How can AI simplify HR, boost employee engagement, and revolutionize the workplace? Logan Sugarman, founder and CEO of Refresh — a startup in SHRM’s 2025 Workplace Tech Accelerator cohort — dives into how organizations are tackling one of HR’s biggest challenges: creating seamless, personalized employee experiences while cutting through app overload and inefficiency. With the help of AI, HR teams can automate repetitive tasks, streamline systems, and focus on building meaningful employee journeys. As Sugarman explains, “AI lets us take the entire world of data and bring it into a single point, so we can look at it from as many angles as possible.”
How can AI simplify HR, boost employee engagement, and revolutionize the workplace? Logan Sugarman, founder and CEO of Refresh —a startup in SHRM’s 2025 Workplace Tech Accelerator cohort — dives into how organizations are tackling one of HR’s biggest challenges: creating seamless, personalized employee experiences while cutting through app overload and inefficiency. With the help of AI, HR teams can automate repetitive tasks, streamline systems, and focus on building meaningful employee journeys. As Sugarman explains, “AI lets us take the entire world of data and bring it into a single point, so we can look at it from as many angles as possible.”
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Alex: Welcome to the AI Hi Project. I'm Alex Alonso SHRM's, chief Data and Analytics Officer. Thanks for joining us this week. We're exploring the power of AI to make HR systems more personalized, streamlined, and informative and interconnected. Setting the stage for the future of employee engagement, and more importantly, employee experience, how organizations are most efficient in ensuring that they are creating that desired employee journey throughout their lifecycle.
this week is Logan Sugarman, founder and CEO of Refresh, one of the startup companies in SHRM's Workplace Tech Accelerator cohort this year. Welcome to the A IHI Project Logan.
Logan: Thanks for having me, Alex. I'm really excited to be here.
Alex: I'm excited that you're here as somebody who's on the strategic investment committee of SHRM Labs. I'm somebody who's very much familiar with your work. We've had a chance to, to get to know [00:01:00] each other a couple times when you all were on site here and I'm really excited to hear more about what it is that you have really going on in the world of refresh and especially AI enabled kind of personalization as it relates to that, that experience on the job.
Uh, one of the things that I, I, I think is always helpful when we start these kind of podcasts is to talk a little bit about what your, your origin story, if you will. You know, to start us off, can you tell us a little bit about the story behind Refresh? What was it that inspired the creation, the founding of it?
That one stop hub for employee uh, for employee experience, if you will.
Logan: Yeah. You know, we started off as a corporate services provider bringing onsite wellness health uh. Services to large corporations. So massage, yoga, flu shots. Uh, that business really gave us a front row seat to the connected experience as employees experience it. [00:02:00] And I think as importantly as HR delivers it you know, one of the things that that I saw that kind of blew my mind was we would go in and offer free massages or the company would offer free massages on Fridays and.
It, every spot wasn't being taken up right, and and it really makes you stop and wonder, you know. It, it's massage, right? If massages aren't being fully utilized and they're free, what else is happening behind the scenes here? Uh, that is going underutilized and, and underappreciated. Right? as we kind of dove a little deeper and understood the complexity of HR as a, a platform and as an organization where your average corporation has something in the neighborhood of.
50, a hundred, 200 unique systems. Right? The, the increasing complexity that's inherent there is, is, is insane. And that's what's [00:03:00] driving the perceived or the expected success rates within HR tools of usage rate of 15%, right? In any other, in any other industry, 15% would be a failure rate. This is.
This is a success rate. And, and what we were seeing with massage of, let's call it a success rate of 80 or 90% uptake, was actually like a massive raging success, right? Like in the context of, of what we were dealing with. And so as we dug in and actually as COVID happened and, and blew up the services business that we had, because that went away, that went over to zero overnight.
We felt like we had all this internal learning and this internal understanding of some of the problems that confront employers of all sizes and specifically employers with large populations of frontline desk lists, distributed workforces, where the extra levels of inherent complexity mean that these, these, [00:04:00] these workforces completely ignore or, or, um.
Are, are less able to connect and utilize tools that they're probably the most helpful for. Right? It, it, it's something where these time intensive journeys across your insurance, across your payroll systems just become a. A time too much, right? You, you don't have that extra minute, you
don't have that extra five minutes and you don't have the real drive or maybe even understanding of them to, to allocate that time into it.
