From AI Strategy to Execution: Ethical Leadership, Trust and the Operational Reality of Healthcare AI

Chris Hutchins: Today in the Signal Room, I'm joined by Brian Sutherland, elite AI product manager and advisor focused on customer-facing AI and high-consequence healthcare environments. Brian built Humana's first member-facing intelligent virtual assistant, a platform now generating more than $7 million in annual savings while improving patient experience, including a 31% lift in task completion and measurable gains in satisfaction. That combination, financial impact, and human impact is rare. His perspective on AI is not abstract. It's shaped by decades of watching his mother navigate a health system fragmented across payers, providers, and pharmacies, where automation often added friction instead of removing it. That lived experience shows up in how he designs systems, understands the manual processes before automating them, pressure tests decisions before scale makes them irreversible. And he builds AI that meets patients where they are, not where the dashboard says they are. Brian advises leaders who are building out or buying AI and need clarity on what will break first, where trust the roads, and how to make high-stakes decisions they can defend later. Brian, welcome to the signal room. Been very excited and looking forward to having you on. And I just want to jump right in because there's so many cool things that we that we're going to talk about today. Um, you know, in our first conversation, it was clear to me that uh you're you're the right guy to come talk about some areas that I think are needing a lot more attention than they're getting these days. So let's start with one that's probably kind of a top of mind thing for folks, and I seem to be seeing a lot about this, but where do you see AI initiatives most commonly failing? And is it like leadership alignment, workflow design, operating model, or is it a combination of those things?

Brian Sutherland: Oh, it's definitely a combination. It it's I don't even think I could just say like point to one thing. A lot of that I see really happening is leadership will come in, they'll have a really great and strong idea about you know what it is that they want to do. Right. And it it's the you know, almost the classic story. We have an initiative, it becomes a whisper down the alley game, long-winded translation. But then I also believe that we are also up against a relatively young technology still. There's so much that we still don't really know as far as what disciplines we should be really putting forward into this, how we should be positioning this. And so then, yeah, workflow is yet another portion of this. So it really does come down to all three of these things.

Chris Hutchins: You you point to something that I think it it needs to be repeated. Yeah. This is still relatively new. We've gotten so used to things moving at the clip that they're going six months into something that's still relatively new. We feel like it's legacy to us, and we forget that we're we still have a lot, a lot more uh growing and learning to do to really get these things to operate and do the things that they're capable of.

Brian Sutherland: It's so true. And and it, you know, like the technology may be moving really quickly as human beings, right? We're it's in our nature. It's it's just you know, it's just part of us. We're wired for comfort, we're wired to not want to go through difficult growth, difficult challenges. So the technology is is moving faster than people are willing to change. I mean, it's a significant friction point, and it's something that whether we like it or not, it's like like uh what how does the old saying go? It's uh when you know you're you're looking at a sequence of events, you're looking at like you know, a daisy chain of processes, you're only as fast as the slowest item in your your chain.

Chris Hutchins: No, that that's true. It's it's really kind of an interesting dilemma. We've got these two things that are almost d opposite each other right now. So the trust factor in human relationships has eroded significantly, but we're still way too quick to trust technology. And I I we need to find some places in the middle there, I think.

Brian Sutherland: Yeah, yeah, for real. And and I think, you know, even talking about trust in general, there's also because we have plenty of sci-fi out there uh around AI in general, that has really kind of informed us of this could turn into something really terrible, really catastrophic. Not not to go into like doom and gloom, but everybody's first instinct. I I'm hearing my father going, you know, this is Skynet that's coming around. And that in and of itself has has sparked a lot of distrust right out the gates. And that's not even the real distrust that we should be having, in in all truth. There's so many other layers of distrust that we have to actually cut through.

Chris Hutchins: No, that's an excellent point. You know, when you're talking about adoption and you know, whether we do it quickly or not, the interesting dilemma that I see out there is that there's pilots everywhere. And you know, they everyone gets excited about it, but then when it gets deployed and we start to get into it a little bit, it seems to stop really being proliferated and and get the traction that it should. Yeah. So why why is that from your perspective? Why do they start to hit some barriers when they get to an enterprise level?

