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Connor: [00:00:00] you guys are doing it and doing it at a level that most folks are still trying to figure out one on one.
If you're looking for. What does great look like? I think what you just described is absolutely the definition of that.
Lindsay: Would it be helpful to dig into some of these specific?
Connor: Yeah, absolutely.
Hello and welcome to go to market with AI, a podcast for sales, marketing, and customer success leaders using AI to scale their growth operations. I'm your host, Conor Jeffers. And in today's episode, we are talking to Lindsay Rothlisberger, director of revenue operations at Zapier and previously at Unidays and Oracle.
With over 16 years of experience in marketing and a passion for transforming ideas into impactful strategies, Lindsay brings a unique blend of strategic foresight, People centric leadership and a relentless pursuit of driving business impacts or optimized customer experiences and operational excellence.
We talk about real AI use cases. Zapier, how Rev Ops is uniquely equipped to drive AI and organizations and all of the incredible work [00:01:00] that Lindsay and her team is doing. I think this will be our best one yet. Let's listen it.
Connor: Lindsay, hello and welcome.
Lindsay: Hi, thanks for having me. I'm so excited to be here.
Connor: I'm so happy to have you. you are my capstone, headliner, end of season one. person. And I think the combination of RevOps and Zapier and your illustrious career is extremely interesting. And I feel like I got off of the highway and took a hard right into entrepreneurship.
And, had I not done that you are in one of the couple of dream job categories that I would put up there.
Lindsay: Oh, yeah. Wow. That's quite the compliment. Yeah, I feel very, very lucky to have sort of fallen where I am today. It's been, I think also at the cusp of like, I feel like I started my career when marketing automation was really like a new thing. So to see it evolve, like toward AI now is like just really exciting.
Connor: Well, I'm curious to sort of just start from like, If you're marketing automation into RevOps now more into AI generally, which I [00:02:00] think in hindsight is like, oh, that makes sense. But at the time, these are all brand new things of sort of what's the path that you charted background to where and what you're working on now?
And then we can talk about all of the cool, amazing things that you're doing that. Get us so interested in learning about.
Lindsay: Yeah, that sounds great.
started my career in event marketing and demand gen which
during that time, I was always just tended to be very interested into both, like why we were doing the marketing activities that we were, and then how could we do them better and more efficiently?
So that led me into a marketing ops role where I got to figure out like the how of how to make these marketing campaigns much more personalized in these much more scaled great customer experiences, which was sort of new at the time. and really interesting for me. I'm particularly excited about RevOps though.
I kind of made the transition from marking up ops into RevOps here at Zapier actually a couple of years ago. And I found that RevOps [00:03:00] really combines like everything that I love. it's allowed me to be able to find these like. Disparate go to market motions across our go to market org and figure out how to connect them, make them more efficient, use automation and AI and workflows to really like grow and scale efficiently.
and in a way that is like. It creates a great customer experience, which I think is also really interesting. I'll just like plug, I think RevOps is becoming just such a critical function in organizations. Because we have this really unique view across the entire customer experience, as I mentioned, and then also like this understanding of how everything works under the hood that nobody else in an organization has.
And that gives us such a leg up. When it comes to being able to contribute to strategic decisions for the business. So I just think that's a really interesting specialization that I've sort of found myself in and being [00:04:00] at Zapier at a place that. Like services RevOps orgs like us has also been just really cool because I get to sort of like connect to the customer in a really unique way as well.
and sort of like provide product feedback and learn, based on the use cases that we have and what might work for customers. So that's been a dream really.
Connor: Yeah. How were you the first RevOps official person at Zapier?
Lindsay: Yeah. So I was the first marketing ops hire
Connor: Oh, really? That's wild. Okay.
Lindsay: And we did not have a sales team at the time. We've been product led for a really long time. So marketing ops at Zapier was more oriented around like the personalization at scale and like, how do we use marketing and our product to, to fuel growth.
