Overcoming Knowledge Gaps That Make Organizations Resistant to Innovation with Eric Saylors

Speaker A: You're listening to the Change Management Review podcast where we bring you the best tactics, strategies and actionable insights for organization change. We talk with impactful organizational change practitioners and leaders. And now your host, Teresa Moulton.

Speaker B: Welcome to the Change Management Review podcast. My name is Teresa Moulton. I'm the editor in chief of the Change Management Review. And today I am very excited to introduce you to Eric Saylors. And, uh, let me tell you a little bit about Eric because we've got an exciting topic, uh, about the impact of human AI relationships in the fire service. Let me tell you, this is a very interesting topic and I'm so excited that he's here to share more about it with you. So our guest is, uh, Fire Chief Eric Sailors, a third generation firefighter and a pracademic. We're going to talk about what that means. With nearly three decades of experience in the fire service, he currently serves as fire Chief for the El Cerrito Kensington Fire Department in California and previously led the EMS division of the Sacramento Fire Department, overseeing a $30 million budget and more than 100,000 incidents per year.

Speaker A: Every.

Speaker B: Uh, Eric holds a doctorate in leadership and organizational change from the University of Southern California, as well as a master's in Homeland Security from the Naval Postgraduate School. And he is known for bringing rigorous research and models like the Clark and Estes knowledge motivation organizational framework into real world fire ground decision making, risk assessment, and leadership development. He describes himself as a pracademic blending practical command experience, academic research and consulting to help fire and public safety organizations improve performance, safety and community impact. In addition, Eric works with hen as the product strategist for, um, developing fire products. So, Eric, fill in anything that I've missed and welcome to the show.

Speaker A: Well, thank you so much. No, I, I don't think you missed anything. It's always weird to hear your own resume because you're thinking to yourself like, who is that person? So.

Speaker B: Exactly.

Speaker A: Yeah, no, no, I'm, I'm excited to be here and I'm very happy to talk about this topic.

Speaker B: Great. So let's dive in in terms of, um, the human AI relationship in the fire service. Tell us first a little bit about the fire service because a lot of my audience is, uh, from organizations like Incorporate, some nonprofit, but definitely not a lot from people from the fire service.

Speaker A: Fire service is such a unique environment, mostly because it is always dealing with a complex or chaotic environment that is handed to it. So one of the distinguishing differences between it, uh, and most other problem spaces is that the fire service doesn't choose its problem problems, it gets handed to them and then it is forced to solve them in a short period of time. Decision making is always done with limited information. Consequences are always at the highest level, which means that if something goes wrong, you're going to wind up going to a funeral and speaking to some spouses and kids. Uh, so you are trapped in this process where you are going to these events that are unpredictable, something that you didn't choose, and somebody's life is almost always on the line. What it does is it creates an incredible sandbox for washing out the difference between theory and practicality. It creates an incredible sandbox for leaders to learn some of those lessons early on that there are amazing theories out there that sound like they're going to solve all your problems, but sometimes practically, they don't work. And you have to figure out in a short period of time what does and doesn't work. Now with that said, it's also vested in culture. There is a lot of culture in the fire service that keeps it grounded in its mission and what it's been doing. And it is always evolving as fast as it can to keep up with the problem space. So that is the unique world of the fire. Fire service.

Speaker B: Yes. And one of the things that you've spent a lot of time doing in your work with the fire service is creating leaders. And you told me a story about how you actually prospect future fire chiefs and what it takes to become one. Could you share a little bit about that?

