Steve  0:09  
The HR Happy Hour Network is proudly supported by Workhuman. We talk a lot on this show about where HR is headed. One of the most exciting developments I've seen lately is what Workhuman is doing with future leaders. Future leaders is the first talent intelligence tool of its kind, helping companies identify high potential VP plus talent already inside their organization up to four years before they step into the role, and it's not based on gut instinct or visibility. Using real-time recognition data and proprietary AI, it reveals who's already influencing and elevating the people around them. Future leaders from Workhuman is setting a new standard for internal talent development, and you can learn more at workhuman. com. Thanks for joining us. Welcome back to the At Work in America show. My name is Steve Boese. Man, I am super excited for today's show. Of course, we've been talking about AI for a few years now on the show. We've covered tons of AI topics, talked to everybody in HCM tech and workplace tech about AI, but we haven't spent enough time talking about how AI will change, needs to change, and and will just make work a little bit different for folks doing a lot of these HR processes, like performance, like career development, and just getting the most out of people. To help us dive into some of those topics today, we have Bryan Hancock. Bryan is a Partner at McKinsey. Bryan, welcome to the show. How are you?

Bryan Hancock  1:43  
I'm doing well, thank you. Thanks for having me.

Steve  1:45  
It's our pleasure, Bryan. It's great to see you. I have very fond memories. I mentioned in the pre-show, I did some contracting work at McKinsey ages ago and left there thinking, man, these are like the coolest people I've ever worked with, and some of the smartest too. So, so kudos to to that team. God, I missed that team. That was a long time ago, but it brings back some fond memories. But first of all, Bryan, how are you? Great to see you. So how are things going in your world these days?

Bryan Hancock  2:11  
No, things are great. Things in I think you know a lot of you know our clients are talking about AI all the time.

Steve  2:18  
Yeah.

Bryan Hancock  2:18  
And you know, and what we're seeing is folks trying to move from kind of individual use cases and individual here and there AI, but really you know thinking through end-to-end workflows, and then on top of that, kind of what's the vision for the HR function or the vision for all the functions of the organization. So I think what we're seeing now is our clients are moving from the experimenting, what is this phase, to the wow. Okay, this is going to really, if you take that workflow view or if you take that enterprise wide view, it's going to look pretty differently, and that means our talent processes need to be different. It means that you know how we in HR show up need to be different. So it's it's a pretty exciting time and one where I'd argue, you know, HR is more important than ever.

Steve  3:04  
Yeah, Bryan, I'd make that argument too. We've tried to make that here for a while on the show. As you guys work with clients, or you think about how AI is getting into organizations, certainly probably starting with individuals, just maybe even carrying it in on their own in small experiments, which are now certainly growing. Right, it's becoming more embedded into processes and into workloads for something like performance management, which, as you know, I don't have to tell anybody, right, has been fraught with challenges for decades before AI, right? Performance management was a challenging process, which many folks just didn't like at all, and others probably felt we didn't get much value out of, and we couldn't really do a good job at it. But maybe bigger picture, how are you guys thinking about how, in the AI era, how performance management needs to be kind of rethought.

Speaker 1  4:05  
I think one of the things that's exciting about the AI era and performance management, the AI era, is that you're able to make it easier on the humans to run the process. I don't think anybody wants to have you know 24/7 surveillance by a bot, and then have the bot provide your review at the end of the year. But in a world where you can be getting continuous feedback on your own in the flow of your work, that it's helping give you signals as to oh you know you know real time feedback that hey maybe this PowerPoint page that I've made doesn't make sense or isn't clear and if you have an agent do that hey that's that's not performance management the way we think about it but it is managing the performance of the individual.

Steve  4:52  
Yeah.