Brian Sutherland: I mean, it it's all over the place. It's it's pervasive, right? The this endless supply of pilots. The the common pattern that I I really start to see does come back around to starting with that trust factor. Usually the big thing I I tend to spot out almost immediately is the ambition is larger than what was actually designed in the system. It's not designed to build trust, it's technically designed to give maybe a quick answer. And we it maybe covers I will say like 20% of the big use cases that are out there. But in in trust building, you can't just say 20% and you're done with it, right? The old 80-20 rule. You do have to still care for that other 80 in some fashion. That doesn't mean we have to build everything out, but it does mean that we have to be considering what do we do for that other 80.

Chris Hutchins: That's a really interesting challenge challenge to try to overcome. I think expectations are something that we don't do well with sometimes in terms of setting them at the right levels. And I, you know, I've been talking to some folks recently just about the the criticality of some of the roles that are are not necessarily put close to this front and center of the process that need to be. And those are the ones that are really going to be helping to drive the execution and help to really make things stick. Make sure people are understanding what the value really is and why it's important that we're we're we're trying to tackle these things. Because it's not a shortage of administrative functions that really could be done much more efficiently. We just need to listen to the folks who know exactly where some of the pain points are so that we can address the right things. I think yeah.

Brian Sutherland: And you know, one of the ways um, like I I actually wrote about this recently on LinkedIn. One of the ways that we can actually go about that to really get as close as possible um is to become one of the operators ourselves. I actually had helped Humana with building out um one of their first AI customer-facing AIs that that they had out there. It was in their chat platform. And uh one of the things that um that I recall from it was uh I actually took calls for two weeks prior to even working on the project. And it the exposure that you get from that, like hearing what the customers are actually going through, in in the case of Humana, we'll say members, because it's member experience, patient experience, it actually created a couple of different dimensions of thought, right? I didn't just see where the pain was, I also saw where it maybe was more appropriate to put that technology and where it was less appropriate to put it.

Chris Hutchins: Yeah, that's interesting. There's uh I think I'm thinking back to you know when I was working in New York. Um, on one occasion I got to go have a uh a tour of the one of the office suites where they were taking care of uh cancer patients. Right. And what was really remarkable to me is that things had changed dramatically over the course of a number of years. And not only did it help me to understand where some of the pain points were, it gave me a much better appreciation for what people are doing day in and day out that are really right on the tip of the spear when it comes to taking care of people. We were just at a very different place. So as frustrating as it can be for people like us, sometimes doing implementation because we're not getting where we want to go fast enough. Sometimes we need to pause and take a look back and just see how far we've come. And it's it really makes it worthwhile when you when you take the time to do that. And it you undoubtedly will come across things you had no idea how big the impact was, and it makes the work feel much more rewarding.

Brian Sutherland: For sure. And and you know, even funnier too, and I haven't done this in a while, but returning back to that workflow, right? Like you just said, how far we've come. Uh, we we forget as as implementers that we moved the needle and our understandings are all built and predicated off of an outdated workflow. You know, and it and we're seeing coming back to like the original question, we're seeing a lot of that that we have new workflows that we really need to be assessing and evaluating, and then figuring out where it's best to place the technology, where are the friction points within that existing workflow, not the workflows of of yesteryear.

Chris Hutchins: We're talking about the expectation component there for for a second here. F what do you think gets uh most underestimated between the time of the executive approval and the frontline deployment? Because there's usually some level of expectation that we've missed somehow.

