Driven by Purpose | Peak’s Chief Commercial Officer, Zoe Hillenmeyer (Podcast 209)

And there were some very interesting studies about money's effect on that in particular. And in certain circumstances, money is a very useful motivator. But in most circumstances it's not. Yeah, it is not, because it actually it puts a monetary value on something. Oftentimes that's valued there. There's a higher order value. So people are willing to make an economic payoff on something that they see as being like, oh, well, there's these other things I value more. So what are those other things? Well, typically they're about people pursuing the fullest version of themselves. That's what it kind of comes back to. We all have some destiny we're after on our vision of who we want to be. And so I think of that as intrinsic motivation. That is somebody's intrinsic motivation for why they showed up every. Why did they end up here in the first place? You can get paid a thousand ways. Why are you here? What's that about for you?

Our guest today is peak's Chief Commercial Officer and head of US Operations, Zoe Hillemire. Zoe has a decade of experience in the artificial and decision intelligence field and has been part of over 20 product launches in the category during her time with AI pioneers including Peak, AWS and IBM. Zoe holds an MBA in strategy from Washington University in St. Louis and was trained formally in the fine arts as a sculptor. Both experiences mean she approaches the AI and decision intelligence space with a human centered outlook and builds teams and communities that thrive on collaboration, creativity and diversity. Zoe lives in Seattle with her wife and two dogs and is involved in several initiatives to drive greater inclusion, diversity and equity in AI and business. Zoe, welcome to the Second in Command podcast.

Very excited to be here.

You are in one of my favorite cities. I love Seattle.

Having lived in many cities, it is also one of my favorite cities. It is the only city as an adult I've managed to stay in for more than a year. I've now been here for eight.

Okay, so you hopped as well? Yeah. When I first moved to Seattle, I was driving around and I saw a bunch of people driving their cars with bike racks and mountain bikes on the roof. I said, this is my neighborhood. And I ended up in Fremont. It was just this kind of fun local neighborhood. So tell us about your journey. How did you get to where you are right now with Peak?

Yeah, it's been a journey of, I think, curiosity and just always sort of being interested in how people make decisions. So that started actually during my mba. I was really interested in how different decisions got made at the time. I was studying organizational behavior, economic, sort of like microeconomic decision making. And then I became quite interested in a lot of science around decision making. How different teams make decisions, how people come up with ideas and then how people evaluate them. I guess I've always been kind of a right brain, left brain kind of person. And decisions are where a lot of that happens is like you're bringing together data, you're bringing together intuition, you're bringing together teams, and then you're putting together a plan and then seeing how the plan went. So it started a long time ago, but I was naturally very curious on what was happening within the science and became very convinced that the next big strategic lever that was going to impact most businesses was actually going to come from decision science. Because there's so much power in what was happening with data when applied to science. And has sort of been chasing that ideal for my entire career, to be honest. So yeah, followed that research, I did quite a lot of research on who was making investments in that space. IBM at the time is making an extraordinary amount of investment in their R and D, more than any other strategic consultancy at that time. So I went into strategy consulting, but spent loads of time with the PhDs back in the labs learning about their science and went on to kind of build out a lot of the initial work around how you could apply that into businesses. At IBM, went to aws, led the evolution of a large portion of the AI technology at AWS and how to put that in the hands of more customers and, and in particular more developers. And then I met Peak. Through that, Peak was one of our top partners at aws. They were extremely proficient at bringing together not only the underlying machine learning aspects which I'd been spending loads of time on, but actually how that creates commercial impact and really connecting the dots for customers between the science and the decision. And in fact they were category creators and innovating this new category of technology that is decision intelligence. And to me, we're by far the most compelling product and team in the market. And so yeah, I joined the team in January to help drive the overall global expansion and lead the US crazy.

I could geek out and listen to you talk about this for days because it's just such a super intriguing space and I love really smart people. Who does Peak sell to? Who are your customers?