Bryan Hancock  4:52  
And if you're a manager being able to prep for that conversation getting more inputs and more you know things you know fact. Of what happened in the week, and where are things, and where are people spending time? So you're prepped for the conversation of saying, "Hey, I noticed you connected with, you know, these sets of folks, but not these. Tell me about it. Let's have, you know, let's do that because those other folks were important too. Like, what's going on? How can I help? It just provides more fact, more context into the regular conversations than if you think of the end of year process. You know, we at McKinsey for our partner evaluations. You know, we use a holistic evaluation, and and what that means is, as evaluators of our fellow partners, you know, we interview each other, and this is an inordinately time consuming process. And what we have is we have AI running the background of the interviews that we do, so that using the evaluator can then have the synthesis of okay, what are people saying? What are the areas against our leadership development model that we're hearing lots about, and where are we? So we can follow up and have the right human conversation on the areas we might have missed. So, what's exciting to me about AM performance management is it allows the individual to have more real-time feedback and allows managers, reviewers, whoever's doing the ultimate review, to spend less time navigating through the paperwork and more time on oh, those are some of the interesting things that I want to have the next conversation about. So, I'm actually pretty excited about where it can go, and I'm also excited that you know organizations want to do this. I was with a private equity firm, and they had all their CEOs in. We're talking about AI, and one of the CEOs said that they that their CHRO wanted to change his or her name from the chief people officer to the chief performance officer.

Steve  6:46  
Okay.

Bryan Hancock  6:48  
And the reason for that was they said because you know what what is it? What are we going to do in an AI era? Everything that we're able to do on talent on people, you know, all the insights we have are going to drive performance. Whether it's from making better selection at the front end of the process to better onboarding to providing coaching in the flow of work to the individual as it's happening, to how we're better equipping managers, to how we're ultimately holding people accountable. Like we're able to drive performance in a way that's not. Did you fill in all nine pages of the online performance thing. That oh, by the way, we streamlined, so it's eight pages. But you have got to re-enter stuff, you know. You know, but it, but it's actually driving performance. And the the that chief people officer really wanted to make the point that hey, in an AI era, it goes from, you know, we always thought that you drove performance through people in HR, but now we've got the tools to actually make that kind of promise a reality, and it's super exciting.

Steve  7:48  
Yeah, Bryan, thank you for that. That's a great kind of reset and overview, and really encourage us to think about the really positive ways some of these technologies can be brought to bear for employee performance, for enablement for even development too. Those development conversations, which I want to come back to as well. But when you first at the beginning of your your answer, you mentioned surveillance, right? We no one really wants that, but yet, right? There are some organizations out there, probably more than some, who their initial kind of forays into this has been to get lured right by this, by this these capabilities into a little bit more of those kind of surveillance things. I've heard some, you know, read some stories about Facebook doing things like this, and other even stories about companies who were just rewarding the wrong things in an AI world, like mainly how much AI were you using, right? And and you know the the token maxing you know concept came up a few weeks ago, maybe longer, maybe it's been longer than a few weeks, but I only heard about it a few weeks ago. So there is a little bit of an element of, and and maybe that's part of what you guys do with with the teams you work with to help them understand sort of, where's that line, right, between gathering all this information for for many of the positive examples you you mentioned, Bryan, and and maybe just going too far with it, right? Which were some of the other examples we've seen.

Bryan Hancock  9:14  
Absolutely, I mean, it it's on the one hand, all of the data allows you to have a customized experience.

Steve  9:21  
Yeah.