Brian Sutherland: Oh yeah. And you know that the uh that that timeline is gonna vary. I s I, in my opinion, it's gonna vary from organization to organization, right? You're your your smaller shops, like yeah, it will say like startups, it it's probably pretty quick, right? The it's such a small company, the nervous system isn't really completely blown out yet. Contrast that to a Fortune 500 or higher, right? Um it it all of the the things that I'll say we take for granted in maybe like your standard rollout, right? So you usually have a PMO of some sort, or maybe a change management office. There's a a running tally of these are the the key essential items of anything that has to be gone through that has to be done prior to saying we feel confident and comfortable with a production release, right? Now it seems like that's all been forgotten. That the technology, because it introduces this new degree of speed, um, the thought appears to be that really the wrinkle was all around how do we get the technology built faster? That was never really the the issue. The the issue is actually centered more on how do we coordinate all of these different competing audiences together and how do we get them on the same page? What do we need to do to instill confidence in all of these individuals that we're working with so that we're we can approach a production environment free and clear.

Chris Hutchins: Yeah, I think some of that kind of leans into you know governance conversations to some extent, um, not from the way that people traditionally think about it, what I I hate to even say traditionally because it's it's been such a an academic exercise for for the most part over the last decade or so that I've been more heavily involved in um you know the technology uh development or even data analytics. How do you see from a functional standpoint where governance really can help be an enabler versus that the the external factor that feels like somebody's trying to keep us away from things or trying to limit what we can do?

Brian Sutherland: Yeah, right. Uh it and and unfortunately, uh I know how like how you feel about you don't want to even say the word traditionally, right? Governance in and of itself now has this perception that's attached to it, uh, that governance slows us down from the speed that we're trying to gain. What is unfortunate is governance really protects it, it has the interests of what you're doing in mind in the sense of enabling for going forward. Uh, governance has to have a very significant play, at least at some point in the process, trying to help navigate um a partner right now through trying to get in something into production at this um current stage. And I won't name names for the time being. But the the short story is uh it's not me, by the way, everyone. Uh but the short story for for that particular group, that there's a lot of pioneering that's going on. And um in that pioneering, we have to at some point pull governance in. We have to make sure that when we pull them in, that we are not just allowing governance to turn it into a death by committee. Um, we want governance to inform. We want governance to feel confident that you know we have been thinking about these different areas that we have to be really mindful of and we have to be careful of. Um, but that we can't do it without them too. That their input is important, it's valuable, and that it helps to then shape and crystallize what are the controls necessary to be able to freely scale.

Chris Hutchins: You're talking about something that I think is like is really needs to be understood much, much better. When you're thinking about it as an enabling uh function, you need to think about the the different perspectives you want to make sure you have in the room to make sure that you're asking the right questions, make sure that you're solving the right problems. Oftentimes there's the the initial what I would call the survey, or if you want to want to call it something, I don't even know what to call it. But you know, you have a listening session, you take have you identify a few different takeaways. Right. Then people go and will build something in a vacuum and come back, and there's a surprise waiting for them that it actually is not meeting the need that they thought they were trying to address. Yeah. That it's it's really an involvement in a relationship that has to actually be engaged. And when you're dealing with AI, there's an evolution. Your models are always going to be training. So there's got to be a really functional cycle that accompanies development and deployment, an ongoing operation of the solutions we put in place because it's going to continue to learn, at least theoretically, and it's going to solve a whole set of problems. And then at some point in time, we should see enough of a transformation that we can actually start adjusting some deeper things or go further. But we've got to have that the rigor and uh the procedural um aspects of making sure that we are governing and we are monitoring and we are constantly having the different perspectives looking at the things that we're working on.

Brian Sutherland: I mean, it's the yes and at this point, um, because it's everything that you just highlighted, Chris. And with those additional perspectives, um the the failure of uh of what happens when you have somebody take these specs, go off into a silo, start building the blind spots that no one can really predict or anticipate. You're never going to see those blind spots on your own. Uh, and then this introduces yet another perspective that wasn't even considered. When blind spots start to get surfaced and we start to pressure test against those blind spots, there is a natural order of resistance. And from what I, at least my personal experience, usually that resistance is based on I didn't really think about that when we first started. We've moved down the journey so far already, and I've made some pretty big commitments right now. So now what do I do? Do I go back and now have to renegotiate on those commitments? Right. And what kind of fallout am I gonna feel from that? Um, so it that's a little bit of my own um recognition of what an appreciation of what certain individuals who are getting that pressure, what they're feeling, and how do you really navigate through that challenge, right? What what expectations do you set? And what's your your most people would say the uh the get well plan, right? How do you get to launch and then how do you do your fast follow?