Yeah, so we have our existing customer base today is a range of a lot of brands that people might be really familiar with because we really specialize in kind of a consumer focused space. So across retail and cpg, but also Increasingly in manufacturing, construction. Ultimately our goal is to make the power of kind of AI within a decision accessible to all companies. These were companies where it was very clear that there would be a major impact very early on. But oftentimes they didn't have the data science resources in house to do that at scale in a really effective way. We started working with companies in these industries five years ago. We've built those into common applications that now are accessible and being used by companies in financial services, skateboards, all sorts of different types of brands that you may not expect. But they can access the technology because we've kind of packaged it up in a way that's a bit more connected to a business use case that people can understand.

I was about to say, can you explain this as if you were explaining it to your grandpa, what you do? And then I'm painfully aware that my hair is going gray. So I'm going to say can you explain it to a 7 year old? Tell a 7 year old what data intelligence is, data science is, and how companies are using it so they would understand.

Actually, I did a session on this for a bunch of 10 year olds last summer and it was probably the coolest session I've ever done. Because really I always say that machine learning, artificial intelligence, data science, they're all synonyms for each other. They mean slightly different things, but they are collectively very similar. I always say that it's the journey of applying math to the chaos of reality. That's all it really is. Fundamentally, when you're using an approach in machine learning or data science, you're using the data you have with some really cool math to predict potential outcomes. For many leaders, what this means is that they now have a new tool in their tool belt for how to make a decision. I think the era of the last 10 years has been about leaders learning how to look at the data about their past and ask different types of questions. But the era of the next 10 years will be about how to look at predictions about the future and make different decisions. Sometimes I'm talking with leaders, I'll call this probabilistic decision making. But essentially we're all always thinking about the probabilities of different possible outcomes anyway. You can use data to project different outcomes from that or different probabilities, and then as a leader you can make decisions within those parameters. And so that's, it's the way that we've built Peak is about simplifying that process. Because what I just explained is complex. That's like hard, it's like, hard to do. So what we've done at peak is simplify that process within a single platform, allows you to bring the data in, build models, and then also, like, put them against a UI that a commercial leader or someone within a business context can interpret, understand the impact that would have in their business, and ask questions against it to make better decisions.

This is really cool. I mean, there's a saying that I heard years ago, I think it was Google that used to say, I don't care what you think. What's the data say?

I actually care about both.

Oh, interesting. Because you care. You care. That's interesting because you care about decision making. So, and so is it a blend of data and intuition then? Or is it a blend of data and what?

Yeah, exactly. Yeah, it's absolutely a blend of data and intuition. And, you know, not like, it's not an oil and vinegar thing. It's like a balsamic vinegar. It's a balsamic vinaigrette thing. Like, you mix it up, you don't want it to be two parts that don't interact. Really the power is when you bring the two together and you're able to say, well, what data do we even really need? Like, that's a question that somebody's intuition might lead you toward the answer about. And then how do I interpret the data that we have? What if I don't have data that I should have thought of? And then even if you have all the best data in the world, a lot of your data is telling you something about what's. Usually data is a footprint of what's happened, which isn't necessarily what will happen or what's coming or what you need to plan for next. That is also going to come through experience and intuition. And it's when you bring the two together that I think you see decision, intelligence. And that's where I think the major opportunity is. It's awesome.

Well, it's interesting because you're right, because the data that you have inside of your company, or data, I guess, American, Canadian data. Data that you. Now I'm going to be super cognizant of the fact that I don't know how to say it properly.

No, no, it's because I hang out with a bunch of Brits all the time.

Oh, gosh. Maybe I got confused. So the data that you have inside of your company. Yeah, you're right, because it doesn't. The data you got for your company doesn't talk about trends. It doesn't talk about what's coming it doesn't talk about what you're seeing in the market because it isn't inside your. So you have to be aware of that. Are companies using that external data as well then? Or are they using.