Bryan Hancock  9:22  
As an individual employee, customized learning, you know, that's hitting you where you are. A better conversation with your manager that better reflects the reality of what you're doing and where things are. I mean, these are the things. If you think about what a really good, you know, hotel concierge or really good, you know, front of a restaurant does with the rotating customer. They know you, and they know your preferences, and they know you like sparkling water. And no one thinks that's creepy. People think that that people think that is good service. Yeah, you remembered, you picked up on it, and for AI to be able to help. All of us improve in our own way, and then our managers to provide better service to the people they're leading is great. Now, if that same restaurant then says, "Well, I noticed that you wore the blue suit the past two times when you were with the redhead, and the gray suit, when you're with your current companion, not only would you find it creepy, you would be like livid. You would be incre insanely because it crossed the line. And I think what we know is in a restaurant or service or where we encounter those types of things in other parts where we kind of intuit where the line is between customized and creepy, and I think what we need to do in HR and in our organizations is think: Yeah, what's our compact with our employees? How can we communicate what we do? What are the promises we give them in terms of how we're using the data and where, so that we're able to live into the customized experience? People want to be seen in their job? They want something that's customized to them. They want to go to a training that's not like, gosh, I already know this stuff, and yet it's, but I got to complete it. They want that level of customization. You see it from employees, but at the same time, they don't want it to be weird. And to me, where that you know intersection happens is you know at the manager, and you know it's like okay for a given area, what what's the kind of data that that a manager would say you know is helpful in the in their realm? What is it that they would have? What is it that they're able to do? And then you know separating that from maybe some of the things that like, and now this rolls up into a broader firm surveillance system. Like, whoa, whoa, whoa! This is this is our coaching conversation here. This is me providing insight, you know, to somebody individually. We've got to think of where the boundaries are, and I think some companies are thinking through. I think many companies are thinking through kind of what the appropriate data usages are where it goes. You know, some of it pushed by regulatory, some of it pushed by kind of their own, you know, sense of responsibility. But I think it's an area that we're still navigating. And so, what I counsel my clients is take that part of it very seriously.

Steve  12:18  
Yeah.

Bryan Hancock  12:19  
Not just for the legal parts, but because of the trust, we have employees and we know trust is critical. So actually, let's break down, let's do research, and figure out what are the areas that would cause higher trust, and what are the things that would cause lower trust-not just in the AI, but in the company's use of it. And let's use that as one guardrail. And on the other end, let's think about what the promise of it is, and how it can provide a better experience, and so if we can maintain the trust and provide the better experience that actually drives performance, that's the home run. But to do it, we have to frame the problem that way because if we don't, we end up either in one ditch or the other, or we're stalled because we say, "Hey, if we're using this performance, that's going to be a bad thing because performance has to be human. Well, what if we just make the human stuff better?

Steve  13:07  
Yeah.

Speaker 2  13:08  
Like I said, like I like maintain the human part, just make it better so that your manager is having a high quality conversation with you, not a mediocre conversation. I think that would work. So, but it requires that kind of north star vision for okay, how are we using AI in this area, and how are we being thoughtful in navigating the trade offs, and what's the research we're doing, and who makes the call? All of those are conscious decisions that need to be made, and it just can't be made by somebody who happens to control the data set to say, nah, nah, yeah, sure, let that data through. Like that, that can't be the answer. You've got to have some broader vision of how it of how it connects.

Steve  13:48  
Yeah, and and there are real concerns out there, whether they're coming from CEOs or CIOs on some of this stuff, or or even legal, right, in the organization. And you know, we'll mention one example, which is the ongoing litigation that's happening with Workday, and that that's a talent acquisition story. So not not a performance management story in particular, but I could easily see some enterprising group of employees take issue with maybe what they might consider maybe inappropriate or unfair application of AI tools in a performance process, which ultimately impacts things like promotions, things like compensation, things like even employable your employment status certainly right can all factors there. So there's a lot to consider here, right? As as you as you're wading into this, and maybe that's maybe I don't want you to have to comment directly on those things, but but just in general, like hey, we're concerned. I'm if you're talking to an organization, and maybe the chief people officer or the chief performance officer is is pretty excited about this, but. They've got to have conversations inside the organization with legal and maybe IT. Is that something you guys are running into in helping folks work through?

