The Neuron: AI Explained
How ZoomInfo's CEO Rewired a 3,500-Person Company to Be AI-First with Henry Schuck
24 Aug 2025
What does it take to steer a 3,500-person company into the age of generative AI? ZoomInfo founder and CEO Henry Schuck joins us to unpack the company's journey from data powerhouse to AI-first GTM platform, the cultural shifts that enabled it, and the hard-won lessons any leader can borrow. We explore how they reduced teams from 26 to 2 people using AI agents, why 2/3 of employees now use AI daily, and the critical role of data infrastructure in AI success.Subscribe to The Neuron newsletter: https://theneuron.aiLearn more about ZoomInfo: https://www.zoominfo.com
Chapter 1: How did ZoomInfo's CEO decide to go AI-first?
So how did ZoomInfo CEO Henry Shuck rewire a 3,500-person company to be AI first? Stick around. He's about to tell us. All right, welcome, humans, to episode 10 of The Neuron, AI Explained. I'm Corey Knowles, joined as always by our resident wordsmith and rabbit hole navigator extraordinaire, Grant Harvey. How are you today, Grant?
Good. I'm diving into all the rabbit holes of all the rabbit conspiracies that we mentioned in one of our last podcasts. For all the rabbit truthers. No, I'm good. How are you, Corey?
I'm good, man. I'm good, I'm sure. Glad, excited about this one today, because today we're tackling a question that is keeping teams and executives across the business world up at night. How do you lead an AI-first team without burning mountains of cash or your people? To help us out, we've recruited Henry Schuck, founder and CEO of Zoom Info.
Henry turned a scrappy contact data startup into a 3,500-person go-to-market intelligence machine powered by its own generative AI co-pilot. We'll break down the cultural shifts, the data hygiene, and the hard metrics that prove the tech is really working. They're doing some great things over there, and I really think you'll be fascinated with it.
But first, before we dig in, Grant, what would you say it means to be AI first?
So this is an interesting one. Being AI first means that a company product or strategy is designed from the ground up with artificial intelligence as the core enabler. So rather than treating AI as an add on or an enhancement, they're actually trying to build AI into the core mechanic, like basically the into the core mechanism of how it works from the jump.
As opposed to it being like, you know, the little deer antlers around Christmas you see on the side of a car.
Instead of that, it's building it into the engine and making it- Which is kind of how a lot of products have built AI into their- Well, actually, a lot of companies have built AI into their products at this point.
It is.
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Chapter 2: What cultural shifts were necessary for an AI-first approach?
It's like we've done it for 10 years. Well, we didn't know you guys did that. And so I think the quick aha or the first real aha moment for us and for me especially, was when you saw that one of the early things that we saw generative AI be really great at was taking a whole bunch of data, making sense of it, and then giving you the most important pieces of it for your daily life.
And so we realized, hey, if you could just take all of this data that we have on companies and people and signals happening at those companies, you can marry that up.
with the customer's first party data so you know which accounts are theirs, which ones are in their territory, which accounts they care about, and then actually bring together that first party data underneath their emails they've had with those accounts, the engagements, the calls, the call transcripts,
then that could much more easily triangulate which accounts you should be reaching out to and what you should be saying to them based on the signals and the first party engagement. And you don't need to be a computer science PhD to pull that off. And then you don't need the world's greatest UI experts to create like the perfect UI to do that.
You really need that underlying LLM engine that can go triangulate that and deliver it to your end customer. I think when we first saw that, we realized, okay, this is going to help us solve a lot of really meaningful problems that our customers are having when they want to leverage the right data, the right signals to go to market.
Wow. Yeah, that's super interesting. And you know, when I think about that with software, one of the things that comes to mind is how often people underutilize the features that are there just because of how much there is to learn in every tool. How big of an undertaking with knowing that was it? To get ZoomInfo AI ready.
That sounds like it was a lot. Yeah, look, I think we benefited from a couple of things. One, we sit on this proprietary data asset. And so we didn't need to like fully pivot the business. We just needed to make it easier for our customers to get value out of that underlying data. data asset. And so that was one thing.