Chris Hutchins: You know, that's an important point though, is uh I think when uh oftentimes we are working on developing and deploying a system, which is you know that that happens every day, all day long in in most organizations, the if they've got technology there. But the the challenge is the the things that you're not anticipating that do come up, no matter what it is that you're developing, if it's a system, an application, a platform, there are always these things that you come across that you didn't anticipate. And the governance in the context of seeing all the angles, to me, that's one of the most valuable things you can have because it's much harder to miss potential gaps if you have as many perspectives as you could have looking at it from the different angles. Um, you mentioned blind spots. The hard part of them is we don't even know we have them. And so we need to have people that are seeing all these different perspectives.

Brian Sutherland: Some of those blind spots are are in all truth, sometimes they're benign. Oftentimes they are actually very detrimental, especially if we start getting into some of the the the more uh le like you know the the legal aspects of things and fines, penalties. That's and that that's only just scratching the surface. That's not even exploring branding impacts, that's not exploring your lifetime value impacts, um, anything that basically could have negative effects on your business.

Chris Hutchins: These things that as hard as they are to kind of implement and get moving on, they do finally get to get you to a much better place in terms of what I think what I think is important is trust. So in your experience and going through different implementations and figuring some of the things out that you've been talking, talking about already, you know, what does that trust start to look like inside of an organization as you as you're maturing and going through these processes?

Brian Sutherland: No, it's uh honestly, it's not easy. So it it it looks like a number of different things. Uh it it's most I think most people who probably will be listening in are gonna be no stranger to walking the tightrope. You obviously have some uh we'll say there's some sort of objective that's been set, you know, probably at the most senior of levels in you know executive leadership. And you're you're now trying to also balance out against what you know could have some very adverse effects while also balancing against that. You know, one of the things that I look at is are there alternative ways to still get to, you know, say you had a um we'll say you had a savings goal for the year, right? Are you are you do you have alternative ways to obtain that savings that perhaps was not what you originally thought? You know, maybe it your original thought was let's automate something in the foreground. You know, let's say maybe we do a generative readout to your customers, but then that has a lot of risks attached to it. Is there a way to be able to build trust in your experience first while also building the muscle for generative experiences maybe in the future? And how do you also gain some efficiencies leveraging those generative experiences to streamline or reduce effort when you get into and we'll we'll use a contact center connection? When you finally get to say that upfront IVR 800 number and you get it to the to the agent or the associate or the person at the front desk, whomever's taking that call, what are ways that we're offloading the effort on their part um so that the costs that are associated with that start to become less?

Chris Hutchins: I think the an interesting piece of it is there's there's a lot of different angles that we look at this uh, you know, the the trust piece of it. But there's the trust in people, which we we touched on a little bit. But now when you're talking about the recommendations, uh how do you see uh really overcoming some of the barriers to get to helping clinicians to get comfortable, to trust recommendations or or to at least You'll be able to assess whether they're they're you know really solid or not. And then similarly, if you talk a little bit about the what it's like on the other side of it, is it's really the patient and the provider relationships or the patient and the nurse relationships. Those are really what we're trying to make sure that we protect and preserve. But it seems like if we're not doing things purposefully, we could really mess up in the trust arena that could cause those things to go completely opposite for where we from where we want to go.