Bryan Hancock  15:09  
Absolutely. I mean, one of the things that we do with clients is we help think through the broader human side of AI adoption. Is we have a survey that we use. It was developed by this our same team that's had our organizational health index survey for a long, long time. You know, develops kind of an AI readiness survey that looks at the organizational behaviors as well as the outcomes that we're seeing organizations, and how that correlates to being ready to to do AI at scale. And and it's particularly important for areas like performance management, and one of the things that we see as most critical is this idea of trust. And you know, we need for employees to have trust in the data, and trust in the underlying process. And we can see that there are ways in which good use of AI and data can actually enhance trust, and we can also see, you know, for some of the reasons I don't know, how it can take it away. And so this is where, you know, we think there needs to be a intentional act on the part of companies to say, okay, how are we maintaining trust as part of our change management story. What are we doing here? And from a procedural justice standpoint, who gets to weigh in on it? Right? Is it the employees that get to opt in or out of individual data being collected? Some people are going there. Some of them are saying, "Hey, we're going to do this overall because we know it'll create a better employee experience. But what we're going to do is we're going to put the boundary between what an employee sees themselves and a manager sees, or we're going to put the line between the manager and the broader organization for this sets of data and insight, so that the manager can have the coaching conversation. You have the personalized conversation there, but it's not attributable more broadly. So, skip level managers looking for reporting in parts of the organization to say, ah, who's slacking off or who's doing this? Not available for that, but is available for managers. Like those are the types of things that you really have to think through on a workflow by workflow basis. Okay, in this workflow, like, and you know, and for these sets of activities, you know, what's in bounds and what's out of bounds, and what's going to improve the individual? What's going to improve the team? And what are we comfortable with leveraging? But it requires that, and so you've got to have a real robust organization that is thinking through. As I'm redesigning a workflow, this is not just putting in the technology because we can do it. It's thinking through how are we going to manage performance in this workflow. What is in the bounds of trust between, you know, the the employees, the managers, and the broader organization? What's fair game? What's not? Even pushing through, you know, on a workflow level to think through how are we going to redesign work to optimize learning? Because you could probably redesign the workflow multiple different ways, but is there a way that you can intentionally optimize how you're redesigning work so that the human AI interface is in a way that that optimally coaches the human? That means you've got to have learning in the workflow redesign too, right? But that's where I think you know we see the leading companies being very thoughtful and proactive as to say, hey, okay, this is not just hey, we're going to turn on this module of this vendor.

Steve  18:24  
Right?

Bryan Hancock  18:25  
For everything, we're redesigning our workflow, and as we do it, we need to think about the responsible elements of that, including how do we preserve trust? We need to think the learning elements of it, and obviously, you know, the productivity and outcome elements.

Steve  18:37  
Yeah, Bryan, thank you. Yeah, and I think that reflects some of the evolution we've seen from the provider side, particularly HCM space, right where the early forays into AI capabilities that were more generally available were those answer question tools, chatbot tools, maybe book calendar meeting things, and generate text. We see a lot of that. You know, generate a job description, generate the language around, you know, a goal language maybe that an employee might have, and now the there's a drumbeat in the industry, and this is why I think where organizations need to be careful, right, of listening too much to to sort of bender hype on some of these things, but right now we're hearing, hey, this is the evolution of these tools is now towards full end-to-end execution and delivery of outcomes. Right, depending on the on the nature of the process and and the tool itself. But I think it's right for as you suggest, Bryan, for organizations to really approach these things a little thoughtfully and carefully with their team. You know, all the impacted organization departments kind of involved before we start all of a sudden AI agents are going willy nilly and hiring people over here and giving people compensation increases over there and you know doing performance plans over here. I think it's something to also think about. Totally, because there are some ways in which the agents talking to each other and doing things on their own help speed up processes where it's been bottlenecks for a long time. So think, you know, enterprise software, you know, company where they get a signal from their sales team that might actually have the the build team actually have to go out and hire new folks. And hey, maybe you can, you know, publish the rec. Maybe you can start doing some of the early sourcing, so that by the time the deal clears, you're two steps further down the road. Hey, that's great.