But I think the big thing was we had to change the way we were thinking about building software for our customers, where a lot of that was focused on the application layer and how you improve the application layer. And we really had to shift people to start thinking about how do you improve that underlying data layer that drives insights up to the application layer. And so
You know, the platform itself, the ZoomInfo platform itself, when you log in, you know, we are gathering data on these 100 million companies, 500 million business professionals. We're gathering them from a multitude of different sources. We have hundreds of thousands of users who use our community edition, who get free access to ZoomInfo in exchange for their contacts and their email system.
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Chapter 3: How did ZoomInfo reduce its team size using AI?
2021. Yeah. A ways back. My apologies. No problem. But how did that change your approach or direction? Has that been a key element?
You know, it's so interesting. When we first acquired Chorus, I think AI felt more like something that people talked about, but you never saw... well implemented in a company. These tended to be like before chorus, all I ever saw inside of our business. And I had invested a lot behind AI was a bunch of like academic projects.
People would go like do some data science projects, almost nothing made it to production. It was this incredibly frustrating experience where you put a lot of money behind AI, but you never really saw it come to fruition in the platform. Yeah.
Um,
Then when we acquired Chorus, Chorus was way ahead from an AI perspective and they had built a lot of AI technology. And so they kind of like led the way or shined a light on the opportunity to leverage AI in the products and the platforms. And then over time, what we found is some of our best AI talent, my chief product officer,
our chief strategy officer, they both came from Chorus and they are driving, both of them are driving internally with our chief strategy officer and externally with our chief product officer, all of our AI initiatives. And so you don't like really appreciate that when you're making that acquisition, you're thinking more of the product, our ability to take it to market.
And then tangentially, you're thinking about the talent that comes in with that acquisition as well. But in this case, the talent is running. The talent from Chorus is running our key AI initiatives in the business.
Wow. That's really interesting. And I guess that's a good place to segue into, you know, what does an AI-first team look like at Zoom Info? And how much of it is new team members versus a shift in mindset for existing team?
Yeah, so I think... I don't think there's a one size fits all here, but I do think there are people within your organization that are well-suited to become AI native. They're systems thinkers.
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Chapter 4: What role does data infrastructure play in AI success?
We're delivering better output faster to the end, to down the line constituencies. And we've taken that team from 26 to two. And I think there are probably other, I know that there are other opportunities across our business where that's happening as well.
And as agent technology gets more and more advanced, you know, if I have one of my executives told me his team asked him this question and said, like, should I be concerned about my job?
And he said, look, if ever there is a job that takes French fries from the fryer and lifts them up and put them over into another bin and sprinkle salt on them, I promise you someone is trying to build a robotic arm to do that action.
And so if you're in a position that's just moving information from one place to another place and sprinkling a little something on it, someone is going to try to replace that job and automate it. And I think there are a number of areas around every business that historically relied on people to move information from one
repository to a dashboard somewhere else and click a bunch of buttons and it goes downstream. And that was very valuable to a business. And if you perfected the technology enablement along the way there, you could have a competitive advantage. That just is not a competitive advantage anymore.
And those are areas in the business that to be competitive, we have to go use AI and leverage agents to do. And so I think there's a lot more of that coming for one. And then internally, you know, we also focused a lot on how do we build the infrastructure, the data infrastructure for all of our employees to also take advantage of AI and its power and use it in their day to day.
And so today we brought together our product data, Zoom Info's data, I'm sorry, product usage data, Zoom Info's product data, that company professional data asset,
our financial metrics data, all of our calls with customers, all of our emails with customers, our calendar meetings, all across the business that's in one data asset, that now our reps are able to go query and ask questions of, and they can say, hey, today I'm a mid-market rep,
which clients in my customer base should I be focused on today who show the most renewal risk and tell me the reasons why. And then the AI goes through and it says, these are the three, these are the reasons, this is what you should engage with them on. Here's a PDF of a deck that you should share with them. And so we're really driving efficiencies in the business.
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Chapter 5: How did the acquisition of Chorus AI influence ZoomInfo's strategy?
You know, that curiosity, the ability to see that step-by-step, really programmatic view of problem-solving, I think, is very key. On a kind of unrelated note to that, do you have any advice on...
monetizing AI in the upmarket and downmarket in your customer base? You know, our customer base is interesting because we have 35,000 customers and those 35,000 customers, they range everything from the Fortune 10 to, you know, a small commercial cleaning business in Toronto and really everything in between. And so they all have like very different AI wants and needs.