And then on the HR side, you have all of the manual effort that is put into communicate, engage, educate. Folks on each product and to, to get the products out into the world. Right? Just that, just that the distribution is, is, is, is a heavy lift. So what we really took to heart with, with, with Refresh, was that we needed to provide a place where we could [00:05:00] build a solution that was configurable.
Through every use case, right? There was truly future proof where as organizations involved and as their benefits needs changed, right? The system would change too, where you move away from, you don't go back to a re a full rebundling, right? Like we had back in the day where everything was in a walled garden, you couldn't get out.
Uh, and certainly cer certainly we're looking to address the, the app sprawl that is currently kind of uh, taken over. Uh, but we do it in a way where. It's agnostic, right? We, we wanna mirror what works and what exists in companies and allow them to provide that and push that back to their employees.
So um. It's funny, like now that we do demos of this for companies, the response they get back consistent. It's like, I had no idea this was even possible or even existed. Right? Because their options usually are like, Hey, if you want, if you want best in class products, you gotta, you gotta go point solutions if you want one best in class products and then all the other stuff [00:06:00] that.
You know, a common provider is willing to, to do for you, you gotta go into a walled garden and you're gonna get what you got. You know, I think, I think our Ali value add is definitive in that we allow you to create the system that's right for you. While aggregating all that stuff and creating that single resource for employees to personalize.
And I, and I know we'll, we'll probably talk more about this
through AI and data, what they're seeing, how they're engaging with it, and what their journeys look like. Right. And I. Where we see ourselves is, is not just another tool in the staff, but something that one, ideally puts hard dollars back in, in HR pockets by knocking out a few of the existing tools because there's just so many point solutions you have.
You don't need all of them. And usually we'll have a solution for, for 3, 4, 5 of those. And then saving a tremendous time for HR employees from, from an, from an operational and engagement standpoint as they, as they go to actually utilize these systems.
Alex: Yeah. You know, it's funny, it, it, it is frightening to me how much and and frightening, but in a, in a good way. Right? [00:07:00] It's frightening to me how much we have access to all this data that we generate in and of its our own operations, and somehow we don't leverage it to the degree that we should. Right? I think about a provider like refresh.
Being someone who can actually help us really take advantage of that, to create that personalization that you're describing. But then beyond that, to help us make better decisions in a, in an automated kind of way. Right. It takes me back to a joke. I, I feel terrible for doing this, but it takes me back to a joke that my grandson told me recently, and he's, he's a teenager, right?
So it's not like he shouldn't, it's not like it's not age appropriate. He's not a a genius or anything like that. But he he said to me, what, what did the AI say to the HR professional? And I said, I don't know what did, what did they say? And he said, the AI said, I'm not coming for your job. I'm coming for your data.
That's what I want. Right? And yeah. Yeah. And it was like I didn't think of it that way, but you're right. It is, it is you're, you're helping me do my job better by helping me analyze my data and turning it into an [00:08:00] output that is so valuable for everyone down the stream. Right. So we've talked a little bit about ai, right?
And obviously we're ai, we gotta talk a little bit about that. Uh. I, I recognize that it is so central to the functionality that Refresh brings in terms of their offerings. Talk to me a little bit about how you see the AI fundamentally changing the way organizations manage, but also deliver offerings all the way around.
Right, and, and more importantly, both what they're doing now and what you anticipate might happen in the future.
Logan: Right. Well, I think I think the interesting thing, at least from my perspective on AI, is that it is an organizational construct, right? It's. It's only as good as the training that you put into it, right. As where we stand at this point in time. Right? Sure. I'm sure at some point in the future, AI will start training itself and then we'll get Skynet and everything will change.
Uh, but, but at this point, from, from, from, from an HR perspective, and I think [00:09:00] a, a industry perspective it is really great at taking in large amounts of data and organizing it in a way that maybe. Machine learning and some of the other tools that we've had in the past made the promise of, but weren't able to fully fulfill.
What I don't think people have to be worried about is it's, it's not coming for your job. I, I, I think of, and I, and I think we'll probably talk a lot about this 'cause this is a, this is kind of how I view refresh, is that AI is a really great co-pilot and I think Microsoft taking that and, and, and, and putting and branding that is, is, might be one of the better decisions they've made recently because I do think of that when I, when I think of copilot, um.