And then it, since adding in sales. We hired a couple of sales ops folks, a CS ops person, and we made the decision to centralize into a unified RevOps [00:05:00] org, which just works so well for our business model because sales cycles can be quick subscription based products, like the tight coordination.
For Ops is just more important in a lot of ways than like the stakeholder coordination.
Connor: Sure. Sure. Sure.
Lindsay: Yeah.
Connor: And does RevOps at Zapier own like service marketing? Say what's the breadth of like the RevOps department, the RevOps function?
Lindsay: Yes. So we own audit, but all of the day to day automation and operations process workflows for go to market. And that includes marketing, sales and customer success. And then functionally. We own strategic insights and analytics, so we have sort of a specialization there. And then sales enablement, which is also CS enablement and and tooling and technology and integrations.
So the sort of like platform of our operations teams.
Connor: So for anyone listening what Lindsey is describing [00:06:00] is the modern best in class RevOps organization. And it's so funny because I could sit here and ask you about RevOps is happier all day. and that's very much my background and my love. But I think while we are here for AI and GTM, I think what's super interesting is.
I, I was just even this morning on the RevOps bootcamp with HubSpot Academy talking about for people who are like getting into RevOps the first time and trying to build that department and trying to build that organization. And it is so interesting to go from that conversation this morning, which is very one on one to you guys are doing itand doing it at a level that most folks are still trying to figure out one on one.
and if you're looking for. What does great look like? I think what you just described is absolutely the definition of that. Which maybe gives you the jumping point for the next of how AI is coming into GTM at large and RevOps driving that at Zapier.
Lindsay: Yeah. No, absolutely. I think AI. Is going to really transform [00:07:00] the platforms, the tooling, the product landscape over the next several years. I think. Longer term, what we'll start to see is these, platforms and solutions that are so much more easy to customize for your unique business needs.
Being at the forefront of like the new tooling and tech for go to market is is a really great place to be right now. But I think like even just in the short term, AI is going to make go to market teams, just like. 10X more efficient and effective. When you think about qualification, campaign development, like intent signals. In the short term, there's just so much that you can do
I think to harness AI today, that then is going to make you really well positioned as this, like transformation happens over time. I also think you called out something really interesting that I wanted to highlight too, which is that RevOps and AI, like, I hate using jargon like synergy, [00:08:00] but like,
Connor: It's true. I agree.
Lindsay: pair, like, literally when it comes to excelling at both personalization and efficiency, like, that's exactly what we're trying to do in RevOps and like AI is, I going to be such a powerful way to like 10x those outcomes.
Connor: I think to your point, something that you mentioned around having a. holistic view of the entirety of that customer journey and all of the teams in the processes. I think one of the things that ends up happening with Bad or just sort of phase one deployments of some of the AI tooling is there's so much that happens in our world that just goes through like the human being litmus test of a filter that no one documents and no one writes down because no one needs to check.
Like, it's just very obvious. Like, oh, that's wrong. And there is no check on that with AI. And so you have organizations where They want to deploy this into part of the function, and then it rolls to the next department, the next function that's downstream. And they're like, what on earth is this? Now that you just ran this through AI, like, this is terrible.
And I [00:09:00] think as a result, RevOps is really uniquely positioned to say, these are the actual areas of opportunity versus this is just the thing that maybe hurts me the most, or I have the least capacity to and RevOps can probably deploy that more effectively than other groups might be able to.
Lindsay: Yeah, like my mantra is RevOps is closest to the problems like RevOps is closest to the problems around how things work. And even just at Zapier, like our RevOps team being being so close to like the AI implementations, we've started leading these internal AI strategy forums where we actually share out like learnings from AI with the rest of the business.
And I think that's been a really useful way to like get feedback and buy in and ideas, but then also be like the facilitators of the change.
Connor: who's involved in that? As a peer, is that like you, someone volunteered and said, Hey, we think this is important and we're going to go and lead this. And it's kind of like a center of excellence or a working group or like, what's the level of formalness to that?