Speaker A: Yeah. It was a few years ago when we were, uh, uh, another chief friend of mine and I, we were trying to figure out why the fire service at times felt like it was in an internal loop. Uh, and this is, this is a parallel to a lot of organizations that we were just doing the same thing over and over again. And the big contributing factor was, was that leaders that wound up in the top position that were accountable and responsible for everything that happens frequently, were unprepared for that position. They, it wasn't their fault. You can't, you can't say that they were, um, they were negligent at some point. Just the systems in place were not there to prepare them because they were moving from a tactical operator into a strategic thinker in a very short period of time. And they just did not have the ramp up period. So we wanted to create this ramp up period. We look back, we analyze, we said it takes about 15 years to build a fire chief. So from the time that you are a ranking officer till the time that you Find yourself in that seat. It takes 15 years to develop the hard skills and soft skills that it takes to manage an agency and also keep people alive. So we dug back and we said, let's pull in a group of people that are roughly in their 30s, that have already promoted to a rank officer, and let's start exposing them to what it takes to be a strategic thinker. What is going to happen to them in 15 to 20 years from now when they realize they're the last person standing in the room at the age of 30? They may have no intention whatsoever of being the fire chief, but what they don't realize is that just through the process of attrition, they're going to find themselves there like many of us did. And if they truly want to solve the problems, they need to start preparing themselves. Now, we did that process with roughly 30 candidates, and almost all of them are chiefs now. Um, they are incredible thinkers. They really upgraded their level of education and training and experience that required to sit in this position. And it was, it was impactful. And since then it spread to other organizations across California and up in Washington and Oregon. The people that are reproducing the same process realizing that if you want success after you, you have to spend a huge amount of your effort ensuring your replacement. It is as good, if not better than you when they step into that spot.

Speaker B: Yeah, it sounds like a mentorship apprentice model.

Speaker A: It really is. And, um, we called it the Secession Project because we had used the term mentorship in the fire service so many times that it almost become a cliche, a cliche of failure. Um, not that the intentions were wrong, but the actual outcomes just weren't there because the agency didn't have the support. So we turned it on its head and said, the true measure of a successful leader is one that is able to self replicate. And we put that on ourselves and said, look, if we're able to self replicate, then that's a true measure. Then we're going to have to make this one of our key priorities.

Speaker B: Yeah, that's amazing. Can you help us get inside the head of, um, a leader of a incident, a fire incident, um, someone who's on the ground kind of running the show and what it's like to make the decisions on the fly and process so much information, uh, while they're leading.

Speaker A: So it is two things. It is highly stressful, uh, because you do not want to go to another funeral. And I remember my first funeral and, uh, it's overwhelming. So it's highly stressful. But you also have to compartmentalize that stress because you have to make decisions. You, there is no opportunity to pause. There are decisions that need to be made with limited information and you always want more information. But that's a challenge with running an incident is that it is keeping pace with or ahead of you. And if you don't make decisions, it is just going to outrun you. So you have to sit in a vehicle or behind a command post, manage your own internal stress, look calm and collective and think objectively. It is a skill set that takes a lot of internal self regulation and reps to do it. And you know in the end that you are going to own those decisions. And that is part of, of the harbor. You know, when this is all said and done, success or failure, you're going to be held accountable. Right. And the difference between responsibility and accountability, you are going to be held accountable. You're going to be standing in front of a court, standing in front of a group of SMEs armchair quarterbacking you and pulling your tactics apart. Right?

Speaker B: Yeah.

Speaker A: You may be standing in front of a spouse, giving them a flag, saying I'm very sorry. You have to know and accept all that. The second you step up to that and realize, you know what, that may be my destiny, but I'm still willing to stand on this proverbial bridge and do this because somebody's got to make these decisions. So that, that is what it's like to be in command of uh, some of these incidents. Both those things running in the background and still being able to focus and say, this is what we are going to do, we have to do it now.

Speaker B: That's really well said. I felt like I was in the position for a minute there. Um, I know you've done some work with AI, you have um, a physics background. And we were talking a little bit about how AI um, can support someone in that role. Can you say a little bit more about that?