That's what AI is, right? It can sit alongside you, it can see what you're doing. It can use that data writ large across large organizations and begin to chip away with some of the problems that we just discussed in my origin story, right? Like that we live in a world and a workplace is flooded with apps, right?
And tools and resources. So much so that we can't [00:10:00] fully utilize all the value that's there. So the question, and this will sound a little bit funny, is. With ai, how do we get it back to basics and what does that look like, right? Like with the ra, with the rise of ai now we have better ways to harness and aggregate and organize data.
And at least from the refresh perspective, we wanna use that power to create a a, a truer consumer experience for employees, right? I think, um. This isn't unique in the world, HR, right? This is showing up in industries like insurance, but you know. Solutions are too often built with backend efficiency as their primary goal, right?
And, and, and, and not incorrectly so either, right? I mean, who's buying these products if people are gonna be using them from an administrative side on a daily basis, right? So if it isn't easy for me to use my payroll system as an HR professional or admin, I'm definitely not gonna buy it, right? [00:11:00] User experience has gotten short shrift throughout most of, I think employee tech and HR tech's building you know, we consistently hear the same thing, right?
Tools are too hard to understand, too hard to use. They're, they're, they're too sort a genius in the way that I can't know how to use my payroll just because I know how to use my health insurance. Well, that's, that's a terrible experience. Right. And especially when we think of distributed workforces, right?
Which. Distributor workers are 80% of the workforce. It's, it's the majority of people out there who do not sit in front of their computer all day and do not have people often talk about the, the, the issues with context switching in the workplace when you're kind of knowledge worker in sitting of your desk.
Well, I mean, I. That's a multiplier effect when you are a retail worker or a frontline worker at a hospital or, or, or somebody who's fixing the streets of, of a Manhattan, right? I mean, it, it only gets worse. It doesn't get better, and the technology gaps become more real. So my question was how can we harness this [00:12:00] this data aggregation tool to.
Salt plus create a journey for employees that looks more like a top end retailer and less like a clunky HR tool that no one really wants to, to, to, to engage with or, or has a harder time engaging with. Right. I think, I think h ai by itself you know, I have, I have a, I have a joke that, or maybe it's not a joke.
It's, it's, it's more of just an adage, right? Like, it's, it's that, you know. We went from Doss prompt right to Microsoft GUI interfaces that were beautiful and easy to understand and we can move things around and, and that was the mass adoption of computers, right?
We've got back to dos, right? AI for most of us in most use cases is dos.
And that anticipates a certain level of either understanding or drive on the part of that user to get the most out of it, right? I, I think back when, when I was using dos and I [00:13:00] would go in as a kid and what did I know to put in, I put in games, right? What would the system gimme? It'd be gonna illustrate games.
So my highest intent feature was games. I would put games in and games is what I got now maybe if I'd known there was other things in the system, I would be somebody else in the role and not have a kid who
played video games all the time. I think combining together that user interface, right, which is dynamic, modular, personalizable on that user level with data that can adjust that user experience or what I'm seeing.
Mm. That's the, that's the end result that really kind of takes all the power of imagery, right? Which I think if you look at ad sales or even marketing, right? Like it's pretty clear people don't really wanna read that much. Uh, they'd rather look at images and, and things that can direct them in that way.
And. Doesn't implicitly expect you to know what you're looking for, right. And allows us to drive journeys that are, that are [00:14:00] interesting and useful for users in ways that we never could before. So I think of as ai, as a, as an intelligent engine
Alex: Yeah.
Logan: that can be trained up for organizations to help.
Promote, create drive journeys for employees that are important to the, or to the employee, important to the, the organization. And do it in a way where we take some of that load off of the HR professional, right? Like, so going back to the co-pilot analogy, right now we have a copilot sitting in your app.
This is your HR professional or HR team leader who's now sitting with that individual employee who's popped open the app and thinking about them, right? And what should they be seeing? What should they be doing if they click on this button or say they want to progress in this direction within the organization.
I. How do we advise them? Right? And so we've, we've got this dynamic system that can take them through those journeys that are prescribed at an organizational level, [00:15:00] right? But are utilizing cutting edge tech, but doing it in a way that's accessible. And it's it's a combination of what we do in, in consolidating.