Yeah, yeah, absolutely, yeah. I mean there's quite a. It's a really interesting universe around, you know, data, data in general. Of course, there's been loads of momentum around, hey, how do we get more data, better data, cleaner data? Like, you know that that's absolutely part of what's happening in the market right now. But I still think people struggle even with all the best data to then figure out what to do with it. And that's what we've seen. Like that's why I spent a lot of time in the last 10 years simplifying how to build an ML model, simplifying the data streams to create an ML model and continuously tinkering away at making it easier and easier and easier to create a great machine learning model. But what I found in the last, particularly the last two years is we were getting the models easier and easier to build and that wasn't where the sticking point was anymore. Like, I could build a neural net recommender in two days. Like that is a thing that used to take nine months. But if you're a commercial decision maker, you don't know if it's any good. You don't know what to do with it. And so people were really like, the science has advanced massively in the last couple of years. But a lot of commercial leaders aren't super deep on the science. That's okay. I am, I love it, I'm a super nerd on it. But loads of people aren't and they struggle to translate between that and a decision or an impact they can, a commercial impact that they can create in the business.

It's interesting. I've been going to the main TED conference since 2010. It starts again this weekend and I super geeked out because I get 120 TED talks over five days and my mind just blows. I call it ideas having sex because I take all these random ideas and trends and things that are happening from these super geeks and there's such a tiny little space, but it's, it's really cool to see what's coming, like to see the future of stuff that and kind of pull it all together. So when, when Peak is working with clients, are you just a software company? Are you a software and a consulting business as well?

So the history of Peak involved quite a lot of consulting right back in the early days when nobody knew what would work in machine learning. Right. It was a lot of science, a lot of experimentation. And what we've done is essentially through all of those engagements, lot of learnings. And those learnings have basically been packaged into now applications that we can deliver really efficiently on the platform. So we don't do consulting work now, but we recognize most customers need, many customers need support through the adoption cycle. It's not just technology, it's technology, it's processes, it's people, it's learning and there's a certain amount of configuration. Most machine learning models need to be their best. And so we work with most of our customers, with one of our data scientists that's a specialist in the area, to configure the application so that you can deploy it really quickly and efficiently, but also it's tuned to your business. We don't actually think that like as a theme, I think that there's been like a whole lot of discussion around off the shelf AI, pre trained AI, AutoML, these kinds of things, they're all techniques and they're okay. But for most enterprise grade applications you need a bit of configuration. Most businesses have IP that they want to bake into their models and they should. Right. It's that fusion of the data and the experience. You want to get both. So we, we include a configuration layer in all of our deployments with customers to make sure that it's tuned. Now a data scientist from the customer can also do that work, but we also just include it for our business and enterprise customers.

I'd imagine that you're dealing with enterprise level clients. Are you not, are you into the medium, enterprise or are you really kind of enterprise level only?

Yeah, I mean it's, you know, if you come back to our mission, which is to democratize AI and build a company that everybody loves being a part of, like we open the door to loads of different customers. So one of the things that we've done in the last year, which is then really exciting, and I'm like, I'm personally really, really stoked about it, is we've opened up the platform in a way that invites more and more users onto it. We've opened a community where data science grads who maybe have learned loads about the science, but not necessarily all the commercial application pieces can come join and learn. And we're kind of continuously bringing it down our entry points and bringing that configuration piece in so that even if you're a data scientist, a company that isn't huge, you can still work with Us, you can still get an application into production. We're able to kind of take that repeatable approach and make it much more accessible. So we have small and medium sized businesses, we worked with a number of different customers at different sizes. But I do think the benefits for an enterprise are quite extensive. And that's just because of the connected approach to how the platform is built. So we connect your customer data, your product data, your supply data, your demand data altogether so that when you make an investment in, you know, in a personalization campaign, you can translate that to your demand forecast, which then you can translate to your supply chain implications all quite easily. And so it's not, it's not like a point solution approach. So for enterprises I think that the boon is really significant. But we want it to be accessible to everyone.

Ten years ago the term big data was like everywhere. It was like big data, big data. But now it's like I never hear it at all. What happened to that? Is that just. Have we got a new term for it? Did something happen? Did we?

