Artificial intelligence is transforming every industry—but how do we make sure we don't automate away the very thing that makes organizations successful: human judgment?
In this episode, I welcome back keynote speaker, employee retention expert, and author Lisa Ryan to discuss her new book, Smart Plant: Aligning AI, Automation, and People. Although the book is written around manufacturing, our conversation quickly expands into leadership, workplace culture, employee engagement, and the role of trust in an AI-powered future.
We explore why AI should be viewed as a tool rather than a replacement for people, how organizations unintentionally erode judgment through a series of small decisions, why psychological safety matters more than ever, and practical ways leaders can introduce automation without losing the experience and wisdom of their teams.
Whether you're leading a manufacturing operation or a knowledge-work team, you'll walk away with practical ideas for embracing technology while keeping people at the center of your leadership.
Key Takeaways
- AI is most powerful when it enhances human judgment instead of replacing it.
- Human judgment should be treated as organizational infrastructure—not a soft skill.
- Small leadership decisions can quietly erode trust and decision-making over time.
- Veteran employees possess valuable "tribal knowledge" that organizations need to intentionally preserve.
- Psychological safety encourages employees to speak up before problems become crises.
- Before implementing automation, ask "why" repeatedly to uncover the real objective.
- Automate the work employees dislike most before replacing decision-making.
- Introduce AI first in stable, low-risk processes rather than your biggest operational problems.
- Include frontline employees early in technology decisions to increase buy-in and improve outcomes.
- Appreciation and recognition remain powerful leadership tools—even in highly automated workplaces.
Relevant Links
- Lisa Ryan's website: https://lisaryanspeaks.com/
- Smart Plant: Aligning AI, Automation, and People: https://a.co/d/05Z0GSHp
- Lisa Ryan on LinkedIn: https://www.linkedin.com/in/lisaryanspeaks/
- Manufacturers Network Podcast: https://www.manufacturers-network.com/
- Avish Parashar: https://AvishParashar.com
- Say Yes, And! to Change: https://a.co/d/0dLj8ppw
Unedited Transcript
Avish
Hello, Lisa, and welcome back to the podcast. How are you, my friend?
Lisa Ryan
I am fantastic. It's great to be back with you.
Avish
Excellent. Well, I had you on a little while ago—almost a year ago—to talk about your book "Gears of Greatness." And somehow, you know, it took me 12 years between books, and somehow, you know, like a year—less than a year later—you've got another book out, which is what we are here to talk about.
Lisa Ryan
Yes.
Avish
So before we get into the ins and outs of what that's all about, for people who didn't catch you the first time or are unfamiliar with you, could you just give us the one-minute Lisa Ryan, who I am, and what I do, overview?
Lisa Ryan
Sure. I am Lisa Ryan from beautiful Cleveland, Ohio, and I work with associations focusing in manufacturing, construction trades, and healthcare—long-term care in particular—to help them keep their top talent from becoming someone else's.
Avish
Love it. That is very concise and very clear. So let's just jump right into it, right?
Avish
You're—the work you've done in the past—your previous book was about gratigy, which is like gratitude as a strategy, and how to kind of weave that into businesses. And this book is a little bit different. I don't know, maybe it's—maybe there's a lot of parallels there that you could share with us.
Avish
But so your new book is called "Smart Plant," which is not a horticultural book. It is a book for manufacturing plants, and about how the—the aligning AI, as the subtitle is—aligning AI, automation, and people.
Avish
So I've read the book. I went through the book a month or two ago when you launched it. I revisited for this. But could you just give, sort of, the people who haven't read the book, the overview of what the overall premise and point of the book is?
Lisa Ryan
Sure. I mean, AI is something that is—that genie is not going back in the bottle. I mean, we are—and we're becoming more and more dependent on it.
Lisa Ryan
I had—as on a podcast earlier today—and we had the conversation, it was kind of, "Remember back in the days when the internet first came out and everybody was like, 'Well, I saw it online, so it must be true.'" Well, now it's getting to the point of, "Well, it was written on—it was done by AI. It has to be correct."
Lisa Ryan
And what we're doing is we are turning over so much human judgment to the machines that not only are we putting us into some potential danger, because the AI is really great for what's happened in the past, but it's not that great on the one-offs, the things that humans who basically have been building up their sensory library the whole time they've been working in the plant, where they can see and smell and feel and touch when something's going wrong—something that AI necessarily can't do.