Integrating with every tool that's in a stack and making it all accessible from one point, and then using AI and some of the higher level data features to personalize and customize that experience for that user.
Alex: Yeah. You know, it's, it's fascinating to me 'cause I almost think about it as sort of like a lot of health apps and wellness apps today. Uh, if you think about the, the, the journey that
kind of electronic medical records were on, uh. 20 years ago, right where it was, we're gonna have 15 different kinds and they're all gonna have different information.
But all of a sudden now they all integrate into one portal. And as a patient, I may have 17 different providers, but it's all integrated and I get to do a, a meta view of that and make decisions on my health in that regard. Right. Without [00:16:00] sort of endorsing any one system. Right. But that, that's sort of what you're talking about here is that next level of journey that takes us back, but also takes us forward in terms of what we can do with that.
Personalized thinking around the holistic view of what I, the data and, and offerings that I have available to me.
Logan: Yeah. No, and I think that that medical model is exactly right. I mean, your doctors can now see things across doctors that maybe they would've missed before, right? And, and.
In theory, at least,
Alex: Yeah.
Logan: your standard of care should be, should be, should be better, right. And and, and benchmark you too. Right?
So now those doctors too can see that data across other patients that are of similar age and similar background. And they can look and if you have a wearable, they can see how much exercise you're doing, right? And they're like, okay, well this, this all tracks. And like he's a little bit outta the benchmark here.
Let's, let's, let's dig into that, right? Same way in HR, we can now benchmark and see. What's effective, what isn't effective, and refresh as a, as a corporation can [00:17:00] come back to you and say, Hey you guys aren't having a lot of success with this one tool that you have. We have other groups that are like you.
They use this other tool, their results are like three to four x higher maybe you should knock that one out and and use this other one, or vice versa. Right. It's, it's a way to use data across systems too, to make sure that everyone's getting the most out of whatever they're spending money on.
Right. From an ROI
perspective.
Alex: Yeah. You know it makes me think about the power of personalization in all this. Right? And I think specifically about what a big part personalization is in terms of what refresh offers the market. I. Offers employers, but also employees. Right. What, talk to me a little bit about what that secret sauce is.
How is it that AI is, is sort of tailoring that experience and making it so that you have access, self-service access, not just to the tools, but also communications and information you need in, in the moment Almost.
Logan: I'm gonna make a, I'm gonna a [00:18:00] grandiose statement here and we'll, we'll, we'll see if I can back it up. But I, I, I think, I think this is actually a rather simple problem because the core is to think about. Personalization especially in HR is that a lot of the hard work is just being able to meet people where they are right now, right.
Today, right. That I, I know that you're married, I know that you've got kids. I know that you live in San Francisco or Sheboygan, right? Uh, I know that you've worked with this company for 10 years or two years, right? Those core attributes are a lot of what we need to make a an initial personalization until you tell us more about what you want or your manager tells us more about what they'd want outta your HR, tells us more about where you're going to get, to get to a point where.
We can be pertinent every time you open the app, right? And maybe [00:19:00] prescriptive as you start moving down a pathway, right? You know, it's funny, one of the most common issues that we come across in talking to clients is their ability to engage both covered and uncovered employees in a single system.
Right. Like that's from a conceptual standpoint, that's not a, that's not, that's not complex, right? It's two groups that, that can't see the same thing. Right. For compliance purposes. And it's wild to me that I. Seemingly, we're one of the few systems out there that can, that can achieve that goal, right?
So if you think about personalization in its levels, right? It stops at group wide things, and then the audiences get smaller and smaller and smaller until it's just you. And you're the one person who's a covered employee in San Francisco and has all these other different things. So personalization to a certain extent is.
Accumulation of data around you as, as, as a specific person. And, and having a system that can key off [00:20:00] of each of those different little data aspects and then ask you for more information to further enhance that journey or make sure that you're getting what you want on us or ask you the right questions.
Right. I think one of the things that's interesting about a lot of the systems that we see out in the world is that and it, we actually touched on this at the, at the top of the the podcast is, is their ability to. It's to cycle data, right? To provide that feedback loop. To not just take data in, but then re be responsive based upon that, right?
