Lauren Schuman is VP of Product Growth at Close, a sales CRM for small scaling businesses, where she is building out the new product-led growth (PLG) function and working on setup, activation, and monetization for new AI-native features. She has over 20 years of experience in product and growth. Most recently, she was VP of Product Growth at Bitly, where she managed a self-serve P&L of over $75M in ARR, and before that at Mural. As Senior Director of Product Insights & Growth at Mailchimp, she started the company’s Growth discipline and led the Product Insights and Growth teams, contributing to revenue growth from $500M to over $800M. She began her career as a buyer in corporate retail.
PLG was built around predictable software. What happens when the product itself becomes dynamic, personalized, and probabilistic? When Lauren joined LogRocket’s LaunchPod podcast in 2025, she predicted that traditional onboarding playbooks would give way to natural-language, genuinely personalized experiences, and that faster AI-driven learning would create new bottlenecks, particularly around discovery and adoption. She predicted it, and now she’s building it.
In our conversation, Lauren discusses why AI can pull teams away from growth fundamentals, how a widening tech-savviness divide is reshaping onboarding and adoption benchmarks, and what it takes to earn trust in products that won’t be right all the time.
Leading with the problem, not the technology
You’ve built PLG engines around established concepts like activation, time to value, conversion, and retention. How does that change when you apply them to AI-enabled features?
What I’ve been finding is that some of the new technology is distracting us away from the core principles. You’re so eager to say, “What could an agentic onboarding experience look like?” that you’re sort of forgetting the whole point of what you’re trying to solve by leading with the technology.
With my team right now, we’ve talked a lot about understanding what is possible, but not jumping to what technology to use to solve the problem. I give credit to Max, our senior engineering manager, who’s done a lot of pushing to think about the problem we’re trying to solve and not the technology, and have the engineering team partner with us on the best way to think about implementing the solution.
A lot of the fundamentals are still important. What are the jobs that the customers are trying to accomplish? Those jobs pretty much haven’t changed in nature. How they accomplish them may be changing. But at the root of it, if you still think about what a prospect or customer is trying to accomplish, how they’re feeling in that moment, and the steps that it takes, I think you ultimately end back at a similar place.
You actually have to be a lot more strategic. We’re saying, “What is the right vehicle to solve a problem at a specific point in time, and what is the right approach?” There are more and different vehicles and approaches to consider, but staying rooted in the fundamentals — what is the customer trying to accomplish, what are the things that make them successful in your product — is really important.
A widening divide in tech-savviness
Where are you seeing things actually change?
I don’t have the evidence to necessarily support this yet, but I feel like there’s a larger divide in tech capabilities, especially for small businesses. You’ve always had a continuum of early adopters and then your laggards. I think now, with the speed of innovation and the accessibility of certain technology, that gap is widening. That makes it even more difficult to figure out: Should your product be well-suited for the early adopters, should it be suited toward the laggards, or are most people in the middle? Those experiences might look entirely different.
If we’re talking about time to value or activation for someone who’s super tech-savvy, they might be able to get up and running having a conversation with an AI agent, and that’s a no-brainer. But somebody who really doesn’t understand how agentic technology is working, or any of those patterns, or how to do good prompting — that’s an entirely different experience that might be needed to serve that type of customer. What I’m getting at is that I think you have to be even more segmented in your approach. It’s not a one-size-fits-all.
In some ways, the growth practice was built for this, because testing and learning quickly has always been important, but now it’s even more important. If you go listen to any of the companies that are thriving right now, they’re all like, “Our speed of learning is just so fast. The faster that we can learn, the better that we’re doing.” Companies that are not having that rapid speed of testing and learning toward what’s really new are the ones I think are sort of falling behind.
One other thing I would add is that trust is even more important. With deterministic products, we knew we could control the outcome: do this and, most of the time, you get that. But now that products are more often not deterministic, we’re like, “Ugh, how do I get you to trust this thing that may not be correct all the time?”
And by the way, what is acceptable? You’re actually having to teach customers how to use the product that isn’t going to be right all the time, and what to accept, and what is good and what is not good. The core principles remain the same, but the tools that you have are a bit different, and there’s probably even more to consider around how comfortable people are with technology and teaching them alongside you.
When different segments use the same feature in different ways, how do you measure success?
I think it’s more about being honest about your benchmark for adoption. We launched a voice AI agent, and while we worked at making it as approachable as possible, we realized there are some people who will be entirely intimidated by the idea of answering any questions to set up a voice AI agent.
Some of this factored into our initial thought of what success would look like. Success wasn’t going to be a mass market adoption. Success was going to be like, “Hey, we had X amount of customers that were really using this product and getting a ton of value out of it. They were raving fans, and they were telling everybody about it.” Maybe that was only 5 or 10% of our core customer base, versus another feature that is much more widely distributed, and you would expect 50% of your customers to use it. Some of it is in the features that you’re building up front, and who they’re aimed at in terms of that tech-savviness.
