Our guest today is Peak’s Chief Commercial Officer, Zoe Hillenmeyer.
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.
In This Conversation We Discuss:
- How to understand and use data for better business decisions
- The benefits of running an enterprise
- What happened to the concept of BIG data and how data has evolved
- Zoe’s experience with Amazon and how she adapted to Peak
Resources:
Connect with Zoe Hillenmeyer: LinkedIn
Peak – https://peak.ai/
Connect with Cameron: Website | LinkedIn
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Our guest is Peak‘s Chief Commercial Officer and Head of US Operations, Zoe Hillenmeyer. Zoe has a decade of experience in the Artificial and Decision Intelligence field, and has been part of over twenty 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 show.
I’m 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.
You hopped as well. 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.” I ended up in Fremont. It was this fun, local neighborhood. Tell us about your journey. How did you get to where you are right now with Peak?
It’s been a journey of curiosity and just always being interested in how people make decisions. That started actually during my MBA. I was really interested in how different decisions got made. At that time, I was studying Organizational Behavior, Microeconomic Decision-Making. I became quite interested in a lot of the science around decision-making, how different teams make decisions, how people come up with ideas, and how people evaluate them. I guess I’ve always been a left-brain kind of person, and decisions are where a lot of that happens. It 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.
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. I’ve been chasing that ideal for my entire career, to be honest. I followed that research. I did quite a lot of research on who was making investments in that space. IBM, at the time, was making an extraordinary amount of investment in their R&D more than any other strategic consultancy at that time.
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 build out a lot of the initial work around how you could apply that into businesses at IBM. I 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 in particular more developers.
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’ve 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. In fact, they were category creators and innovating this new category of technology that is decision intelligence. To me, we’re by far the most compelling product and team in the market. I joined the team in January 2022 to help drive the overall global expansion and lead the US.
I could geek out and listen to you talk about this for days because it’s such a super intriguing space. I love really smart people. Who does Peak sell to? Who are your customers?
Our existing customer base is a range of a lot of brands that people might be really familiar with because we really specialize in a consumer-focused space. Cross retail and CPG, but also increasingly in manufacturing, construction. Ultimately, our goal is to make the power 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.
Decision Intelligence: Ultimately, our goal is to make the power of AI within a decision accessible to all companies.
We started working with companies in these industries a few 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 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?” I’m painfully aware that my hair is going gray. I’m going to say, “Can you explain it to a seven-year-old?” Tell a seven-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 ten-year-olds. It was probably the coolest session I’ve ever done. 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 have a new tool in their tool belt for how to make a decision. The era of the last ten years has been about leaders learning how to look at the data about their past and ask different types of questions. The era of the next ten 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. 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. As a leader, you can make decisions within those parameters. The way that we’ve built Peak is about simplifying that process, because what I just explained is complex. That’s hard to do. What we’ve done at Peak is simplify that process within a single platform. It allows you to bring the data in, build models, and then also 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. 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?” Have you heard that?
I actually care about both.
That’s interesting because you care about decision-making. Is it a blend of data and intuition then, or is it a blend of data and what?
It’s absolutely a blend of data and intuition. It’s not an oil and vinegar thing. It’s like a balsamic vinaigrette thing. You mix it up. You don’t want it to be two parts that don’t interact. The power is when you bring the two together, and you’re able to say, “What data do we even really need?” That’s a question that somebody’s intuition might lead you toward the answer about. Then, “How do I interpret the data that we have? What if I don’t have data that I should have thought of?”
Even if you have all the best data in the world, your 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. It’s when you bring the two together that I think you see decision intelligence. That’s where I think the major opportunity is. It’s awesome.
That’s interesting because you’re right, because the data that you’ve 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 company yet. You have to be aware of that. Are companies using that external data as well then?
Yes, absolutely. It’s a really interesting universe around data. In general, of course, there have been loads of momentum around, “How do we get more data, better data, cleaner data?” That’s absolutely part of what’s happening in the market right now. I still think people struggle, even with all the best data, to then figure out what to do with it. That’s why I spent a lot of time in the last few 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 to create a great machine learning model.