Lisa Ryan
So it starts with a cautionary tale of what can happen when we become too reliant on AI. But the whole message of the book is really that human judgment is infrastructure. It should be every part as big a part in your organization as your accounting and finance, your inventory, all the other systems we have in place.
Lisa Ryan
How can we make sure that with the decisions that we're making to bring in automation and AI, that we are not completely removing human judgment, not only for the staff that we have now, but you look at people coming in, we have so much of that tribal knowledge that's leaving industry, and we have to figure out a way that human to human we can continue to learn from each other. So that's really the running theme.
Avish
Yeah, and I love that. I love how the word "judgment" is pretty much throughout the entire book, and that's kind of like the—you know, it's like as speakers, we're supposed to, like, try to pick a word or a phrase that we own. And I'm like, "Oh, like, judgment would be a really excellent one for you to own," because it's like, it's woven through there.
Avish
And the book is targeted towards, you said, manufacturing and plant leaders. But as I'm reading it, you know, it's not in—that's not my niche, or niche, as they say. But it seems like it would just apply, really, to any industry.
Avish
Obviously, it's focused on that, but—because a lot of my listeners might be from other industries—but the whole idea of judgment, especially when it comes to AI, is pretty universal.
Lisa Ryan
Right. Well, and it was funny, because when I did the book launch party and the book launch training for it, one of the women that had attended that—and she, too, not even in a remotely similar industry as manufacturing—and she said, "Lisa, this is not a manufacturing book. This is a leadership book."
Lisa Ryan
Now, I chose manufacturing as kind of the story to build it around, but yeah, it is appropriate for any leader who's bringing in a team and wondering what their future is going to be based on the AI that is—that's coming in and taking over.
Avish
And I want to—I want to talk just a little bit about the manufacturing specifically, because it's something you said, as you've done this before, is, you know, we jump around a lot, whatever plan I might have. So you talked about the knowledge that's getting lost because, you know, people are leaving. And I have spoken to some kind of—like some manufacturing type groups, you know, we call it for lack of a better term, I know some people don't like that, but, you know, that—people who work more in shops and things like that. And one of the common conversations I have in prep for those is that there's a whole generational thing where they're like, "It's hard for us to attract younger people into this."
Avish
So it's like the population that work in that industry are getting older, they're getting less—fewer younger people coming in. And I, as you're talking, I was like, "Oh, are the plant leaders then going to make the mistake of saying, 'Well, yeah, we don't—we're not getting younger people, but that's okay,' because now we've got AI to kind of do the thinking that the younger people would be doing. So it's okay that we're not attracting newer, younger talent to our plants?"
Lisa Ryan
Yeah, that would be a big falsehood. You know, and it's funny, because we want to make sure that we figure out a way to bring down that industry knowledge. And one thing with the baby boomers and older Gen Xers is they've always felt that that knowledge is their power. "Well, I know stuff, so they have to keep me here." And at some point, physically, it's like, "I'm sorry, Billy Bob, you've been here for 40 years. There's a really good chance that you're not going to be here for another 40 years."
Lisa Ryan
But we want to keep that legacy that you've built, and that is the reason that we really want to sit down with you, sit down and do interviews with you to capture your knowledge, to have other people shadow you, to maybe have interns or mentor relationships or whatever that looks like, so that the knowledge that you have in your head can continue to prosper this company for long after you're gone. If we put it in a way that is honoring them and the tenure, I think that that will get rid of some of the fear around it. But it's just so critical.
Lisa Ryan
Here's the thing with AI, and it talks about this in the book. The decisions that we're making, where we're removing human judgment, where we're removing human interaction, because, you know, the beginning story with Marta and Devin, they hear, you know, Marta saying, "Hey, this bearing sounds like it's going to go," and Devin's like, "Should we report it?" And Marta's like, "Oh, why? They're not going to listen anyway." So Devin tries to report it, and they're like, "The dashboards are green. Mind your own business. Get back to work." And then, of course, we know two hours and 17 minutes later, the line crashes and the plant is idle for six days.
Lisa Ryan
But think about what just happened to Marta. She's been there for over 30 years, and she feels like she knows stuff that the machine didn't know. And she even said, "Devin, it's a waste of breath. They're not going to listen to me anyway." So what is going to encourage your tenured employees to come to you who know stuff when you're just listening to what the machine says and not them? That's the problem that we have to figure out.