So I think of it in, in, in, in a, in a, in a super simple example of we sent out a pulse survey, right? 1, 2, 3 stars. How'd your week go? Super simple. Um. People get that data back in, and then they're like, all right, what do we do with this? Right? Do we send it back out? Do we send, do we, do we send it over to somebody who can send something to HubSpot or to our sales?
Like, how are we managing this? Our system's easy, right? Like we have preset what maybe a path looks like for a one star user to see how we can find out what, what went [00:21:00] wrong. You know it, it some things we could put in there based upon that to, to help maybe elevate that person and try and get them up to two stars next week.
And then at the three star level, try and get some information about why was the week so great, right? Like, what can we bring to other people? What can bring to those one star users to help them feel better? And so personally, I think everyone thinks of personalization as I like this very unique thing in the world.
And the truth of the matter is we are all unique flowers but we're still part of a garden, right? And there's still other flowers like us out there, and it's. You don't have to know every little thing about somebody to, get it right. Especially when we're talking about HR and making sure that your benefits and, the tools that you're being shown and the options that you're seeing from a learning and development perspective, are the Right.
ones for you and.
We also don't be exact. If we're on the 60, 40, 80, 20 side of the bet, [00:22:00] we can get better and you could just tell us like, Hey, I I'm not interested in upper management. I really wanna stay in operations, or whatever it is. Like those are all pieces of feedback that makes the system better and makes the system better for you.
And we give these away all the time to Facebook or to Amazon and whatever, and they. Crush it with these right little tidbits of data,
our view is, okay, great. Right. You know, it works in a quote unquote evil empire situation where we're giving it away and getting nothing back. Well, in the employee environment, we give away a little bit information and we can get a lot more out of that employee experience, a lot more outta that benefits experience.
And it makes. I think the organization better. I think it improves cultures. Certainly I think a lot of the stuff we're doing makes it easier and, and less time intensive for the employee to go in and, and understand and utilize their benefits. So it's a win-win, win across the board and, and it is personalization, but I think you have to think about it in, in scales and stages.
Alex: You know you've [00:23:00] talked about so many different ways in which HR operations can kind of be or in some ways, shape or form. efficiencies. Right. I, I think a lot about that. And our own research talks about how when we see people creating efficiencies in HR, typically it's on the talent acquisition side and not on the benefit side.
Not on the learning side. As much as, as you would expect I'd love your opinion. Where do you see that actually playing out best? What is the, the biggest opportunity that people aren't scratching the surface on yet when employing AI to create efficiencies?
Logan: You know, I'm I, I'm gonna, I guess I'm gonna, yeah, I'm biased. I'm biased.
I'm gonna start, I'm gonna keep preaching from the same book and, and I think it combines a couple of things that we've, we've chatted about. I think it's, it's that, it's that concept of the co-pilot, right? Like think if I could think if I could clone an HR executive or, or, or an HR professional, right?
And, and. And we could create a [00:24:00] clone for, of that HR person, for every employee in the organization, right where that person is sitting on their phone thinking about them every day taking in all that little data that we get along the way and incrementally personalizing the system for them. You know.
It cr it creates an efficiency in so that all the manual effort that goes. And I, and we talk to HR professionals about this all the time, like all the effort and time that goes into picking products, distributing products, educating employee products, I mean answering emails, phone calls, whatever bene, I mean the, the benefits.
Education is a huge. Uh time spent. Right. Uh, a a lot
of time goes into that and it's, and it's all for the greater good. And it's, I mean, listen, you can't, you can't buy these products and, and want them to be a powerful source of good in the world and not support them. Right. So, I guess my question is, well, if we could automate that support, if [00:25:00] we can automate that, that, that Sherpa, that copilot portion of that for you.
Doesn't that give HR more time to focus on the people and journeys they want to create and make available to them? So you are, you're providing a really good experience for the user. And at the same time, HR now has more time to uplevel those journeys and those experiences and, and, and think about when I personalize this for someone where do I want them to go?
How do I want them to, to create a path for themselves or create a path within our organization that's great for that person and great for the organization. I mean, I think it's kind of the definition of culture building to a certain extent, right? Um but you know, AI in the systems around it should, should automate the mundane.
