Trisha Price: [00:00:00] Hi everyone, I have an exclusive discount for Hard Call's listeners to Pendomonium - Pendo's Product Festival happening March 24th through 26th, 2026 in Raleigh, North Carolina. Listen in at the halfway point today to get this special discount to the product festival, bringing today's top leaders in product, AI, and software.

Naomi Lariviere: I oversee our client facing AI program at ADP. It's called ADP Assist. And we've been working on Agentic AI and you know, in it, in the space of payroll. The hard call for us is as we started to talk to our clients and we were showing them early concepts of what we wanted to do, what we realized is that our innovation was colliding with the trust that our clients had in us.

We service over 1.1 million clients. We pay one in six people in the United States. That's a lot of people. When you say you can't get things wrong. Imagine getting a [00:01:00] paycheck that's not right? So you know what we learned is yes, speed matters, but trust compounds.

Trisha Price: If you build software or lead people who do, then you're in the right place. This is Hard Calls, real decisions, real leaders, real outcomes.

Hi everyone, I am Trisha Price, and welcome back to Hard Calls, the podcast where we bring on the best product leaders from across the globe to talk about those moments, the decisions that mattered, the hard calls. Today on Hard Calls, we have Naomi Lariviere, Chief Product Owner and VP of Product Management at ADP.

The company many of us rely on to get paid, including me. Naomi and I first connected when she was a keynote speaker at Pendo's user conference a few years back. We had the joy of backstage jitters together, and that comradery was maintained as we continue to get together. To share strategies, challenges, and just network CPO to CPO.

What I love about [00:02:00] Naomi is how she thinks about innovation, especially in an industry where there's little to no room for error. In today's episode, we're digging in to how Naomi balances innovation with trust, builds psychological safety on our teams and makes the hard calls that define great product leaders.

Welcome to Hard Calls, Naomi. 

Naomi Lariviere: Thank you my friend, for having me. That was such a nice introduction. Thank you. 

Trisha Price: Well it's easy to do a great introduction when you mean it and when you know someone and admire someone. So I'm really looking forward to our conversation today. 

Naomi Lariviere: It's very mutual. Thanks for having me.

Trisha Price: So before we jump into hard calls, could you share a little bit with our listeners about your journey into product leadership and what your role at ADP looks like today? 

Naomi Lariviere: Yeah, that's a great question. I'm somebody who kind of fell into product management. I did not go to school for computer science or kind of any of the other [00:03:00] paths that tend to end up here.

I have a business and management background. My first job outta college, I was in an early career program, which I think a lot of people coming outta university are - I got into business analysts and over time that role with the companies that I worked for kind of merged into product management and really understanding both the business needs, the client needs, as well as being able to talk to the development team about like what we were actually trying to accomplish.

And maybe 15-years ago, I got put into a leadership role and it's just kind of snowballed ever since with additional responsibilities and bigger portfolios. And I think when you love what you do, your career just kind of keeps feeding you. And that's what I've gotta say about ADP.

I joined here, I had a very small team and ended up overseeing our large enterprise portfolio, and now I oversee one of the largest portfolios at ADP, which really [00:04:00] services all of our business units in a shared product fashion. Which really for us means we build products that we can build once and deploy multiple times across our product ecosystem, which is, which helps us be scalable and helps us grow as we need to.

So, it's a fun job. Love working at ADP and love to bring what I know about product back to my team. 

Trisha Price: Great, well I look forward to digging into that a bit more as we go through the podcast, but this show is Hard Calls and we like to start every episode with a hard call that our guest has had to make.

So tell us, looking back over your career. Recent or a while back, tell us about a hard call you've had to make. What made it challenging? You know, what considerations or process or data led you to make the decision and what'd you learn from it? 

Naomi Lariviere: Yeah, no, I think in our job, in our profession, we make hard calls kind of routinely. It's part of the [00:05:00] job and you're prioritizing things all the time in terms of what you wanna do. But maybe I'll talk about something that's a little bit more recent. So I oversee our client facing AI program at ADP, it's called ADP Assist. And again, this is another initiative where it's build once, deploy it across our entire ecosystem.

We've been working on agentic AI and you know, in it, in the space of payroll and sometimes in the space of payroll. You know, and with this technology we wanna go as fast as humanly possible. And there's so many wonderful things you can do with it from just task automation to like actually doing the entire process for you.