Yeah. It's really funny to watch the language, isn't it? Because the language is, it's an approximation of a theme and oftentimes it's established by non technical people. Right. Like it gets established by somebody who did a, I don't know, show, you know, some sort of journalism show or something. Yeah, there's a technical, but it catches a theme of something that's coming. I think a couple things happened with big data. I've been thinking recently about the same thing with AI. Like what happened to AI. Well, it splintered. It's actually loads of other smaller things that are all real, some of which are more advanced and more mature and some of which are still maturing or quite experimental. But big data, I think what happened was it, it kind of, it's split up into a whole bunch of things. In fact, with some machine learning use cases, I would argue we're in a small data world. Actually it's quite cool. You can do some machine learning workloads today, very small data sets. Like that's one of the coolest things that's happened. You can create data now that is representative of a simulated environment. You could start a whole project with zero data. So I think there was a theme for a while that was like big data, more data, all the data. And I do love data. But bigger and more is not always better. You want to have good data, clean data that's representative of the decision and the information flows that you need to run the business that's cool.

All right, let's go into the org itself. What was it that got you to be. How big is the company at this point? Roughly how many people?

Yeah, I think we're at 3, 10, but we're hiring, so I could be a little off, maybe up or down by 10.

And are you all in Seattle? Are you global?

No, we are definitely a global team. In fact, I am our sole Seattle representative. Most of our US Team is based in New York, and we are hiring a small team here in Seattle as well. We have teams in the uk, both in Manchester, which is our founding home, and then London, and then we also have teams in India in Pune and Mumbai.

All right, so how. I mean, clearly during COVID companies have gotten used to hiring people that are remote. Was that how this happened for you, that you were able to get into the great organization kind of during COVID.

Yeah, I mean, I started in January, so we all sort of were like, oh, we'll be able to travel again. This will be great. No, you know, I think, like, Peak had always. Peak has a, what we call a clubhouse first mindset. So actually the intention is to give teams a space to collaborate and build together. I think it was a little bit of. It was a bit of like a kismet between. We'd partnered together for so long for three years, done a lot of amazing work around the world, and it was very clear to me that it was the right moment in my career to step into a role like this. And I didn't really look around. I mean, I've met just about everybody in this space over the last decade. I called Rich. He was my first phone call and he was my last phone call. We spent about nine months figuring out what the right role was and when and timing between what was right for Peak and what was right for me. By the time we decided, it was really clear that it was going to be a good thing for both of us. Peak has a great relationship with partners here on the West Coast. AWS is one of our. Obviously, one of our great partners. And so being here in Seattle, that's not a bad thing because partners is part of my remit. And then we've got loads of great talent here in Seattle and up and down the West Coast. So it also gives us a way to attract some new talent to the team and continue to grow the team.

That's cool. So you were at aws, Amazon Web Services, and you moved over to Peak, right? Yes, I was kind of same size company. Right. Like, what was it like leaving this behemoth of Amazon and going to still a pretty nice size company at 300, but clearly very different. What was that like for you and how did you have to adapt?

Yeah, well, my team size is not that different. So in that way it's sort of similar. But I really wanted some place that was wholly focused on machine learning and artificial intelligence. And that's just because it's such a unique market, it's such a unique space to build within that, I think to really deliver the full value for customers and to really bring it all together in the product as well. You need everybody after it. Everybody bought in that decision. Intelligence is it. Let's all learn AI. Let's be in the weeds on it. Let's make it simple. So for me, there was a big attraction to that in terms of learning to adapt, I think. I mean, between moving through. Yeah, a very large company to, you know, a scale up, there's obviously changes. Probably the most significant one was my schedule, actually, which has nothing to do with the size. That has to do with really where the heartbeat of a lot of the team is. I wake up at 4am most days now and I'm done by about too a lot of the time. So I have my afternoons back, which is cool. I spent a lot of time paddleboarding. But I think there's an element of just trusting yourself. Like when you're at aws, there's a lot of guardrails. Right. Like, there's an element of. There's so much momentum, there's so much foundation that's already been laid. You kind of need to just like, stay in the lane. Be great, but stay in the lane. Whereas at peak, you know, there aren't. And you have to know which. Which boundaries are the right ones to push because that's why that's the power of being in a smaller organization is you can push some boundaries that others might not be able to.

That's why you're there too.

Totally.