Lindsay: [00:10:00] Yeah. Well, Zapier has really truly embraced AI across our entire organization, like even within our product itself. So we have some really Smart folks who've been working on AI projects on the product and engineering side. And then we have senior leaders involved in those forums as well. So like senior leaders in go to market and then across other AI areas.
and so it's them. And then it's a few members of my team or whoever's working on an interesting AI project. And it allows us to like, share ideas, get feedback share resources, because all of this is so new. It's allowed us to like, just to have a little an element of collaboration from like different specializations across the org.
Connor: I think a lot of organizations get stuck in this. We don't know how to start. We don't know how to get people engaged. We don't know how to educate folks. And it sounds like you've really leaned into, we're going to be that center of education. We're going to really, Share that information.
How are you identifying what to show people [00:11:00] and what gets them excited, what gets them engaged and from sort of this AI center of excellence that rolls to the rest of the organization that they latch on to, they're excited about. And they, it kind of makes it click versus being maybe scary or hard?
Lindsay: So the first step was creating ample space for this. So in our RevOps team, we are busy people. We have a lot going on.
Connor: You haven't solved all of the operational
Lindsay: No, like, as we're still struggling with reactive versus how do we be proactive? How do we
Connor: Always, always
Lindsay: these, like, needs coming down the pike and being 10 steps ahead, but we really, really had to be intentional about carving out time in our week to be able to.
experiment with AI, whether it be building a workflow that uses an AI step to unlock a new use case. So we even had an AI hack
week at Zapier where everybody could sort of set aside a whole week and just play with these new tools and learn. And so setting aside time is the first step to explore.
The second thing is actually being able to show [00:12:00] results. I think Is really what drives the buy-in cross-functionally when we can show, and I have some specific use cases we'll walk through later, but when you can show that you've literally opened up sales capacity by two demos, a rep per week with this one use case a lot of people internally start listening and want to make sure that you're supported and that you have the resources you need to double down there.
So I think once you latch on and you find something that works, like. Going deep with those types of use cases and really pushing the boundaries to show those like very tangible results.
Connor: I feel like something that everyone forgets when new stuff comes along. And I just thought that answer did such a excellent job crystallizing this, where it's like, well, how do I get everyone to care? And how do I do it? You're like, we'll show them that there's results that they care about and then they'll care.
And it's the same as all things forever. And it may be a very new mechanism of doing it, but I think the process by there is the same and. I think that is extremely actionable.
I'm eager to jump into like what you guys have been working [00:13:00] on
and what you're seeing in the GTM org and the more tactical stuff that you guys are deploying.
And even the we're opening rep capacity alone is super interesting, but what's the coolest stuff that you guys are working on?
Lindsay: yeah. So I 1st kind of want to break down into, like, the different sort of, like, categories of problems that we're solving with AI, because I think it helps sort of crystallize and I can show exactly sort of where we're focusing. So when you think about, I, there are a few different ways to implement it.
So you have sort of this use case where you're using AI as like a concierge or a co pilot where you have someone, like you have chat GVT, or you have some tool next to you assisting you as you do some tasks, like creating marketing content, writing blogs, like developing new strategies or templates.
So I think the concierge use case is very interesting. But I think where you start to see AI being even more powerful is when you get into using it to analyze information. So pulling out trends, pulling out insights, like you actually [00:14:00] don't have to be a data scientist anymore. If in RevOps, you have a strong business acumen and you can think strategically about what problems are we trying to solve as a business to ask the data, the right questions.
The technical skills become a little less. Important, which I think is really interesting opportunity. in RevOps And then our main group of use cases. So we've got the concierge. We've got analyzing information at the main use case and where we're finding the most immediate value is around what I, I don't know another way to sum it up other than synthesizing information.
So you're taking information across several sources or inputs, and then using it to using AI to create outputs that mean Faster access to key info for your reps and then much better personalization. And the the way that we're using AI Zapier is mostly utilizing our own tool. So Zapier, so like using OpenAI and Zapier together in our workflows.
But I [00:15:00] also would be remiss if I don't mention Similar to this vein of analyzing information and synthesizing information. You've got all of these really interesting and intent tools on the market that I think are going to get so much better and more sophisticated around outbound and demand gen at scale.