Lisa Ryan
And even if the case with Marta, that she heard something with the bearing and she reports it, maybe they stop the line. And if the bearing was bad, then Marta's going to be a hero, right? "Yay, she saved the line." But what happens if there was nothing wrong? Is Marta going to get in trouble for that? Because, "Well, look at all the money you cost us because you just shut down the line for no reason at all." Do you think she's ever going to shut that down again? Because now there's punitive with that. So it's just a lot of different considerations to take the step back and to listen to your people when they're coming forward and sharing stuff with you.
Avish
Well, that's why I love—that's why it really resonated with this book. What I loved about it is, on the surface, it seems like, "Oh, it's a book about AI," which obviously it is, but the human side. And so much of what you're talking about, things like that, are stuff that I talk about in a different way, right? It's about saying yes and your people instead of yes, but it's about listening and letting go.
Avish
And, you know, so much of that, it's—in some ways, it's really simple, right? It's like just, you know, treat your people like human. You know, don't just trust the data, don't just trust the machine over that and bring in your own judgment. But it's simple, but not easy. And I think that's a big chunk of the book is about, like, "Well, how do you—like, how do you do this?"
Avish
And before I get into some of those how's, I want to circle back to one other thing. In the process of writing this book, you've had your own podcast where you talk to a lot of leaders, right? And a lot of the information and ideas and even quotes in this book came from those interviews. So before we kind of get into some of the content about it, I'd like to hear a little bit about the process and how you took—I mean, I don't know how many—it was like 100 or so on podcast interviews, right? And then use that as some of the foundation for the material.
Lisa Ryan
Yeah, I've had—my podcast has been—it's about five and a half years old now, and I've had more than 200 manufacturing experts on my show. And so the interesting thing is, when this book came to mind and I was looking for, you know, really harvesting the content from all of these conversations I've been having for over five years now, I actually found this app on—well, I asked ChatGPT about it, and Chat told me about this app. I couldn't tell you because I used it for 10 minutes and then canceled it.
Lisa Ryan
But what it did, all of my podcasts are on YouTube. They have their own little file folder or whatever it is. And what this program did is it harvested all of the transcripts from those—at that point, it was 192 interviews. So I had more than a million words of content to go through. And so just asking, you know, using that really large document, but again, using the power of AI to see where are going to be the best quotes, the best stories, and kind of having the model for the story in my mind, but wanting to back it up from real-world experience.
Lisa Ryan
And so I'm really happy that I was able to include—I believe there was 23—there was over 20 of my experts from the podcast that are featured in the book. And not only my friend and mentor, Randy Gage, on the cover, but one of my customers, Roger Atkins, who's the president of the National Tooling and Machining Association, one of my good buddies, he is also on the cover.
Lisa Ryan
So it's nice from the standpoint of putting out a product, because, as you know, books are just fun to write. But really being able to honor people and honor the experience that I've been—had firsthand seat to for the last five years, it was really, really a fun project to do.
Avish
What's cool about that is, and I mentioned at the beginning, like, I'm not, you know, sure what the—this is a different direction, but everything you said about that, and even, like, when you're talking about your kind of senior people and honoring them in there, like, it's all gratitude, right? Like, it's sort of like, instead of treating your people as cogs in the machine who can be replaced by AI, it's be grateful for your people and trust them and build with them to use the tools more effectively using judgment. So I can—I didn't quite grasp that myself before, but now I can totally see the alignment between gratitude and gears of gratitude and smart plants. So it makes total sense.
Avish
All right, let's talk a little bit about some of the ideas from the book. And there's a lot of information in there, so we can talk about stuff, and there's still plenty for people to get reading it afterwards.
Avish
So first off, you talk about the smart plant framework, which is sort of like three steps or three things to remember about judgment. Really, it's all about judgment and using judgment. So could you kind of go a little bit into that and how people can use that?
Lisa Ryan
Well, it's really taking a look at the processes that you have and the risk of removing judgment.
Lisa Ryan
You know, for example, one of the parts in there is talking about the fact that there were—they had six at Oak Ridge Industries, they had six managers working the shop floor. And they said, "Well, you know, from a business standpoint, you know, there's a little bit of redundancy. Let's just cut that down to only having four managers on the floor, you know, where that's going to save us payroll and reduce the redundancy."