Brian Sutherland: Yeah, that's precisely right. And and part of that trust building is also knowing when it's personal, which the more personal uh something gets, the less likely you're gonna get trust out of a person to want to go to a machine. Whereas if it's something that is like, you know, say I went on Amazon to go buy a pair of sneakers, it yeah, that there's some personal aspect to that because maybe I just need the new pair of shoes. But if I don't get that new pair of shoes, you know, I I'll still be alive. When we're talking about healthcare, which is extremely personal, and we we talk about you know, we'll we'll even look at it through the lens of provider and patient, and and not only provider to patient, but also we'll add in the insurance company, the payer, right? It's very personal at that point, especially for the patient in the middle, right? Patients don't go to the to the hospital just for fun, god forbid, and I'd never I never want to like you know stomp on anybody who actually suffers from from conditions like this, unless you have a you know a medical condition where it's like, ugh, but um patients are going for to providers because there's something that they need to have addressed. There's something with their health that's at at stake, and it's survival. It's not um it this isn't something that you know you want to put in front of a machine. So trust building in in a personal experience, you really need a human in the middle in order to be able to build that trust. A machine is never going to be able to quote unquote understand uh what it is I might be going through. It can mimic language to suggest that, but a human on the other side is going to snap and react very quickly to that.

Chris Hutchins: Yeah, and that's a huge uh huge point of emphasis that I think we should really make sure that we we touch on more probably even more so uh in in the coming uh months ahead, because the human relationship piece of it is absolutely essential. And we when you have when you think about where somebody's state of mind might be, when they're going to the doctor, there's already a deficit that we need to address in what that there's something that's not going just swimmingly for them. Right. And so it's really, really important that we we make sure that we're not trying to get the technology to somehow act empathetically. That's not that's really not gonna be helpful because it is it cannot it can't read body language. Well, I mean there there could be some technology on this, you know, theoretically, but I don't even think we care about that. We don't want that. People don't, you know, if when something breaks, I don't know about you, but I don't want the automated line when I call.

Brian Sutherland: Right, right. I I want to talk to a person. It's funny because you mentioned body language. There's there's also a hidden language because most of the applications right now, you know, to your point, it's not able to read body language. It's and Larda, a lot of that is because of the limitations of what medium is being used. I'm picking up a phone. Well, it's that's purely audio. I'm typing in a chat window. Well, that's just words on a screen. But there's a hidden language when we're in in an audio um conversation, for example, awkward silences. Maybe it's a you can hear a tonal change in the person's frustration, uh sure, yeah, or distress. And and these are are things that the machine is not able to respond and react to effectively. Um plenty of people that are trying to to make that a reality, but when you have a a you know, a very personal situation going on, you don't want to guess that they're having a bad situation. You want to be absolutely pinpoint right that they're having a bad situation.

Chris Hutchins: Right. Yeah, I think you you're you're hitting on something that I think is is another place where we need to build some things into our workflows that purposefully give us a moment to pause and and really consider where our things exactly at this moment, and are there are there things that I should be thinking about differently based on what I just heard or what I just saw? AI is basically, for for lack of a better term uh phrase, it's it's almost a decision tree type of thing that you're dealing with. And it's looking for one of three answers, and you give it a fourth one, what's it gonna do with that, right? We have to have some mechanisms, at least from a procedural standpoint, that we're when we're working in in deploying the technology, that there are pauses at strategic moments where you're enabling the human judgment to step in and do what it needs to do.

Brian Sutherland: Exactly. And and now it it's with now we have um generative capabilities. I really do call it like mimicking a person at this stage, where you know you have your branches, um, you you supply this fourth option, it's an unpredictable item, right? People in in their very nature, in the in the core of how we engage in dialogue. You can predict to a certain extent, but there's always room for unpredictability. So now we are also in the realm of you know, you maybe you have the guardrail to stop a random inject and contain it. Without that guardrail, though, you could run into a situation where, depending on how creative you set it up, it the bot will start to, and I hate this term with all my soul, hallucinate. I more personally say it's just making stuff up. If you were to treat it like a person, what would you say? Well, the person's thinking on the fly, and they're making up whatever, like they're thinking in real time, and they're making up an answer based off of what knowledge they have and what intelligence they have exposure to.