Speaker A: Yeah. So we've been studying firefighter deaths for a long time. Um, every time a firefighter dies in a fire, the incident gets investigated. There is a government entity that comes and investigates all of these deaths and we see the results. And what we see are some persistent trends. We call them the NIOSH 5. NIOSH is a terrible government acronym, the National Institute for Occupational Safety and Health. But they are the ones that come in and investigate the incidents. And, and we see these five factors come up over and over again of somebody dying in a building. And um, the death of a firefighter is not only incredibly consequential to the agency and the community, but also when you have been fighting fires for so long, you have to have empathy for the person that went through that. Because to burn to death or suffocate to death in a trapped environment and is one of the worst human experiences that you can imagine. So we have these events, and you have leaders that go, we need to change this. How do we get our head around moving out of these five repeatable steps? Now, a lot of it has to do with the human experience and limitations of sitting in that position of command and keeping up with those rapid calls. And humans, as amazing as they are with coming up with novel and emergent solutions to things that nobody could ever think of, they do have limitations. And under stressful environments that go into gross neurological default. Right. I find myself doing it. I recognize my own trends. So when I get super stressed on incidents, I start to learn. I start to lose the ability to speak clearly. I sound like I have marbles in my mouth, and it's one of my flags that pop up. You'll other. Other humans, they will manifest in other ways. Right. Sometimes they'll start to lose their temper. Sometimes they'll start to grip the steering wheel really tight. We see triggers in ourselves. But the point is that humans have the limitations. And when the stress kicks in, they start to miss stuff. And even if you tell them that this is the thing they're going to miss, the limitations are still there.

Speaker B: So interesting.

Speaker A: AI has the ability to monitor what is happening on scene at a large scale.

Speaker B: Yeah.

Speaker A: And if you give it the correct priors, meaning that if you tell it what to look for, it can pull things out of radio traffic or situations that you might be missing as a human because you're in gross neurological default and grab your attention and say, hey, look, somebody just said they're lost and they're low on air. Now, that came coupled in a bunch of other radio traffic that seemed really important and was driving the lanes of this incident. But that little piece was a leading indicator that you are probably two to five minutes away from a death.

Speaker B: Wow.

Speaker A: So right now you have to stop what you're doing and reconcile that. Now, one of the things that happens in gross neurological default is auditory exclusion, which means your brain purposely triages things that are coming through it and it will kick stuff out. And it's incredible to go through after action reviews and play the tapes of the radio back and, uh, see the incident commander. Like, the sound comes out of the speaker and the incident commander is there listening to it. M. But it's clear, it's clear their brain did not register right.

Speaker B: Wow.

Speaker A: They did not hear it. So there are useful tools out there now that we can monitor radio traffic, give it a set of priors and things to look for and then grab that incident commander's attention. If I can't do it auditorily, I will do it visually. Right. If you're looking at a command board, I'm going to start to flash things at you as if it's a red light on your dashboard. You may be driving along in your car and there's a ton of stuff going on your dashboard and you're ignoring most of it. Right. You're driving along, you're focusing, but all of a sudden that red light starts to flash. It's there to grab your attention, to tell you, hey, right now I, out of all the things that are happening, this is the most relevant thing.

Speaker B: Wow. So it's interesting. So the relationship between the human and the AI is that of really the AI being more of a tool kind of um, an indicator. But then the human has to use the judgment to say, this is a situation that I need to prioritize 100%

Speaker A: as an assistant to watch what's happening in the background and then allow the, allow the human to use their superpower, which is emergent and creative solutions. Right. That's the, the, the interesting part of the fire service and the challenging part of the fire service is that it's never the same, it's always new. You were always stuck in this process of probe, figure out, make decision, probe. Nobody ever gives you all the information. And that's, and that's the human superpower is to kind of work our way through that. So assistant to tell you, hey, here's a new problem you need to solve. That is where the AI comes in as a really useful tool.

Speaker B: Now are there situations in the fire service where the AI would be enabled to make a full decision itself?

Speaker A: I would say no. The AI can suggest, and we have been working on that as well. So there are times in the fire service you get stuck into a math problem.

Speaker B: Yeah.