Lisa Ryan
But what they didn't take into consideration was the fact that those managers, when you had six of them, they had time to spend talking to the people on the floor and finding out what's really going on. And the people on the floor trusted the managers because they saw them all the time, and they felt comfortable saying, "You know what? I'm not quite sure about this right now."
Lisa Ryan
Or just bringing some of those issues to bear, where when they went down to four, then everything was, you know, "I don't have time to have those connections anymore." So the whole framework really gives you the questions of capturing, you know, of looking at ways to make sure that judgment is part of your infrastructure and the specific ways to do that.
Avish
Yeah, and it's interesting you cover the cutting down, you know, with AI and just everything about being data and metrics and KPIs and efficiencies. There's a certain benefit to the inefficiencies, like that space, right?
Avish
I mean, I guess there's a famous story about, you know, Steve Jobs when designing the Apple headquarters only put restrooms in the lobby, which would force people to have to go all the way out of their floor from their office, which kind of created these collisions where they would, like, bump into someone and start chatting.
Avish
And it seems similar there. It's like, "Well, if I have a little space to breathe, I can think, I can chat." And you can't really put that on a dashboard, right?
Lisa Ryan
Right.
Avish
And AI can't replicate that, at least not yet. Who knows, in 20 years, you know, we got Androids walking around. But, you know, you can't right now.
Avish
So it sounds like some of that is the effective versus efficient debate. It's like, "Yeah, it might be more efficient to cut this down and use AI, but is it as effective?" Cool.
Avish
And kind of along those lines then, and I really like this idea because I feel like this kind of ties into something I talk about, about apathy and as people sort of. You talk about the erosion of judgment and how, you know, we might think that, "Oh, judgment just stopped one day because X, Y, and Z happened." But really, it's a slow and steady undercutting of trust and removing the judgment. So could you share a little bit about that?
Lisa Ryan
Yeah, and that's why—and it was kind of funny because some of the early feedback that I got on the book was, "Well, you're talking about 2029 as if it was in the past." It's like, "Well, actually, according to the book, it is in the past because the book starts in spring of 2030."
Lisa Ryan
So what I wanted to do was start with, you know, what happened on that fateful day when the line went down, the decisions that they made in 2027 and got approved because it just made sense and it wasn't that big of a deal. And then what they did in 2028, which again, wasn't a big deal, made sense on paper. Yes, let's remove these managers. Let's, you know, cut the training down from three weeks to two weeks. And just, you know, what they did in 2029.
Lisa Ryan
So little by little, it was just a lot of small decisions that ended up having that huge impact. The plant was a lot—it was very productive. I mean, 500 days of perfect uptime, which was a totally reason to celebrate until it wasn't because they basically made themselves—they weren't prepared for the one-off of something bad happening.
Avish
Yeah, and it's just—I like the—it makes so much sense, the idea of how judgment slowly erodes, but it makes it so much harder to know you're eroding there. Like, it's easy to not make the big giant stupid decision. It's harder to not make the small mistakes that kind of set you off course.
Avish
So if there's a manager or leader listening to this, whether they're in manufacturing or just kind of a general industry, and you kind of go into the book, but what are some of the things they can do now to identify, like, "Oh, what's this? How can I not make these small mistakes that are going to blossom into big problems down the road?"
Lisa Ryan
You know, it's interesting because it's really looking at when you are going to automate a process or bringing in AI to look at all the future what-if ramifications. I have a friend of mine, Ray, and he talks about putting it on a murder board, which basically is you put the idea on a whiteboard and then the people in the room try to murder it. Like, "Why is this a bad idea?"
Lisa Ryan
So if we had, for example, put on, "Let's take it from—we're going to go from six managers to four managers." You know, it sounds like a good idea, but why would it be a bad idea? So if we look at the case of the worst-case scenario, well, what happens if one of those managers quit and we only have three managers? Or what happens if they don't have the time with their people?
Lisa Ryan
Or what happens if, you know, we have the pro of, "Yes, we're going to be saving money," but if we just start looking at where are our people making decisions today and what are the critical decisions that they have? Like, what could go—what could end up going wrong?
Lisa Ryan
And because we live, you know, we want to keep everything positive and we want to keep people motivated and happy, we don't—we're not really wired in the workplace to go to the worst-case scenario, what could happen. But in these cases, when we are looking at ways to automate with the potential of those losses through those small decisions, it's really looking at everything that could happen and being prepared for it, or at least having a plan B.