So, think, no more spam outreach. Like, I think we're going to get much better at, like, reaching people at the right time with the right message. So there are a lot of really interesting tools on the market that I think are going to be pretty groundbreaking when it comes to like sales efficiency and doing effective demand gen and sales outreach.
Would it be helpful to dig into some of these specific?
Connor: Yeah, absolutely. I think what I'm so excited about is especially in talking to you is I think previous episodes of the show have been Talking to folks who are building AI companies or AI products. And so we get into like a lot of product we get really, and then maybe like conceptual and some arm cherry stuff.
And the tactical area that you guys are [00:16:00] hands on doing real things is so interesting and I'll give the plug because I think you did it very subtly, which is if anyone has not checked out Zapier's AI functionality, it is probably the most go to one that everybody references as, have you seen this of being able to.
Just write what you want to do and get automated actions out of it. It is pretty mind blowing. And so I'm really excited to hear the actual stuff that you guys are working on.
Lindsay: yeah. So our RevOps team I might've mentioned this before. I think it's around 50 percent of our Zapier workflows now utilize some form of an AI component. I think being as happier has given us this unique leg up because we have the ability to actually use AI in our workflows, which I think is like, just can be extremely powerful.
So the first use case that I want to highlight. is lead routing in particular. So we have a contact sales form on our website. We get a fair amount of inbound traffic which is great. [00:17:00] And so we, you know, typical form, we're able to collect the information that we need. And we have this hypothesis where we thought that maybe the free text portion of the form.
Could do a better job of getting customers to where they needed to go than the traditional drop down menu.
Connor: select a handful of things. Like what's your use case that where are you trying to go? All of those pieces.
Lindsay: Exactly. Exactly. So we set up a test where we looked at the inbound free text information coming in from customers. And we were able to correctly identify a pretty large volume of support inquiries. People who just needed technical support with Zapier that weren't really meant for sales requests, but they needed to get to the right place.
Like
Connor: Yeah. And they're hitting whatever form they can. Cause they can, they're just like, can someone please get back to
Lindsay: We want to have them have a good experience. And also it filled up. It's also not great for our sales team because those aren't the right conversations for them to be [00:18:00] having.
Connor: Sales reps love getting non leads
Lindsay: yeah,
Connor: lead rotation. It's their
Lindsay: exactly. So they were very happy with us when we figured out that. And now this is, this form is a hundred percent powered by AI to identify these unique inquiries and get them to the right place.
So we've been able to correctly route our customers more successfully than when we have them self select. So that's an interesting use case there, but I would say another really powerful one. And I kind of framed this up as. Go to market alignment type use cases, but also customer experience. So, as you know, like when you work in RevOps, you are responsible for making sure that customers have a seamless experience across marketing sales and CS.
So when a deal closes and then the CS person or the account manager takes over, like there's a lot of context that they're missing. And so our refs were spending a lot of time kind of walking CS through. through like the use cases or the things discussed during the deal. So what we do is we, this is one of this is a really [00:19:00] powerful workflow for us because it saves about two to three hours of like prep time between those two functions.
We take all of the notes and deal information and we summarize GonCon transcripts of like what the customer is interested in, and we use Zapier to like. Connect to those sources, have an AI step that summarizes the customer experience up until this point, and we and and then it automates a handoff to customer success of customer success sort of has all these notes and information and so they're ready to, like, really kick off a positive working relationship with the customer off the bat.
Plus it saves a lot of time internally. So, that's another use case that we love. And we do something similar for product as well. So when we're having,
Connor: like, we, you haven't even told me what this is yet, but this is one that I'm so excited about because I feel like AI is going to be the world's best product manager. Because instead of. Sales told me this, I'm going to interview these people. It's aggregate all of the information and kick it [00:20:00] back.
Lindsay: yeah, you nailed it. You already know this use case, basically, In a product led org, it becomes so much more important for product to get the feedback back from customers because we're using our product to fuel a large portion of our growth. And so those feedback loops are crucial and that's another really unique role that a RevOps team can play that I think often gets overlooked is how do you also like align.
