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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 - Emilie Schmitz
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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 Emilie Schmitz, the chief human resources officer at Ryze Claim Solutions. And I wanted to get Emilie on because she's leading HR inside a business where judgment calls are not [00:01:00] hypothetical, they are actually the whole job. So Ryze handles claims for insurers and self-insured organizations across auto, trucking, construction and catastrophe response, which means a large share of the workforce that Emilie supports is adjusters and frontline teams that are making high-stakes calls under pressure sometimes while a storm is still on the ground, which actually just sounds like a good day in HR, right?
Emilie has built her career on talent development and change management with 16-plus years leading people operations across multi-state organizations, and she spent recent years watching AI continue to move deeper into exactly the kind of judgment work that the frontline teams at Ryze do every single day.
So today, I'm excited to talk more about the line between human and AI and how that factors into people decisions and what it actually takes to get a workforce like Ryze's workforce ready for that shift. [00:02:00] Emilie, welcome to the show.
Emilie Schmitz: Thank you, Barb. I'm so excited to be here. This is, meshing AI and HR, and those are two of my favorite subjects, so being able to talk about them both is exciting
Barb Bidan: Awesome. Coincidentally, mine too, so I'm glad we're here together. You have spent 16-plus years leading people operations across ~some~ some pretty big companies, right? Multi-state organizations. You built your foundation in talent development and change management, which is critical for the time we're in right now before landing as a CHRO at Ryze.
What is the through line that you would describe for your career path and how did that through line lead you up to where you are today?
Emilie Schmitz: Yeah, so I think it goes back to just the change management as the core. I think in all the roles, change management is a part of the workforce no matter what role you're in. But I found myself tying the things that I learned back in my early career around change and restructuring organizations into what I do now at Ryze, [00:03:00] and at the end of the day, the root of most of what I do is that change management.
So as I look at my career and how I've evolved and how I got to Ryze, it's not being afraid of any kind of change, and it's being able to guide and mold others through change, and it just so happens that it, HR is the sweet spot of where I landed and what I enjoy doing every day
Barb Bidan: Completely. I'm curious because a lot of folks with depth of change management expertise it becomes so ingrained in how you think and your approach that it ceases to be like a skill set and is just embedded in how you think. But for those who don't have depth in change management, what are maybe some of the things that go through your brain intuitively when it comes to change that maybe someone who's not as adept in the subject might not be thinking of?
Emilie Schmitz: Yeah, no, and that's a great question. I was recently told that not everyone is as excited about change as I am, and that was really good feedback for me to [00:04:00] remember that it is pacing and that communication around change. I think instinctively someone like myself who enjoys making things new, enjoys refreshing things, for someone who's not as keen on change, that's scary and they need to know more details.
And so I have someone on my team who she's wonderful at execution, but I need to make sure I do a better job of communicating. And so I think for people who may not be as excited about change or maybe not as experienced in executing change throughout different organizations, it's really looking at what is broken and what can we fix and how can we improve through efficiencies and then communication.
Communication is the biggest piece that I think someone can do around change is just over-communicate . There's no too much communicating when it comes to making changes in organizations and on teams
Barb Bidan: Completely. And I think in HR we especially now, but always, right? Change management is such a critical [00:05:00] part of our role, right? We're leading teams and companies through change, and so we just do a lot of it, and such a critical piece of advice is really remembering that your own orientation to change is not what everyone else's orientation is to change, right?
And remembering that ~in,~ in your approach and how you communicate, which is actually easy to forget. I've been reminded of the same thing ~that,~ that you have too, right? It's that I am like, "What are we changing next," right? But so hopping into Ryze a little bit because it is a national claims operation, right?
Across auto, trucking, et cetera. You've got a workforce that's making real time judgment calls, right? Under pressure, I'm guessing out in the field, right? Actually investigating claims, et cetera. How does that reality of that pressure and that real time judgment shape the way that you and your HR team think about supporting that workforce?