Speaker A: And the math problem we, we don't do, we don't do math well on the fly especially. We're, we're stressed out. So I'll give you an example. Uh, one of the things that uh, I'm known for is my seven year study on active shooters and the process I went through on trying to change the way the country responds to active shooters. And activ frequently gives you a math Problem that you can't solve on the fly very well. So if you have 50 victims, uh, very similar to like the Pulse nightclub. And those 50 victims, their number one priority is bleeding control and transport to definitive care, which means they have to get to a hospital. Well, 50 victims, you can only fit two victims per ambulance. Uh, so that requires 25ambulances minimum for 50 victims. Most areas don't have 25ambulances, which means some of those ambulances are going to have to hot lap, which means they're going to have to go to the hospital, drop off two victims and come back. But then that is determined on how far away the hospitals are. Right. Driving 20 minutes to a hospital, 30 minutes to a hospital. This is that old classic high school problem of a train Traveling south at 22 miles an hour. Well, we don't do that well, but I can hand that to an AI and say, look, here's my parameters, 50 victims, here's the 10 local hospitals, here's the distance from, here's the distance from my incident where I'm at right now. Tell me what I need and it can produce an answer. Look, 10 of these ambulances are going to hot lap. The other 15 are going to do a one way trip. And you have victims at the hospital in 26 and a half minutes if you do this Right.

Speaker B: Right.

Speaker A: So that is a fantastic tool and decision making process, decision support process that will give that incident commander solutions. Now the funny thing is, as a human, we may not be great at doing the math, but we are fantastic at recognizing the right answer when we see it. Right. So when it gets flashed, you're like, oh wait, that makes sense. I need 25ambulances. I only have 15 in the system. That means some of them are going to have to hot lap. And um, it just mapped out its solution and all of it makes sense. Right. These hospitals are that far away. That means I know that this many, um, this many ambulances are going to hot lap. And now I can start to make the next critical decision is which victims go first.

Speaker B: So this is, this is very analogous to what happens in organizations like for example, for the organizational change professional, their traditional toolkit is being absorbed by AI. So the stakeholder analysis, the change impact analysis, the um, relationship analyses, uh, that they do, all that is being absorbed and then they are then freed up to do more of the consultative work. Um, and so they have more headspace to build relationships with people who are, you know, opposing the change or with their sponsors. So it's very interesting to hear, hear this uh, example, and then, you know, because it's so not corporate. And um, and then think about, okay, so if we can incorporate and um, really think about what it really means in the fire service on the ground, then it really shows and illustrates the power of working in that AI human relationship. And I would think for um, leaders, fire service leaders, it would give them the opportunity to also, uh, just have more data so that when they go back to their office and they're working with their teams now, they can look at decisions and figure out how much more accurately the decisions were made and um, be able to learn from that with their groups.

Speaker A: It's a brilliant, it's a brilliant observation on two aspects. Number one is all about cognitive load. Right. So the humans have limited cognitive load no matter what domain you're in. And you want that cognitive load to be a force multiplier in the domain the human is good at. Right?

Speaker B: Right.

Speaker A: You want them thinking next, you want them ahead of the problem, you don't want them drug into what's, what is distracting right now or go down another rabbit hole. So that cognitive load, if you can free that up from doing the things that humans aren't great at, then you're going to have much more effective outcomes.

Speaker B: Right, Right.

Speaker A: I like, I like the analogy of uh, of being a pilot. So I'm a pilot. I'm an instrument rated pilot. I've uh, been flying for well over 20 years and in my plane I have an autopilot. I think the autopilot is actually one of the first AIs because, uh, it has a sensor, it has an environment it works in. It's got agency over the system that I give it. Agency over. Right. And it's got a performance evaluation that I need so I can fly my plane no problem. But then I can also turn on my autopilot and I can say, hey, look, hold this heading and hold this altitude. Which is a ton of work for me. It takes a lot of cognitive load to constantly engages. But I can tell the autopilot, do this for me. And now I'll take my cognitive load and I will shift into the critical decisions of what's happening next. Right. How I set up for next, my next approach, what is the weather doing. Right. And that is a critical feature for being a pilot. And, and safety escalated massively in aircraft accidents at the advent of these simple autopilots. So that is an analogous historical relationship between an essential machine doing the thing that requires a lot of cognitive load from a human, freeing it up and allowing the human to do the thing that they are really good at. Right.

