Chris Havrilla - HR Tech 2024 - How to Reduce Technological Adoption Barriers in Organizations
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Hello and welcome to the HR Data Labs podcast. I'm your host, David Teretsky. Here at the HR Technology Conference 2024, live and in person at Mandalay Bay Exposition Center in beautiful Las Vegas, Nevada. And today I have with me one of my BFFs forever, Chris Havrilla from Oracle.

Best friends forever. Forever. We're yogurt buddies. Yes, we are. Yes, we are. We will always have that New Jersey yogurt place. That's right. That will always be our, that'll be our Paris. It will be our Paris.

In our quasi-residence in West Orange. It was a residence. Residence Inn, yeah. That's right. And Marguerite Santamaria. Oh my gosh. She was our buddy. Family. Oh my God. Mishpocha. Exactly.

I learn something new every time I'm with you. And I do too. And that's why I appreciate your friendship. But Chris, as we do with every podcast, we need to know one fun thing that no one knows about Chris Avarela. Oh, wow. I choose the hard ones first. You do. You do. What is something fun? Fun, new, different. Something new, different. I think...

Little known fact. Okay. Little known fact. Everybody knows I'm into F1 right now. Yes. Everybody knows I am a big supporter of the Oracle Red Bull team, but very, very few people know how that got started and where the love came from. Yeah, I'd love to hear that. And I will tell you that from the very, probably...

Maybe 14. I used to go to dirt tracks because I'm a Southern girl. Right. And we love all sports. Sure. And we love cars and we, you know, just the whole thing. Yeah. Cars and trucks and everything. So I would start, you know, I would go to these dirt track races. And then I kind of evolved. Yeah.

into sports car racing. Wow. Right? And I would go and I would work at the Road Atlanta track doing timing and scoring with little stopwatches because I'm that old. And then I went into race control where...

where with our walkie-talkies, we would find out what was happening at other turns and put it on the master board for the chief steward. And then I had a race car driver come and ask me to be on his pit crew. Wow. So I could do what is known in NASCAR today and stuff like that as a spotter. Wow. But it did not have a name like that. I literally would time everything.

And tell him the differentials between the car in front of him, if there was one, and the car behind him. Split times. Split times, all the differentials, how much he'd need to increase his speed, and I'd write it on chalkboards. Right. Because that was not technology at that time. It's gotten a lot. The data and the technology and how it's evolved into how you build a high-performing team is like...

People are like, how do you learn all this about F1? I'm like, it's like everything I love all coming together. And that transitions very well into how to create a high-performing team in the world of business. That's right. It is. Because finance and HR and IT have to work together, don't they? They have to be a big pit crew. Yes. Yes. They really do. And I am the spotter for now. Okay.

There you go, people. There it is. Comes full circle. That's right. That's Chris Evrilo. Okay, we're going to end the podcast right there. We're done. That's all she wrote. No, I'm kidding.

So one of the beautiful things about being at the HR Technology Conference is that you look around and you see the vastness of how HR has changed. And you could say it's HR process. You could say it is the HR technology, but they go one hand, right? One hand in the other. So, Chris, you've been in HR for a long time, just like me.

What have you seen as far as evolutions of HR? What are the things that you're seeing as trends as evolution of HR? I think the thing that really stands out to me is the fact that it is, I think we're finally at this point where the technology can work for us. Okay. Right. It has been the work of,

Like we fed the beast, we fed the beast, we fed the beast. And we just didn't get, it became the work itself. And for me to see that in a very organic way, this could actually change how people work and operate if you let them, especially. You know, and that is super exciting to me because every time I think about some of these things, the lens I look at now when I walk this show floor is everything

Will people just change how they work? Will they see the opportunity and go, oh my God, I can do this better, faster, this, that? Will they see it and

And start to explore it that way. Or is it just going to add another step because they're not going to change working? But it should be so seamless. It should be so organic that I change how I operate. Right. Right. And I see that promise in so many of these booths.

It certainly is at the heart of what I do when I'm thinking about what our investment strategies is. Will this actually fundamentally change the way a manager manages investments?

Without some big top-down transformation, you know, where everybody takes the same training, and then now you know how to go be an empathetic leader. Yeah, that doesn't work. It doesn't. And we saw that over and over and over again, even in, you know, where we've worked together in the past. Yes. Right? No names. So I just, that to me is the promise. I also think it's going to change the way HR operates, right?