Emilie Schmitz: Yeah, so when we talk about, things like training, we have to make our training available for someone who's on the road. We have to make our [00:06:00] training available easily and efficiently. It can't be a virtual call every time ~we do--~ we wanna do some type of training because we have people who are on roofs.
We have people who are driving to the next roof. We have a workforce that is spread out across the country, time zones but then also what they do. ~We have some...~ Most of our desk adjusters are remote working from home, but then we have field adjusters that, like I said, they're climbing roofs, and so they can't, ~i, I--~ we had an all-hands meeting yesterday, and I guarantee you there's at least one person sitting outside of a residential property listening to the call from their car, getting ready to go do an adjustment. So when we look at how we are rolling out policies or meetings or trainings, we have to take into account just the diversity that we have in where people are sitting when they're doing, or standing, when they're doing the work.
And and then we have our CAT season. Even things like PTO. ~During a hurricane, ~during hurricane season, we have to make our policy flexible enough [00:07:00] where our team knows if a big storm comes in, your PTO might be revoked because we have to ~all be all ha-~ all hands on deck come a big hurricane to service our customers.
So really it's thinking about that, and it, again, it goes back to the communication. We have to make sure we communicate that with our group because they're all over the place doing all sorts of different things that we have to communicate almost three times for every topic or every chance we get.
We communicate in three different ways to make sure we're hitting everybody the way they need to hear those policies and procedures and trainings and all of that.
Barb Bidan: What do you do when you've got such a varied workforce and you may need to communicate to them differently? My guess is in certain places, maybe you even have like slightly different policies depending on the workforce, right? It just like what might suit a field workforce might not suit a desk-based workforce, et cetera.
How do you accommodate that need for difference but still [00:08:00] create cohesion in your culture so it still feels to those three groups like they work in the same company?
Emilie Schmitz: Yeah. That's over the last year, we have made a conscious effort to improve on our method of communicating with our workforce. So we have a heavy 1099 workforce, which, allows different rules, right? They're not technically employees, but they certainly are contractors for us, and we still want to treat them as if they're an important piece of our team because they are, right?
And so we have started newsletters and we use the same voice in all of these things and all of these methods and, it's a lot of emails, but then we also have chat boards within our organization that are all ran by pretty much the same people, so the voice is consistent. But we've gotten better on the back end of communicating as a team, which allows when we go and communicate with our workforce, it really allows us to have the same voice because we're on the same page.
And I think that's a big improvement that we've had over the [00:09:00] last year is really just getting that message and who we are to our team out better and that allows for all that communication to be more consistent.
Barb Bidan: Completely. And it's easier to get messages out in when someone's at a computer looking at their email than maybe if you have to get something out to a field workforce. So it is something that requires some thought. So I'm gonna, I'm gonna get us to shift into the thing I kicked off with, the human versus AI boundary and people decisions.
So I wanna get into that. As the CHRO of an organization that's built on judgment calls, where do you draw that judgment call line between what AI should touch in people decisions and what, in your opinion, needs to stay more human?
Emilie Schmitz: Yeah, so it's really clear in our world that AI cannot be used to make claims decisions, and that is a hard line that we have as an organization where we encourage the use of AI for documentation, for gathering information, but [00:10:00] it cannot be used for decision-making . And that translates into HR as well.
If I'm doing an investigation, I'm not going to let AI make the decision on what we're going to do, right? And so I think that is where you have to draw the line on when a decision is being made. You can't rely on AI to make any type of decision on behalf of the organization. But I think it's a really great tool to help you get information faster, help you consolidate information, help you be more consistent in how you're communicating with people.
All of those ways, we are using AI in all of those ways to help our communication, to help our consistency, to make sure that we're more clear and concise in the way that we're writing documents. But I think that line is still in the decision-making. It still needs to be made by a human. No matter what the role, no matter what the responsibility is, it's still, humans need to be making the calls on things.