What I found in the last few years is we were getting the models easier and easier to build. That wasn’t where the sticking point was anymore. I could build a neural net recommender in two days. That is a thing that used to take nine months. 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. 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. They struggle to translate between that and a decision or a commercial impact that they can create in the business.
I’ve been going to the main TED conference since 2010. It starts again. I super geeked out because they’ve got 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 they’re such a tiny little space. It’s really cool to see what’s coming, to see the future of stuff, and pull it all together. When Peak is working with clients, are you just a software company, or are you a software and a consulting business as well?
The history of Peak involved quite a lot of consulting. Back in the early days when nobody knew what would work in machine learning, it was a lot of science and a lot of experimentation. What we’ve done is essentially through all those engagements, a lot of learnings. Those learnings have basically been packaged into now applications that we can deliver really efficiently on the platform.
We don’t do consulting work now, but we recognize 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. 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.
As a theme, there’s been a whole lot of discussion around off-the-shelf AI, pre-trained AI, auto ML, 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. It’s that fusion of the data and the experience. You want to get both. We include a configuration layer in all of our deployments with customers to make sure that it’s tuned. A data scientist from the customer can also do that work, but we also include it for our business and enterprise customers.
I’d imagine that you’re dealing with enterprise-level clients. Are you into the medium enterprise, or are you really on the enterprise level only?
We come back to our mission, which is to democratize AI and build a company that everybody loves being a part of. We open the door to loads of different customers. One of the things that we’ve done in the last few years, which is then exciting and I’m personally 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.
We’re continuously bringing it down our entry points and bringing that configuration piece in so that even if you’re a data scientist or company that isn’t huge, you can still work with us, you can still get an application into production. We’re able to take that repeatable approach and make it much more accessible. We have small and medium-sized businesses. We worked with a number of customers at different sizes. I do think the benefits for an enterprise are quite extensive. It’s not just because of the connected approach to how the platform is built.
We connect to your customer data, your product data, your supply data, your demand data all together so that when you make an investment 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. It’s not like a point solution approach. For enterprises, I think the boon is really significant, but we want it to be accessible to everyone.
Many years ago, the term big data was everywhere. It was like, “Big data, big data.” Now, I never hear it at all. What happened to that? Have we got a new term for it? Did something happen?
It’s really funny to watch the language, isn’t it? Because the language is an approximation of a theme. Oftentimes, it’s established by non-technical people. It gets established by somebody who did some show or something. It isn’t technical. It catches a theme of something that’s coming. A couple of things happened with big data. I’ve been thinking about the same thing with AI like, “What happened to AI?” It’s 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.
Big data, I think what happened was it 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. 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. There was a theme for a while that was like, “Big data, more data, all the data.” 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.
Decision Intelligence: Bigger and more is not always better. You want to have good, clean data that’s representative of the decisions and information flows that you need to run the business.
Let’s go into the org itself. How big is the company at this part? Roughly how many people?
We’re at 310 but we’re hiring. That could be a little off maybe up or down by ten.
Are you all in Seattle? Are you global?
No, we are definitely a global team. Most of our US team is based in New York. We are hiring a small team near in Seattle as well. We have teams in the UK, both in Manchester, which is our founding home, and then London. We also have teams in India, in Pune and Mumbai.
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 during COVID?
I started in January. We were like, “We’ll be able to travel again. This will be great.” Peak had a what we call a clubhouse first mindset. Actually, our intention is to give teams a space to collaborate and build together. It was a bit of like a kids’ mitt between we’d partnered together for so long for three years. There are a lot of amazing work around the world. It was very clear to me that it was the right moment in my career to step into a role like this. I didn’t really look around. 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, obviously, one of our great partners. Being here in Seattle, that’s not a bad thing because partners is part of my remit. We’ve got loads of great talent here in Seattle and up and down the West Coast. It also gives us a way to attract some new talent to the team and continue to grow the team.
You were at AWS, Amazon Web Services, and you moved over to Peak, right?
Yes, I was.
It’s of the same size company, right? 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?
My team size is not that different. In that way, it’s similar. I really wanted some place that was wholly focused on machine learning and artificial intelligence. 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 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.” For me, there was a big attraction to that.
