GA 307 | The Role of Automation and AI in Continuous Improvement with Lauren Hisey

It becomes a balance. That's where I said it was coming, a balancing act. And how do we balance all that technology, all of lan, Six Sigma, plus the people, and make sure that we haven't lost the people side of things.

Today I'm excited to welcome Lauren Hisy to the show. Now, Lauren and I connected through our mutual friend, Karen Ross. So, Karen, thank you for the introduction. And today, Lauren and I chat about a topic that we've maybe touched on a few times over the years on the podcast, but the whole idea of the relationship or the harmony that can exist between Lean Six Sigma and artificial intelligence, or maybe just automation in general. So that's what Lorne and I really kind of dig into. Today. We talk a little bit about what is it? What is artificial intelligence? And then what role does things like automating tasks that people are doing, what does that, what does that do from a Lean perspective. But then we also touch on some of the tougher topics, like, okay, so we're automating some work that people used to do. Aren't we going to be putting people out of work and that sort of thing?

So.

So we definitely chat about that as well. So it's really fun and it was great to, to have Lauren on show. Notes, which will include everything that we talk about can be found over@gembapodcast.com just look for episode three. And yes, I am battling a little bit of a weird Texas allergy thing in my throat, so sound a little bit like a frog. It's not your speakers, it's me. I'm sorry, but we fought through it. But again, check out the show notes. Gembapodcast.com, look for episode 307. Okay, enough from me. Let's get to the show. Lauren, welcome to the show. How are you?

I'm doing great. How are you doing?

I'm doing well. Yeah. I'm so excited to have you on. So we have a mutual friend and Ms. Karen. So Karen Ross actually was just on the episode, I think it was last week's episode actually. She introduced me to you and to actually several other fine folks who are going to be coming on here in the coming weeks. So, yeah, thanks for coming on.

Thank you. Thanks for having me. I'm excited.

Yeah. Well, Lauren, we like to begin all of our episodes with our guest sharing a quote. So do you have one?

I sure do. My favorite quote is, a bad system will beat a good person every time. It's one of my favorite Edwards deming quote.

Yeah, Dr. Deming, he rocks. I love it. All right, well, today we're going to have a really interesting conversation. I feel, Lauren, I'm fascinated with the topic, which is kind of the, I guess maybe the relationship, the harmony, if you will, of Lean and Six Sigma with maybe what we look at as the future. Not the future, maybe even the present really with artificial intelligence or AI. And so before we get into that topic though, maybe it would help if we understood a little bit more about you, Lauren, maybe your background, how you first got into all this continuous improvement stuff and then definitely what you're up to these days.

Yeah, definitely. So I got my start in, well, I started in Six Sigma first about, Let me see, it's coming up on 13 years ago when I was working with AT&T. AT&T started with their continuous improvement program where they're using Six Sigma. And then about a year into Six Sigma they also launched Lean. So they did Lean Six Sigma. And then from there I went into, I went moved over into Nielsen, which is the media research, and did work there for a few years. And then just about it's coming up on a year I went out on my own to start my own consulting business within with Lean Six Sigma.

Nice, nice. So what kind of consulting is it? Just traditional kind of Lean Six Sigma consulting or it's going to be it.

So it's a blend. So I like to do. There's two, two avenues I like to do. I do like to do the traditional consulting consulting routes, but I also like to do a lot of education as well. So I like working with education facilities or universities to provide education on Lean Six Sigma.

Love it, Love it. All right, so let's talk a little bit then about maybe this relationship between Lean Six Sigma and AI. But before that I want to just presume anything because AI is a big, big, big, big, big topic. So what is AI? Let's start there.

So AI would be anything from using Alexa to using Siri to putting together what they call rpa, which is the robotic process automation. So it's. AI is a huge flavor of everything. And then in addition to that is machine learning. And one of the biggest things that I find is that now that we have these new shiny, I call them shiny new tools out there, I think a lot of businesses, regardless of their size, are trying to use a lot of this AI to implement automation or implement faster ways of doing things. But like I said with that quote with Edward Denwing, it's a bad system is always going to be, is going to be that person or your employees every time. So that's why what I think AI reveals is how bad of a process really is. So like I said, AI is just a big blend of things and it's a lot of shiny new tools, which I don't think a lot of people completely really understand what it's about.

Yeah. Yeah. Okay. So how can it help a Lean and Six Sigma kind of deployment or culture or whatever you want to call it? Talk about the genesis of how it could work together.