Speaker B: And Right.

Speaker A: Is, uh, what we want. Also for the incident commander, using that analogy, looking at the fire service, that's something I'm looking into. What I would like is an autopilot in a metaphorical sense for this incident commander so that I can get their eyes up at the incident, seeing what's happening, and m thinking, next. Now, in the fire service, we talk about the OODA loop. This is stolen from the Air Force. And I know the OODA loop. The OODA loop, right. So it comes from a combat pilot. Uh, and the combat pilot says, this is a framework for understanding how decisions are made. You observe, you orient, you decide, you act, and you go in this loop over and over again. Observe, orient, decide, act, observe, orient, decide, act. Now, incident commanders do that. With every decision as this is going, we can watch them think their way through this.

Speaker B: Wow.

Speaker A: Where they get hung up. The critical failure point is orient. They, they observe, they'll see it. But then to orient. And that's a fancy way of saying sense making. Right.

Speaker B: Okay.

Speaker A: Oh, what, what am I dealing with here? Right here. That's where they tend to lock up. That's where they tend to make their bad decisions. That's where they tend to misunderstand the situation and go left when they should have gone right. Goal is, is to have an AI assistant help them orient. Right. Right. Now, you're not dealing with a fire, you're dealing with a hazmat with a fire, which is a different problem. Don't treat this like a fire. You've got a burning exotic material in there that's going to kill your firefighters. Now make a different decision. Now take a different action and then loop again. Right. And that's where we want that AI assistant is to help the human orient so their next decision is relevant to what they're actually facing. Very similar to the pilot.

Speaker B: Yeah. That's really interesting. So, um, what's going through my mind right now is that sense making has been identified as one of the things that AI can't do. So how does that help with the orientation for the pilot in that example? It seems like. Go ahead.

Speaker A: No, no. If I understand the question correctly and you. So if you lay 20 things out on the table and ask the human to choose the most relevant ones, it takes time to process it and it's typically overwhelming. Yes. However, if you have an agent, an AI agent that is very fast at, uh, processing known information. Right. That's the critical.

Speaker B: Right.

Speaker A: Like known information like now what it can do is it can see those 20 things laid out on a table and it can sort them for you really quick. Boom. Here's the three things that you should probably worry about right now. Right?

Speaker B: Okay.

Speaker A: And here's the 17 distractors. Now, you're going to deal with the 17 distractors right now. However, in an order of events. If you don't deal with these three right now, those 17 aren't M going to matter. Uh, okay, so it puts you into the orientation phase of, oh, man, this is the thing that's going to kill us in a chain of events that nothing else will matter. Right? So back into that plane analogy. If I fly into the side of a mountain, my radio traffic, my navigation skills, and, uh, my weather, it doesn't matter. And we have a version of AI running in planes now that have been there for well over 20 years that will yell at you over the headset, terrain, terrain, terrain, terrain. Because it knows you're probably distracted with talking on the radio or selecting your radio channels or looking at some gauge. Right? And so we're looking at. All right, now let's bring that out of a domain that's a little bit simpler than the fire service. Bring it in and allow that to happen to an incident commander. You know, you're facing a mayday. You're facing a mayday. You're facing a mayday. You just got flashed over. You just got flashed over. This is not a fire. This is a hazmat. That hazmat is going to kill you. Don't treat it like a fire. Let's orient to what the problem is right now so you can decide and act and keep going around the loop.

Speaker B: That's really fascinating. It's so helpful to hear this, because when I think about employees and organizations in a corporate America or in a nonprofit, but in a, um, white collar organization, uh, it is hard, I think, harder for people to really picture how the AI human relationship is going to work, um, in terms of the role shifts, where, where the AI starts and stops, where the human starts and stops. You know, and it's so clear in these examples that you're giving that is,

Speaker A: um, and what you just described is probably the leader's biggest challenge moving forward right now is to figure out how to use this tool, when to use it, when not to use it. Right? Where it fits, where it doesn't fit. Because if you overplay it in the fire service, it's going to kill someone. Right?