In the upmarket, those like Fortune 100, Fortune 500 customers, they want data. They want our data. They want our data ingested into whatever AI they're building. They want our technology that matches and merges data together, but they want to take all of that data and then they want to use it internally across a number of different AI applications. That's a really interesting business for us.
It's actually the fastest growing business within Zoom Info. It's growing 20% year over year, more than 20% year over year. It's our operations business. And it has this incredible tailwind because in the super enterprise, They're hungry for data to build into their internal AI applications. And so we monetize that way. In the down market, they're not building their own AI applications.
They want to leverage AI because they recognize that it'll drive efficiencies in their business, but they want it out of the box. And so they want the ability to go pull something off the shelf, leverage that, that drives their efficiency. And so we're kind of building solutions, not kind of, we're building solutions for both ends of the market.
We're making it really easy to consume our data for the upmarket business. And then we're building out of the box solutions like Copilot and GoToMarketStudio for our downmarket clients as well. In all you've done over the last couple of years here, would you be willing to share maybe a failure story and how you pivoted along the way? Maybe there are two.
I think one was, you know, pre us going all in on AI, we were really going all in on this idea of an integrated platform. So we thought like, hey, we have conversation intelligence. We have sales automation. We have the core data asset. Let's just build one platform that's an all-in-one platform that everybody can get. You could get everything you need from a go-to-market perspective out of this.
And that integration was hard. We probably didn't have the right engineering or product. We didn't. Not probably. We didn't have the right engineering or product talent at the time to pull that together. And so that project was like slow going and the pathway to delivering it was not obvious and didn't really leverage our key skill sets either.
And so when we first saw the power of generative AI, we had to make a decision to go leave that vision behind and go drive towards a future vision. And I think like that's probably where most large organizations get stuck. There's this innovator's dilemma, like this is the thing I'm doing. How do I abandon this whole thing and like chase after a new shiny object?
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Chapter 6: What does an AI-first team look like at ZoomInfo?
in our space. And so we had to abandon a bunch of other projects that we were working on that we thought were the future of Zoom Info to go all in on AI. Got a funny text from my chief marketing officer yesterday. She said, I had a dream last night that you were very disappointed in me because my kids were still human and I hadn't turned them into AI agents yet. Oh my gosh. I love that.
I'm glad that all my AI pressure is working. I'm trying to really press the team and drive leaderboards on who's leveraging AI across the company, who are the leaders, spotlight the champions. And so I think the business now has made a mental pivot to we win if we win with AI.
Yeah, and I think that's exactly true. And I guess next, how do you prove it? What KPIs really show that this new AI feature, this new AI pathway are delivering value for you?
Yeah, so two ways. I think like internally with our AI chatbot, we're looking at daily active users. How many of our staff are using our AI chatbot every day? Today, it's almost two thirds. They use it every single day. And that's like really exciting. They've created over a thousand agents to do work for them. There's a Henry coaching agent. There's an account planning agent.
There are all sorts of things that our teams are leaned in on. And so we're looking at adoption there. And then ultimately that's gonna come down to sales productivity. So great, I have this tool that could create an account plan for me. coach my calls and build content for the calls that are coming up for me, amazing. That should show up in sales productivity. It should show up in win rates.
It should show up in conversion rates. And so we're expecting to see all of that efficiency gain now show up in productivity on the P&L statement. Yeah. And it should show up with more growth. Like right now, we're not in a place where we're like, okay, we had 1000 sales reps, but now that we've driven 30% efficiency, we only need 700. I would rather have 30% more
growth than we have today, then 30% less heads. And so you're looking at sales productivity on that side. Obviously on the software side, we're looking at velocity. How fast are we releasing software and is it improving month over month and quarter over quarter? Is the speed with which we take an epic and deliver it to production faster than it's ever been?
Are we removing bugs from the platform faster than we ever have? And so we're really measuring velocity in the best way we can there. And obviously adoption. Who's adopting on the engineering side as well? What tools are they adopting? How much are they using it? How much of their code base is being written with AI? We're evaluating those metrics as well.
