What is the future of the GTM tech stack in 2026 and beyond?

Speaker A: If you build your AI layer on top of garbage, you're going to get garbage results from your AI. And so honestly, when it comes to data, that needs to be the cleanest.

Speaker B: Welcome back to the Sales Transformation Lab, the show where we dive deep into the people, ideas and innovations reshaping how modern sales teams win. Matt. I'm your host, Matt Milligan and today we've got another exceptional guest joining us in the hot seat. Today's guest is a builder at heart. He's a revenue leader go to market strategist and he is currently head of revenue and go to market at uh, Spiky AI. They're a techstars backed company that's turning how teams understand sales behavior and revenue performance on its head. And at Spiky Waleed helps organizations scale behaviors that actually win deals and drive performance predictable revenue growth. Today we are going to unpack a really important topic that is front of mind for many revenue leaders as we record this heading into 2026. And that is the future of the go to market tech stack. Stay tuned, you're not going to want to miss this one. Welcome to the show. Ah, leed, great to have you and really glad that we could finally get this episode in the diary. I know we've been sort of jumping back and forth between SF and trying to make our schedules work, but great to have you here.

Speaker A: Yeah, thanks Matt. I'm really happy to be here and I'm really excited to go into this conversation. It's uh, definitely a topic near and dear to my heart, of course, because of the space that Spikey's in. But yeah, I'm excited to get into it.

Speaker B: Awesome. And I guess, you know, today we're going to be unpacking the go to market tech stack will lead. Obviously there's been a lot of change in the last 18 months or so. A lot of new innovations coming to market. This is a really hot topic that we're going to be diving into. I guess before we jump into the topic, if you wouldn't mind, just, you know, help our listeners understand the context that you're coming at today's conversation from in terms of your personal background. And then also perhaps you can share a bit more about Spiky AI. Um, in terms of helping you contribute to today's conversation.

Speaker A: Yeah, yeah, absolutely. Um, like you said in the intro, I'm a builder at heart. Five years of entrepreneurial experience. Basically worked at big tech before was at Opentext and then Twilio. In between those gigs, uh, I built my own company With a founding, another co founder of mine, um, it's called Dream Philly. And basically what that product was was it was taking the comms and the headspaces of the worlds and making that more of a visual experience. You know humans, we're always consuming information through our eyes, right? We're constantly watching, learning anything basically through our eyes. And so why not meditation or kind of relaxation to be done through our eyes as well. So we took that kind of visualization space and kind of the calm and my calm space and we ended up basically scaling that out to 2.1 million users exposed. And then we went through an aqua hire after that. I was on to my next gig with Spiky. First started off kind of as like a operational advisor. Helped them kind of build from 0 to 0.5 in a way and we scaled that out. They brought me on full time and I've been here for about three and a half years so far. Yeah, it's been an amazing journey so far and we've been doing really exciting stuff in the space. And yeah, I'm happy to talk about, you know, the go to market stack tack tech stack in general. But in terms of spiking, what it's about is I'll start with the kind uh, of challenge that we're solving for today. And so revenue teams today, they're drowning in disconnected data silos. They have all these different tools but very limited actionable intelligence. And most importantly they're reactive. So they're analyzing performance after the fact. Spiky comes in as a predictive revenue operating brain. You can think of us as uh, a layer that sits on top of your tech stack to understand and analyze the data for you automatically and winning, actionable and predictive. So at the same time it's predicting these losses and fixing them before they even happen. So this allows you to get real time guidance to kind of replicate winning behaviors while deals are in progress. So Spiky's understanding what's happening with your revenue, diagnosing why it's happening and scaling what's working and fixes what isn't, all

Speaker B: in real time, you know, and obviously I'm referring to, you know, organizations like Gong and Jiminy and others. And then there seems to be this kind of newer uh, breed of AI native companies that has come, come to the party so to speak. And obviously, you know, Spiky being, being in that category. How do you think about like that, that shift that's happened between kind of maybe like the maybe not legacy is a bit of an unfair word, but those kind of companies that first came to market and sort of built out the kind of adoption of an acceptance of recording sales conversations to then companies like yourself that are uh, taking that one step further.