And, and in so doing free up those teams to focus on the sublime. I mean, I, I I know I'm right. I mean, right, right, right. I mean, however you want, however you want to frame it. I mean, it's, it's, it's, it's, it's [00:26:00] uh it, it's something maybe a little bit grandiose here in the terms that we're using, but the opportunity is real.
Right. And I, and I think,
you know, people go into HR to. You know, be people, people to a certain extent, right? To, to, to help the organizations be better, help the people that that work with them to be better. Uh, and I think systems that, that allow you to improve the quality of the product that you're delivering and allow you to deliver at greater scale.
I mean, that's the, that's, that's the holy grail. That's the gold standard. Um you know with something like refresh. You don't have to be trapped in the walled garden of, of some of these large entities. You, you really get to build the products that you want and still, still achieve the efficiencies of, of kind of having a single rollout, right?
Or having a single place for people to go for everything. And I think. AI in its forms and, using data better and [00:27:00] using the concepts of, copilot or Sherpa and, scaling workforces through ai right? By, getting rid of some of the mundane tasks and putting them into systems and.
allowing AI to, do what it does best, in all honesty, right?
And then allowing the human beings to be creative and, thoughtful and relationship builders within the organization, I think is, a huge boon to companies longer term at least from an internal perspective and HR perspective.
Alex: You know it, it, in listening to you, it's a, it's like you've been listening to my, my Future book or my playbook. Here, which is achieving and it's, I can't take any credit for it. It's actually Salesforce is an, an agent force's dream, right? But the, the basic principle of creating human agency through AI agentry, right?
And, and ex speaking to that so that you can create personalization as at scale and [00:28:00] with actual kind of direct impact, huge impact to you to what it is that you do. You've described that all. I I I wanna take a step back from HR though, 'cause we talk a lot about HR here. Where is it that you see AI is actually making monumental shifts, reshaping the world of business and, and, and kind of thinking about how it might reshape all industries in some way.
If you had to throw a dart out there on the dart board, where is it that you see, holy cow, this is something I'd never thought of, and, and it's going completely gonna change the world completely.
Logan: I don't know that I ha I, I think the force multiplier is, is, is, at least for the foreseeable future one, I can see how it iterates too over time. And at some point you will allow it more autonomy. But it's that, I guess the agent, the copilot and how much autonomy you'll allow it to have.
Still coming back to that, that kind of human. Controller or [00:29:00] pilot, right?
To be able to go out in the world find the data that it knows that I wanna see, or the things that I need to see to affect, right? My business, my business plan, my, my my goals, right? Like, is that factory running as efficiently as it could is, is my, is my oil pumps producing as much oil as they could, right?
If you think about all the efficiencies across the world, a lot of it. At least the ability to see it hearkens back to big data, right? Like if you're able to break down data, aggregate data, you are able to see the inefficiencies, right? Because it's all gonna be there. Available to you if you have the right data sources and are able to, to aggregate them and, and, and, and bring them in front of you in the right way.
And then also maybe have a little bots around there saying, Hey, maybe you should look at this. Maybe you should look at this. Right.
I think the, the structural inefficiency of the world, I. [00:30:00] Are always there until someone spots them. I think AI gives us a, a better tool to go out and, and, and see them and understand them at scale within our own businesses, maybe outside of our business.
I mean, I spent I spent a bunch of my career on Wall Street building out training systems that were specifically geared towards taking advantage of structural inefficiencies, right? And, and, and, and going and finding those. And that's all just a big data problem I think. You know, AI certainly is a, is a, is a, is a, is a level up from the systems and, and resources we had to deploy at these issues before.
But you know, to be able to have bots where you can decompose discreet tasks and just say, Hey ai, ai buddy, go, go handle this, right? Go, go do this on a, on a number of levels, and then bring me back what I'm looking for and let me, let me use my creative. Judgment, et cetera, to, to figure out where we go with this next or how we solve the problem, or [00:31:00] at a certain point, and I'm not, I'm also one that uses this too.
I, I'll, I'll, I'm not I'm not afraid to go into chat GPT and, and, and say, Hey, here's kind of what I'm thinking. You're an expert in this, right? Like feeding in all the right prompts or whatever. And using it as an ideation machine too, right? To, to help me think of maybe things that I'm not thinking of.
So I, I think it's, it's, it's a great way to take the entire world and bring it into a, a single point so that we can try and look at it you know, from as many angles as we possibly can.