On the customer side, we're looking at how many of their daily workflows are running on Zoom Info without interruption, and then how much is that growing month over month, quarter over quarter. And we want our customers to be leveraging our AI on top of the Zoom Info data. And so we're looking at their adoption, their utilization, and how much of their daily workflow is running on Zoom Info.
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Chapter 7: What metrics are used to measure AI adoption and success?
I think 2026 is the year where you're going to have, you know, one or multiple breakouts across go-to-market, where instead of just talking about, a year ago, I went to a conference, they asked the folks there, a group of chief revenue officers, what they're doing with AI. And it was like, some guy was doing something with chat GPT on the side. And it was just a bunch of like randomness.
And I think that randomness changes in 2026 to real concerted, articulable workflows that are being run and replaced by AI. I think that's the real exciting opportunity in 2026.
I think you're spot on. It's going to be an interesting market. And we've seen a lot come to fruition with agents this year. And as that continues to expand and more and more people not just learn what they are and how to think about them, but learn how to build them themselves in their day-to-day work. I think that's key. Totally. It's not just a thing for developers.
I mean, this is a thing you can do yourself. Yeah, we had a moment internally where one of our chief data officer built an agent on our internal chat bot. And one of the other executives were like, oh, I need that agent. Can you send it to me? And he was like, I can, but like you just create it yourself.
Just ask it a couple of questions and put it in the prompt and you're gonna have your own agent. So you don't need my template, just go do it yourself. And it's like that mentality shift from like, oh, I need someone who's technical. Oh, I need somebody who's in engineering.
to a mentality of like, I can just go do this and leverage the AI and the infrastructure there to build my own agents, to build my own tech. Like that is a major shift. And look, you know, I'm not a coder, but a month ago I got on Cursor for the first time and I developed a project. And after I did that, like my brain looks at things differently. And I think about our products differently.
And I think about who leverages Cursor and who doesn't internally at Zoom Info differently. And like, if you're not taking the time to lean in on those areas, you're going to get behind and you're going to see a different world than your counterparts who are AI first and that you're going to be behind.
And I'm like, I've heard people here say like, oh, like the like threatening thing of like, you're going to be behind doesn't work to motivate people to leverage AI. It's like, I'm not, I'm just telling you, Truth. I'm not trying to like scare you into doing your job. You're either going to do it or you're not going to do it. And if you don't do it, I guarantee you, you will be passed up.
Like just with anything, you know, I used to have a, when I, When I started Zoom Info, I had a sales rep who always put in the minimum amount of effort every quarter. He's just like, just enough to not get fired.
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Chapter 8: What advice does the CEO have for leaders looking to adopt AI?
Just two more quick ones here for leaders. What are the first three moves you think they should make right out of the gate to go AI first?
Look, I think the word leaders is the first part of this answer. You have to lead it, which means you can't just like say words and hope that everybody underneath you does things. You have to demonstrate that leadership here. They have to see you leveraging AI. They have to see you pushing them to not get that report again in a non-AI automated way. You have to see the playing field differently.
Then, you know, I think like... highlighting champions and showcasing wins and failures every week, that gives you an opportunity to really empower innovators on your team.
And so like, if it comes from the top, you're leaned in, you're driving the team, and then you're showcasing and highlighting winners on your team and people who are leaned into this, champions, those things are critical to making AI work at your company. I agree. One last note.
What are some advanced ways you see enterprises today using AI that might not be on everyone's radar?
Look, I think the first thing that advanced enterprises are doing is they are figuring out what their data foundation has to look like for them to leverage AI. And so they're going, okay, I have a bunch of information about my customers. I have a bunch of information about my prospects. I have a bunch of interactions we've had with our customers and our prospects.
I know how our customers use our product or service. Because I have email history with them, I know what they think about us. Because I have product usage history, support tickets, I know whether there have been bumps in the road along the way. I need to bring all of that data together in one place.
That is the plumbing that has to happen in order for you to start building a home around the foundation and the plumbing. And so the most advanced enterprises, they're thinking with that mindset first. How do I give my organization a robust data foundation that takes the plumbing work away and lets them be creative on top of that foundation? I think that's not the most... glamorous work.
But the minute you get that infrastructure there, like creativity abounds from there.
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