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AI is reshaping leadership, strategy, and the very role of HR. In each episode, the host Barb Bidan explores how AI drives innovation and leadership in HR with actionable insights for the future of work.
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The Human Element - Maura Stevenson
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INTRO: [00:00:00] Welcome to The Human Element, presented by Wisq. I'm your host, Barb Biden, and in each episode, I sit down with CHROs and senior HR leaders to explore how AI, innovation, and human insight are reshaping the future of HR. We'll explore how technology is reshaping leadership, strategy, and the role of HR by sharing candid stories, practical ideas, and strategic perspectives to help you shape the future.
The Human Element is brought to you by Wisq, the leader in agentic HR and creator of Harper, the world's first AI HR generalist. Learn how Harper can resolve up to 80% of routine HR tasks autonomously. Learn more about Wisq at wisq.com.
Barb Bidan: Today my guest is Maura Stevenson, chief human resources officer at MedVet. Many of our conversations on the show assume a natural tension between AI and people decisions that data pulls one way and humanity [00:01:00] pulls the other, and as someone in that situation has to decide which Maura's argument is that the tension is the wrong frame which I love. She has spent 20 years building the evidence that data, when used well, is not what you trade humanity for. It is how you see it at scale . Maura built Starbucks' people analytics function from scratch in 2004, before people analytics even had that name leading surveys of 120,000-plus employees globally.
That's some data, for sure. Maura then went on to become the VP of talent management at Wendy's, where she used statistical modeling to connect crew and management directly to restaurants' business outcomes, and then presented those results out to franchise owners. So what an impact.
Maura has a PhD and is now the CHRO at MedVet, which is a rapidly growing veterinary specialty and ER hospital with 3,500 team members across 45 hospitals, so still very distributed and a sizable workforce. Maura, I am happy to have you join me on the show today. Thanks [00:02:00] for coming on.
Maura Stevenson: Thanks so much, Barb. Delighted to be here
Barb Bidan: So in 2004 you built Starbucks' people analytics function from the ground up, right? So you have been thinking about people analytics and shaping it for a while. The term I'm curious actually what the first team was called. Was it an analytics team in 2004? And you were running surveys on a massive ~employee size m-~ employee base and having to design tools to utilize that data after the fact.
I would love to dig in more on that role first and what you learned by having to deal with data on such a massive scale.
Maura Stevenson: We were called Partner Insights. So at Starbucks, where I'm still a customer the employers are called partners, right? Because everyone gets equity in the company. And we had a new head of HR, his name was Dave Pace. He's someone I've kept in contact with, and he came out of kind of that Pepsi generation no pun intended, of leaders, and he had a vision [00:03:00] for bringing more data into HR.
And so there we were, Partner Insights. And you start with some of the things that have been happening for a while. There is nothing particularly jaw-dropping about an employee survey, right? We've been doing those for a long time. But the trick then is to connect your employee survey data to other sources of data.
So in retail, and this is a theme both at Starbucks and at Wendy's, I have thousands and thousands of restaurants. And so I can connect that data to manager effectiveness or to pick up window speed of service or any of those kinds of things to show sometimes ~counterintuitive~ counterintuitive findings.
So really what we are building out at Starbucks, yes, we had the survey, but we did other data, three sixty. We pulled in some operational data. We did a big project on Frappuccinos one time. Really [00:04:00] pulling in that operational data back at a time where even a big company at Starbucks didn't have the platforms and layers in the tech stack that they would have today to connect all those things.
You had to do some of it manually. But it was, so you could tell that story. I remember going in early on to meet with Howard Schultz. There were a group of us to talk about the survey results, and we had engagement. And, one of the brilliances of Howard is that he was always anxious and trying to make things better.
And we're showing him pretty good data, and he said, "But I go into stores, and they're not like this." And I said, "Turn the page." We just showed him the distribution. We said, "You do have a segment of stores that aren't delivering the experience." And so I think sometimes, particularly as data has gotten fancier, we don't want to [00:05:00] lose the key insights in pretty basic data storytelling.
It doesn't have to be fancy. It just has to illuminate and give us an insight that we maybe even knew but couldn't prove
Barb Bidan: Completely. In a workforce that size the data is, has got to be... It's like the flashlight that you shine, or the spotlight that you shine on something that makes you ask the follow-up questions, right? And immediately you've probably showed him the stores where that was an issue, and then I'm guessing you can dig in on that and focus.
It just tells you where to place your focus, which is I would imagine, totally necessary in a,
Maura Stevenson: It's not one size fits all, right? And so I think whether you're looking at the store level or the hospital level, or you're looking at the individual person level, there's a lot of variability, and sometimes our solutions do tend to be one size fits all, right? And you [00:06:00] might be able to do one size fits most but I think, understanding what's really going on and often testing and validating hypotheses is really important.