And that's actually this initiative had that goal. We are just gonna do everything for you. You don't need to worry. And the hard call for us is as we started to talk to our clients and we were showing them early concepts of what [00:06:00] we wanted to do, and you know, we were iterating on it. What we realized is that our innovation was colliding with the trust that our clients had in us.

We service over 1.1 million clients. We pay one in six people in the United States. That's a lot of people. When you say you can't get things wrong. Imagine getting a paycheck that's not right, right? Like that is not a good scenario for us. It's not a good scenario for our clients. So you know, when you are doing things that may erode the trust of your user.

You might wanna reconsider what you're doing. So we actually, as we talked to the clients, we're like, okay, well if we wanted to get to that, they're like, well, that's a really great thing in the farer in the future. Like, how can we get you on that trust building journey? So what we learned is yes, speed matters, but trust compounds.

So, to a certain extent, we've had to [00:07:00] delay kind of what our overall goal is to really ensure that we have long-term credibility with our users and that we're bringing them along on this innovation journey as we continue to progress where we're going with Agentic AI. 

Trisha Price: I love that you shared that story.

I feel like. So many of us building AI and agentic experiences are going through, I mean, yours is magnified even more so when you talk about something so critical as people's paychecks. But it's true for all experiences. It's like our customers want AI, they want the ease of use of it. They want the value it provides the automation it provides.

And if you're not doing those things, you're falling behind in their expectations. But at the same time, the very few are ready to jump from here to here. And maybe if it's something really not critical to their business and it's like a [00:08:00] playful tool, that's fine. But I think in anything, that's critical to people's business.

This like crawl, walk, run strategy of building trust and sort of being in more co-pilot mode than full agentic automation mode is something we have to get people comfortable with on the journey, even though the real value unlock comes when we can get to automation. 

Naomi Lariviere: Yeah. And you know, like we view what we're doing as does like our job is to design for people.

This is the world of work. We are changing how people experience their day-to-day lives and. You know, just as much as you or I walk would walk into our C-suite with you know, "Hey, we're gonna do this." They want the data behind it. They want us to be able to explain it. They want it to be very transparent.

And that's what we are definitely weaving throughout what we're doing as we reimagine how work is actually done. So explainability, transparency, like those are kind of like, [00:09:00] especially with AI, those are quintessential elements that all of us have to be paying attention to. 

Trisha Price: For sure. Well, I know ever since I met you, one thing you and I have always had in common and believe in is everything that we do has to deliver client value.

And the great product management starts with client value. And you know, your example here is, is clear that that matters to you. How do you help your teams stay focused on delivering client value, and delivering outcomes not just for your clients but for ADP. 

Naomi Lariviere: Yeah. it's funny 'cause I was just talking with some folks just this morning, earlier today, a about like, what's the prioritization rubric like? What are we looking for when we sit down and go, is this an idea that has merit, that we should progress? So there's really three kind of things that we're looking at is one, is [00:10:00] it good for our users? So is it good for the client?

Like is it going to help them have a better day or experience our product in a way that makes them happy or delighted? Right? So that's question number one, yes or no. Very simple.

 second one is, does it help ADP? So will it help us grow our revenue? Does it help us acquire new logos? You know, will it help us deflect service calls?

Like what that's kind of like, does it help ADP? And then third is, how does it help us with either our market position or competitive position? So we're not necessarily the organization that's trying to do everything the same as what our competitors are doing. We are really trying to service the needs of our clients and really think about.

How work does evolve, so we don't have to be the same as everybody else, but there are things that if you have a sales prospect come to you, like there are [00:11:00] certain table stakes. And so in, in those instances it's either a yes or a no on whether it helps us with that. Now, this is where I love Pendo.

We leverage Pendo a lot in every single aspect of that decision making process. You know, in terms of how our users are using our system, where they might be running into problems, kind of what the journey they're taking through our systems are. And you know, that data is so critical for us in terms of how we answer those questions and how we then make the ultimate decision about what we're gonna do.

Trisha Price: I love to hear that. You know, that warms my heart to hear that Pendo is driving decision making and an important part of how you measure value for ADP and for your users. Naomi, do you guys have scorecards, goals, KPIs like that you think about for the product [00:12:00] team to make sure that these outcomes are achieved?