Speaker B: Uh, right.

Speaker A: If you don't understand it well enough you're not going to use it to its full capacity. So this is, this is analogous to the, uh, to the old stories we would hear about the original Google Maps and people following Google Maps off a cliff.

Speaker B: Right, right, right.

Speaker A: You don't want to do that. You want to use Google Maps at its capacity, but you still have to be in control. You still have to be cognitively engaged to what is going on.

Speaker B: Right.

Speaker A: You can't allow an algorithm to end your life because you've simply offloaded everything into it. So understanding what, what we call AI right now, and I don't even, I'm not even bought in on, that's the correct term to call what we are seeing right now. Yeah, but that's what it's been called. Fine. But to understand what this actual type of machine learning does and doesn't do is one of the greatest challenges to leaders right now because they're going to be forced to make decisions with limited information. They're going to have to do their homework. It is always come back to keeping the main thing, the main thing which is the human in the loop. And now how do we help that human in the loop to do more of what they're good at without pulling them in a wrong direction? And so you may have corporate losses or file bankruptcy in the corporate world. For me, I'm going to wind up at another funeral. Right. And I'm, uh, accountable in a court of law. Right. Two different problems. So I have to think about it deeply to understand how this tool works.

Speaker B: Absolutely. And so just kind of holding that frame and jumping, like if that's frame A and then we jump over to frame B. Um, given what you just said, how can leaders leverage some of these learnings and dynamics when they're leading AI adoption? What are some of the lessons learned? What are some of the insights that they can take about how the human AI relationship works to say the right things, behave in the right way, make the change sustainable.

Speaker A: So let's talk about a framework that allows, allows a leader to make sense of, uh, what is going to happen and how they make decisions. It's called the KMO framework. That is another acronym for Knowledge, Motivation and Organizational Support. It is a form of gap analysis that I have been using for years to gauge my own change initiatives, to understand what is happening. So let's start with the knowledge framework. Right. Because critical to AI, uh, academics break knowledge up into multiple levels. One, starting with declarative knowledge, which means you can say the word, oh, fancy, you can say the word AI. You sound smart. You can say the word, right?

Speaker B: Yeah.

Speaker A: Then conceptual knowledge. Do you actually understand what the concept of our current level of AI is? LLMs, large language models. Right. Neural networks that form, uh, a type of machine learning. Then the next level is procedural. Do you actually know how to implement an agent or an LLM Right. Now, Then finally, the highest level is metacognitive. Do you know when to and when not to do this? Now, so many leaders usually stop and start at the declarative level, right? They learn the word, it'll be. It'll become a cliche, they'll spit it out all the time. Well, I think this is one of those most important events where you actually are going to have to climb that ladder yourself. Right.

Speaker B: Interesting.

Speaker A: You're going to have to have a good understanding. You're not going to be able to BS anybody on this one. You're going to have to do your homework and understand what an LLM does and doesn't do. Involve yourself as much as you can on what the agents do and don't do. Watch how they hallucinate, watch how effective they are when you give them strong priors. Right. Watch them do their job. But then also you have understanding what they don't do. Okay. So that's, that's the knowledge framework.

Speaker B: Yeah.

Speaker A: You move on to. If you've got your knowledge down and you've educated the people around you, you move on to your next potential gap, which is motivation. How is this actually working in the real world? So motivation sometimes is, misunderstand, misunderstood this. But when people see this tool coming in, do they see it? Do they see its value? Do they understand its task value? Is it just one more thing that they have to deal with or is it actually making their life easier? This is something. Hm, I always try to have empathy with for the people, I'll say the people that I work for. But it's the, the firefighters and BCS that work for me. Right. I work for them to help guide.

Speaker B: Right.

Speaker A: You know, am I making their life harder or easier? Right. If I'm making their life easier, then I'm on the right path. Because the easier I make them their life, the better they can focus on the mission.

Speaker B: Right.