And I think that's super exciting because we've said that for a long time. And by the way, I want to touch on that, if you don't mind. We just keep taking old process and putting it into a new technology. Right. And we go, why isn't it getting easier? Why isn't it getting better? Why don't we add more steps? We took a personnel action form that was a piece of paper. We put it in that very secure manila envelope with the red thread. And then we'd send it through into our office mail. Yeah.

We just have automated that thing and keep on it. Maybe we put it in Excel instead of putting it in through PeopleSoft or whatever. But we haven't fundamentally changed how that works. There's still transactions in that same facet.

But doesn't that have to break down? Don't we have to get rid of that shit? We absolutely do. And I think it's probably a lot of the FUD, the fear, uncertainty, and doubt around AI and things like that. But the interesting thing to me is,

Right now. And, and I've, and I can already see it like, you know, just even in, you know, a product we, you know, we announced, you know, back in May and in, and I saw a lot of the HR, you know, people look at it and go, that's our job, right? That's our job. Like we define the roles, we do this, we do that. And, and, you know, it definitely takes some conversations where you can finally just say, you know what?

Look at yourself as a facilitator. Think of yourself as a solution provider, not a service with answers, but how to facilitate solutions because you are operating in the dark. And if I could show you in a way not to operate in the dark where no data is left behind that will actually make your job easier, that's

Would you be open to that? And it's not going to mess up your structures. So that you still have the safety blanket of a structure, but what if we do something over here and it's all the same data? And it really is making people step back and think and to start kind of small and move fast. Because I think the thing that is really messing people up is this notion of transformation. Yes. We have to change everything, everything like, and, and so, um,

There's risk, and all of a sudden everything stops because they can't boil the ocean. But if you can show people how to take baby steps, it actually, the momentum builds pretty quick.

The key that I love talking about when it comes to that is the moving of cheese. Yes. People freaking hate cheese moving. Yes. And it's not even... And sometimes even if you prove to them that it's better to be in a refrigerator than sitting on a plate in the open where it's getting moldy, they don't care because it's comfortable there. Yeah. They're okay with it there. They can look at it. To me...

HR is the epitome of don't move my cheese. Yes. How do you get them off that? You were speaking really wonderfully about all these great benefits to doing it, but everybody's so resistant to it. They're resistant to it because...

The exact reason they will zig and zag all day long and complain about it, right? When you can show them a straight line, look, I can show you a straight line to get from A to B, but you can't guarantee it, right? They can see it, but it's almost like a barrage, right? But I know if I zig and zag, then I know what to expect, even on that journey. Of course. I think what was fascinating about COVID is...

That cheese got moved for them, right? Oh, yeah. They had to. And productivity soared, by the way, outside of HR, inside of HR, everywhere. Productivity soared.

And then it started to drop back down. And it was right about the time command and control came back in because we want to, you know, we want to, we want things to be normal. Right. Quote unquote normal. And I do think you're seeing all these companies now, like people have to come back to the office. We have to do this. Things have to go back to normal and productivity. Yeah.

is going is going down and down and down again right yeah in the same breath saying we need to be more productive we need to be more effective and so i do think that the one thing that we can do is if we can show people those baby steps right where it takes the risk out yeah but um but shows them that they can get to those outcomes a little bit faster but that trust has to be built

That trust has to be built. But this notion of command and control does have to go away. Like the fact that the only reason, the only way I can see forward and where I've seen it work, and I have seen it work. We do have customers where we have gotten traction, but it's only been when they did baby steps to build momentum. They did everything with purpose. They broke it down into, let's say a three month project. Like we're just going to focus on this.

Because once it starts to elongate, leaders change, this changes, the stress comes in that makes everybody want to go to safety. Yeah. Or you get some periods of downward pressure on finances. Totally. And it all, everybody goes, oh, we have to come back to the office. All those disruptions we always hear about, right? Whatever form it takes.

you have to just time block stuff out and have, you know, it's kind of design thinking one-on-one, right? But let's, let's just get a, an MVP, right? An MVP, an MVP. That's the only thing I've seen work that are making it makes it a little bit more organic and,

Because people can see if I do an outcome, like an MVP, right? If I do that and I get it, like I'm building the trust. And so whether that's in a system or a process, right? But this kind of notion of a process needs to go away. Like what I did this week to get to my outcome and what I do next week is all going to depend on my surroundings. Everything might change. Absolutely. So we have to get out of this...