Chris Hutchins: Yeah, this this is a this is a crazy time that we're we're living in. I mean, uh I remember back in the was it the two early 2000s, you know, when the internet was was still relatively new. I mean, like you could hear me get firing up the modem, my my tiny little uh Macintosh computer that my phone can outperform now by what I don't know, a million percent.

Brian Sutherland: Yeah, right. It's insane. Back in the uh, well, so let's see. Uh so when modems were like first coming out, like 14.4K, I think, and then you know I had a 9600 or whatever. Oh boy. Oh man. So like that's that's uh even slower still.

Chris Hutchins: Yep. Yeah, it is it it's crazy. I don't know if I wouldn't have believed it. We would you know we would deal with the technology that we are back then. I I was I thought what we were doing then was pretty pretty amazing. Yeah. The fact that I could actually I could actually write basic and make that make a create a formula that would just count infinitely. I thought that was great. I created the nice little loop. Yeah, not not so earth-shattering anymore. So I want to kind of pivot a little bit because we're we're kind of getting getting to the end. I won't I don't want to miss the an opportunity just to talk a little bit about where you see some uh opportunities where leadership needs to start to think and and do things a little bit differently. Because I think they when you're talking about governance, again, that we're not talking about you know controlling and stopping things. We're talking about enabling them. But there needs to be some structure and some framework and guidelines that really help to keep us focused in the right areas, make sure that we're moving in the right directions. We understand the organizational tolerance for risk. Uh, we also understand, you know, what what the legal ramifications are for the what-if scenarios that maybe we're thinking about, maybe we're not. Um but what would you what are you thinking about and and what are you telling folks as you're talking to organizational leadership, um, potentially board members, how should they be thinking about it? And you know, what do we need to be uh considering differently than we do today in terms of how we over we're overseeing things?

Brian Sutherland: Yeah, my I my so first and foremost, uh with with all of this, right? A lot of what my messaging tends to be is centered on getting it your framework in order, treat what you have as an employee as as maybe as untasteful, maybe because I I know that there's some folks uh like McKinsey has uh some order of of like some thousand employees that are uh all badged as AI employees, but there's some soundness behind that, right? If you treat what you have as an employee, so remove AI from your vernacular and and just think of it as it's a junior grade employee, what would you be doing to support that employee? Right, you would have onboarding um instructions for making sure that that employee can become effective within their first you know 30, 60, 90 days with an AI tool that you're introducing. If you treat it like an employee, you might have a longer runway, but you'll more than likely develop something that will actually benefit you for far longer than your average average employee that you hire. And then we have to also look at it as it it's never going to mature past the junior employee stage. So what does the support staff look like around supporting that employee? What kind of remedial training do we have to administer on a routine basis when a new policy is being determined and it is being implemented, just like as you would with a a product um and another employee within your organization? Uh, you need to consider the training timeline for getting that person up to speed. So that that that was definitely the the uh epicenter of a messaging that that I have. And then um uh there was a second point, and uh and unfortunately my thought train took off and uh we got very wrapped up in that one. I'm sure it'll come back to me at a later day.

Chris Hutchins: Well, I I think the the really important distinction that and I love the way you frame it, think of it as an employee because it makes a lot of sense to do that. And why it's so different is we're used to uh putting technology in place that's really automating rule-based tasks. And it's you can turn it on, you have to, you know, feed it some electricity, you might have to, you know, tune things up once in a while, whatever when we're talking about equipment, computers, or whatever. This is different in that what we're implementing will continue to evolve, it'll continue to learn. It actually is interesting because it if we don't miss the opportunity, we might be able to make some significant improvements in how we even understand medicine because medicine is an evolving science, always has been. But yet when we try to measure things from a quality standpoint, we're treating it like it's static and it never has been. Right. So, you know, this governance in a process in how we think about it really needs to be construct constructed differently so that it has that uh understanding and that it has to continue to be adapted. Because when we approve something today, six months from now, the approval is based on something that's no longer true, usually, because this this capability is going to continue to advance. Right. So I I love the way you put that.