Go to market and product teams. And so we take Gong transcripts. Again, we do a lot with a call transcripts and we Analyze those. We use an AI step in his app to look at all of the product feedback, mentions of new products, things the customer got stuck on or didn't like, and we summarize that and we send it off.
We aggregate it, send it off to the appropriate product manager based on the piece of product
that's mentioned. Yeah.
So it's
Connor: whole extra routing workflow on top of those individual pieces.
Lindsay: Exactly. Exactly. So that's completely automated. And then [00:21:00] reps and product managers don't have to sort of go back and forth. Let me get on a call.
And of course, our product managers still talk to customers, but this can even help them pinpoint like which customers they should be talking to.
Connor: Yeah. Instead of like the age old slack of, does anyone know a customer that. Insert here and hoping someone gets back to you. And then when you talk to that person, it's maybe the right people to even be on the phone with,
Lindsay: Exactly. Exactly. I have a lot more. I can keep going.
Connor: give me another one. And
we'll talk about other
Lindsay: cool. Okay. So the, this next one, sales efficiency is a huge focus for rev ups. So we're always looking for ways. How can we make sure sales is spending the right time on the right things, and we can minimize admin work and we can just make their lives so much easier.
So there's a few things that we do around sales efficiency that we use AI for. So. Again, with call transcripts, we can actually take data from those calls and use that to populate our CRM. Think about things like additional stakeholders that the person might've mentioned on the call, [00:22:00] their budget, their timeline, things we might ask reps to manually take note of and like add into deal notes, et cetera.
We can automate that with AI call prep guides and post call follow ups. That was one of the first use cases, the most obvious low hanging fruit where our reps are having to check a lot of sources to prep for a call. They might want to look at the details of the support tickets.
This customer has had or their product usage. They might want to check the web to look at what What types of things this company might be interested in based on what they do. And so what we do is we use AI and Zapier tables to combine all that information into a call prep guide for the day so they can go in and they can see, this is the customer I'm talking to.
Here's the synopsis of this information that's going to inform my call for them.
Connor: and is that in And structure of that is essentially data intake across. Recording support systems, a whole bunch of other sort of data sources and then combining all of that. And then is the [00:23:00] output a doc? Like, where is the rep consuming that particular piece of information?
Lindsay: We've sort of gone back and forth. This has been iterative. We used to have it in a note in HubSpot and I think we do still have it there but we also created a Zapier table that the reps prefer. They want to see their list that, they want to see things in a certain way.
Connor: Everything goes back to like, can we put that in this spreadsheet though?
Lindsay: Yeah, but at least it's a Zapier table. And so, so yeah, right now I think we're in a table where there's a column for like company use cases, support tech tickets. And then we also have that for the post call follow up. I think the post call follow up is actually a note in HubSpot where it gives them a suggested email template suggested next steps.
Steps based on the call transcript as well. And that's been. Really, really useful for them to be able to like, have that info at their fingertips and just save time. the last use case I want to talk about is my favorite. So, and this is our newest, our newest one. So [00:24:00] like one of the problems, so we have one sales enablement person and he's wonderful but he only has so much time.
And then we have, our sales managers are super busy as well. and coaching in sales is just. So important and consistency around, like, the narrative for sales. So, our sales enablement team member, what he actually did is he he created this method of taking call transcripts.
So Gong transcripts then using AI. And he wrote this prompt that looks at our sales methodology, how well the rep is doing discovery. Negotiations, objection handling, and he creates a database of feedback for each rep that then the sales manager can reference. So then the sales manager doesn't have to go through every call, every transcript.
And it's actually a huge result right out of the gate. We literally launched this a couple of weeks ago and we're seeing we've actually 3xed the amount of feedback that. Managers are providing to their reps and we've already [00:25:00] seen like the consistency in ours Zapier pitch just improve.