So in bringing you in as a senior player reporting, right, you report the CEO, you're kind of in this head of us and running the teams. What was it like coming in over top of people that have been there and working hard and two groups. One, I'm sure there were a couple of people that maybe wanted the role. And two, it's the group of people that you had to kind of come over top of and start managing and leading and building those relationships. How did that go? How did it work? Any tips for the people that are doing it or about to do it.

Yeah, I was nervous about it, right, because I had enormous respect for the team. I mean, I'd worked with a lot of them, so I knew I wasn't coming in on, like, a broken team. They were great team, terrifically talented, and had done an enormous amount of terrific work. So I just spent. I spent time on the learning spectrum, I think is really where I came in. I said, look, this is what I've done. This is what I'm really good at. These are the things that I bring to the table. Like, tell me about you. What are you great at? I know you've done all this great stuff. Tell me more about what that was like for you, what you led, what you loved about it, what you didn't. At the end of the day, a big part of why I'm in the space of machine learning and artificial intelligence, but besides geeking out on decisions as like, the whole theme is actually that really early in my career, I walked into a couple of rooms that didn't look much like me and didn't look much like my community or my friend group or the people who I saw in my world. And I felt really strongly that the space could be meaningfully more welcoming. And so I've always been in, like, stayed in the space because I'm really passionate about the people and developing the people, not just wherever I work, but much more broadly than that, in the community and with our customers and everyone else, and making people feel welcome and empowered to use the technology. And that's the same way I approach the team, which is, you know, I've launched a lot of products in this space. I've been. I've now been, as far as I know, and I might be tooting my horn a little bit much here, but I don't think I am. I think I've been selling AI and taking AI to market and creating products that simplify the adoption of machine learning longer than just about anybody in the market. Because I started, like, with the very first team at Watson, so it was kind of this era, and there are loads of people who've been doing it, like, much longer than that, before this era. But, you know, it's 10 years of doing it, so I've learned a lot about what can go right, what can go wrong, and really, like, how to connect customers to the technology. But we had loads of people who were fantastic marketers, amazing at building partnerships and those bits. And so it was like, how do we use our strengths to be better together?

I think you're right in Saying that you had some of the relationships with some of the people as well and they knew you before as well. So there probably was a mutual respect there. What I mean, there's been books written on the first hundred days or the first 90 days. What was your first kind of 30 days? Like, what did you do and what did you try not to do and any. Any specific things that you did there?

First thing I thought was going to happen in my first 30 days is that I was going to go to Manchester and spend two weeks on the ground meeting everybody. And then we got Omicron and I spent my first two weeks in this office reading like crazy. That's what I spent the first week in particular. I just. Loads of people were still off in England, but I'd started, so I just asked for everything that they could send me, anything I could read. I spent hours reading all the old documentation. I lit a candle, I had some tea. I just sat and read and read and read and absorbed and sent questions like crazy across the line. Hey, I saw this. It didn't make sense. I saw this. Didn't make sense. Help me understand that, et cetera. Our second week was actually our annual strategic planning week, which was kind of a wild ride because I just absorbed all this information and then we were going into all the strategy work. But it was amazing because you had all the kind of folks who'd been around and had all sorts of great ideas and knew what was going to be important. And then I was coming in quite fresh with a different lens, a different perspective, and it was really productive and really helpful. And then the next two weeks were really about then, like, from that kind of strategic work, looking with the team and asking, like, now that I know roughly what our strategic objectives are for the year, is the team set up for that? Really, at the end of the day, I work for the team. And are they in the position? Do they have no surprise? Do they have the data? They need to be empowered to make great decisions for this strategy. So, like, where's the data? Do we have. Do we have the head count that we need in the right places so that we can do that? And, yeah, there was. There were some things that didn't quite line up, but I think having fresh eyes on that element actually was really, really helpful. I spent a good amount of time just trying to suss out where the fires were. The big, you know, the big kind of strategic push from the team to open up the platform and to, like, create more accessibility, and that opens up new questions. So I just wanted to know, like, where are the things that aren't really working well today? And then I could roll up my sleeves, work with the team to try and build out some plans against them.