Onboarding by intent
Given these different segments, do you think about onboarding differently? Do you set up different flows for different types of customers?
We are very actively working on this, and we have been thinking about the different channels and modalities in which people would want to learn. We’ve also been thinking a lot about what it means to get started with a product for different people.
For you and me, we might have very similar businesses, but you want to come in and just kick the tires. You want to click around, you want to test things, you want to make sure the features exist. You’re OK with maybe using fake data, or something that just gets you a sense and a feel, and that’s how you make your purchasing decision.
I might be more of the camp of like, no, I actually need to try it out. I need to have real customers in, or real prospects in, added as leads. I need to make a real phone call, see how the phone system works. I need to send an email. I actually need to see it in action.
We’ve been talking a lot about this related to intent. Are you there to explore? Are you there to actually set up and get going with the product? What is the best way to meet the needs of somebody who’s exploring versus somebody who is ready to set up and get going? If I’m exploring, maybe I need a lot more different paths that I can possibly take, because my chosen path might depend on what features matter to me. Maybe a conversational approach would be great, because it’s more open-ended in nature.
We tried using our AI chat agent, and we created a version of it that was like an onboarding coach. We said, “What would happen if the onboarding coach was the first thing you saw, and we had a couple of prompts that were like, ‘Tell me how to do these particular things,’ right off the bat? How would that work?” I think we had roughly 20% of customers engaged with the chat. But the experience was just like, “Here’s some information, go figure out what to do.” It wasn’t at the point where we were doing actions on the customer’s behalf yet, so I don’t know that it was super valuable.
On the flip side, you might have folks who are ready to come in and immediately set up. What are things that we can do to support you in that process? Should we be super opinionated on what the first step is for a customer like you? Does that mean we actually create workflows on your behalf — you give us a few pieces of information, we go ahead and set it up, and you get going? What level of control is needed?
I’d love to say I’ve solved this problem. I haven’t solved it, but I’m thinking a lot about the idea of intent mixed with level of control versus opinion, and then what’s possible to either guide you or to do on your behalf.
Some of the principles we’ve been working with are: “tell me,” then there’s “do it with me,” and then “do it for me.” How can we pay off each one of those? I don’t imagine that everybody will want to fall in all three camps at all different times, but how do we start to feel our way into the areas where people do want it to do it for them versus do it with them?
What signals do you use to decide which flow to put someone in?
Right now, we are actually going to ask people their intent. We’ve tried to infer some of that, but when I originally came in and took over, we had a very long signup flow. It was like 12 steps to get to the product. We cleaned that up and basically said, “OK, you have to earn the right to ask questions.” We’ve got it down to two steps into the product. We’re adding one or two back. Once you get into the product, we’re adding back a question about your intent, and then one about which channels you’re interested in using, and we’re using that to personalize the experience.
We’re actually about to launch an experiment around this where, based on your inputs, you literally watch your onboarding recommendations populate in real time, so you can see that payoff for you. And then we’re doing a whole simulation for phone number setup. Say you are here to explore and you do make phone calls. We know that phone calling is one of the most valuable parts of our product. If we could just get people to experience what it’s like to make a phone call in the product, then they will be much more likely to get started. We’re setting it up where you get a real phone number, you make a phone call, and your very first lead is actually our AI assistant. You call our AI assistant, you have a conversation, you watch the CRM update with all the details that shows you exactly what would happen, and then you can do it for yourself with your own information.
Those are the kinds of things that we’re exploring: How much can you infer versus when is it useful to actually ask questions and then show the personalization payoff in real time?
Trust in a probabilistic product
You mentioned trust earlier. What practices do you rely on now to build trust, or to avoid eroding it?
That’s super hard. I think trust is more important than ever before. In the past, trust was logos of companies that you do business with, social proof, those types of things. That’s still relevant, but what’s really difficult about the trust piece is that the bar for “this is no good” is different for everybody.
Say you’re interacting with an AI chat product and you have one bad experience. That could be enough for you to say, “I no longer trust this.” But it’s really unpredictable in terms of what that will be across the different customer base and whether or not you’ll have it.
I know a lot of product teams are working on evals to say, “How often is this getting these things right, and what is the quality?” But it’s still to the point where your product could be good 95% of the time, but for that 5%, one thing happens and all of a sudden somebody doesn’t trust this product or feature anymore. How much that extends to the rest of your brand and your product is not really super well known.
I actually think it’s really important to use AI where it is the best solution, but not all the time. Sometimes a programmatic automation is actually going to be a better choice than having an AI agent do it, because it is predictable. Maybe it’s like 90% good, but maybe that predictability is actually better than the real wow factor and the risk that’s associated with the agentic version.