Connor: I think you're totally right. And I think that the coaching element is going to be so powerful. And I think what people in every role. Really crave and want is help me get better and teach me what I don't know.
And too often I mean, always managers are slammed overwhelmed and the ones that are the best are nights and weekends. I'll listen to call recordings and I'll try to get back to you on some stuff when I have time. And I think synthesizing that and doing that real time coaching element is so powerful to make people more effective.
Lindsay: Yeah, it's this one has been particularly exciting just because it's saved time and it's improved rep performance. Like, what a win. What a result. The other thing I just want to call out to is, like, all of these use cases that I went through, they were not done by 1 person on the team who's like an AI expert. This was really across the entire team, a lot of different people experimenting with a lot of different [00:26:00] things, trial and error and some folks not technical at all, like, and able to implement these solutions pretty much autonomously.
Connor: What do you think the key is that a unique attitude? Is it a unique environment? Is it that you guys have the tools exposed to people? I feel like that's where a lot of organizations get stuck. Is there like, how do we start? Do we need to go higher ahead of AI? and you're basically suggesting like, no, you don't need to do that at all.
And do you think there's anything unique about Zapier about where you, what you're doing that enables you to do that? And is it a hard or is it a soft thing that other people can embrace?
Lindsay: Great question. So at first I was Not convinced that everybody on the team would be able to pick this up out the gate and run with it. I guess what I thought initially was that what we would do is we'd have one or two sort of AI evangelists on the team who are just super skilled at this and can go just focus on the AI workflows.
And I don't think that's the right approach. I think that the right approach is inspiring your team to focus on what problems are we trying [00:27:00] to solve. And AI as a tool to getting there more effectively. And so that mindset and building that muscle again, of like, I have this tool at my fingertips, I know how to use it like at a high level.
Yeah. I have to like tinker a little bit to kind of figure it out at first, but knowing what the goals are, like our enablement manager, like he was like, I got to get managers coaching more. I got to get managers coaching more. I've got to improve consistency. What are the ways I can do that? And AI becomes a very natural Easy solution to be able to go solve that.
So I think not thinking of AI as sort of this like separate entity that we've got to go figure out, but like, what are the problems that we're solving in RevOps? And how do we really leverage this new technology?
Connor: I think that's incredible. And I think something that you said, which is a phrase that I repeat constantly all the time, which is what is the problem that we're trying to solve? And. You can [00:28:00] get very farwith just that phrase. And I think that combining that with a framework on where is AI going to give us a superpower on actually solving this problem.
Is there anything that you found that isn't a fit or something that you guys have tried to do that didn't work or areas that you are like, don't apply AI to this particular thing?
Lindsay: Yeah. So we did a lot of experimentation around, can we use AI to generate automated marketing emails? And we kind of got it there, but it, we did a lot of experimentation, like we were a little nervous, like if these emails are going out to customers, like, are they going to be consistent enough?
Is the AI going to go rogue? So I would say. What we were leaning in heavily in that direction early on, because it felt like the most obvious thing, like, how do we generate marketing content at scale? And so I would say it was like, okay, but I didn't find that, like, the emails that we sent that were AI created did a whole lot of a [00:29:00] better job than just our most simple.
Segmentation could do. And so we've really started to, when we, I think the first thing was that call prep use case, and when we really started to see like, Oh, we increased rep capacity pretty significantly with this. And then we started to lean a lot more into the efficiency use cases. And like, I think what's really interesting about revenue facing or revenue driving teams and customer facing teams is like.
Not only does it really matter from a revenue perspective, how much time we're spending on these things, because of course you want to be focused on revenue generating stuff. But secondly, they are the front lines with the customer. And so they're almost embodying like that experience for a customer.
So if we can arm them with all of this information that has just had a much bigger impact for us. At least. In the short term and for now.
Connor: if I was to summarize that back to you it would be focus on ways to drive [00:30:00] efficiency and enable your human folks to be more impactful versus automating away the humanity from the interaction.