Yeah, good for you. Did you, did you try to avoid making any of the, you know, firing decisions or the canceling decisions or any of that? Or were you, did you react to some of those because you saw it and wanted to just jump on some?

No, I think, like, for the most part, our fires were help needed. They weren't like. Yeah. I'm not one to immediately assume I have the answer. I think I spent the most of my first 30 days on a bit of a listening tour curiosity journey. Where are the fires? And then what's under that? What's the symptom? Sometimes the symptom is people, but oftentimes people are doing their best with what they have and they might need new information or they might need a new forum to express that information in a way that's really productive. Um, and so, yeah, mo, mostly it was about making sure we had enough people in the right places and that those people had the information that they needed. We had great people. So that was a benefit for me.

Smart. You mentioned the scale up. I think Vern Harnish popularized the term a scale up. What do you think companies have to do or leaders have to do in that stage of an organization? When you're in that scale up growth, you know, stage?

Yeah. I don't mean to sound like a broken record on it, but in some ways I think you have to ask yourself, like, am I putting, am I setting up my team to grow and obviously to scale? How do you do that? It means you have to build out the information flows and the process like spaces to make great decisions as you grow. And that is for anybody that's brand new all the way through to an executive that, you know, might, you know, to the co founder. So like, all of those decisions are equally important when you're scaling in some ways. And I have a saying which is when you're scaling, and this is my first time doing it with a startup going to kind of scale up stage, but certainly I've scaled teams within other organizations. When you're scaling. The one thing that I've learned probably the hard way is you don't want to scale up inefficiencies, you want to scale up your efficiencies. So you want to set up. You'd rather find an inefficiency now, fix it right now so that it'll be efficient because if you scale efficiently then you're really in an amazing way to provide durable long term value for customers and of course for investors.

I was talking to someone the other day about some of the projects and stuff, the work that they were doing and we talked about optimization and automation and outsourcing and I said, but before you optimize or automate a process, let's optimize it because it might be a shitty process. We don't want to automate a shitty process. It doesn't make any sense. Or before we even optimize something, do we even need to do it?

Like maybe let's even need to do it. Yeah.

Do you, do you say no much to, you know, like it's really hard and we're in this era of like inclusion and you know, make sure that Gen Y gets like, feels good and they get to give us all their ideas and. But it feels like they get their feelings hurt when. And this isn't just Gen Y, it's like, you know, Gen X, Gen Z, baby boomers. Everybody kind of gets their feelings hurt at times. But I think leaders need to say no often.

Do you think everybody needs to say no more often? I don't think it's the leader thing. And I think that we have a peak, we have a sort of culture of what we call sustainable high performance, which the only way you can get sustainable high performance is if you learn where your boundaries are and what your nos are. But the good news about that is it's exactly the same thing as strategy. Strategy is exactly the same thing which is being willing to say no to some stuff and say nope, this isn't in. And I think as long like one of the things I'm quite passionate about is removing a decision from the person, a person is doing the best they can with the experience that they have, the data that they have and the brief that they have. And I tell this to my team, I'm like, here's my best idea given the data I have. In my experience, this is the best idea I have. Somebody has a better idea, I would love to hear it. And we don't have to do this, but this is the best one I could come up with to solve this problem. If you don't think it's good enough, we should can it. So I think it's about making a culture where no is not a personal statement and everyone can say it and we can say it about many things so that we can do the things that, so we can say yes to. The stuff that's really important.

Yeah, yeah, I agreed. All right, you mentioned a couple more questions. You mentioned developing people as being a core kind of focus for you. How do you focus on them? What do you focus on? Developing them. And do you have like any areas or thoughts around that framework?