So I think it’s about being really smart about where you apply AI, and helping benchmark. If you can provide benchmarks for customers, that lets them know how good something is. It’s also about getting folks in the mindset that they can help improve your products. If you could get somebody invested enough to care in providing feedback to make your product better, and then seeing improvements, that can help build trust.
There is such a thing as speed to trust, or initial trust, and that’s one benchmark. But then there’s the ongoing risk that you could earn trust, and then something happens, and all of a sudden the trust for that product is gone. I don’t have a good answer for how to measure that, and also how to get people to come back and give your product another try.
Pricing in the wild, wild west
How are you thinking about pricing and packaging for AI features?
We keep saying pricing is like a wild, wild west, and I think nobody’s figured it out. The thing that I do like about it is that we’ve all agreed that you have to test and learn regularly. Back when I joined Mailchimp they hadn’t touched their pricing and packaging in 10 years. Nowadays, can you imagine? The best companies are rapidly iterating regularly, and I think that’s new and different.
I started my career in corporate retail, so I was a buyer in the apparel world. In that world, everything is about cost plus margin, so you’re very used to having a cost of goods sold. As I moved into SaaS, it was like people just put your finger up in the air and be like, “I think this feature’s worth this amount and I’ll price in this way,” because you had no real costs. It was really your people costs. Now I think the industry is learning what it’s like to actually have costs associated with technology.
The mistakes I’m seeing are where it’s just like, I’m adding a markup to whatever the cost is and passing it along to the customer. I think that doesn’t work well, because that may not equate to value. Just like in a grocery store or a retail store, you’re going to have things that maybe lose money, but they’re table stakes and you have to carry them, and there’s a willingness to pay that maybe doesn’t match the cost. There are other things where maybe the cost is actually quite low, but the willingness to pay or the value is super high.
Yes, you need to think about cost, but you should make decisions according to the value, not necessarily just the cost. I’ve noticed that’s often a really hard muscle to build for folks who don’t come from the world of actually thinking, “It’s not just about, ‘This thing costs this much, so therefore we’ll pass it on with a 20% markup or whatever.’” You have to consider how it all comes together.
How are you approaching monetization for your own AI features?
Right now, our AI features are included in our plans, so you don’t have to buy them separately. You get AI credits as part of your plans, but if you are using above a certain threshold, that’s when you would purchase an AI subscription or eventually a top-up package.
We purposely did that because we didn’t want monetization to get in the way of adoption. I think right now, our biggest hurdle is just more awareness and adoption of the AI features, and we’ve given a lot of leeway for folks to try our features. Our goal upfront was not to try to get monetization off of it. It was more about, “Hey, is this feature actually useful to you?” If it’s useful to you, it’s going to be a no-brainer that you’ll want to buy more credits, because you’re clearly getting a lot of value from it.
Protecting the core
Are there assumptions you had about building with AI that have proven untrue?
There’s so much hype about what the technology can do that I thought everybody was going to run super fast and, for instance, have agentic onboarding experiences. I thought that classic product as we know it, where you have a UI and you’re clicking UX elements and doing your tasks that way, would be a pattern of the past.
But I don’t think most products have gone there. Many people have added in AI agents and agentic capabilities, but I haven’t seen the uber personalization payoff yet in many of the products. I’ve seen a lot of add-on AI agents. Many of them are not good. I still think a lot of the most advanced users go to Claude, and they hook up via MCP, and they’re using products through Claude as the main interface. So maybe that’s the learning: How much do you build out in your own product versus how much do you open your product to be consumed by other products?
I think everybody’s grappling with this: If it’s open, then what’s the true value prop in your product if any of a number of products could have the same access and create the same thing? I think the best teams are thinking carefully about the best jobs to solve with AI, not just trying to use AI to solve all the jobs.
How should teams balance building new AI experiences with the product most customers still use?
There is the notion of “be where people already are” versus trying to fight that. If there’s a subset of your base that’s already in Claude, and that’s how they want to run their workflow in their day-to-day, don’t fight against that. What would be the right strategy to monetize that, or what’s the unique value there? But that segment is not going to be 100% of your customer base, so how do you evolve who you’re looking for and servicing?
The big thing that I’m wondering with a lot of companies is, if we’re all so invested in building new AI features or being AI native, what about the rest of your product experience? What’s happening to it, and are people paying attention to it? I can’t overstate the number of companies that I’ve been in where they’re so focused on innovation and the next thing and the next thing and the next thing that they forget their core product and their core customers. Then that becomes stale, or broken, or out of date, and they can really lose foothold because they’ve lost sight of that.
I’m not saying you should bury your head in the sand and not innovate or do anything new and interesting. But at the same time, I wonder what percentage of organizations’ teams are focused on all the new versus still making sure the core does what it needs to do. I bet if you were to ask a lot of product leaders right now about the percentage breakdown of who’s using their core product versus their AI features, the answer would still be overwhelmingly their core product.
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