Yeah, definitely. So I think that one of the bigger things I would say is that if you put lean, if you leverage Lean Six Sigma with AI, you're going to go down a road or a down path where you have continuous improvement, but you're going to have continuous improvement with that, with the technology there. So I. I have in the past have implemented a lot of Lean Six Sigma projects or Kaizen events where, you know, we have some leaders that want to implement some type of technology. But I always tell them, let's look at the processes first. Let's improve the processes, standardize them, and then let's look into putting in the first. Let's do the automation, the rpa, and then you can move into that machine learning. It's an evolution, and it's not something that you can implement and you just throw technology at it. You got to make sure that your processes are stable before you can even move into putting anything, any type of AI or technology at that process.

Yeah, I'm so glad you said that, because so often we see folks just really trying to automate their waste. Right.

That's a huge thing. I did a talk on that last December where people just, oh, let's just automate the process. I'm thinking, well, if you have a lot of waste in that process, if you don't even look at those eight waste, you're just gonna. Just show how bad your process really is.

So before we were getting started, we were kind of conversing and sharing some ideas, and you made a comment that organizations could become lean and rigid, perhaps in a more traditional sense, if they perhaps shun the whole idea of something like AI talk. What do you mean by that? Becoming rigid?

Yeah, definitely. So I think there's. While many organizations today have been using Lean Six Sigma, they've actually, I would say a lot of organizations are becoming too lean, so they have made the processes too lean. And then they maybe are implementing the technology, but then they're becoming too rigid because then they're not one. Their culture, I think their culture might be with Their employees is becoming damaged and then two, they are becoming less fluid. So change is not easy. So in a world of transformation, while we need to be stable and have, we need to be very stable, we also need to have that flexibility because we all know that our markets are completely changing. And then as that technology changes and then as people change, we need to become a little bit more fluid. So it becomes a balancing act. Mm.

So what do we do? Like, how do we fix it?

There's a, there's a lot of different ways we can fix it, but I think one of the biggest things we need to do, I think if we just get back down into the roots of what Lean, what I. Not even Six Sigma, but what Lean is. Right. So if I think about Lean removes that waste and variation out of the process and we set up those right measurements and we remove those non value added steps. But I think the other big, the biggest approach we need to take is look at it as a toolbox, a toolbox approach. Because if you put Lean and Six Sigma and AI together, you have this toolbox, you have all these great tools. But I think the biggest thing that is also missing too is that we don't look at the human side or the people side of things. So if you think about the one waste of underutilizing talent, I also look at it as a whole perspective, as a whole, with people. Because if we aren't looking at our people and we're not looking at our culture, then we're underutilizing those talents at all levels. So I'm not just talking at the person that's doing the job, and I'm also talking up to the CEO as well. How do you. It becomes a balance. Oh, that's where I said it was coming, a balancing act. And how do we balance all that technology, all of lan, Six Sigma plus the people, and make sure that we haven't lost the people side of things.

Yeah. What's interesting, talking about people with AI is one of the. Oh, I don't know, what's the word I'm looking for. One of the complaints, if you will, or concerns with AI is that we're going to have these people, less factories and, you know, just a bunch of intelligent robots running and no one will have a job and that sort of thing. So what's your take on that?

Yeah, so that's a good question. One of the biggest things I think is people are becoming afraid. So if you look back in history, you looked at way Henry Ford put together the Model T Right. For example, he developed a really great way of developing this, the Model T and making it really fast. But there they were using machines and they had a lot of unskilled workforce. But then if you go back to, if you think about what Toyota did with lean manufacturing, this is where I think where Lean did is put the human back into the manufacturing. That's where I think here we can say put lean, put the human side back into technology. So for example, yeah, today, some of the jobs today that we do today can be automated and especially if the process is very stable. But if you think of the evolution of how things are moving and how fast technology is moving, some of the jobs that we do have today will be automated. But then we're going to be creating new jobs and new skill sets. So that's where I think if we can put together that put the balancing act there where we have Lean, Six Sigma, we have AI and we have the humans, because our humans, human workforce is going to show us the best places to standardize and automate our processes. But also if you intermingle them together, this empowers our workforce to make those changes to allow for that customization that we need. And then also instead of having an unskilled workforce, we're going to have a very high skilled workforce going into the future. Yeah.

So I wonder if you could, you know, you mentioned earlier, you know, some Kaizen events and whatnot, where maybe we remove some waste and then we use some sort of intelligence to automate something or to complement something. Can you give us like some examples? You don't have to name any companies or anything like that, but some specific examples of where you've, of where you've leveraged AI.