Speaker A: Yeah. So I kind of see this shift almost like the go to market tech stack is kind of going through the same shift we saw in data, uh, warehouses and marketing automation. They started off first as kind of like systems of records and then now became systems of intelligence. And so I kind of see that space, our space, heading in the same direction. Legacy, if we want to say that word, like gong and chorus and all of the bigger players they first optimized really for call reporting, transcription, post call analysis manager led coaching, dashboards, really all of these types of things. And so this was awesome when it first came out in like the uh, early 2000s. And it created visibility, but visibility alone didn't change outcomes. Especially when you're leading a team that as a CRO, for example, let's say you're leading a team of 100 plus, you know, reps under you, or including mid level managers, VPs and all of that. How do you leverage GONG to be able to get the most out of that? Right. So I think right now there's a huge shift, like I said, going from that kind of capturing these insights and needing to make decisions. And that's where the intelligence layer comes in. And of course with the shift of AI, that's what's allowing us to do all of that.

Speaker B: Yeah, totally. Um, and I guess it's really interesting that comparison uh, to the data space because you talk about that system of record. I mean I guess the kind of first SaaS player in the go to market space, probably Salesforce, that popularized SaaS and that system of record we recently had. You know, uh, I know there was Dreamforce in San Francisco in October and I think they just wrapped up the tour in London in November. Uh, there's a ton of noise coming around. You know, obviously Agent Force and, and the kind of plays of the big, you know, HubSpot and Salesforce in the space. Maybe we start there. In terms of thinking about the future of the stack. I mean what are you seeing in the market in terms of CRM as that system of record? Are you seeing organizations still purchasing a CRM system in the same way? And do you think in the not too distant future we're actually headed towards a world where actually the CRM system as we know it kind of ceases to exist?

Speaker A: That's a really good question. And I've seen this broken down kind of two ways really dependent on which company you're a part of and the space you're in. I would say the more traditional spaces outside of tech, they still heavily rely on CRMs just because already they don't kind of have the infrastructure around like call intelligence or any sort of recordings or anything like that. When it comes to the tech space, I see this change happening more often and I don't think it's to say that a CRM is useless now. So like for example, kind of how Spiky started off with is we started off as a, you know, note taker and that uh, quickly translated into, okay, this is going to really help salespeople and their teams. How do we kind of plug and play into a lot of the tools that they currently are in, one of which is the CRM. So, and then you can imagine if a sales rep is booked five times in a day. You know, they have a bunch of demos, prospect calls, discovery calls, that starts to scale out and become a huge back burner. Right. Uh, that's where we kind of figured out, okay, hey Spiky, how do we basically enrich the CRM for them without having the reps to be able to do it? So we basically push in those notes, those meeting notes. Custom field sync basically can update the fields within your Salesforce HubSpot and then can even generate follow up emails or emails based off the conversation that you just had, eliminating basically all of your admin work that has to do with the CRM. And when you go back into the CRM, you can focus on whatever you need to do, whether that's learning more about your prospect, enriching that relationship, warming it up more, whatever it is, rather than just having to manage it and create, creating more backlog in terms of like the workflow and admin tasks I think right now. And that's kind of like what Salesforce is doing with, you know, their, their Einstein and all the AI stuff that they're coming out with is that they're trying to make their CRM smarter, intelligent, less for a rep to be able to go back into the CRM and needing to do things, but the CRM actually works for them. And I think I see the same thing with Gong as well. Like, well actually Gong, um, and I've seen like whispers around this and I've seen it from kind of just my own eyes with how they've been moving in the market is they are trying to become a CRM because once you meet with someone and you have the recording, you can create an account just based off of that. And if you're plugged into the right enrichment tools, then you have all the data that you need through your gong. And so why do you need a Salesforce if you just have everything living in gong? And so I've been seeing that shift as well from the gong perspective. But yeah, I think every basically tool in this kind of go to market tech stack is trying to go into a different direction. For Spiky, we're kind of trying to consolidate tools into one actionable intelligence layer rather than just being a CRM. So you can think of us as like a revenue operating brain. So we operate based off of the tech stack that's below us. So we sit on top of that and operate off the tools that you currently have.