In terms of learning to adapt between moving through a very large company to a scale-up, there are obviously changes. Probably the most significant one was my schedule actually, which has nothing to do with the size. That has to do with where the heartbeat of a lot of the team is. I wake up at 4:00 AM most days now, and I’m done by about 2:00 a lot of the time. I have my afternoons back, which is cool. I spend a lot of time paddle boarding.
There’s an element of just trusting yourself. When you’re at AWS, there are a lot of that guard rails. There’s so much momentum, there’s so much foundation that’s already been laid. You need to be great, but stay in the lane. Whereas at Peak, there aren’t, and you have to know which boundaries are the right ones to push because 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.
In bringing you in as a senior player, you report to the CEO. You’re 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 in two groups? One, I’m sure there were a couple of people that maybe wanted the role. Two, it’s the group of people that you had to 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?
I was nervous about it because I had enormous respect for the team. I worked with a lot of them. I knew I wasn’t coming in on a broken team. They were a great team, terrifically talented, and had done an enormous amount of terrific work. I’ve spent time on the learning spectrum. It’s really where I came in. I said, “This is what I’ve done. This is what I’m really good at. These are the things that I bring to the table. 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 a 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. I felt really strongly that the space could be meaningfully more welcoming.
I’ve always stayed into 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. That’s the same way I approached the team, which is I’ve launched a lot of products in this space.
As far as I know, and I might be tooting my horn a little 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. I started with the very first team at Watson. It was of this era, and there are loads of people who’ve been doing it like much longer than that before this era, but it’s ten years of doing it. I’ve learned a lot about what can go right, what can go wrong, and how to connect customers to the technology. We had loads of people who were fantastic marketers, amazing at building partnerships, and those bits. It was like, “How do we use our strengths to be better together?”
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. There probably was a mutual respect there. There have been books written on the first 100 days or the first 90 days. What was your first 30 days like? What did you do and what did you try not to do? 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 and meeting everybody, and then we got Omicron. I spent my first two days in this office reading like crazy. I spent the first week in particular. Loads of people were still off in England, but I’d started, so I just asked for everything that they could send me, anything that 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 absorbed and sent questions like crazy across the line, “I saw this, it didn’t make sense. I saw this, it didn’t make sense. Help me understand that.”
Our second week was actually our annual strategic planning week, which was a wild ride because I just absorbed all this information and then we were going into all the strategy work. It was amazing because you had all the folks who had been around and had all sorts of great ideas and knew what was going to be important. I was coming in quite fresh with a different lens, a different perspective, and it was really productive and helpful.
The next two weeks were really about from that strategic work, looking with the team and asking, “Now that I know roughly what our strategic objectives are for the year, is the team set up for that?” At the end of the day, I work for the team. Are they in the position? No surprise. Do they have the data they need to be empowered to make great decisions for this strategy? Where’s the data? Do we have the head count that we need in the right places so that we can do that?
There were some things that didn’t quite line up, but I think having fresh eyes on that element actually was really helpful. I spent a good amount of time just trying to suss out where the fires were. The big strategic push from the team to open up the platform and to create more accessibility, that opens up new questions. I just wanted to know, “Where are the things that aren’t really working well today?” Then I could roll up my sleeves, work with the team to try and build out some plans against them.
Did you try to avoid making any of the firing decisions or the canceling decisions or any of that? Or did you react to some of those because you saw it and wanted to just jump on some?
No. For the most part, our fires were help needed. I’m not one to immediately assume I have the answer. I spent the most of my first 30 days on a bit of a listening tour/curiosity journey, “Where are the fires? 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. 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.
Decision Intelligence: Make sure you have enough people in the right places and that those people have the information they need.
You mentioned the scale-up. I think Verne Harnish has popularized the term of scale-up. What do you think companies or leaders have to do in that stage of an organization when you’re in that scale-up growth stage?
I don’t mean to sound like a broken record on it, but in some ways, I think you have to ask yourself, “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 spaces to make great decisions as you grow. That is for anybody that’s brand new all the way through to an executive that might be to the co-founder. All of those decisions are equally important when you’re scaling in some ways.