Naomi Lariviere: Yeah, we use OKRs. So everything is outcome based driven. So we have our vision, our mission the outcome that we're trying to drive. And then as we decompose the idea into a roadmap, then we actually are going, okay, in Q1, we're gonna achieve this part of the outcome. And we keep tracking towards it.

 outcomes for us always are metric bound, so it could be that we're reducing service calls. It could be that we're helping those new logo sales. Whatever that element is, and then we're just tracking that as we go along. I mean, what I love about ADP, I mean automatic data processing, but if you think about data, data, it's our middle name and everything we do, we are probably one of the most metric'd organizations that you have, and I love that about my job is we, we can pretty much tell you anything about what it is that [00:13:00] we're doing and how we got to an outcome that we were trying to drive.

So, yeah, just lots of, you know. Really monitoring it because just because you made a decision to actually invest in something doesn't mean that you actually have to continue to invest in something.

We've had things where as we were building it. And maybe we put it in pilot, we just weren't getting the adoption or we weren't getting pilot clients to sign up for the idea. And given some time and some, some more analysis as to what might be happening. We kill ideas all the time.

We kill projects. There's probably a lot of stuff that goes on here that never sees the light of day. And that's okay. And that's how data can really help influence your decision. And not just at the inception of an idea, but as you are continuing to go along. And the SDLC, 

Trisha Price: I love that that is - as you said when we started off, you and I, this role, [00:14:00] we make hard calls every day, and killing a product or pausing something is probably one of the hardest calls that we have to make because sometimes it's easy to say like the outcome's right around the corner. We're just not there yet because we fall in love with our ideas and we're trying to innovate and we're trying to do things different.

 and that I think is just one of the hardest calls that we have to make. 'cause you just want it to be, you knew it was a good idea. and it's like, oh, but we just have to do this one more feature and the outcome will come, but sometimes it doesn't. 

Naomi Lariviere: Exactly. I think I really believe in, in the phrase, and you've probably heard it before as well, is like, you're not the user.

I'm not the user and so I never actually get too caught up in whether my idea is actually gonna make it into production or not. For me it's all about that person at the end of the computer screen or the mobile device that is actually experiencing them. I believe what they tell me, [00:15:00] and that is how you make your decisions.

Because if they don't see value in it, then why are we doing it? Right? 

Trisha Price: Yeah. Then they're not gonna pay for it. They're not gonna appreciate you. 

Naomi Lariviere: Exactly. 

Trisha Price: We to listen. We have to listen. 

Naomi Lariviere: Exactly. 

Trisha Price: Well, as you mentioned, and we all know, ADP is a highly regulated space. and your role is fascinating to me around bringing AI to your users, bringing AI in a scalable way to ADP. and you have to do this in a place where precision matters, right? Mm-hmm. Even a small mistake has major consequences. As you said, none of us want our paycheck to be wrong unless, unless. It's in the positive direction, but then there's probably still somebody there who's not happy.

 so tell us like, how do you approach bringing AI in? How do you balance innovation with the need for almost perfect reliability? 

Naomi Lariviere: Yeah. Very [00:16:00] carefully. So I think, And I'm gonna apply what I'm about to say as like before AI, so BC so before AI happened, generally most organizations, they would build their products.

Test it and then hand it over to your security, your legal, your compliance team, and they would look at it and do a checklist of yes, yes, yes. And then it would actually go and become generally available or be released clients to use. That we have completely shifted left. So as we come up with our ideas for AI, what we realize, because we do have a lot of data, we have the largest HCM data set in the industry.

We service organizations in 150 or 140 different countries, so there's lots of laws, regulations, especially like in Europe where there's been a a new legislation around that, even [00:17:00] here in the US, California. So what we, we did realize is we need to shift that entire process. Left. And now any idea that comes in for AI it goes through, we call it the CDO process.

It's governed by our Chief Data Officer and that team. And basically it's looking at the security elements of the idea. It's looking at the data, how we wanna use it, are we using it in a way that complies with privacy laws? We look at it in terms of how does it watch or observe compliance laws around you know, the different statutes across the world.