Definitely this, this is a great, that's a great one. I would say one good example I have is I was working with a client and they knew we needed to do some type of automation because we couldn't hire more people to do the job. And they wanted to completely redo the way they were doing all the work. So what we did is we got together with the team and I always make sure that I have all the right people that are in the room as to. So we have your leaders, you have the people that are doing the job. And we do a. I want to say this, we had to do a virtual gamble walk because we were just, we were pressed for time. But once we got everybody in room for about two to three days, we walked through the process, got rid of all the waste. But one of the things we Saw that is we had some marketing people doing operations jobs, and then we had some people that were in operations doing marketing job. And then of course, we had product leadership that was doing. Wasn't doing, working on the products and thinking for the future. So what we do is we completely redo the whole process. But as we were doing that, we were able to see within the process some, Some steps in the process that could be automated. So instead of if it was a very manual task, take that manual task and automate it. That's where, like.

What do you mean? Like, give me an example of a task that you. And then how you automated it.

1. Oh, this was, this is a good one. It was just data entry. So we had someone that. This was a marketing person putting data entry in for a new. For new, new customers that were coming in. So instead of, you know, capturing your email address and from all these different forms and their contact and the type of company and things that. That were being sold, all of that was being put in to. Into another database, from one computer to another. So instead of having someone mainly put that in, why can't we scrape the contract or scrape the screen or some do or do some type of API to just automate that? So it goes from one database to another database. That's just a very. One simple thing that we put in to get that we put in. And that is considered AI because it is automation and that, I mean, just that taking away that step from a human and putting that in. Yeah, I think we probably, I mean, that went from maybe 30 minutes of data entry down to maybe a few seconds.

Yeah, no, that's great. Yeah, because I think sometimes we think of AI, it's too big, you know, it's like, oh, I can't do that. Right. But yeah, you're right. I mean, now some purists out there, it's not AI, you know, AI is this big Elon Musk robot, you know, doing this thing, you know, But I think, you know, I think that's beautiful. Like even, you know, when you saw the podcast, kind of standard work, whatever you want to call it, that I send out, you know, like, I literally, I use this little snippet program that I hit a key keystroke and boom, it's there. You know, like all the, all that information is there. So I can literally, someone says they're going to be on the podcast, three seconds later, they've got this epic email, or people save it in their email signatures. There's all different types of ways of doing that. But to try to type out all out every time would take me 30 minutes instead of five seconds.

Exactly. I have another good example too. This is something I worked with a client in the talent acquisition space that you have a manager that needed to post a job that was going to be out on LinkedIn. Well, they had to mainly type all of this stuff into a form, which is great. But then what we were finding out that the data that was being collected right from that manager all the way to the point where the jobs getting ready to be posted on LinkedIn was that there was a lot of, lot of data errors. Well, there was no error, there was no error proofing. I mean, come on, just think about it. Something as simple as poke yoke, right? You can automate that. So where it's automation where you have drop down menus and you can put some intelligence in that. So if they say if they pick one field, the next field that pops up is only going to give certain answers. So that way the data that you are collecting up front is going to be great data on the back end. Just some quick examples that I've done with some other clients on that area. Another one more quick example.

Yeah, these are great.

Another quick example as I worked is a very small client where it's an accounting firm and they are doing a lot of data entry between the different accounting systems that they work with their clients on. So we just automated so that all the different, all the different databases were feeding into each other so they didn't always have to go in and put the same number in five different databases. And what was really great about that too is that when they went to go run the reports at, you know, at the end of the quarter and for the end of the year they, it was all very quick and automated, just something they just hit a button and it was intel and then they put some intelligent learning in there as well. Got it. Like you said, it's not always a fancy robot.

Now can you think of any examples and this is a maybe unfair question to ask you, but where AI has gone wrong and not done what you wanted to do or it perhaps made something worse or maybe not for you, but maybe you've heard of it.

I would say I haven't had, I personally haven't had done one of the things I do know that I, that I've read about. So if you think about, I did some, I was looking at some data and some statistics is that, you know, 30 to 50% of the initial RPA projects failed. And the reason why is because one of the biggest things is that the data coming in into either that the, that the AI or the robot is collecting is not the perfect data. So they're not doing, like I said, something simple as mistake proofing. So that's one area that's really bad. Another one is where AI can go bad is with the wrong processes. So sometimes if you, if you think about it, everybody wants to automate some type of process, but not every process can be automated. An example of where a process can't be automated is if that environment, that or the industry or that part of the process is too dynamic. So something that's not repeatable. There are areas in different industries where a process can't be, just can't be automated because it is too dynamic. And that's reason that's where you need to have that human touch point in there.