It’s my first time doing it with a startup going to 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. You want to set up. You’d rather find an inefficiency now. Fix it right now so that it will 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 about some of the projects and stuff and the work that they were doing. We talked about optimization and automization and outsourcing. I said, “Before you optimize our automated process, let’s optimize it because it might be a crappy process. We don’t want to automate a crappy process. It doesn’t make any sense. Before we even optimize something, do we even need to do it? Maybe let’s just stop it.”
“Do we even need to do it?”
Do you say no much? It’s really hard, and we’re in this era of inclusion and make sure that Gen Y feels good and they get to give us all their ideas, but it feels like they get their feelings hurt, and this isn’t just Gen Y, it’s like Gen X, Gen Z, Baby Boomers, everybody gets their feelings hurt at times, but I think leaders need to say no often.
Everybody needs to say no more often.
That’s true.
I don’t think it’s a leader thing. At Peak, we have a culture called sustainable high performance, which the only way you can get sustainable high performance is if you learn where your boundaries are and what your noes are. 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, “No, this isn’t in.”
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 briefs that they have. I tell this to my team like, “Here’s my best idea. Given the data I have in my experience, this is the best idea I have. If somebody has a better idea, I love to hear it. 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.” It’s about making a culture where no is not a personal statement and everyone can say it. We can say it about many things so that we can say yes to the stuff that’s really important.
I agree. A couple more questions. You mentioned developing people as being a core focus for you. How do you focus on them? What do you focus on developing them? Do you have any areas or thoughts around that?
No, I don’t have a three-point plan. When I was studying the outcomes of behavioral economics, particularly at a micro level, there are some really interesting studies. This is all a bit dated, so you’ll have to forgive me. I haven’t updated my research. There’s a really interesting study done 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 is not.
It’s a demotivator.
It is not because it puts a monetary value on something oftentimes that’s valued. There’s a higher order value, so people are willing to make an economic payoff on something that they see as being like, “There are these other things I value more.” What are those other things? Typically, they’re about people pursuing the fullest version of themselves. That’s what it comes back to. We all have some destiny we’re after on our vision of who we want to be.
I think of that as intrinsic motivation. That is somebody’s intrinsic motivation for why they showed up. Why did they end up here in the first place? “You can get paid 1,000 ways. Why are you here? What’s that about for you?” Most of my development conversations start there. I just say, “Why are you here? What’s next?” By the way, what’s next might be at Peak, it might be somewhere else. I’m really open. I want you to be on a quest that’s great for you. Along the way, I’d love to empower you to be great in decision intelligence and machine learning. If there’s going to be a pass-through where you pick that up and take it to wherever you go next, that’s success for me.
That’s interesting. Was it Dan Pink that did a famous TED Talk years ago on the science of motivation and talked about the monetary side? It can be a demotivator for sure.
Massively.
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” Anyway, let’s go back to the 21-year-old, 22-year-old Zoe who’s just getting ready to start on her career. She’s super excited. What advice would you give the 22-year-old that you know to be true today?
There are many things I would say to 22-year-old Zoe, but one thing 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 doing all this to retire really early so I could go back to being an artist. I’d read a story about Rauschenberg, and he’d worked on the stock market and then got in, been a starving artist, but not starving anymore because he saved all his money. I thought that’s what I was doing.
Along the way, I fell in love with the technology and I fell really in love with the people and with the space. I pushed myself harder than I needed to because I felt a sense of Imposter syndrome. I was a sculptor like, “What was I doing here?” It turns out, there’s 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’re willing to learn. I should have just given myself a bit more grace on that and not necessarily try to pedal to the metal. It’s part of why I think that I value so much the sustainable high-performance mindset. This isn’t about five years. The machine learning thing will be my whole career. I’m not ever going to work in tech and not have it be around this space. That’s pretty clear to me.
I’ve probably got another 20, 30 years to go. I don’t want to burn myself out and be exhausted. That’s one thing. The other would just have been to believe in myself. I ended up jumping in with two feet 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. Even though I’m fairly mathy, I don’t code still today. I’ve debugged a couple things, but you wouldn’t want me writing your enterprise grade code.
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. I just would tell myself, “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.”
It’s amazing. I hope you’re still doing sculpting.
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.
Zoe Hillenmeyer, the Chief Commercial Officer at Peak, thanks so much for sharing with us on the show.
Thanks for having me.
I really appreciate the time.