Speaker A: So do they value this task? Right. Will they be persistent in following forward it? Because if they value it, then they're going to be persistent and they truly have to believe that this is going to work for them. Because if they don't believe it's going to work for them, they will tell me they like it and Then they'll undermine it later. Right? Or they'll only, they like it and the second I'm gone, it'll, it'll swing right back the way it was. Right? It'll just be one more initiative. Right? So digging your way through the motivational framework and really understanding what is happening at that human level is critical. And then finally the O is the organizational support. This is where we fail the most usually. So we'll come out with initiatives as leaders, right? We'll make these assumptions and we'll say, all right, look, this is the thing we're now going to do. However, I'm not actually going to make an effort to support this change in a sincere way that it's supposed to be done. So organizational support happens on multiple levels where you not only have uh, your written policies, but also understanding of your cultural policies and the correct training and education to understand how this change is going to help you. So I'll, I'll give you, I'll give you a parallel example of organizational support and then we'll, we'll tie it into AI. So, um, active shooter has been a challenge across the nation since 1999. Um, I did this thing called a seven year active shooter study. I applied a specific framework to do this and one of the things that I spent most of my effort in was priming the 500 firefighters in Sacramento, priming them with knowledge of what the problem space actually was. This was the organization coming in and helping them understand what was actually happening in active shooters. Basically washing out their imagination, watching out the die hard movies that they have watched and brought down into reality to say, no, this is what the evidence is telling us and if we have a clear understanding of the problem space in the mission now, we can make some good decisions. Opposing most agencies just laid down rules and laws and never made an effort to get their employees to understand what the actual problem space was.

Speaker B: Okay?

Speaker A: And they failed over and over again. That's why it's been such a challenge for so many years is they started it from the top down. But I made a massive effort for, uh, well over two, uh, months of me personally meeting with firefighters in groups and explaining the problem space to them. That was organizational support. And then we bought them the right equipment, we bought them the right type of training and gave them years to evolve through reps and exercises and training. It wasn't a memo that I forced out and said, here, confirm, uh, that you've read this memo and then moved on. That would have been guaranteed failure.

Speaker B: Right? Right.

Speaker A: So you Take that. Organizational support. Whenever I actually have a poster hanging on in my office that says KMO Knowledge, Organizational support. Ah. And I always point to like, where did we fail? Did we fail here at the organizational support side? Right. When someone made a poor decision, did we fail to train them? Right.

Speaker B: Right.

Speaker A: Did we actually invest in them? So when you, when you bridge this into AI, this is not going to be an easy button push. It's going to take that, uh, organizational support to ensure that you engage with the people that you work for.

Speaker B: Right.

Speaker A: Them understand. Just like you had to understand the knowledge levels of what this thing does and doesn't do and how to help you and how it will make their job easier. Right. Sincerely, yeah. Um, and then provide them the tools and the training so that they can become a force multiplier. That framework has guided me to my successes. It's helped me understand my failures because I have more failures than I do Successes over my 30 years of trying to push my way through certain things and looking back and going, you know what, that's where I made my mistake right there. Organizational, uh, support was not, was not invested in enough.

Speaker B: Yeah.

Speaker A: The other two.

Speaker B: Well, I applaud you for, uh, doing, um, the retro look back, uh, on your work, on the learning experiences or failures that we have, um, because that's where the learning is and that's where we can improve and become more expert in what we do. And that's the gift that I think we bring forward to the people that we are trying to help improve. And that's the tacit knowledge that, um, I feel so many organizations are losing as they lay people off is they're getting rid of this tacit knowledge that the AI hasn't captured yet. You know, and, um, that's the part of this AI human relationship that is hard to bring back to, um, reality is that the AI is only as smart as the information it's given. And you know, we have a, we have a responsibility to give it the right, the right information. And sometimes it doesn't have the context, it doesn't have the judgment, it doesn't have everything that it needs. But, um, coming back to the whole thing that you shared with us today, you know, I can see how AI can be used as a tool for, uh, kind of helping us hold a whole bunch of information and then winnowing it down to priorities. I can see, um, how AI can be used as a tool for, um, identifying, uh, different issues that we might not be able to have seen because we weren't able to Cognitively hold that information so it can actually be a bigger brain for us. Um, and I can see how, uh, also some of the work that you know, you were talking about on how AI and humans work together. I can see how um, leaders really need to, they really need to respect the employees ability, um, to understand how the AI actually can serve them. Uh, it's not necessarily, take, uh, this AI, use it, go faster, do more work, do more work, do more work, do more work. It's, wait a minute, here's the work that you do. Um, now let's talk about what of that the AI can actually do for you. And now let's talk about maybe the new types of work that you can take on that are more aligned with what you're good at. And I think that that is like really important. Those are really important lessons, Eric, that you brought forward.