And then last but not least, like legal. So are we thinking about bias? Are we thinking about the ethical use of it? All of that is kind of like our shift left philosophy. Now, it doesn't just happen at the first time that you. Come up with the idea as we are going from [00:18:00] A-A-P-O-C to a pilot to generally available, that analysis or that work that our CDO office has deployed gets progressively more difficult.

So there's harder questions as you go through. So by the time that actually is in our products, it's. We been thoroughly vetted based off of our understanding of the way the world is right at this second. I mean, laws are changing every single day. So our process does adapt as we go through it.

But that is generally what we do now, overall, our principles And how we have been thinking about AI. We started an AI and Ethics Council in 2019. This is made up of subject matter experts in the field of artificial intelligence and ethics from some of the major universities out there.

And they work alongside us to help us lay out our plan in terms of the things that we should be watching for in this space, because it's not just about your payroll. You [00:19:00] know, being your paycheck being correct. It's also about how you're recruited into an organization. It's about your performance review.

It's about hiring and firing decisions. All of that is as we want to apply AI to it. We just have to be super thoughtful about what it is. Now you can pass all of these checks that we're doing internally, but again, it goes back to does the user need this solution? And is it good for a DP and does it help us with the competitive?

And so like this, all of this process goes in tandem with how we actually are making decisions about what we bring. 

Trisha Price: So fascinating when you think about all of the aspects that you're bringing AI all the way from first touch of candidates to hiring, to onboarding, to performance management.

I mean, that's just critical, critical to how so many all companies run their business. I mean, our number one [00:20:00] asset is our people. 

Naomi Lariviere: Yep. 

Trisha Price: And so it is fascinating to think about the legal implications of everything you're doing across that life cycle. Naomi, can you give us a concrete example of a new AI feature or product that you've launched into your products?

 And the impact it's had. 

Naomi Lariviere: Yeah. We've done a actually quite a lot and we actually, so I mainly important to note, we don't talk about like, ideas that we have that we're just kind of like thinking about today. We only talk about things once it's actually in our product. It's. Being used by either pilot clients or by or it's generally available, but we actually have quite a lot that we've delivered across our six major platforms that we've got.

 And I'd say like probably the one I'm most excited about, it's been in pilot now for several months. And I say pilot, it's like we're rolling pieces of [00:21:00] it out to generally available as we go. So it's not fully GA right now, but like clients do have pieces that are using. and it's called payroll anomalies.

So the bread and butter of what a DP does is a while we are a HCM provider, what people mostly use us for is the payroll process. Payroll is a very complex process. on average, a payroll practitioner or the HR department, they do about a hundred different activities to make sure that you get a correct paycheck.

 That process is done over the course of generally two days. Most organizations, usually Monday and Tuesday are kind of big days for, for the HR department. They're running all of their checks. So this is like. All the new hires that came in, are we, are they accounted for people who left?

Are they accounted for anybody going on leave of absence? Do we have our benefits data, our 401k [00:22:00] information? All of that information's coming into the system. And then payroll basically checks all of that data to make sure that it's correct. And we call that. Anomalies. So what we're looking for is anything that is out of the norm.

So maybe you're an hourly worker, but all of a sudden you have like 80 hours on your weekly pay stub, and that's kind of odd. So did we overpay you? So it's flagging that those kinds of decisions back to the user to go, Hey, you, you wanna look at this? And what we've what? Used to happen is our payroll practitioners is, they would, we would flag all this information, put it in a PDF, they would have to print it off and then go through it line by line.

Some of these reports can be like 200 pages long, right? And we're like, there's gotta be a better way. On average it takes 'em about 90 minutes to do this process. 90 minutes is a lot of time. And you know, I always talk about it like, we want you to get in, get [00:23:00] on and get on with the rest of your day because you shouldn't live in our systems.

 Our systems are used to facilitate work and we really felt that problem. It was important because out of all the payrolls that we run, we can see that at least 70% of payrolls have at least one anomaly that will show up. So it, it is a critical step in the process that people need to look at. It's high impact it's had. But it also had high feasibility in terms of can we apply agentic AI to it. So what we've created is the ability to detect, make the user aware, and then actually resolve the issue for them. Now, this is where kind of the trust factor came in. So when we first started, we were like, yeah, we just wanna, you know.