'Cause oftentimes, leaders, they already know what the problem is, and they already know how to solve it, right? And sometimes our job is to say, "Oh, that's really interesting. I'm gonna get into that a little bit and see what else is also interesting." At my current job, I work with a bunch of highly trained veterinarians.
They're scientists, so I do actually use the term hypothesis with them and that testing and learning, and it's okay if your hypothesis was not substantiated. That's okay too. We're gonna learn more there, right?
Barb Bidan: Right. And it's okay if we ~lear- if we~ add on to your hypothesis, right? You had half the picture, and when we dig in on the data, we see the rest. And so you your PhD, Maura, is as an you're an affiliated research scientist at USC Center for [00:07:00] Effective Organizations. How does the academic side of this work shape how you're thinking about what you're willing to trust from a people data tool, what you're not and maybe how you shape this work overall?
Maura Stevenson: And USC is a center, and so we are very much grounded at CEO, we're very much grounded in practical applications. So right now, my colleagues there are leading an AI adoption set of workshops, right? So really getting into the practical thing. And where we focused a lot at CEO is, and my work has focused on we jokingly ~s-~ say it's two twins separated at birth.
There's the data people, and there's the OD people. And historically within the profession, those people have been very different, right? So when I went to grad school, I took seven graduate level statistics courses, and my first job was actually as a [00:08:00] statistician, right? Versus Team dynamics or process, the thing is they're the same.
And it's the piece of whether you're doing an analytics project or whether you're doing an org design project or a team effectiveness project, you're following a research path, you're just learning as you go. And one of my coworkers is at CEO he was commenting and he said, "And we're saying that these are the same things, OD and like people analytics."
And I said of course they're the same thing." He says, "But see, nobody says that." And I think it's really going back to the core of what I did learn, right? Back in the day that organizational psychology is about human behavior, and it's about understanding that individually and at scale, right?
So it has to be both. And so when you think about [00:09:00] our research methods, I think one of the things today, back when big data became popular we can have a lot of data, but sometimes it doesn't give us the richness. So I have always been a big advocate of pairing quantitative data with qualitative data.
And so if you're starting looking at an organization, you wanna help them go through transformation, you pretty much have to use qualitative data 'cause your sample size is one. It's one organization. And so I think we don't want to let the whiz bang that has become quantitative data override the richness in our qualitative data, and they're best paired, right?
And AI can help us a lot with both of those types of data and the analytics around them in ways that we didn't use to [00:10:00] have. But it has to be an and
Barb Bidan: Completely. And if ever there was a time for this to be accessible for any size company I think AI um, is a huge help with that when you think about qualitative data ~and~ and trying to theme, right? Like what we used to do before, right? Like who here has marked themes on qualitative data and tried to figure that out?
And ~the ~the heavy lift can go to AI at this point, and we can pull themes out of qualitative feedback, like written feedback that sort of thing so quickly. How are you... this has been one of the biggest areas, I think, where we've been able to pick up pace on surveying, but also understanding what's happening across like performance reviews in a company, that sort of thing.
Like stuff that would've been impossible to get at when we were having to more manually... use more manual approaches to theming the data. What sort of things are you seeing unlock maybe because we can get at that qualitative data so much more readily than we could [00:11:00] before?
Maura Stevenson: ~I, ~I think we did have ways even in the past. It was an intern, right? And they'd come in and read thirty-five hundred comments on development, and they knew all about development. But I think, for us, it's really we ask a lot of open-ended comments in our surveys, and our caregivers have a lot to say.
It can help us quickly validate or invalidate, right? As we're moving through. So we had launched a new technology platform, and there was some chatter that this wasn't going well. And so we dug into the survey data, and we'd asked the questions like: What one thing, could get better?
There were only eight comments that even mentioned the new platform. So there was maybe eight people who had a problem with it. And so we were able to save the leadership team and the rest of the organization from spending time that they shouldn't have spent solving problem that we didn't have, right?
Putting all that together. [00:12:00] It's also been really big for us as we have started going deeper in medical quality. So in veterinary medicine, medical quality is harder than in human healthcare. We don't have third-party payers, some of those-- You can't survey the patients those types of things. But it's really helped us to look at what medical quality issues are being reported, and we have excellent reporting frequency.
And then so we can figure out what are the things that we can take action on, and we can know those things quickly, right? So if you're gonna pop a pop-off valve, or you're like, "Hey, we gotta get the cages out of the way 'cause people are tripping on them," or the big one that we saw, lookalike, soundalike medications.