No, I don't have a three point plan, but I do have, I think I do have. Yeah. So when I was studying, studying like the outcomes of behavioral economics and particularly like at a, at a micro level, there's some really interesting studies. This is all a bit dated, so you'll have to forgive me, I haven't gone and updated my research. But there's some really interesting studies done, I think in the late 90s, early 2000s about how people were motivated and what motivated an individual. There were some very interesting studies about money's effect on that. In particular, in certain circumstances money is a very useful motivator, but in most circumstances it's not. Yeah, it is not because it actually it puts a monetary value on something. Oftentimes that's valued there. There's a higher order value. So people are willing to make an economic payoff on something that they see as being like, oh well, there's these other things I value more. So what are those other things? Well, typically they're about like people pursuing the fullest version of themselves. Like that's what it kind of comes back to. We all have some destiny we're after on our vision of who we want to be. And so that I think of that as intrinsic motivation. Like that is somebody's intrinsic motivation for why they showed up every. Why did they end up here in the first place? You can get paid a thousand ways. Why are you here? What's that about for you? And so most of my development conversations start there. I just say like why, why are you here? What do you and what's next? And by the way, what's next might be at peak, might be somewhere else. I'm really open. I want you to be on a quest that's great for you. And along the way I'd love to empower you to be great in decision intelligence and machine learning. Like if we can, this could be a pass through where you pick that up and take it to wherever you go next.

That's great.

That's a success for me.

Yeah, that's interesting. I think. Was it Dan Pink that did a famous TED talk years ago on the science of motivation and talked about the monetary side and it can be a demotivator for sure.

Massively. Yeah.

I apply a lot of my leadership with people to Maslow's hierarchy of needs. And just looking at are we as a company delivering on all five layers? Because we often miss on a few and we talk about the, you know. Anyway, all right, let's go Back to the 21 year old, 22 year old Zoe who's like just getting ready to start on her career. You know, she's super excited. What advice would you give the 22 year old that you know to be true today?

There's probably at least there are many things I would say to 22 year old Zoe. But one thing I think I would have told myself is to say no and to realize that it was a marathon, not a sprint. When I was younger I thought I was going to get in. I thought I was doing all this to retire really early so I could go back to being an artist. I read a story about Rauschenberg and he'd worked on the stock market and then got and been a starving artist but not starving anymore because he saved all the money. I thought that's what I was doing. And along the way I fell in love with the technology and I fell really in love with the people and I fell in love with the. And I think I pushed myself harder than I needed to because I think I felt a sense of imposter syndrome. I was a sculptor, like what was I doing here? But it turns out like there is a lot of space for people who don't have a background in computer science to participate in this domain. Particularly if you're curious and you want to learn and you're willing to learn. And I think I should have just given myself a bit more like grace on that and not necessarily try to pedal to the metal. It's part of why I think like that I value so much the sustainable high performance mindset. This isn't about five years. It's a, this like the machine learning thing will be my whole career. Like I'm not, I'm not ever going to work in tech and not have it be around this space. It's, that's pretty clear to me. So I've probably got another like 20, 30 years to go. I don't want to burn myself out and be exhausted. So I think that's one thing and yeah, I think, I think the other would just have been to believe in myself. I think I like I ended up jumping in with two feet like fully and just learning the data science and meeting all the data scientists and everything else. But I harbored a lot of insecurities about that. Lack of technical background for quite a long time. And even though I'm fairly mathy, I don't code still today I've debunked a couple things, but you wouldn't want me writing your enterprise grade code. But there's a real space in the market for people who are willing to be translators that want to learn enough to be partners. Not necessarily always the expert, but who can partner really well and translate well between and so yeah, I think I, I just would tell myself, like, let that little insecurity go. You don't need to carry that one with you. Learn, but don't sit around and be hard on yourself about it.

That's amazing. I hope you're still doing sculpting.

Yeah, the sculpting, it takes a slightly different form now. I do a little bit, but largely we're doing a lot of home remodeling and that's taking a lot of the creative energy and design element for me.

Been there. Zoe Hillenmeyer, the chief Commercial officer at Peak, thanks so much for sharing with us on the Second Command podcast.

Thanks for having me.

Really appreciate the time. And then he'll be a better employee, but he'll be a better father, husband, partner. He'll be, you know, more relaxed. Like, again, if your physical health is crap, your physical health is crap across all of your thing. And I started realizing that actually we had been training as an organization across those same disciplines. We had been helping our leaders understand their strengths, weaknesses.