Everything's all solved. Like the world is beautiful. That 90 minutes it's maybe a five minute process where you just kind of check it. That's where clients are like, "no, no, no, no, no, no, no, no, no, no. Show me the math. How did you get here? Show me why you, you did it." [00:24:00] And really what we've went back to the the tinkering board to, to go and actually look at okay, how do we make it more explainable?

How do we make it transparent? How do we actually show them our homework? Right? And also, how do we let them make the final choice? What we understand about our users is because this is such a it's one of the most audited processes. In a organization we wanted to make sure that they felt comfortable and they could check off.

So while we have automatic detection awareness and resolution capabilities, they are the final human in the loop to actually go, yes, I accept this work. Yes, this is the right thing to do. And then it moves on. But the other part that we then wove into our process from an audit tracking perspective, while we have audit logs.

For every single process in our, in our ecosystem, [00:25:00] we actually brought in an agent control center. So this tells them all of the things that the agent is doing versus what the human did. So that way if they ever were audited or you know, God forbid they were sued or something like that, they have that information at their fingertips.

They can produce it and they are good to go. So our clients, and now let's talk about impact because that is something that we track, right? You know, what we can actually see is how many, like literally, and this is where Pendo helped us, is to be able to tell when they see the anomaly how they click on it, and then actually how many go and do the action to say, yes, I'm okay with how you solve this.

 And that we can see like they prioritize some of the anomalies that they look at it, you know. Basically varies by user. And then last but not least, it generally it's taking 90 minutes in the [00:26:00] process that they use, if they're going with the PDF, it now is reducing up to an hour's worth of time.

From that process. So like, it's significantly, significantly improved. Kind of like their happiness with that part of the process. And you know, clients are just you. I think we have a quote on our website, like the client was just like, this means so much to me because it's. It's easy, It's smart.

It's doing the things that help me with my job and hopefully we, we'd say maybe that makes them the situation a little bit more human for them in that process.

//AD//

Trisha Price: Registrations for Pendomonium 2026 are now open. We are bringing together the most inspiring minds in product and leadership who will challenge your thinking on everything from product-led growth.

To the future of product to gaining value from your AI investments, [00:27:00] it is likely you'll even run into some of our guests from hard calls. The product festival is designed to spark curiosity, create conversation, and build community while spotlighting the newest tech for software experience leaders. I would like to invite you to join me in Raleigh, North Carolina from March 24th to 26th with an exclusive 30% discount when you use the code HardCalls30 That's Hard Calls, all lowercase and the numbers three zero. Get your discounted ticket at pendo.io/pendomonium. See you there.

I mean, that is real value and you know, we hear so much. Around AI and everybody's building AI features, but in a lot of cases, for a lot of people, AI has yet to give an ROI, and this is a real example of your customers getting actual time back from your AI investment. [00:28:00] and that's incredibly impressive.

All of us have had to pivot and learn new skills in this era of AI. Whether it's the engineers or product managers, designers, in terms of how to build trustworthy interfaces and interactions with agentic interfaces for our customers. How did you do that as a leader with your team?

Did you have to go out and hire people that had experience? I mean, it's kind of new for everyone, so how do you find that experience or how did you upskill your team so that they were able to have the kind of success you've had so far? 

Naomi Lariviere: Yeah. I'd probably say a little bit of gorilla tactics. So I think you just said something that's really important that everybody should understand.

This is new technology, we're all learning together. Right? You know, we weren't sitting as PhD students at Stanford like learning this as part of our coursework. [00:29:00] So we're all learning it together, including our users. They're learning it together for us, we have a really great leader who was like, "Hey, you know," Maria Black, she's our president, and CEO, she basically said, "listen, I think this could really be the wave of the future, especially in our industry, and we can really think about how we design the work for people and reimagine work." And she was just like, "I need everybody."

To jump on board. So, especially within the product and technology organization we have had coursework that we've all gone through. We do a lot of webinars where they're more like a lunch and learn. So here's a team that was doing early experimentation, what they've learned, what they understand.

 And then when I say gorilla tactics we've also leveraged content from the [00:30:00] big LLM companies, they all have free learning available to them. Coursera has a ton of learning as well. The universities are making education available for free if you want to do some of that.