Barb Bidan: That we've talked about that a lot on the show. It's come up in different [00:11:00] ways from comments like AI can recommend, but the human decides, right? And like remembering that your human workforce still needs to own the decisions even when those decisions are made based on AI recommendations.
You're not gonna blame the agent, you're gonna blame ~the,~ the human making the decision, right? When you think about some of the more human people-related decisions that we have to make, right? Hiring, performance promotion what are some of the ways that you're thinking about injecting AI to help support any of those processes, but also where you're drawing that, ~that human~ human must decide at this point?
What does that look like for you?
Emilie Schmitz: So one of the big initiatives we had this year is a full and complete compensation analysis in HR, and it was also creating job levels and, we had sixty-five different job titles, right? And consolidating that, doing the research on compa ratios, that is great for AI. AI was super helpful [00:12:00] in navigating how to structure things, how to set this up properly for our workforce.
But when it comes down to those decisions around what level an employee is at, are they really a level two adjuster or are they a level one adjuster, and what does that mean to be one or two? That's me having conversations with each manager for about an hour, going through each person and talking about each individual person and what they're doing and how their skill sets either align to this role or another role.
Same with their compensation, right? It's, we're talking about I have the data and the levels that, that AI helped me build, but I can't determine what that person actually is based off of AI. We need to have a conversation, and I need to know what their claims look like and how well they're performing.
And so that's a piece that AI is certainly helpful. When I look back ten years ago, doing all that manually seems, ~seems~ wild, but we did it, right? And so I was able to get it done a lot faster than [00:13:00] I would have been ten years ago. But you still need to have that human element in how I'm going about placing people in the levels that they should be in.
Barb Bidan: Yeah, ~and I'm, I had a very, we-~ we're living parallel lives. I just wrapped an interim assignment, and I was doing something similar with job architecture, and we, again, used humans at the same stage that you're describing, right? The AI pointed us in the right direction and then we took it the rest of the way in a very human one-to-one manner.
But I'm actually curious about the thing you said about it used to take us forever to do these things like 10 years ago. And I'm ~genu- like,~ I've had this thought as well, and I'm curious the first time you remember doing something that you're like, "It took us months and five people or something," right, crazy like that, to do this before, and is this even possible that I just did this in this short of a time?
What was your prevailing thought ~whe-~ the first time you realized how big that makes this shift, and what are you gonna do with your extra time?
Emilie Schmitz: [00:14:00] Yeah. You get more tasks that you get excited about, right?
So I think that the first one, and it happened about a year ago, right when Claude was coming... I know people had used Claude for a while, but it was really becoming more in my world I guess I'll say.
And our CEO came in, and he asked for in-depth turnover data, year over year, department by department, and we had an ATS system or an HRIS system that probably wasn't well-maintained for the last four years or so, so the data was not as accurate. So I built this spreadsheet by hand
And this is only a year ago, and I had all these calculations, and I built it all combined into one. And then after I finished it, I said, ~"I probably could...~ Let me just try out and see if Claude could do it." And I put it into Claude, and I put the raw data in it, and it just did all of it. And it was able to give me by department, ~by mana- ~further detail.
And so now when he asks for that quarter over quarter, I'm able to just upload the raw data, and [00:15:00] it updates exactly in the format that, that he's looking for. And so the first time I did that, I said, "Oh my gosh." Like ~I,~ I built this whole thing, and I was so proud of the spreadsheet that I built, right?
It was one of those spreadsheets you walk away ~from, you're~ just so excited and proud of it. And then Claude replaced all of that work I did. But then I said now what else can it do?" That was that light bulb moment of what else can it do that maybe these tasks that I, like doing, like getting into the data, but, how else can it help us?
And so it reduced our time of reconciling health bills from about four hours it was taking the team to about four minutes to reconcile our health bills. And so things like that, where we are now able to change from spending the time building these systems to now I'm able to work on, okay, how can we develop our people?