Lindsay: Yeah. Yes, I think so. I think so. I think that touch point is crucial. Like companies are human. I found that,
Connor: the good
Lindsay: yeah, the good companies are human. Like, I wouldn't want to take that element away. At least not right now, who knows what I'll, I mean, AI is moving so quickly. It's really hard to make guesses right now, but, it's definitely helped us improve customer experiences overall.
Connor: That's amazing. In terms of sort of the areas where, is there anything that's in the, like we Don't aren't touching this. we're, this is in the, like, do not AI category. is there anything that is in that versus maybe things that we've experimented with and failed? and is there something that maybe is in that category that you don't think should be there?
Lindsay: That's a really good question. I would say at Zapier, we're sort of like, let's shoot for [00:31:00] the moon. Let's try it. let's go for it. Let's see what happens. Let's take some risks. Think in the tech industry and at Zapier in particular taking risks, making, big swings, creative bets.
What I will say is I think early on. We probably weren't taking big enough bets, like, I think early on we were, incorporating AI sort of a little bit here, a little bit there, but we weren't thinking of it as like, how do we 3x 3x sales? Capacity, like, and I think we've sort of evolved toward, let's be a little bit more focused now that we've found some wins and we found some areas where we're having an impact and let's like go all in and make that.
Workflow or that process like even better. So I would say for us, nothing is really off limits. We're, we're trying it all and seeing what works and learning as we go.
Connor: So, so if I was to, and I'm going to [00:32:00] start, I could. Ask you about all of this all day. and you guys are bleeding edge. So I describe not only, I think the RebOps organization is absolutely the best in class structure and the things that you guys are doing are very bleeding edge. And I, I say that from seeing a lot of stuff and thinking it's pretty sophisticated and a lot of what you're describing is outclassing that.
So nothing but admiration for. The work that you're doing and I think key takeaways that I would grab from this would be surprisingly bigger goals, try to solve bigger problems which is the first time that I've heard that in these conversations, which I think makes it extremely interesting.
We have the concierge use case, the trends and insights use case, the synthesizing of information use case. The coaching use case is sort of the core ones that maybe to look into and evaluate. And maybe the last thing that I would ask you with in terms of a closing thought for anybody is you guys are bleeding edge.
I think you've given amazing tactical things for people who have a foundation [00:33:00] in place for anyone who is on the starting line and is saying, this is really exciting. I'm extremely inspired by what you're doing. And, We don't even really know where to start. What's step one?
Lindsay: Step one just Do it like, if you have access to chat, there's a lot you can do with data analytics. If you have access to their more premium plans. And then when it comes to automation, like Zapier has a free plan. You can start playing around with it. I would say like, you really just have to lean in there.
I think the thing that's a little bit tricky is there's not a whole lot of content out there, right? There's not like a whole lot of courses on this stuff. there's not a ton of materials, but I think there actually is. There are some now, like if you. Search these RevOps communities and the internet.
There are lots of materials and videos that can kind of help you get started and walk through things. Zapier has a great Zapier community and Zapier learn resource too, if you're looking to explore with Zapier in particular. But I think, yeah,just try it. I think in [00:34:00] RevOps, we're such tinkerers, right?
Like we love new tools. Like we're the perfect people to like, kind of just roll up our sleeves and see what we can make.
Connor: Love it. I think if you're in RevOps, take this as Lindsey's call to action. and for, last question for me is, when, how, you guys have only been doing AI stuff for the, I mean, we're talking maybe a year and a half max?
Lindsay: Yeah, I would say our first use case we implemented was probably just about a year ago. Yeah.
Connor: So take all of the progress. Lindsay, what you guys have done in the last year is incredible. And it makes me really excited. I'm leaving this conversation with a whole laundry list of stuff. I'm like, Oh man, we gotta go do that. And across the board. And thank you so, so much for joining us, sharing your insights and sharing what you guys are doing.
It is incredibly amazing.
Lindsay: Thank you. Thank you so much. It's been so much fun talking about this. So I really appreciate you having me.
Connor: Absolutely. I hope to catch up with you more soon.
Lindsay: Definitely.
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