Bryan Hancock  20:30  
That's not interfering. That's just good prep. But we're still going to need to have somebody interview those folks. We're still going to have to do the hard decision. Okay, well, you know, where does compensation fall into? How do we make it, you know, fit for them and for for us and our structures? Like all of those next things require the human judgment, but can the automatic AI take a demand signal from the business and turn it into you know a candidate pool that we're ready to act on? Absolutely, that's moving fast. I don't have. I I think that's some of the exciting parts. But by the same token, if you said, "Hey, what we can do is we've got enough data points and are confident enough that we can issue ratings for everybody on your team, I would. And yeah, and we've got all the statistics to back it up, and we can normalize this. We've got all the we can put it against the framework and we talk about the points of evidence. This is going to be more objective than it's ever been before. I would say at that point, like, hey, just because you can, and it may be accurate, and that may actually be a good thing down the road. But what you need to do is you need to have that accurate data presented to a human, and maybe you know certainly the manager, and if there's a calibration committee or broader committee discussing, you know certainly have that data could be one part of it, but it needs to have the other human elements because I think where AI can be particularly good in some of these processes is it can help the human be better seen because it's got the real data that maybe the manager didn't see these four things or maybe didn't say right. So they can help you be better seen. At the same time, the risk is that you, as the human, feel not seen.

Steve  22:12  
Oh, sure, there's that.

Bryan Hancock  22:13  
Right. And so, how do you? It cuts both ways, and it's both about being seen. And so, that's where having the right policies in place, having the managers trained in the right way and the right way of thinking of approaching it, not just in the macro level, but in the workflow by workflow, including the performance management workflow why it's so important.

Steve  22:31  
Yeah, Bryan, there's so much we could dive into here, and one area I want to make sure we talked about even just a little bit is how AI is working to help managers and help improve the quality of those coaching conversations, and honestly, even the relationships between the manager and their team members, right? Through through through the application of some of these programs, we have heard forever, right, the importance of that relationship as critical to things like engagement, things like retention, and overall satisfaction, engagement, right of the workers themselves, which is we've we've got to care about immensely. Are you seeing really impactful uses here that are actually you know in clients you guys are working with, or examples you've seen where, yeah, we we can find ways to help managers empower them to to to lead and coach you know more effectively via these these tools.

Bryan Hancock  23:29  
Yeah, I mean some of it can just be tools that are reflecting on how the managers are spending their time.

Steve  23:34  
Yeah.

Bryan Hancock  23:35  
Hey, how much time have you spent on one-on-ones in the past month?

Steve  23:38  
Yeah.

Bryan Hancock  23:39  
How much time you know where are you in meetings that overlap with your team? Where are you doing? I mean, and these are things that you know many existing vendors have tools that help you do the analysis. You can get your Microsoft Copilot summary of your week.

Steve  23:52  
Yeah.

Bryan Hancock  23:53  
That can provide insight to you as a manager as to whether or not you're spending the right amount of time with your team. So that is just a straightforward reflection of where you're spending time and playing that back.

Steve  24:04  
Yeah.

Bryan Hancock  24:04  
And so I think there there are things that can help there. There are things that you know using any enterprise LLM to just have you know you reflect on the conversation that you're about to go into. Hey, what I'm heading into this conversation? Yeah, I wanted to be productive on these elements. What things might be on the mind of the person I'm going to talk to? And the LM can pull in earnings reports from the company. Can come in, pull in competitor intelligence call, other things, and you can create a you know you can create create enough of a of a pattern of how to do it so the LM can do it you know easily for each conversation. But then that in and of itself preps you say oh I hadn't made the connection to that.

Steve  25:00  
Yeah.

Bryan Hancock  25:01  
They might be worried about this announcement because it doesn't actually pertain to them, but it sounds similar. Ah, I'm heads up. Yeah, so I find that that awareness, and then you've got the next level of coaching tools that help you, you know, construct a difficult conversation, role model a difficult conversation. All of those things are helping amplify the human part: Are you spending the right amount of time with them? Do you have a right awareness? Are you in their shoes so that you're heading into the conversation? If you're not naturally adept at having these conversations, do you have some reps as to what the things might be? None of those require a coaching app. None of those require they just require the manager to be thoughtful of, oh, she's spending time how can I use these tools to help me be better? Then you can layer on top of that coaching apps that help that have memory that know all the people on your team and all the feedback you give them and you know and maybe that coaching app can also give you feedback across the you know because at some point if that's the repeating feedback across the team that's also feedback for you as the manager. So you know, and so things I think build from there. But there's a lot that managers can do, and we're seeing organizations start to take a real view of kind of empowering the manager layer and starting with what is easy, which is kind of training on just the things that amplify the human capabilities of the manager before you get to the better insight that you have for them, the more detailed information, et cetera, et cetera. 