Got it. So if someone's listening to this right now, Lauren, and they're interested in perhaps taking advantage of more automation, more intelligence, whatever we want to call it, what advice do you have for them? What are some ways people can kind of step into this?

Yeah, definitely. So one of the biggest things I would say is if you want to implement some type of automation into your processes is first look at the process itself. So looking at it from an end to end view, not just, you know, just not in just one area. And then what you want to make sure is that you are collecting correct data, the data coming in is good, that you look at all the eight waste that look at the eight waste and see what you can do to get rid of all the as much waste as process as you can. And then the other thing to you is look at the non value add and value add stuff. So if you could take out all of that non value add out of there, that will definitely help you create a stable process and stable environment to operate, to start that automation quickly. Yeah, because you also, like I said when I had said before, you want to make sure that your processes aren't too dynamic. There's instances where you can make a process as stable as possible to a point and that's probably where you can automate part of it. And then once it becomes fairly dynamic, that's where you can have that interaction with the human side of that on the human side of the process.

Yeah. Beautiful. Hey, so the last question, we were talking earlier about people maybe doing different types of work 10, 15 years from now than they are today. Just like you said, when industrial revolution came around, people ended up doing different work than they were doing before that. Right. So I don't think there's any harm or I don't think it's a bad thing. But so my oldest daughter is 17 and you know, thinking about her future and this and that, like, what do you think, you know, for a young person who's gonna seek education and you know, do you, do you have any thoughts on what folks going through this whole education process should be focusing on? I saw an article, Elon Musk a month or so ago, had some hot sports opinions. Imagine that, you know, on what he thinks. But what do you think?

So what? I think so, yeah. If you are in high school now or going into college or in college, one of the biggest things I think to do is learn. It's not, I think we have such an emphasis right now on learning different types of technology, which I think is great. And then of course the data, the data science. But I'd say find something that you really like to do. So I know it's not. That's just my, always my genuine answer. Because my son, I have, my son is now, he's gonna be 26 and I've always told them, I'm like, do something. If you want to do something in business, that's great. But also make sure that you learn. You have an open mind and you learn the technology side of things. Always be open to learning because once you're done with school, you're still going to continue to learn. There's always going to be new technology coming. Yeah, that's probably my best answer because I always tell the students that I mentor that do something that is great for you because what might be good for someone else might not be good for you.

Yeah, yeah.

And just be open minded and be willing to learn because technology, no matter what work, where you work, is always going to have some type of impact.

Yeah. I was actually scrolling through Audible the other day looking for some more audio books. People are probably sick of me talking about all my audiobooks that I. But I can't remember the name of the book, but it was, it was all about the, the specialization versus being a generalist. And I think the book was more a proponent of being more of a generalist and not such a specialist, you know, early on. So I thought it's an interesting take, you know, on, on really the world. Because, you know, how many of us are using exactly what we learned in school, right? Probably very, very few, you know, so.

Exactly, exactly. I gotta say, if you ask me, my background, my background's in Political science. And now I'm doing something different.

Right, right, exactly. Exactly. All right, well, Lauren, thanks for coming onto the show. This has been fun. If folks want to connect with you or learn more about your consulting business, what's the best way for them to do that?

Yeah, there's two ways. So find me on LinkedIn. I'm very big on LinkedIn. And then of course, my website, which is laurenheiseconsulting.com.

Okay, and your last name is? It's Lauren. L, A U R e N&Heise H I S E Y. Right?

That's correct, yeah.

Okay, cool. So, yeah, and we'll link to everything in our show notes. So that'll be GembaPodcast.com. this will be episode 307. So great. Thanks for coming on, Lauren. Karen, I know you're listening. Thank you for introducing us and definitely thanks, Karen.

Yeah.

So do you ever get out to any conferences or anything, Lauren?

I do, I do. I'm coming up on one this week. It's going to be, well, cyber security, which is not Lean Six Sigma related, but something I wanted to learn a little bit more about cybersecurity. So going to the one here in Atlanta this week.

You know, it was funny. I was in Atlanta a month or so ago and I was Uber driver was telling me all about. He was actually in school for cybersecurity. That must be a thing in Atlanta, huh?

It is, it is. It's a big thing right now here.

Yeah, well, we need it. Goodness.

Yeah, definitely, definitely.

All right, well, thanks again. Take care. And maybe we can chat again down the road.

All right. Thank you so much for having me.

All right, take care. Thanks for listening to the Gemba Academy podcast. Now we invite you to take a note, no strings attached. Fully functional test drive of gembacademy.com gain immediate access to more than a ,Thousand Lean and Six Sigma learning resources, all free of charge at gembacademy. Com.