Speaker B: Yeah, it's a really interesting observation. And so from what I'm hearing from you with regards to system of record CRM probably here to stay for quite some time and maybe more traditional industries or industries that have more traditional field teams where they don't have digital tools as abundant as more, more tech focused firms. But then for those customers that uh, are perhaps even using tools like Salesforce, those incumbent players are going to be innovating on top of their stack to try and become more than a CRM, whilst certain players in the market like gong might be kind of coming the other way and trying to reimagine what

Speaker A: a CRM could, could become.

Speaker B: How do we think about how you think about data in this regard really? Because obviously, you know, as we think about this evolution of the go to market tech stack, so much has been spoken about of your data architecture is everything, you know, rubbish in equals rubbish out. How are you thinking about approaching that problem? You obviously spoke about being that kind of intelligence layer, which is, which is interesting but uh, how do you think about the kind of data quality?

Speaker A: I think data infrastructure is more important than AI infrastructure because like you said, it starts with kind of rubbish in, rubbish out. If you build your AI layer on top of garbage, you're going to get garbage results from your AI. And so honestly when it comes to data, that needs to be the cleanest. Now a lot of these tools currently in the market are helping with that. So like for example, Spiky Gong chorus, like all of these different types of tools now you have conversational intelligence, listening to the recordings and being able to store data, uh, correctly. Now that's not to say that, you know, some transcripts based off of, you know, different tonality or Internet connection or even like, like language barriers and accents. It's could kind of affect the transcription sometimes, but as that gets better, the data that's coming in from the conversational aspect is going to help a lot with kind of like setting up the framework for a lot of these AI layers to sit on top of and build workflows or operations off of it. And so I think if we kind of head towards tools kind of building the data piece more and more with of course supervision from the human element, then I think we can get a lot more accurate data rather than just having a human person come in, update things based off of what they think, maybe make a couple of mistakes. And now you have like kind of this heap of garbage that you're sitting on. So I think pairing kind of like that AI ah plus human aspect is going to help a lot with that kind of transformation. And yeah, that's, that's kind of like my thought m on that space.

Speaker B: Yeah, I concur totally. And I think a lot of the founders in my network, you know, went through YC or they raised money to build companies at the application layer and then they've actually realized, well, hang on, holy shit, we've got to fix the data problem first. And they actually pivoted a lot of their AI apps into building AI infra companies, which I think is just, it, uh, kind of speaks to that challenge that you shared. The point we were making a minute ago around the data source as well, where the more modern companies that have adopted call recording, you know, they have the benefit of that extra data source. But you reminded me of. I went to a conference a few weeks ago in London and I was actually gifted as an attendee a device called Applaud. It's a really slick little wearable. It comes packaged like an Apple product. So it's very premium and it comes with a wrist attachment, it comes with a necklace attachment and a pin. You can basically pin it and two taps and you can record all of your in person meetings and then you can sync them to your call recording tool. So I must admit I haven't used it yet because it kind of feels a bit like a spy device. But then it got me thinking. You know, it was only five, six years ago the first time I was using uh, call recorder on Zoom or teams. It was like kind of you felt a little bit nervous at the beginning of the call that the customer was gonna freak out. But that became normalized pretty, pretty quickly, unless you're in certain parts of Europe. So it just Makes me wonder like, could that acceleration maybe be happening in the kind of field sales environment quicker than we think?