Steve  26:30  
Yeah, Bryan, thank you for that. I do think we're finally at the point. I've been doing systems for a really long time, and I know I've been trying to sell them and sell the implementation of them using lines like, "Oh, well, this will free you up to do the important work, or this will free up the team, right, to do the real, meaningful, important human work. And I got to tell you, we were kind of bsing people with that line for a really long time. Like we thought, you know, big ERP was going to do it, and it didn't really do it, and the cloud was going to do it somehow on its own, and it didn't really do it. And mobile, all that did was let people work even more from different locations and all the time, right? And but these tools do feel a little different in that they are so capable that HR departments and other departments in the organization can really take offload, I guess, if you will, a lot of that low value add kind of stuff, the rote stuff, the routine stuff, and open up lots of possibilities for people to do much more meaningful work. I think maybe the the last topic I'd love to get into, Bryan, in that vein. First of all, hopefully you sort of agree with the thesis. But secondly, what does that kind of mean in your perspective to the function of HR? I know you told that great story about the the person wanted to go from the you know the the chief people officer to the chief performance officer. Is that indicative of where HR is going in your mind somewhere else? What how do you see sort of maybe five years down the road, how are HR people having these kinds of conversations about the the role of HR in the organization?

Bryan Hancock  28:07  
Well, I think a lot of the day to day delivery of you know HR products, if you will, is going to be done by AI or by agents. If you've got an address change, you've got married, you have a something wrong with your pay slip, you know those day in day out pieces I think are going to be energetically, you know, even some of the you know workforce planning and some of the coaching that can be provided for managers. They'll have a first draft that'll be created by agents, and what that means is that that HR business partner to that leader, that leader's got a lot of information on their phone, on their laptop. They they've got the information. What they need is they need a coach to help them interpret it, and they need and so that so the the free time is actually going to happen because the data pulls, the cleaning, the getting to the insights, all of that stuff that is still what a lot of people spend time doing, or the actual organizing to deliver. A lot of that's going to be done agentically. So now what we can do is jointly problem solve off of oh, there's a new factory opening down the street that looks like they're going to be hiring our folks. How do we do that? Like you know we've we've got all the pieces. We know who's at risk. We know all the pieces. We didn't have to crank through a bunch of data for that. Now let's problem solve, and I think we're going to get to on the HR business partner side that the you know more and more time of problem solving, and then you know in the core delivery and the COEs, a lot of that is going to be done attemptically. And so what they're going to be doing is they're going to be thinking through again their end end workflows. Okay, what different pieces of technology are happening where? What's the overall user experience, you know that we're doing. So it's less about hey, I'm creating one learning program. It's more hey, I've got a set of learning products, and here's how they are, and here's what's customized, and here's who we've got. You know, so they're thinking about it from a technologist standpoint more than maybe the classic COE standpoint. But in all of it, I think. What we're doing is we're going to be spending more time in higher value activities, and the other thing that's going to happen is you know if you think through like one of my clients is thinking through hiring, okay, and is saying you know for frontline hires you know is it possible that the only human that they talk to in the process is the hiring manager? We're able to do the screening, sourcing, interview scheduling. Hiring manager gets agentically prepped for the interview, does the interview, great, and then you know the onboarding process triggered. The the one person they talk to is the hiring manager, and there's a debate over whether that is just manager self service 2.0 3.0 4.0 whichever level it is, where you know the and and what's behind that is the view of like, oh, we're just offloading work to the manager. And in this case, I think what's different, and it goes a little bit to about like this does feel different this time, is as the manager, I don't feel like that feels like it's offloading work to me. It just feels simpler and more intuitive. I don't have to have four conversations with HR to get a hire. I've got a pre vetted set of folks in front of me. I've got I'm well prepped for the thing, and I'm able to rock and roll. This is great. This is not like self service. Now I have to do the interview scheduling, or I have to do this. It's now this is easier. And by the way, if I have a question, you know, my HR business partner or my talent partner-they're going to be able to have a deeper conversation with me because they're not tied up in that stuff either. And so now it's like, okay, now let's talk. The candidates aren't quite the ones we're thinking. What's going on? Like we're able to have more time for those conversations, whereas today they might be squeezed in between all of the doing that we have to do today. 