Complete notion of it's got to be this way or no way. But you mentioned this before, and I think it's very true that people like comfort and people like repeatability. People don't like shocks to the system. They don't do well with change. And so therefore, every time something goes wrong, they go back to their safety and safety is control. Safety is, oh, cover my butt or whatever. Yeah.

And there's been so much disruption. There's been so much change. And we got used to it for a little bit and we started getting pretty good at it, but now it's all gone away. Yeah. It's reverting back to old ways. It is. And I think a lot of that is trying to kind of bring that control, you know, back into things. But I...

But I do believe that's why things have to be kind of broken up. And, you know, the power of data right now, you can't deny it. Like, you can start to see, you know, and thank goodness for things like ChatGPT that kind of, you know, there's all kinds of things you can say about it. But it really made this so mainstream that it gave people a reason to think differently and to say...

You know what, if somebody's going to try to cheat the system, right, because it's going to do this paper, but at least they're thinking outside the box and they're just trying to make something easier for them. And that's the lens, you know, and that's when the guardrails and controls can come in after that, right? Because you have to kind of give, like, what's the rules? Well, you can't copy. But Chris, you and I both lived through probably high school, high school.

when we were still using typewriters, but then there were some computers around. Some kids use computers, some use typewriters. And we went through the transition where people were like, no, you can't use a computer to send this assignment or to print this assignment out because it's going to help you with spell checking, whatever. All right. Well, we went through those times. Now there's Grammarly. Now there's ChatGPT and Gemini and blah, blah, blah. And all of them can help kids to your point. Right.

Those are the tools that those kids have available to them. When it was us, it was either a Selectric or it was a Corona or it was an Apple II or a TRS-80 from RadioShack. But those are the tools. Apple IIe for me. I had an Apple II Plus. Oh, okay.

But that was before the E. Yes, it was. And then there was the Mac that I actually had in college. The box. Yes, the little box. The rectangular box. Exactly, that had the monitor inside it. Absolutely. And it was a gray screen monitor, if you remember. I do. Or black and white, but it was still one little thing. Yeah. And so...

We've got to get over this, you know, how work gets done. Yeah. Stop worrying about that. Like what you hear so far? Make sure you never miss a show by clicking subscribe. This podcast is made possible by salary.com. Now back to the show.

Now, there's the other sides of it that I've been talking about all week here, which is the risk of people putting stuff in the wild and having the walled garden and having, you know, the data sources that are trusted. Right. Having the, at least the data be curated to make sure that we know what's going into these models and what's going into the algorithms. Right.

But for frack's sake, use the tools at your disposal and get smarter about them before everybody else is doing it and you're not. You know, bingo. The key you just said there, I feel like I preach this day and night, but if you don't

Open your eyes to the technology and learn what it is. You can't learn how it can help you. But I do think the lens we all need to look through is, well, you know, and look, one thing we're all good at is human nature is being selfish. You know, and so embrace that and say, well, how can this help me? What if somebody let me hire somebody tomorrow? What would I have them do? Right? It's the same thing, you know? And so knowing what...

Look, if I hired somebody new tomorrow, it would be awkward. And I'm not going to trust them. They're not going to trust me. We kind of have to feel each other out. But we do it, right? And the faster we do it, the faster we kind of embrace that person, you know, ask some questions.

see what they're, you know, it's the same thing with this. Right. Right. So like be selfish, go learn about it, go see what it can do, play with it, see what it can't do. Right. And then figure out like, be lazy, be selfish. Right. And figure out how this can help you do something different. If it's creating more work,

or distracting you from your outcomes and not helping you get there, then move on. It's not going to be for everything. So what you just said made a lot of sense so much that I started thinking, you know, I could actually ask these technologies to help me sell. Yes. And then I thought, oh, well, you know, I also have Salesforce and I know there are some tools built in Salesforce that do something like this. We just need to embrace them. Right.

Absolutely. It's going to be awkward. Getting to know somebody is awkward. But, you know, I look at this as I've got...

