talking about that a bit in the book. Honestly, I’m trying to stay away from some of the tools, because I know to try to keep the book relevant for a few more years. I’d rather not be tool tool focused, because I know it won’t be long before some of the tools we’re talking about will be, you know, displaced by others,

Catherine McDonald  40:24

yeah, and I think a lot of the tools are industry specific as well. Like in manufacturing, you’ve got companies out there selling just AI sensors and whatever for machines, and you’ve got, you’ve got very industry specific that we just most people wouldn’t hear of because it’s not in their industry, and then you’ve got all these different AIS doing customer bot, bots, customer service, you know, robots and calls and things like that, chat box and but so many different companies are selling these and providing these. And we just, there’s just so much at the moment. But, yeah, I think it’s good to try and keep on top of and Shane, you should ask your, I’m going to ask my chat GPT, you know, the same question Randy asked, Did we get the same answer? That’d be worried,

Shayne Daughenbaugh  41:10

right? So, so in, in that bigger thing. So, you know, Randy, you have this sentence that I, I don’t know if this went through chat. GPT, I’m just going to say, because it is a mouthful to say, but I wanted you to speak to just as we’re wrapping up here, just some of the things that, some of the transformations that chat GPT gives you know you you have this the I don’t know if this is in the book. I’m assuming it’s in the book, or in a book, or will be, but most people don’t know that they’re living through. This is where it gets me a game changing, disruptive, revolutionary, paradigm shifting, transformation like that’s huge of a sentence. But what does that mean? You know, instead of looking at the tools, what might be some of the things in how, how might AI transform so many things. And let’s just, we’ll, we’ll just talk about, you know, lean. But is, is there just some, some little, quick things that you can grab and say, hey, it’s going to be a game changer because this, or because this, or, you know, whatever.

Randy Kesterson  42:12

This is a perfect example of the use of AI. So I just, I said, we’re going through a transformation with AI. What are some other similar terms that you know might be used? Well, this is a short list of what AI came up for me, tongue in cheek. I came up with the the title for the slide. But it really is these words apply where you know whether you call it a breakthrough or a disruptor or a revolution. That is what I think AI is representing today. And if you really look back a bit, AI started years ago, just spell check and some things that right existed, you know. So there are early, early applications. And now we’re, you know, expanding the field a bit, but I think all these words apply. You can pick the one that best fits your situation, in your environment, but we’re in the early stages of it. And I think having lived through the computer transformation and the telephone transformation and some of the others, you really don’t know you’re in it until you’re kind of looking back at it, and you know, in retrospect, you see, Wow, it’s amazing how much changed in the last next years,

Shayne Daughenbaugh  43:27

right, right? And I’d love for you to wrap up with this, as I don’t know if this is a warning, if this is an encouragement, but your options for AI as a lean continuous improvement, op X leader, you you list three options.

Randy Kesterson  43:48

I, I think this applies to any of those transformations we talked about. So the the early the early non adopters on the automobile were the airplane or email. You know, same situation. So if you’re a leader in Lean, continuous improvement OPEX, and you ignore AI, you honestly, you yourself and your organization have the risk of becoming irrelevant. Yeah, you can fight it. I’ve seen people fight the computer. I’ve seen people fight, you know, all the things that are listed on the transformation slide, but you’re just burning energy and or you can choose to be out in front and lead it and help shape the future of OPEX and make sure that we’re risk mitigating the things that could lead to, you know, a dangerous impact to the organization and to lean but I think those are the choices right now. What I’m choosing to do is, number three, I can’t do it by myself. So I’m trying to understand when people who are smarter than I am with regard to AI and lean you know, what can we do? What shouldn’t we do? And what are the first baby steps you can take to incorporate AI into. To lean in your organization.

Catherine McDonald  45:02

I love that. I love that you’re so passionate about this topic, Randy, I love that you’re learning and sharing that learning as you go. Because I think that’s just so, so powerful. You’re not coming from the perspective of somebody who, as we said earlier, says they know it all and tells people what you’re actually just learning and sharing that learning and obviously using all of the experience and wealth of experience that you have from your previous work in organizations to do that. So we’re all really learning. So I think that it will just be so helpful to everybody. And thank you so much for sharing all of those insights. If anybody wants to keep in touch and follow you and follow your learnings, and obviously, by your book, where can they find you?

Randy Kesterson  45:45

Yeah, probably the easiest way is, I’ve loved to connect with people on on LinkedIn, so please send me an invitation. My website is kestersongroup.com just my last name group.com, either one of those will work just fine,

Shayne Daughenbaugh  46:02

okay? And we will make sure we have those in the show notes. Okay, very good,

Catherine McDonald  46:07

though, definitely. So thank you and Shane, thank you, as usual, for the wonderful questions, and we will wrap it up there so we will say goodbye to our listeners, and we will see you next time on the Lean solutions podcast. Bye, for now. Have a

Shayne Daughenbaugh  46:19

great day. Bye, goodbye