And so really it's that time from getting data to taking action, we have dramatically shrunk that time with confidence, right? The other thing we've really [00:13:00] focused on AI for is improving the quality of work for our frontline caregivers, right? That's really been, for us, that would be the amazing use case so that they can spend more time providing care and less time doing things like charting, for example
Barb Bidan: Exactly. So it's ~the,~ the data is actually in that case allowing your frontline care providers to be more human. Right? So I think it's back to you've done an excellent job and it is actually a very big counterpoint to some of what we've discussed on prior shows where~ a, a l-~ there has felt like more tension, and I really like the way that you are telling us to embrace the tension between like ~the,~ the human aspects, the qualitative aspects, the data aspects in order to allow us to be more human, make better decisions.
I'm curious as you've gone down that path whether there's any story that you might share with us where a leader has perhaps been able to really lean [00:14:00] in and become much more human in their approach because of the data that they were able to access?
Maura Stevenson: I think, going back it's an old set of data, but it's executive assessments. So I'm a big fan of executive assessments, and I think they can be used individually. I think they can be used collectively which can be a very interesting ~cultural, ~cultural point for us. And I have brought in, and we have widely implemented as part of our executive assessment suite, the Hogan suite is something we've implemented.
And again, I work with a bunch of scientists. And so it was something that I had all of our leadership team members take, right? 'Cause leaders go first, and every director and above at MedVet has taken the Hogan. So it becomes part of this language, and it was particularly helpful for my CEO [00:15:00] to have words that she could use to describe her style or where we might have rubs without labeling people, right?
She has a very direct style. I have a very direct style, but some members on our team don't. And so it's really how do you manage how you communicate in a way that improves the functioning of your senior team. And sometimes data just gives us languaging or insights. It can be about ourselves. And so we go, "Oh, I know that under stress and pressure, I have a tendency to do X." Then I can be more aware of that, and I can be more human, my best human, in how I engage and interact with the other folks on my team
Barb Bidan: I love assessments for that. The language that they can embed in an [00:16:00] organization unlock some of the most powerful conversations, right? It goes from, "I feel like I show up in this way," or, "I feel like someone else shows up in this way," to having actual words to wrap around it, and then suddenly people are talking about it in a way that depersonalizes it.
It's actually like ~in,~ in the best possible way, right? So I've seen that happen as well. I wanna actually talk a little bit more about the business side of things with you. One of the core themes that you have from a business perspective is that caring and accountability, I think, belong together.
That is a harder argument maybe than it sounds like because most ~org-~ organizations treat them as a dial, right? Like you... caring over here, accountable over here. And I don't think that's true either, and sounds like you don't either. Take me through what you think that actually looks like when those two things coexist in an organization.
Maura Stevenson: It's been a big focus of ours. And just let me set the context. During COVID, our business was up over forty percent. And we did everything [00:17:00] we could to keep the doors open and keep our caregivers safe and working. But, Americans adopted eleven point six million new pets, and many general practitioners were not open.
And so it got really busy. And when you're growing really fast or you're getting really busy, sometimes you can get a little sloppy. And sometimes we are making decisions that we thought were, ~that we thought were~ kind, but really they were more nice than kind, and they weren't kind to the rest of the team.
At the same time, we, our workforce is over eighty percent female and people who work in veterinary medicine have dedicated their lives for caring for little creatures who can't care for themselves. So these are incredibly compassionate, values-based people, but there are always trade-offs and choices.
We were getting ready to launch our medical quality [00:18:00] initiative, which would require our caregivers to self-report or other-report medical mistakes or near misses. Human healthcare has been doing this for a long time, right? And the primary thing that we had concern about is that these initiatives do not go well if you don't have a high degree of psychological safety.
And psychological safety, we've done a lot of education on it. It's not that I'm gonna let you do whatever you want. It's not a license to whine, but it's that if you see something, you should feel comfortable bringing it up, that your voice will be heard. And so in one of our pulse surveys prior to medical quality, we did a, a suite of questions on psychological safety, really drawn off of Amy Edmondson's work in the area, and we learned a couple things.
First of all, we had shockingly high psychological safety as an [00:19:00] organization. I don't know how you get that if you don't have that. So I think it really speaks a lot to our leaders and the tone that they set, and it made our medical quality initiative go very well because people were comfortable reporting, etc.
But then we were starting to see some things at the manager level where there was a discomfort with conflict. There was a discomfort with holding people accountable. And so we put together a session that's really blossomed about here's psychological safety and here's accountability. And I had started reading some stuff online saying you can have too much psychological safety I would posit you can't have too much psychological safety, but what you can have is high psychological safety and no accountability, and that creates a culture of complacency, right?