And so now I'm working more on developing our people and getting them that training in AI or that training on leadership that [00:16:00] somehow in a very small HR team wasn't always the things you got to work on because you have to work on all of the tasks and administrative things. By being able to use AI for a lot of that, now we're able to develop and grow and do some of the fun parts, I think, of HR, which is the people side of it.
So that's really the moment I think that I got really excited about where we could take all of this and how we could grow and, now we're looking at different agents and different things. My, my team and I are, like, going all in on this because it really can allow us to work on things that we enjoy and work on developing the talent in the organization instead of just being the administrative side of the department,
Barb Bidan: yeah, that aha moment is the thing that I think is... being able to get people, whether it's those in our workforce, those in our HR teams, to be able to experience that aha moment where you realize how much help this tool can provide, where your wish list that was only ever [00:17:00] gonna be a wish list is actually a thing that might be able to come to realization it has really shifted the way that I work. And so that's a thing I try to illustrate for others or let them illustrate for themselves, right? Just get that one big win where they're not ever gonna look back, and then suddenly your whole team is crafting agents and so forth, right?
Which is, sounds like the path that you are headed down. So I'm always just curious about how that's landed with others. So I wanna kinda marry the idea of communication, which is where we started the conversation, right? And kinda communicating to a varied workforce, and then the human decisions and where the human sits in AI-based decisions.
How are you actually talking to the broader workforce about where that boundary sits, right? You mentioned the hard boundary, right? Of claims decisions can't be made by AI. So how do you communicate that out to the team so that they understand the guardrails, if you will and the boundaries that you've set up?
Emilie Schmitz: So one of the things, and I can't take credit for it, ~it was~ [00:18:00] it was the genius on my team who did this because she got excited about Claude and about AI, and she built a training module about Claude. So when someone in our organization is going to get a company Claude account, which we have encouraged they go through a training module.
And that training module not only teaches them how to prompt , but we put in there an intentional mistake of Claude giving them the wrong information, which, we know can happen with AI. It will give you what it believes you want it to know. They want to please, but it doesn't mean that it's always correct.
We built in this incorrect piece of the training module, which allows people to see that they still need to use critical thinking. And so that is what we are encouraging, that AI can help you do things more efficiently. They can help you do things faster . They can help you get to a place of being able to operate more efficiently and faster, but it can't do what you need to do, which is critical thinking. It can't [00:19:00] find the holes. You're gonna have to make sure that it's accurate. You're going to have to double-check the work and understand what you're double-checking because you can't just walk away from it saying, "Yep, it's correct."
We've all been or I've been a part of meetings and presentations where, it was built by AI, but the person didn't quite understand the content, and I think that is becoming The more people are using systems, the more they see when it's being used and people don't understand it.
So at the end of the day, it's great to use it, but you still have to understand what it is spitting out and why, and be able to critically think about it. And so that's the message that we wanted with this training module was, yes, it's great. Yes, we want you to learn it. Yes, we want you to use it. But still use your thinking cap and still use that critical thinking component.
That's even more important when we talk about AI, I think, because, that's how you're gonna make sure it's accurate, and that's how you're gonna make sure you're still putting your name on that material and that it's correct
Barb Bidan: Totally. It's not [00:20:00] only that the human owns the decision, but the human owns the result, and we need to remember that. But I love the novel way that you inserted that into your training because I think it feels very sticky, right? If you missed that mistake and then learned that lesson in the training after, I feel like that is a way to really make that stick with the learner on how important their role still is as a human in this practice.
Super, super creative and smart solution there. So let's hop forward from the philosophical side a little more into execution. And really when you are looking at AI readiness across the organization either within your team, broader workforce, both, doesn't matter what does the picture look like today, and what sort of skill gaps are you starting to uncover in the workforce that will impact, how AI forward we're gonna be able to be?