I got headcount I just didn't get before. Right. And now what can it do for me? Right. You know, and I do think that that if everybody would just take some time and learn about it, just like you would somebody coming on your team. Right. How can we help each other? What's fascinating is there was that kerfuffle on LinkedIn not too long ago. I'm not going to mention the company name, but a company said they were hiring an AI and it was going to have a job description. They were going to pay it, all that stuff. Right.

People lost their freaking mind. Oh, this is a marketing baloney. This is, you know, this is horseshit. Are you going to do development for it? Are you going to give it a seat? Are you going to... Come on, really? You know...

Even if it is a marketing thing, bravo. Right. Bravo. They're thinking about the future. You, you're worrying about, well, why is this bothering you that they did this? Right. Why do you have to beat them up for it? I actually agree with that. I was really surprised by that whole thing. Like I said, you know, I loved that they were,

humanizing it in a way. And I was really shocked by the response. And, you know, in hindsight, I think it did play into fear and certainty and doubt. But, you know, I would say in this world of, we keep talking about skills and capabilities is maybe think of it that way, you know, that, um, because I do think machines are workers in our workforce, but if we don't want to humanize it that way, that's fine. But, um,

These machines have skills and capabilities that we don't have, and we've got ridiculous skills and capabilities they don't have. That's right. So if we can start thinking about how does work get done, and again, how can I embrace this set of skills and capabilities, because I don't have time to cull through a bunch of data in 18 different systems, but if this does and can bring it back to me and

put some structure to it that I can sit around and argue with it and, you know, and then apply my curiosity and my critical thinking and get somewhere. And I just saved two days worth of research. Yeah. A ton of research, ton of time. Right. Absolutely. And that's where this is to me, this is no different than when, you know,

There were cars when there were horses. Right. And when there was the tractor before, there were people. Right. Or oxen. These are gigantic leaps of productivity that these are tools that will enable us to do things. Now, if we have to humanize them, give them a social security number, make them pay taxes, okay, fine. Whatever. I don't care. Right. And that makes the tax base richer. But...

You know, the selfishness. If I can get my job done better. Absolutely. Better, faster, have more of an impact. Yeah. Hello. Yeah. This is exactly what the words competitive advantage mean. Exactly. It's exactly. And if you don't embrace it, get the hell out of my way.

Well, the whole very definition of innovation is it's going to answer one question and three more are going to pop up. You know, and if we're going to get stale, like we're never going to have an impact. Right. We're never going to have an edge. I want an edge. You want an edge? Absolutely. Thank you. Yes. Boom. Okay, hold on. Just drop the microphone. Drop the mic literally. That's the reason why I love Chris Amrilla.

This is epic. But I mean, you know, you think about something like performance. I mean, if we're going to, you know, let's bring it back to performance and comp or something like that.

How many times have you been in a performance review where some, literally the last two months is the only thing we've talked about? Recency effect. Absolutely. Totally. But if I can summarize, if this, if this machine summarizes everything over the last year, probably brings up the stuff I forgot to document. Exactly. The great stuff. Yeah. Brings it together. And, and now I have a better conversation and the,

Then my boss and myself are not jaded with trying to go back through all this stuff. Like if it's going to lead to a better conversation, like I'm all good. And, but guess what?

Guess what? If chat GPT comes back and, or, you know, my wonderful Oracle tool comes back, it says, you know, this, this, this, and formulates a, you know, a, a base of a document for my boss to, to play with and now make it her own. I will know in a heartbeat if it's not her voice. Yeah. So I, I, it was funny. I got a lot of media when we first started to release some of this stuff and

And that said, well, you know, then what if the manager never does anything? I'm like, you think the worker won't recognize that? Like this will self-manage a little bit, but if I can get a good, reasonable performance document, that's comprehensive that now I can take into the comp, you know, kind of process and things like that, I have a much better chance of getting fair pay. But Chris, it is exactly what you said though, before, um,

It's better feedback because it's been summarized over the year. Yes. We are human. We forget stuff. Yes. The good and the bad. Right. The happy and the sad. Yes. I'm going to break out into a song. Wah. I think the good with the bad, the happy with the sad. Sorry. Sorry, everybody. I apologize for that. It's okay. They didn't see my dance moves. That was keeping me going. That was a bonus. Yeah.