Avish
Yeah, that makes me think of kind of in the creative realm, stories I've read about Pixar and how, while they're working on a movie, like every morning they start by reviewing the previous day's work as a group, you know, a small group, and their objective is to sort of rip it apart. You know, because to make it as strong as possible, let's find the holes in this.
Avish
But to make that work, you have to have a certain degree of, like, psychological safety and trust though, right, in the team. So could you talk a little bit about kind of that?
Avish
Because if you just come in on day one, like, "All right, Lisa came up with an idea. Let's all talk about all the reasons it's terrible." You know, that could have the same effect of making you be like, "Well, I'm not going to contribute an idea next time." So kind of what—how do you suggest people approach kind of that fear?
Lisa Ryan
Well, that's safety, and that's part of the framework too, in that you look at what happens when somebody shuts down a line. I mean, from a safety standpoint, for example, we all know everybody on the line has the power to shut down the line. But one of the best examples, I was speaking for a textile association a couple of years ago, and it was one of their safety programs, because, I mean, textile, dry cleaning, it's all about safety. And they were talking about the policies and standards, but then the norms. Okay?
Lisa Ryan
I mean, you can have all of your SOPs in place, you can have all of your policies, this is the way we do things, but what's actually happening on the plant floor? So if somebody shuts down a machine and there's nothing—there ends up being nothing wrong, are they going to get in trouble for it? Because if they say, "Well, that was a waste of time, why did you do that?" and having the person justify.
Lisa Ryan
Because if you think about it, and I talk about this in the book, if something does go wrong, the person who fixes it is a hero. "Yay, we are up, there's no longer downtime." But the person who stops the line to prevent that from ever happening to begin with, they're kind of putting their—they're kind of putting their reputation on the line. Because they could be a hero or they could be a zero if nothing happens.
Lisa Ryan
So, and people are paying attention. They want to see, "If I shut down the line, am I going to be congratulated? You know what, Lisa, I could see that, you know, that was a really good point. We appreciate you letting us know your concern." Because it's not like I'm going to be shutting the line down willy-nilly just because I want to take an extra 30 minutes for lunch.
Avish
Yeah. Yeah, and it's—oh, go ahead.
Lisa Ryan
No, I was going to say, I think that if we get that out of our head, that our employees are going to take advantage of us.
Lisa Ryan
I mean, there's a small percent of your employees that, yes, they are. They're going to jack you at every single moment that they can.
Lisa Ryan
But if instead we treat those 95 to 97 percent of employees like good, hardworking, honest people who are doing the best they can with what they have, then we create a much different work environment.
Avish
Yeah, it's one thing I talk about is sort of giving people the space to fail. Like, trust they'll do it right. And then, you know, it doesn't mean you just let them do whatever they want, but it's like, instead of assuming that they're doing it on purpose to make it a livelihood, or even just incompetence, like, "Oh, you know, you shut it down, you're overly sensitive or whatever," instead of anticipating they're going to do that to put all these policies in place to prevent that, like, give them the room to make those mistakes.
Avish
And then if they do, if you find someone's doing it over and over again, then you go to that, like, training. But that is such a hard mindset to wrap around.
Avish
And I'm curious, you know, I see this in sort of the sort of knowledge workspace. I'm curious, do you find that attitude more prevalent in, like, with manufacturing where it's like the sort of preemptively making sure people don't mess it up or people don't take advantage, or do you think it's kind of universal across all industries?
Lisa Ryan
It's such an interesting question because when I think about the Gallup organization and how they have been looking at employee engagement since the early 2000s, and it really hasn't changed. Okay, 30 percent actively engaged, 50 percent neither engaged nor disengaged, 20 percent actively disengaged. Those numbers haven't changed a whole lot. What has changed is there are companies that are doing really great things and are at 95 percent. And there are other stories, and you and I both hear these stories of workplace, like, "What is going on?" because they're at, you know, 10 percent or 15 percent engagement rate.
Lisa Ryan
So I believe that it's the same in manufacturing. There's companies that are going to be doing it really well, and I see a lot of those in my audiences that they get it, they're sitting in my program because they are doing things well and want to continue to do things better. But as you can tell from the feedback scores, there are always one or two people in the room that believe that this is nothing but a bunch of psycho babble BS, and if they just pay their people more money, they'll never leave and it doesn't matter. Why should I thank my people for doing their job when they come in? And people will go on that little tangent of, "I can't find anybody that wants to work. Nobody wants to be loyal. I can't find anybody anymore."