Brian Sutherland: And with everything that you were saying there too, Chris, you you actually uh jogged my memory. Uh the train came back into the station. The second point is if you are treating it like an employee, assume that it will make mistakes, not that it might make a mistake. And as you think about that, design your support struts, your framework for when those mistakes happen. What is your your policy and process and procedure for those mistakes? In a world like healthcare, uh, the the most common mistake that I see come up is usually it's not intentional in any way or malicious, uh, but it would be an unintentional breach of PHI. And now I've accidentally disclosed information I should not have. And anyone who all of us in the in the healthcare industry, as we all know, you have a policy in place in your organization for a a breach happened. Now we go and disclose it, we self-report, and that protects ourselves, and it also strengthens ourselves so that we protect our our patients and our members in the future. That we we don't have one leak become a million leaks.

Chris Hutchins: Just a little bit of a difference between there's between those points. I think that's a very well put framing for that. Um so as we're kind of wrapping up here, uh, we've talked about you know moving fast and you know, the the areas where we need to make sure that we're taking the right steps. Not we don't want to slow progress, we do want to make sure that we're doing things at a at a logical and a responsible pace, and we make sure we do have to make sure we've got people looking out for the blind spots that we have. It's always gonna have to be there, unfortunately. But that's that's life. We're human beings and you know, I don't know about you, but I have friends and family members who have probably saved my neck more times than I could count because they saw something that I didn't see.

Brian Sutherland: I mean, I I would be lying if uh if I were to say, like, oh yeah, I've never had a thing come up. Um we we've all been there, right? Um, and and call it our guardian angel or whatever you want to call it. But yeah, it we're just it's part of human nature.

Chris Hutchins: Well, if I could have you just think about the this for you know as we wrap up. If you think about looking ahead in your crystal ball, what do you think the next five years is gonna be the thing that's gonna make the difference between organizations that really implement uh effectively and manage well uh using these AI technologies versus ones who may fall down and not have the success that they're they're aiming to have?

Brian Sutherland: Yeah, I I I in my heart of hearts, um, I I think that the the the biggest uh differentiator is gonna come down to the organizations that can effectively structure themselves to treat this AI as an employee, regardless of where you put it in your configuration, regardless of how you think it might be protected, treat it as though it's a person. Don't call it a person, of course, right? It's still not a human at the end of the day. But if you were to to center your thought on that, those organizations that can build frameworks and governance around that, those organizations will have staying power from now until the next big disruptor comes out. Um and then we get to do the merry-go-round all over again.

Chris Hutchins: I I like what how you you frame that, and I I think it's an important thing to note as well. Um, we are dealing with a period of time where people are pretty uh uncomfortable and they're they're nervous about their jobs being replaced and all that. And so when we're talking about you know, treating it as an employee, the the intent here is is really around making sure that we understand that it's going to continue to evolve and grow. It has nothing to do with with what what the full the role or even the function is. It's that it needs supervision, it's artificial. Artificial means not real. And well, in my simplest uh understanding of the term. Maybe it's not exactly correct, but we do need to treat it that way. Um, but not for not framing it as it's it's just a human being. Well, this is evidence you're gonna try to replace me with a machine. Right, right. Not what we're talking about.

Brian Sutherland: It it it's uh I mean to put it bluntly, it it knows how to mimic to a certain extent. Kind of think of it like a parrot, right? Parrots are incredible creatures that can mimic human speech, human sounds, but it itself is not a human being. It's not effective at interpreting um all of our language. There they do, of course, you know, an animal is gonna be a little bit closer to having emotions and feelings, uh, but it it's it's parroting back information that it has been fed. So it it is the ultimate imitator, human um human language and human engagement that we have seen in the entire history of the human race.

Chris Hutchins: That's it for this episode of the Signal Room. If today's conversation sparks something in you, an idea, a challenge, or perspective worth amplifying, I'd love to hear from you. Message me on LinkedIn or visit Signal Room Podcast.com to explore being a guest on an upcoming episode. Until next time, stay tuned, stay curious, and stay human.