We'll put the video as the addendum to this. But seriously, though, if you could give managers and employees an assist by giving all of the really good highlights and lowlights. Listen, performance evaluations have stopped being about

how to be better so that you can change for next year and get more and do more and be better. They've become a, well, my boss hates me because they gave me a one out of five. No, they don't hate. I mean, they may hate you, but they don't hate you or they shouldn't hate you. But it should be an objective conversation about did you achieve the things that you had set out with your boss? Did that align with your job description? It should have. Yes. If it doesn't,

We got other color. I mean, that goes back to the data being accurate from before. But it also comes back to that conversation, right? Yeah. I would tell you that if you asked my boss right now what my PowerPoint skills and what my what I would say my PowerPoint skills are.

Would be two very different scores. But if we, if I just put a PowerPoint on my profile tomorrow and it drove a better conversation, whether that was actually you need some development on that or actually you should be teaching some other people or...

You know, I don't, you don't have all those bells and whistles. And I was like, yeah, but I had a better conversation. Like it's all perspective that needs to be discussed when we're thinking about how to unlock my performance of potential. Exactly. And because that data point could have different meanings and perspectives and uses. Yep. So those are all the things, but we get so mired in what,

Like if you go back to skill data, okay, I've seen this with people as they start to think about adoption. Okay, but what about proficiency? What about validation? It's like, what about the conversation? And then we can, again, those baby steps. Let's just put the PowerPoint on there first. And then we can have a conversation. And then we can start to see...

What is, what we'll say this number could represent? What would be that perspective? And then you can start to build accordingly. It's baby steps. It is, but we have so gotten as a people, as a culture, and it's not just the U.S., it's beyond us.

Performance evaluations as a practice is just garbage these days. Nobody likes it. Everybody doesn't look forward to it. I mean, I love it. I love feedback. I don't get any. You probably get some. Hopefully it's all good feedback. But I mean, seriously, though. I get a lot of feedback. I'm sure you do. Yeah.

I get some too, but, but, but no, but seriously, it stopped being what it was. It stopped being about how do I develop? Like you were saying, how do I become better? How do I get a better career? How do I get more comp, whatever. And it's all about, do they like me or not?

Well, I do think that's the promise and the organic change that I see coming. And or at least when I think about how we're investing, right, how do we kind of democratize the data and the insights? Right.

to drive better conversations. You know, and I think about like what we did with Oracle Grow, right? We're actually putting that down and we're saying in this, we're in your role now, but also, you know, for future potential in your org, right? Yeah.

Doesn't mean everything is right, but it gives that worker something to talk about with that manager because that is perspective. Maybe even that worker didn't think because we still have a tendency to think not you and I know because when you look at our job titles over the last several years, they've changed dramatically.

So I know you and I don't self identify in one title, but a lot of people do. Right. And, and to have that ability to, to, cause we might trust the machine better than even our moms or dads or friends or colleagues or bosses. Right. And to say, well,

Well, why did it say that? Let me look into this a little more. Right. But I would have never known to look there in the first place. Right. That's where I think this will slowly change the way we think because we all are inherently ignorant.

We want to succeed. We want more money. We want more opportunity. We want better challenge. We're selfish. Maybe, you know, I've always said I'm a little lazy because I want to get to this point, be faster, right? But it makes me work more innovatively because I want that edge. So I do think that's the promise of a lot of this AI right now, even if we have to argue with it.

It's going to make us think differently. Hey, are you listening to this and thinking to yourself, man, I wish I could talk to David about this? Well, you're in luck. We have a special offer for listeners of the HR Data Labs podcast. A free half hour call with me about any of the topics we cover on the podcast or whatever is on your mind. Go to salary.com forward slash HRDL consulting.

to schedule your free 30-minute call today. We have to learn how to argue with it. We have to learn how to work with it. We have to build skills to be able to understand what it even means because there's so much noise, especially here. I'm not talking about the noise in the microphones. I'm saying there's so much noise of the differences. What is it? What is AI? Because every single...