Lisa Ryan
What I want to say is, "Oh, honey, they're leaving you." But I don't say that because it's not my business, and that's my inside voice. So the people who get it, get it, and they will continue to get it and learn and fail in all the good things that we're doing. And frankly, the people that aren't getting it, you know, they're going to have to learn the lesson the hard way, and there's nothing you can do. It's sad, but it's true.
Lisa Ryan
I have my best clients, and I'm sure that you find the same thing. The best clients come from companies that are already doing things well and want to get better. The companies that suck, they don't see any need to invest in their employees or be nice or appreciate their employees. They just want to throw a lot of money at the problem and, you know, see what happens.
Avish
Yeah, or they're being sort of like forced to do it from like the parent company or like one person up high is like, "All right, we need this," and everyone else is like, like that cross-arm person is like, "I don't need this, let's do it." And they're like, "All right, fine, they're forcing us to go to this." So I agree, it's like how the best get better. It's like, well, they understand and they keep building off of that.
Avish
So let's talk a little bit about that. One thing you mentioned in the book is like retrofitting a solution doesn't help, which I think is the premise of like once you've discovered there's a problem, then coming back and being like, "Oh, let's put up posters around the wall and maybe throw in a training program."
Avish
And I'd like to get into a little bit what you mean about that, because as a person who is a speaker and a trainer, I'm like, "Oh, is Lisa saying that like training isn't helpful in those situations?" So I'm curious like what you can clarify a little bit about what that kind of section is all about.
Lisa Ryan
Well, when we're—that goes back to basically the very beginning. Before we implement anything new, before we bring in a new program or a new process or anything along those lines, where we're looking at all of the different ramifications of why are we doing it, number one. What are the positives? What can happen as a result? And we build that into our training so we're not implementing a brand new system and it goes wrong or people are like, "I'm not going to use that," and then we're like, "Oh crap, what do we do now?" So starting as fresh as you can.
Lisa Ryan
Now, for some manufacturers, you have equipment that's, you know, 50 years old and there's got to be some retrofitting, trying to figure out how all the AI and automation systems talk to each other. But in the training, it's also getting that buy-in from the people on the floor.
Lisa Ryan
And what I have found in working with my clients, when you find that one person who is just crossing their arms, "I'm not going to do this, this is stupid," if you can get that person to buy in, because you're saying, "Oh, you know, Billy Bob, you've been doing this for 40 years, we'd love to get your feedback and love to know a little bit more." Well, I don't think it's going to work out. Well, why? Because sometimes he just wants to be heard. And when you can flip that person, they become your best advocate for everybody else on the floor. Because the other people on the floor are like, "Ooh, if Billy Bob's buying into it, then there must be something to this."
Lisa Ryan
But we really need to get that, the transparency. Are our employees going to like everything that we tell them all the time? No, of course not. But if we're going through the—if we're going through and actually being upfront, being straight with them, "This is what we're looking at. These are the reasons why. This is what we want to accomplish. What is your feedback? What are your thoughts?" We don't want to do that forever or that we're just going down rabbit holes. Somebody's going to have to make a decision at some point. But the point is to just do it from as early a stage as you possibly can so you're not fixing things that go wrong.
Avish
Yeah, it's like having the foundation in place.
Lisa Ryan
Yeah, absolutely.
Avish
Yeah, one phrase I use sometimes is mindset before mechanics. It's like, yeah, we can get the tactics and the tools, but is the underlying mindset that people have even going to support these tactics and tools we have? And let's get that squared away first.
Avish
So let's talk about that a little bit then kind of we're kind of coming towards the end here. But if you've got someone listening who's a leader, whether at a plant or just, you know, their company team, and they're looking at AI and they're like, "Oh, you know, we got to—we've been sort of avoiding it, but now we really need to start embracing it."
Avish
What is like the first couple steps they should take, you know, to make sure they can avoid some of these? Like, how do they get started down this path to do it the right way?