Emilie Schmitz: Yeah, we are actively having conversations around, we want to be an [00:21:00] AI-forward business for our clients. We want to be able to provide them accurate data as quickly as possible on their claims. Our clients are the carriers really, and we want to be able to use AI to get them the information that they need accurately, efficiently, and, as quickly as possible, right?
And so I think for us it is how do we maintain all of those with, with it still not making the decision, and we have the end person making that decision. And so we're building out methods of being able to... we're right now structuring what that looks like from an IT perspective, from an HR perspective, from an operations perspective.
But what we're seeing is there's still a fear of it. And then there's also people because of the fear of it taking jobs or the fear of it, being, an overlord of information, whatever their fears are, has prevented them from even testing the waters. And so what we have wanted to do is [00:22:00] start testing the waters in a closed environment.
So we use Gemini for that, where we've uploaded our documents, and it's a very easy and safe way for our team who maybe isn't as, as experienced in it and building agents and building systems, but it's just to get their toes in the water And so we've started doing that, and we push them towards training within that and let them see that it's not that scary.
~It's not, it's not... ~but really ~the,~ the break of it is how to communicate with it. I think until you use it a lot, you think it's like a Google, and so using AI as Google is how the majority of the workforce is using it, which again goes back to that training that we created was, not, "Here is how to prompt."
And so if you put in a prompt, it will redirect you to maybe think of another way that you should do that prompt. And so we're working on all of that in how we are going to operate our business. So we're not trying to be reflective and say, "Okay AI is going to help us."
We're trying to [00:23:00] integrate all of that into how we operate the business and know that AI is going to continue to evolve, continue to move down this path but how can we train the workforce to use it in a non-scary way, reassure them, and then communicate with them how we are pushing this, but also how it's not replacing jobs and how it's not scary.
~And it's, it's-- I,~ I don't know that I have a full answer on that because I think we are still evolving in that conversation with how we're trying to roll all of this out to those who may not be as excited to work on, work with it as me and my direct team are.
Barb Bidan: Yeah, human resistance is the biggest risk, in my opinion, to AI adoption right now, and, being able to make this feel safe and accessible for people is critical. The show-and-tell thing has caught fire in the worlds that I've been operating in. So having team members share the things that they have built,
That I couldn't have gotten us faster across the [00:24:00] stop using AI as if it's Google chasm than through the show-and-tell. . It was like seeing people on a Zoom, minds blown when someone shows a more advanced way that they have used AI to impact their own personal workflows, and then their colleagues are working in adjacent areas, and off it goes from there.
I don't think any of us have the code cracked either, right? Which is why I love that we get to share on episodes what others are doing. I've super enjoyed our conversation today. I wanna bring us home with some quick hit hot topic questions that just your hot takes on a couple of things.
What is one AI tool that you've actually used within your team for HR items or workforce planning this year?
Emilie Schmitz: Yeah, we use Claude pretty... we've switched over from Chat to Claude
Barb Bidan: So have I. Agreed. Yes. What is the people decision, I might need to change my trick question here. What is a people decision that you would trust an AI [00:25:00] model with today?
Emilie Schmitz: ~I would trust it with... ~I don't know that I would trust it with any people decision, but I would trust it with gathering and reconciliations and structuring policies and structuring even investigation documents. But I don't think I could still trust it for any decision-making
Barb Bidan: I love it. I didn't really realize that I had written a trick question in there, but I agree. We said no decisions on AI, but recommendations and helping make our work quicker, for sure. What is one piece of work, not a decision, just one piece of work that you feel at this point like you'd never hand over to AI, even if the models and tools get a lot better than they are today?
Emilie Schmitz: I'm not ready and maybe this is a barrier on me, but I'm not ready to turn over payroll to AI. And that one is, we have a lot of changes. At least at Ryze, we have a lot of changes, we have a lot of commissions, we have a lot of things that go into it, and I'm not there yet.
I know that there's a lot out [00:26:00] there that's controversial, but I'm not ready to give up payroll yet
Barb Bidan: I have run payroll. I'm intrigued by that. I would be like, "Oh, okay, computer can do that." But I also know you do not mess with people's pay, and payroll teams that I have led know that this is a zero error environment that we need to have. And so maybe your thinking comes from that too, right?