Except for the Omaha Steaks people. Yeah. Everybody's talking about AI. And I think we have to ask that. Every single person in here has to say, but what's it going to do for me?

but what's it going to do for me? Like quit thinking about the buzzwords and say, what is this going to do for me? And then I have to think about, will that help? Or will that create more work for me? Like we just have to break this down a little bit more simple because, you know, at the end of the day, that really is what is going to actually drive the kind of traction that we need. But I really firmly believe, and I think,

All of us as vendors and all of the people here serving, you know, where we are going to claim AI and black box and things like that is we also kind of have to have a little bit of a why box so we can contextualize. Yeah. If something is giving a recommendation or a suggestion, why? Right. But a why there. Right. So you have something to argue with. Right. Not just...

Oh, okay. Or maybe even that kind of fixed box. Well, what if that's like wrong? Then there's something behind it that needs to be fixed. I learned something new for the first time at the show, and I forget who it was who told me it. I think it was Richard Rosenau. He said that there are bots everywhere.

That will check other bots because sometimes artificial intelligence, it dreams up an answer. It hallucinates. Right, exactly. Because it's using other AI generated content to give back an answer. Right. Okay. Holy shit. Because if that's what's happening. Right. Sorry for the language. Yeah.

But holy shit, you know, if it's coming up with an answer, that's a lie, right? Or it's coming up with an answer that's not exactly based in fact, obviously those two things can exist, coexist, because we know that, right, that there's a difference between a lie and something that's not exactly true.

My eyes are going back and forth. That's a whole nother podcast. That's a political podcast we could have for like hours. But seriously, if the things that we're relying on to give us true answers, because it's a computer and how could it lie? But it's dreaming shit up on its own.

Oh, my God. You know what? We argue with each other. We debate each other all the time. We've got to learn to do that with the machines. But the more we can make it easy by doing that, providing some context, that Y-box and fix box is exactly why I think, you know, we need that, especially while we're gaining trust with this stuff, but also to know if there is a problem and that we do need to go back and check something, fix something. What's the source? What?

What's driving this? I guess at the end of the day, what we're talking about is we still need to do QA on this stuff. We still need to make sure that the answers that are being given not just are accurate. I mean, yeah, we got to check the math. We've got to make sure that we're going to be able to be okay with when it's not. And that if this is what we're adopting and how we're adopting and how we're going to embrace it,

Unless we put in those other agents to make sure that the answers are correct and keep doing unit tests and keep doing accuracy, you know, whether it's spot check or every single one, we're going to have to assume that some of the stuff is wrong. We deal with people. We should deal with machines. Yeah. You know, we, it's, Oh, we should question things and we should train people when they aren't doing things the right way or machines too. Right. That's it's,

Well, the machines are trained by people. They are. And data. And data. And we all know that HR data is not clean. And we know where the data came from. Yeah. So that's why none of this is perfect. I don't know that we're going to get answers. We have to look at it right now, at least in the beginning, as suggestions and recommendations. But, yeah, I mean...

Us not thinking is not a good plan for this. Right. But the world's not perfect. No. And we're not perfect. Nope. So why should we assume that the technologies are? I mean, you know, for whatever reason, my iPhone crashes every once in a while. Yeah. I love it, but it crashes. It does. The internet crashes. Things happen. Yes. So we have to build that into what we're doing. Right. Exactly.

Sounds like we're creating religion. Well, it's perfect, but it's not perfect. It's something we believe in, but we don't believe in it. I don't ever say anything is perfect. Yeah. I don't ever think I have the answers. You're perfect. I know I'm not perfect. Yes, you are. You're perfect to me. You are so perfect.

Beautiful. I won't even sing. I'm already losing my voice. It's me. Can't you see? Actually, it's the first time I've ever sang on the podcast. I was just about to say, it's the first time I think I've ever heard you sing. It's to you. It's not bad. It's serenading. It's to you. And for those of you who are still listening after that, and I haven't actually made you deaf, I apologize, but...

That's what Chris does to me. I mean, this is my yogurt BFF. Yogurt BFF forever. For sure. Chris, it's always a pleasure. Thank you. This was awesome. You know what? I think I should just call you every once in a while to serenade you. Maybe you should. We won't record it like this. We want to subject everyone else. But again, thank you very much. Thank you for having me. This was great. All right. Take care. Stay safe.

That was the HR Data Labs podcast. If you liked the episode, please subscribe. And if you know anyone that might like to hear it, please send it their way. Thank you for joining us this week and stay tuned for our next episode. Stay safe.