 Like Duke University is a good one in your home state. And they all have coursework. But I think what I love about where we've been on in this journey over the last two and a half years is. The collaboration that the organization has. I don't think you, you can just go, I wanna be innovative one day.

It really takes a lot of bold thinking and you have to, to drive kind of that disruption. You can't just be satisfied with, well, this is how we've always done it and this is the way our clients always wanted in order to make. You know, work reimagined, you have to think differently. And this technology gives you that opportunity.

So we have [00:31:00] we I talked about we have outcome-based teams, but we also have fleets of teams that are working on AI. And they work on problems across the, what I would've said might be a traditional silo. They are working across it to go like, "Oh, the payroll team did this, well, maybe we can use that same concept over in benefits or retirement or recruiting." And so they're learning off of each other and I think most of the ways that maybe you or I kind of grew up in the corporate world is.

You learn on the job. And so it's been a great opportunity to see those teams really push the boundaries of we're like bold, you gotta be bold and they from having sandboxes where they have the freedom to experiment with all the different tools to try and determine which one's the best one for the problem that they're trying to solve, to just kind of saying.

We can think differently. We [00:32:00] can do things differently. It doesn't have to be the same. And they have that permission and autonomy to do that. And I think you know, given our predictable approach around how we bring AI to market, that allows us to really kind of celebrate the learnings that we're getting as we're going along it, and just not pushing features that maybe clients don't want.

So it's been a great time. 

Trisha Price: I love that. I mean, I don't think it's common and easy or typical for companies of your size and scale to be able to do a pivot the way you have to this experimentation, continuous learning mindset of AI. And that's clearly showing up in your ability to deliver it to your client and the experiences.

 I think easier sometimes in small companies that are just getting started [00:33:00] to have this experimentation and learning mindset, but I think is often harder for companies who have probably gotten into a pretty predictable delivery methodology, SDLC, we could probably, you and I have been doing this for a long time.

We can probably, with reasonable confidence most of the time, understand when a new product or feature is gonna come to market, what the risks are. But this is a whole different ball game, and you're working through it in a really interesting way that seems to be working. 

Naomi Lariviere: Yeah, and I mean, it starts from the very first use case that we actually brought to production it that went from the idea and the data part of like why it was a good idea to actually execute on that went from you know like day one to in production with clients in 13 weeks.

So I don't think anything at a DP has gone that fast. Yeah. But. [00:34:00] I would say that the process that we've now applied our like when I talk about our shift left on our compliance and our, our AI, responsible AI program that has really actually enabled us to move faster than we probably would have traditionally on a regular capability or feature.

And I'd say it's refreshing to see the pace of what we've been able to deliver over, like we, it was like that from that one use case. Within six months we had 10 and then 20 and like we just keep. Like it's become kind of this like, not necessarily a conveyor belt, but like it we're, it's faster, more predictable.

We're learning, as the technology is changing we're having to go, oh wait, that idea wasn't necessarily so great to do that way. Now we have a new toolkit [00:35:00] in our bag, let's go and use that. And so we're trying to be very nimble and not locked into any kind of form or fashion in terms of how we do this.

Trisha Price: It's interesting. We had the same experience at Pendo Naomi. We were building agents and we built one agent into our listen product. The one that looks at support tickets and call transcripts and looks at like portals And any kind of survey data and helps product managers know what our customers users are asking for.

And we built an agent on top of that. So you could ask a questions like, what are the top 10 enhancements that my enterprise customers are looking for? And then we went and we built an agent for our guides, right? So you can say, Hey, I wanna put a new onboarding guide into my product that does X, Y, Z, and you can ask it and it starts to create the guide for you.

And then we [00:36:00] built one for our analytics. So you can ask questions like, how is this particular feature performing? Tell me what's working well and who's using it. And we learned from e different teams built each one of those because we were trying to go fast and we were trying to experiment and learn.

And then we realized what we really needed to build was an MCP server. And we wanted to do that because you might be building your own agent for your product management team, and you might wanna be able to ask it questions about any of those things, of your Pendo data without coming to your Pendo agent.

And so it's like, okay, well now I gotta scrap all of these and move to this new architecture. And I think like, but that's okay, right? It's not wasted time because we actually learned lessons from each one of those experiences. And I think that's just part of the world we're living in because the technology's moving so fast.