And so we made this little grid of low psychological [00:20:00] safety, low accountability. Here's what you get. And what you really want is high psychological safety and high accountability. Because we talk about leading, we talk about getting better. We have to know how to give each other feedback. We have to know how to get better.
And if somebody's not holding me accountable and I'm messing things up for the team, everybody else knows it. And if it's not addressed, that really does drag us down. So high psychological safety with high accountability is what we talk about and what we expect
Barb Bidan: And I'm curious 'cause you at the beginning of that story you talked about kind versus nice for a minute, and I'm curious if that became a concept that was part of that education for folks. What is the difference between being kind as a human versus [00:21:00] being nice, 'cause I think that plays a role, right?
In how leaders feel comfortable or not comfortable holding people accountable is they're aiming for the wrong one of those. But I'm curious if that's something you all talked about.
Maura Stevenson: I think a phrase that has really taken off is Brené Brown's clear is kind. And that kind of... that sums it up pretty well. It is not kind for me to not tell you that things aren't going well, because then things aren't gonna go well at all, and we're gonna be at a different place. It is not kind for me to, not tell you when you're impacting others on the team, and it is certainly not kind for me to tell you when your clinical skills are not up to par 'cause nobody wants that.
That feels nice. Nice is about avoiding conflict. And conflict can be hard, right? Especially if the other people struggle with it. But we've worked a lot on getting better at [00:22:00] having those crucial conversations. We use the STATE model, but I think it's really that piece of clear is kind, and you may not feel like I'm being kind right in that moment right there.
But eventually you're gonna be like, "Oh. Oh, I see what they did, and I see why." And it's a different framing, and I think there's too much surface nice. But to me, kind is that combination of psychological safety and accountability
Barb Bidan: Yeah. ~There,~ there has never been a single line in a text that I have come back to more frequently than Brené Brown's clear is kind, I will say that. So I am with you there. ~Another,~ another theme is that purpose-driven work still requires sustainable systems. So I'm gonna unpack that a little bit, right?
I think what you're aiming at there clear roles, real support cultures where concerns can be heard, that psychological safety. It sounds obvious that you would want that, right? [00:23:00] But ~sometimes~ sometimes those pieces are things that are really hard for purpose-driven organizations, maybe ties into the nice versus kind thing.
But what do you think it is about mission-driven cultures that makes them maybe resist or move more slowly with building the systems that their people need?
Maura Stevenson: So I'm gonna go way back many moons ago to Starbucks. And caveat, I haven't worked there for, what, fourteen years, thirteen years now. But Starbucks is a very purpose-driven organization. It certainly was when I was there, and you had a lot of energy, you had a lot of people who wanted to do the best for the brand and the experience that we were delivering, and you wanted to go fast and bring it to as many people as possible.
And sometimes your systems, processes, and tools, they get in the way. They slow you down. You're like: "Really? This project is gonna take how long?" So sometimes you might just keep going. And I think [00:24:00] Starbucks went through a phase when I was there where we did catch up on a lot of that technical debt~ from, ~from the fast growth.
But I think it's hard because A purpose feels personal, right? And systems, process, and tools can feel limiting. Now, I disagree with that, right? I feel that well-done systems, processes, and tools can be an absolute enabler if we're doing them the right way so that we're spending time on the right things.
I don't want my local folks having to figure out what kind of gauze to order. They used to be able to order whatever they wanted. But you know what? A, we can save a lot of money, B, gauze is not something that anybody really has an opinion on, and C, I want my healthcare team members doing ~what,~ what fulfills their purpose, which is the hands-on time with the [00:25:00] patients and talking with the clients.
And so I think, purpose-driven organizations, there's often a lot of freedom in them. And it's figuring out where do you wanna scale for good and where do you need to let there be that local freedom and figuring out those things together and knowing that when you harmonize things, you're gonna lose something, but you're also gonna gain something.
And so I think it's really... It's hard until people start seeing it, right? And they're like, "Oh. Oh, I don't have to do that anymore. That's getting done for me, and I can spend more time with hands-on care," right? And I think it's that piece of technology should help us be able to spend more time being relational, right?
A big AI initiative we've had at MedVet ~is~ [00:26:00] is ambient listening software. And so unlike ~human cal-~ healthcare, I don't have HIPAA. Right? But like human health care, I have certain states where I do need to ask you before I record it, but most people say yes. So you're in your exam room. I've got the tech, I've got the doctor, I've got the owner, and I've got the pet.