Speaker A: I, I want to play off the, the last thing that you said. What, what else can we do? What type of new work can we do? So I think many leaders are going to make critical mistakes here, uh, with the advent of AI.

Speaker B: Yeah.

Speaker A: And just the idea of laying off workers or replacing humans I think is a fundamental misunderstanding of actually what your people do.

Speaker B: Yes.

Speaker A: And, and I think it's a critical mistake. Now I, I can't make that mistake in my domain. And I, and I won't. Um, because not to just keep beating this thing in, I'll wind up at a funeral. Right.

Speaker B: Yeah.

Speaker A: I can't make that mistake. It's not declaring bankruptcy or having a red line on a balance sheet.

Speaker B: Right.

Speaker A: But uh, that lesson of what you just said for every entrepreneur or business leader out there is to say, don't think about what you can lay off, think about what more they can do. Additional tool. Right. And think about now what more you can do.

Speaker B: Right.

Speaker A: As a company, where can you expand? Have some forward vision on where this is going. Because humans have always evolved and expanded in our knowledge base.

Speaker B: Right.

Speaker A: Reached heights that were unimaginable to exactly prior generations. So, so I need those leaders to have that vision saying, yeah, this is an amazing new tool. It's a force multiplier. So if I keep the, if I,

Speaker B: if I keep the force, if I

Speaker A: keep the force I have now and I multiply that force, I'm going to be that much bigger. If I reduce my force and have a force multiplier to stay in the same spot, I'm a fool.

Speaker B: Right.

Speaker A: I misunderstood what the human does and doesn't do as a superpower.

Speaker B: Yes.

Speaker A: And fundamentally missed what My job as a leader is to do so, uh, that, that like so many things in human history, will probably play itself out back to the gap between theory and practicality. Right.

Speaker B: I think you're right.

Speaker A: So many of these leaders are going to have a theory that sounds right, they're going to say the right words, but practicality is going to catch up with them eventually. And if you don't know how to use your force multiplier, your competitor will know how to use the force multiplier and you will find yourself out of business when they know how to do it and expand.

Speaker B: I agree. I agree. So, Eric, we're about out of time. Um, if, and I think we've had a really amazing conversation, I'm going to have to have you come back so we can explore some other dimensions of Eric Sailors. Um, uh, what concept or comment or phrase would you like to leave our listeners with? As we close out this interview, be

Speaker A: curious and think deeply about how you are going to help your human people that you work for use a new tool.

Speaker B: That's great. Thank you very much. And how can people get a hold of you if they want to continue the conversation?

Speaker A: They can find me on LinkedIn, you can find me on Facebook, uh, reach out to me. I will reply.

Speaker B: Fantastic. Well, Eric, thank you so much for your time, your brilliance and your, um, service, uh, in the work that you do. And um, for all of the thoughtfulness that you brought to this, uh, to this interview. I really, really appreciate it and I think this is a very rich learning experience for the people who are able to connect with it.

Speaker A: Thank you. It's been a pleasure. We hope you enjoyed this episode of the Change Management Review podcast. To get alerts for new episodes, be sure to follow us on LinkedIn, subscribe to our website weekly newsletter, or subscribe to follow on Apple, Spotify and wherever you find great podcasts.