Steve  31:44  
Yeah, that's such a great example, Bryan. Because I fairly recently sat down and listened to some folks who are doing that kind of hiring at scale at a couple of different organizations and the technologies they're using, and they described it very similarly to you just did. Right, that they've been able to create a process where the hiring manager simply at the beginning of the week indicates I've got these couple windows of time this week to do interviews, and they set a little calendar, and then they go away and do their job at the location, and they see meetings just pop up on their calendar in those windows only, right for the interviews, and that's their involvement in the process, and it's working at a couple. Like I won't say their names here because I'm not sure I'm allowed to. But brand name national places that you've probably gone to within the last two weeks to get something like for yourself or your car. I will say it that way. Yeah. So, yeah, that's a great example, and I think it's a it's a super way to think about the opportunities, the potential, but also just-I mean, like the the like no one really wants to do those the mundane jobs, the difficult jobs. You know, we do them ourselves still now, right? Scheduling a call with someone, we scheduling this podcast is a pain in the butt. I'll tell you quite frankly, right to get people's calendars together, right, so we can do it. So, right, trying to move to a place where we're we're doing less of that stuff and more of the stuff that's that I guess person to person, really too, right? That that that that's having the most impact at work.

Bryan Hancock  33:17  
Yeah.

Steve  33:18  
Yeah, it's exciting, Bryan. This has been super fun. We could go forever. We'd love to have you back, maybe, and talk some more about this. I would for sure. But McKinsey does such a great job at providing research reports, insights. I probably get three or four McKinsey newsletters during the course of the week in my inbox, just on all manner of topics. Bryan, is there anything else you'd like to share with the group, or come and find you, or go to the McKinsey website to read more about what your team does? I, you know, um, whatever you'd like to share.

Bryan Hancock  33:50  
Yeah, no, thank, thank you. You know, feel free to to go. You know, you can Google me. It's Bryan with a Y, Hancock, and on the McKinsey page, I'll have actually links to all of the articles and podcasts that we've done recently, and we try to keep a good cadence out there. And so, so if you want to follow those and track those, we also have our own McKinsey Talks Talent podcast, where we're pretty excited about the upcoming season. So.

Steve  34:16  
Awesome!

Bryan Hancock  34:17  
So that should be that should be great as well.

Steve  34:19  
We'll share some links to those in our notes as well, and man, this has been super fun, Bryan. I love that we've got a chance to dig into things like performance and coaching and just impact as having on HR. So thank you so much for your time today. We really appreciate it.

Bryan Hancock  34:33  
Hey, thanks for having me.

Steve  34:34  
All right, that's Bryan Hancock from McKinsey. We will put links to the resources Bryan mentioned in the notes. I want to thank him. Thank all our friends at McKinsey. I fond memories of working on Madison Avenue, implementing systems there years and years ago. Shout out to the FIRST team, the first team. I wonder if any of those guys still listen to my program, like all the guys I knew 20 years ago. We'll see. Maybe I'll get an email. But thank you again. Thanks to our friends at Workhuman as well for supporting the show. So my name is Steve Boese. This has been the At Work in America podcast. Remember, subscribe wherever you get your podcasts. Go to HRHappyHour.net or anywhere you listen your podcasts. Thank you so much. We'll see you next time, and bye for now.

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