Naomi Lariviere: Yeah. And I think the I talked about collaboration and what I think is good and it sounds like at [00:37:00] Pendo, you guys are practicing this, is that you need to give teams psychological safety in terms of how they're coming to. How they're showing up and how they're actually advocating for what we're doing.

 I think it's really important that we give them the space to, and the power to speak the truth. I don't want people to just feed me a line and go, oh yeah, we're gonna make that date when really it's. Failing miserably, right? I really want them to tell me what risks that we might have early, what challenges we might run into.

Because this technology is new, it's unpredictable in certain elements. And you really have to you know, maybe slow down to go fast. So we put a lot of accountability on our teams in terms of what they're delivering and how they're delivering it and how they speak up because I think a lot of organizations can fall into the trap of like, well, my exec wants it by this date, and then they don't speak up even though they know that it's not doing [00:38:00] the thing that you wanted it to.

Yeah. i talked a little bit about experimentation. We spend a lot of time experimenting in lower environments that are not in production, really looking at all 360 degrees of the issue because we really wanna make sure that we're doing the right thing for our clients and for their data.

And so experimentation happens in lower environments. Never in our production environment. and with that experimentation principle, it's you can fail fast in those lower environments, but then you're succeeding very deliberately in our production environment. So we're really trying to balance creativity, keep that alive, but also your quality and what we bring to our clients.

That's uncompromised. 

Trisha Price: I'm continually impressed with your leadership style, with your ability to drive outcomes, with your ability to innovate in a [00:39:00] complex environment. But I also respect your leadership sort of on a different angle, which is something I know both you and I have been passionate about for a long.

Long, long portion of our careers which is our belief that diverse teams are better teams and we both have put significant energy into lifting other women up especially in technology where that's been a challenge for both of our many parts, of both of our careers. Can you talk a little bit about that and why that's a passion of yours and why you think that's led to your team's success too?

Naomi Lariviere: Yeah. Well, I, when you think about like, at the end of the day, you and I are in the business of building products that people buy, right? And not everybody looks like me. You know, and I really do believe that diversity drives better products. That different perspectives are gonna catch [00:40:00] different edge cases that might happen that I might not think about.

 And then as it relates to like the teams that I cultivate and as I look when I. When I got into this business not, not a DP, but into the tech business I would look around, I was oftentimes the only woman who was the business analyst or the only woman who was on the team.

And it's kind of like, well, sometimes you get afraid to speak up and you, and you don't do that. And I think over the course of my career and maybe like the ambitions that I've had for myself, I really want to bring other females or underrepresented groups along that journey with me because I do believe that everybody has a seat at the table, that we all have a voice and that we all can contribute to the growth and development of our own organizations.

And I've [00:41:00] benefited from both male and female mentors that have helped me, grow my career. And I really do believe that it's our responsibility to be able to give that back to those who are falling behind us. Because having an example of a strong female leader who's getting things done.

That inspires other women to do that. In our profession, 35% of women in tech are leaving the career by the midpoint. And we can save those women if we give them the examples within the profession that they can aspire to because someone just has to tell them it is possible. 

Trisha Price: I love that.

And it is possible. And while I think you and I both have seen change in a positive direction and we see more diversity and more women in leadership positions, there's still a long way to go. and I'm with you. I'm passionate about this, not just because I [00:42:00] think it's the right thing to do, which I do think it's the right thing to do, but I actually believe and have seen it produce better business results when you have different.

Perspectives at the table willing to challenge each other that come from different backgrounds or look different. and you're right, our buyers and our users don't all look like each other like us. So It's helpful to keep that in mind when we're driving business results for sure. Yep. Yeah. Well, Naomi, thank you much so much for sharing your story.

Your passion for people, your approach to leadership but most importantly, for hard calls in our hard calls audience, how you have successfully. Delivered AI features that are driving value for ADP driving value for your clients in a complex. Make no mistakes, almost, space [00:43:00] that you're in.

So I know that our listeners are gonna learn a lot from today's conversation and enjoy hearing from you. So thank you so much for joining Hard Calls. Thank you for having me. Thank you for listening to Hard Calls, the product podcast, where we share best practices and all the things you need to succeed.

If you enjoyed the show today, share with your friends and come back for more.