Lisa Ryan
Well, and I'm going to misquote this. I don't know if it was Einstein that said it, but if he was working on a problem, he would spend the first 55 minutes of that hour figuring out the question to ask. It really is when you're putting in a new automation or a new AI program, why? Why are we doing this? And maybe going five why's deep, doing that whole activity. But finding out the essentials of, you know, why you're doing—what is the result that we are going—that we want to get because of this.
Lisa Ryan
I always talk to my clients that if you're looking at some kind of automation and you don't know where to start, start with the jobs that your employees hate doing the most. Because if you look at the best way to get brownie points is to basically take some of the burden of the day-to-day grind off of your employees to give them the opportunity to use their brains and do something else.
Lisa Ryan
You know, automation should be the—should start with the difficult, the physically demanding, where we can get the repetitive strain injuries or there's more danger involved, of starting there to automate. But always remember that no matter what we're doing, that we need that human judgment to look at the process and to make sure that we're listening to our people when they're sharing their feedback.
Avish
Yeah, I love that. And that's kind of the thing about that, you know, AI should be a tool, not a replacement. It's like, how can it be a tool to help your people to free them up to have that space for the judgment and to do the things that require creativity and that keeps them engaged, kind of maybe moving them out of that Gallup, you know, 70 percent of neutral to disengage. And very cool.
Avish
There's one other thing you said in there I wanted to ask about because I love it. You said the starting point is stable processes is where you should start as opposed to the stuff that's broken. And that resonated with me because it's something that I sort of talk about, but I'm curious, could you go into a little bit about what you mean when you say stable processes are the best starting point?
Lisa Ryan
Well, it's really going through your operation and seeing where it's the—where it's the least—there's the least risk of taking the day-to-day human judgment out of this particular equation. So understanding all the systems that you currently have, who's involved, what the decision-making process looks like. And again, it's taking that step back to assess everything first so that you have a starting point of just looking at the processes that would—when I talk about stable, it's like the least risk, the least problem, the least chance of some kind of failure that could happen if the machine was—that's usually 99.7 percent of the time correct.
Lisa Ryan
What happens the 0.3 percent of the time that it's not? You know, is that something that human judgment because of that person that's been on the line for 30 years and could have told you that? That's what makes the difference.
Avish
Whereas I think the temptation might be to go after like the biggest problem, right? Like we've got this cool tool, like, well, obviously I want to use this magic paintbrush I have to address this giant uncertain thing, but it's probably better and smarter to start with the ones that, all right, if it breaks, it's not the end of the world.
Avish
Which seems obvious in retrospect, but I think it's not how a lot of people approach it.
Lisa Ryan
Well, and you think about when you're doing it like that too, it's easier to get the buy-in because people can see something that's working well in a day-to-day operation instead of putting yet another Band-Aid on a big problem.
Avish
Ooh, that is a good way to put it. So with that, I like that line. So we're going to move to the close here.
Avish
I'm going to ask one question as I do. I don't know if you remember, but I always end with kind of the same question.
Avish
Before I get to that, for people who want to connect with you, learn more about you, potentially hire you, get your book, where are the best places and how can they connect with you?
Lisa Ryan
Sure. My website is lisaryanspeaks.com. I am prolific on LinkedIn, and I have a newsletter called Cracking the Retention Code that comes out every Wednesday. A pretty easy to find wherever you're looking. And of course, Smart Plant is on Amazon.
Avish
All right. Fantastic. And we will link to all of those things in the show notes as well. Lisa, this has been great. Thank you for sharing all this information. I think it applies to all industries, even beyond, like you said, it's a leadership book.
Avish
Obviously, if you are in manufacturing or work at a plant, it's doubly, triply relevant, but I think anyone can benefit from it. So I want to finish up with one simple question, which is, you know, I talk about yes and because I think the world would be a better place if everyone had a default mindset of yes and instead of yes but.
Avish
So I'm curious, what is one small thing that you believe if everyone did, it would make the world a better place?
Lisa Ryan
If everybody just looked for ways to catch people in the act of doing things well. So many times people do not hear feedback unless they're doing something wrong. And if we can turn that around and instead catch people in the act of doing things well and thank them specifically for that, the world would be a much better place.
Avish
Well, that is a great answer. And that encapsulates the yes and versus yes but mindset perfectly. So it's a perfect place to end. Thank you so much, Lisa. And everyone, be sure to go check out all of Lisa's great content, her book, and her speaking website. Thank you, Lisa.
Lisa Ryan
You're very welcome.