The ambient listening records all that and sends it into the medical record. And we have ER docs who are saving ninety minutes to two hours a day that they would otherwise be spending charting, right? That's giving them real time back. My sister-in-law, who's a human healthcare doctor, said, "We finally got ambient listening.
I've been waiting for it my whole life." And now we are piloting a software that answers phones if they don't get answered. And it's very conversational, and I think we're nervous about it, but when you listen to the compassion that it will give you, it can schedule appointments. Again, early [00:27:00] days.
That allows my CSRs at the front desk to spend more time in conversation with the people right in front of them. And so I think it's really... it's like data or technology, it should be an enabler of what makes us uniquely human, and I think relational skills, discernment, judging being able to make a good judgment, being able to connect with other people, these are the skills that are going to be even more important if we have technology supported by data helping us with some of those other things
Barb Bidan: Right. And when we do our job, especially on the people side with systems those systems truly should make people's lives easier once they get through that period of change, right? And can feel their life on the other side . I cringe when sometimes that is not what we're necessarily aiming for.
So I feel if you feel [00:28:00] like your team is going to react badly to tech that you're rolling out and they're gonna feel boxed in by that keep thinking, before you roll that out and figure out a way for it to serve its purpose, right? Which is ultimately to help us, not to make people's lives worse.
It should make it better. So I am going to close us out there, Maura. This has been a very enlightening conversation, but I do wanna go quickly to our lightning round questions, just some quick hit questions that we wrap up each episode with. So just whatever comes top of mind to a few of these.
So what is one tool that your HR team or the team in general is using today and really loving?
Maura Stevenson: There is an AI tool called SixFifty that gives you legal. They are not lawyers, but for quick level research, it's been a game changer
Barb Bidan: Amazing. That I am taking a note of that one 'cause I love when I hear some- of a tool I have ~not~ not touched yet. Next is what is one people decision that you would never [00:29:00] trust an AI model to make alone, regardless of how accurate it proved it could be at doing so?
Maura Stevenson: I think a termination decision because there's often a lot of context there, and I think we go in based on the data we have with certain assumptions, and then we learn more, and we realize that what's going on is-- may not be what we thought was going on
Barb Bidan: Totally. And that's one where the data should be supporting it, and the data could be right most of the time, but in the one time that it isn't, that's ~too big of a,~ too big of a thing to take ~take~ a chance on, too human a thing to take a chance on. In that way, I agree. What does the research actually say about what employees want from leaders that you think AI could help surface?
Maura Stevenson: Employees want growth. They want to be part of something bigger. They want to know that their work matters, and they want feedback. And I think there have been some [00:30:00] good tools and other things developing where as a manager, it's hard to give everybody feedback all the time. I think AI could really help with that on time, helpful feedback, knowing the person, as long as it's, in a nice little ring of safety.
I think feedback could be really helpful because AI is a good thought partner if you know what to take at face value and what not to take at face value
Barb Bidan: Love that one. So I always I try to do justice at, the best I can to some of my takeaways. I feel like our listeners always get so much more from the conversation. But couple of things that I would wrap us up with. I think this you called them the two twins separated at birth, right?
The data people and the OD people, but really they are the same, and just go... I want everyone to go think on that for a while, because they really are, and there is data behind the human behavior, and I feel like that was a, a good sort of starting theme at the beginning of our conversation. The next thing that I took [00:31:00] note of is data, and then particularly through assessments was where we were talking about this, can give us data that gives us the language that allows us to be more human which I think is really really bang on.
So love that. And then I, I'm going back to clear is kind a little bit, not just that, just the idea of high psychological safety and high accountability coexisting together, right? If it, for anyone listening who ~h-~ doesn't know, that has not read Brené Brown and understand where the clear is kind ~come,~ comes from, you should go read that.
But I loved that area of our conversation as well. But I do want to leave you with the last word. We always try to have guests do that. If you were to be speaking to one of the listeners who's maybe on their ride home, listened to us on their drive, what would you want them to carry on thinking about for the rest of their drive home?
Maura Stevenson: That we can really make a difference, I think, in individuals' lives as they go through work because a big part of their life is [00:32:00] work. And although we can't control for everything, and we can't supply everything, having clarity of what their job is, knowing how they contribute to the bigger piece, and knowing that they matter to the organization and to their coworkers, that mattering is so important today, especially with all the technology coming into place.
And definitely get the puppy. So that would be my last thing
Barb Bidan: Absolutely get the puppy, and we're headed into the weekend too, so everyone go get the puppy. So thank you so much, Maura, for joining me today. This has been a really great conversation
Maura Stevenson: pleasure. Thank you