Alex: Yeah, nothing wrong with playing a little bit of fourth dimensional chess.
Logan: Right.
Alex: I read a study recently around uh, from HBR last September, and it looked at how are CEOs, a hundred CEOs, how are they using it? And more often than not, when they were thinking about how they were using it, it's helped me see the blind spots that I don't see.
Logan: Right.
Alex: am I missing in that, in the analysis that I, I should be seeing. Right.
And so to your point, I think that that's where you're creating all that agency. so final question here, and I do this to everybody, it's sort of a wild [00:32:00] card, so please forgive me. advice, what advice would you give a CHRO, A CEO, who's looking to innovate based upon the resources and the tools that they have today?
What would you share with them as a way to kind of think about how they can drive that employee experience change that they want?
Logan: let me start off with how I think about bringing technology into
my own company and, and I, and I, and I think I can answer the question a little more holistically. Is it,
you know, I think I. First and foremost, you have to trust your organization and the culture and processes and people that you have.
And as you look at new technology. It's almost like making a hire, right? You're almost hiring another person. Right. And so, as weird as it sound, I, I, I think technology needs to fit a culture. It needs to fit a company, right? And, and the way that it works, the way that it functions, how configurable it is.
I mean, how and every company's different, right? And every CEO is different and everyone has a different approach to things. And I, [00:33:00] and I think you can really, again, similar to how you would make a, a, a, a higher of a human being. When you decide technology or when you, when you try to look out and, and bring things in to an organization it has to be an organizational fit.
it's, it's actually the principle, one of the principles that was very much a part of, of developing refresh. Right. And like
what we would want to have as a tool. Right. And I, we built our platform specifically to be able to. Match an organization, right? So that it becomes this, it's, it's a, it's, it's a graph of, of, of connective tissue into an organization, but it, it, it, it matches the blood type of that organization.
And I think a lot of the technology out there is built to do something and it's kinda like a bulldozer. It's like, you gotta do this thing, and we're just. Here's the tech. It just does this thing that you, without thought of the organizational complexities that [00:34:00] are inherent in every organization and how somebody might want something to do something a little bit different.
I think of it as like in the in the world of disabilities, right? Like to have a system that, that doesn't conform to my disabilities. I mean, it's, it's not useful for me, right? If I can't see it or I can't hear it, or I can't so, you know. That's obviously a very extreme example and, and, and a point, but the, the point would be that to have a technology that becomes your technology is the gold
standard, right?
They need to be valuable and usable by your people and your organization. And, and, and. Sometimes it's just not a fit. And you know, it's, it's, it's the composability that configurability I think a lot of times. Now, you don't wanna go crazy. I mean, everything shouldn't be fixable, but I think it is something you think about and, and, and how the structure of a [00:35:00] product fits with your structure.
Structure of your, of your, of your company. And I also think, I mean, on the technology build side too, I, I think that people who are out there building technology too, um. I know we all get these like kernels of truth and we're like, I'm just gonna build this kernel of truth. But, but sometimes, you know that core piece of saying, well what are the organization types I'm gonna go into?
What are the cultures I'm gonna go into? How do I want people to engage with this? Um. Really makes for an outstanding product. Uh, so I think it's kind of a, it's, it's, it's, it's kind of a, it's kind of a bidirectional need on, on both sides where the more thoughtful we are on developing product for, for our, our corporate customers hopefully allows them to be more thoughtful in, in, in what they purchase and, and how they think through it and, and, and can kind of match up culture to culture, if you will, from a, from an Oregon and technology perspective.
Alex: Yeah, almost that that leap from being a culture builder [00:36:00] or a culture mandate. Into someone who's a culture crafter or a culture tinkerer, right? And doing it in a way that really kind of does it a data-driven approach. Uh, yeah. So you Logan. And, and that's gonna be it for our episode this week.
A big thank you to Logan for sharing the, his expertise and for a deep insights with us. Before we say goodbye, I encourage you to follow the IHI project wherever you enjoy your podcast. If you enjoyed today's episode, please take a moment to comment, leave a review.
Et cetera. We want your feedback. Finally, you can find all our episodes on our website at SHRM dot org slash ihi. Thanks for joining the conversation and we'll catch you next time.
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