~It's like the- there is... It's,~ it is the most fundamental thing that our teams deliver, is making sure that people are paid timely and accurate, that they count on us for that. It cannot be wrong. So I hear you on that one, for sure. What is one thing that Ryze's frontline field-based workforce has taught you that a desk job never could have taught you?
Emilie Schmitz: Yeah. I think that it is, really they are the front line dealing with the, the insureds who have had these casualties or these massive issues at their house. And so that empathy that our adjusters are able to have, not that you can't learn it from a [00:27:00] desk or from a customer service, but you are really engaging with people when it's a really hard time in their life, no matter what.
And so the empathy that I see our adjusters come to these homes with, and the way that they engage with the insureds who have had this, you know, damage to their home and they're uprooted, and the grace that they hold that with them, is a lesson that I think is one that I enjoy continuing to learn in HR.
Just to remember that at the end of the day, we are all just people trying to get through the day. And so they go to these houses with these insureds, and they really show a lot of empathy and care, and I think that is something that, that you can learn from them, that you can learn from a desk, but I think that it's a different environment being face to face with people when they're at their worst
Barb Bidan: Oh, without any question, right? There, we talk about being close to the customer the amount of empathy from folks in jobs where you get cl- close to the customer or the patient or the student on these really human [00:28:00] hard issues, right? Healthcare catastrophe in your, in, with your home or in, in your life situation that that is like a built-in training ground for empathy and requires people in those jobs who can exercise empathy at the highest degree without question.
So as we were talking, I always try to capture a few takeaways, so I got a, a few. I know our listeners will come away with a lot more. But what, one of the first things we talked about is you started by describing change management as really core, right? And it is core at all times in our profession, but ~es-~ especially core right now.
But I liked the idea of as a person who is helping others through change, taking a moment to remember in your approach that not every person receiving change or working through change feels the same way that you do about change. That's an important reminder. I would be remiss if I did not repeat again that the decisions stay human, my [00:29:00] friends.
So your recommendations the work being quicker, AI can help with that. The decisions and the ultimate ownership over the result right now are human for sure. And then the last thing is I loved the tricky training thing, which I think is so smart. So ~my,~ my comment for listeners was, does your AI training train on how not to lose your critical thinking as a human?
If it doesn't, I loved the idea of injecting a mistake into something that the AI produces through the training and using that as a sticky learning opportunity, so that was a great one. I have so enjoyed our time together today but I also like to leave guests with the last word. So I would love for you to speak to any of our listeners who maybe were listening to us chat on their drive home today, and they've got a little bit of time left in their drive.
What would you want them to carry on thinking about for the rest of their ride home?
Emilie Schmitz: Really, I think it's what are your pain [00:30:00] points? And that's, that goes back to the change management, right? Where are your pain points, and, how can those pain points be used by AI to help you with, right? Because, as you're ~getting~ into AI, you learn that they really do ~help~ with a lot of the pain points.
And but in order to help, you have to identify where your weak spots are and what you need help with. And so I would advise the listeners to think of your pain points, think of where you are spending a majority of your time, and then start finding the ~right...~ Just because, I use Claude or, somebody else uses Chat, you have to find the right source for you.
Because I think all of them have different pros and cons, and for me, Claude was the best. But I know a lot of people use other systems that are better for what the output is that they're looking for. So do some research on that and find your pain points and which solution would be the best for your pain points.
And I think get in there and play around with it because there will be a way for your pain points [00:31:00] to be relieved at least a little bit using some of these tools.
Barb Bidan: Great advice. Get in there and try it. I love it. I am so glad that you joined me on the show. I think this was a great conversation and that our listeners are gonna love it. So thanks for coming on
Emilie Schmitz: Thank you so much. I appreciated the time with you, and it's been great