Speaker A: Yeah, no, totally. And that reminds me also like of the, the other, other side of like kind of the consumer space. When Uber and Lyft came out, it's like, hey, I'm getting into a random stranger's car. Like that's kind of weird. And now it's just a basic verb within her language. Yeah, just take an Uber or you know, take a lift. Like it's. We don't really see taxis anymore, to be honest. Right. So it's all about behavior change. And I think that's kind of one of the biggest, um, hurdles that a lot of innovation needs to go through when it comes to humans. Changing behaviors is most hardest thing that you can ever do. And so kind of trying to figure out how to change that behavior while also implementing innovation is kind of what's going to lead you to success. Right. And so I also think it really depends on the industry. Tech industry always is changing the fastest. So if you're selling within the tech industry, within SaaS, you already got the latest, you know, widgets and solutions and things like that. But again, like if you're again on um, the manufacturing side, that may look a little bit different. They may tend to be still a little bit more conservative with recordings or, or even high compliance industries like healthcare, finance, industry, banking, things like that. They tend to stay away from those types of things and only record kind of like customer success interactions. Like, you know, the typical, hey, this line's being recorded for quality measures and things like that. So yeah, I think it's all comes down to kind of behavior change and how you're approaching that behavior change within the market.

Speaker B: Yeah, totally. It does make me wonder where we kind of are on the hype cycle with regards to adoption because, you know, part of me thinks like a lot of those traditional sectors you just mentioned, quite rightly, that they're still insisting on on prem software. You know, they haven't even moved to the cloud yet. So almost makes you realize that there's this whole part of the TAM that isn't even close to being ready, uh, in some ways. So how quickly will that shift happen? How quickly will that behavior change come through, I wonder? Uh, I mean, how long do you think it would be? We spoke about the kind of CRM being this place. There's a system of record like you think. Is that a 2026 prediction?

Speaker A: Yeah. Are you thinking about like AI, uh, where we're at, uh, in Terms of the, the hype cycle in general.

Speaker B: Yeah.

Speaker A: So honestly, AI has been around for a while. It hasn't been kind of, uh, public or marketed like I would say it's been around even like back in early 2000s, like it's just, it's always been there. Kind of like the basic infrastructure. It just never really woke up anyone until OpenAI came in and completely woke up the whole space. All these companies. As soon as OpenAI came out, Google was on top of kind of releasing their Gemini. Right. And I think that that really started after Covid, like a year or two. Right after Covid. Right. I think we're still like in terms of application and use cases, we're still relatively early on. The tech building has, I think is further down. But actually applying it to real world use cases and getting roi, we're still, I would say maybe three or four years behind that initial kind of infrastructure setup just because we have the tech there. But the result of getting what we want out of these tools is still not kind of at our expectations, I would say. And I've seen this like multiple times, like of course, hallucinations from GPT and Gemini and all the others and those. That's just kind of like the basic chat GPT thing. Right. But now when you start implementing into actual like robots, like uh, a Tesla car or an actual like robotic robot that's going to, you know, come into your house and clean things for you and do all that stuff, the room for error should be very, very small. Right. And so I don't think we're, we're there yet. And so all these startups are popping up to kind of solve these different challenges that are, that are coming up from kind of that gap that we're, we're talking about. But eventually I think it's going to consolidate into a couple of players, you know, in the next boom, for maybe in the next five or 10 years. So we'll see something I think similar to kind of what we saw with like the dot com boom back in the day.

Speaker B: Yeah. And potentially almost what we've seen with the CRM space of how that has kind of consolidated all around these kind of two, two or three big players.

Speaker A: Right.

Speaker B: It's super interesting stuff. So, okay, we've, we've discussed system of record. So the kind of takeaway there is. It sounds like CRM is evolving away from what it has been no longer as a system of record on its own enough it has to develop and generate insight rather than just being a database.

Speaker A: Exactly.

Speaker B: It feels at the moment like we're also in this strange space of vendors, buyers wanting to consolidate. You know, we hear this a lot of like, ah, tool fatigue. You know, we've been through five years of buying all of these different endpoint systems and we want to consolidate, want to consolidate. But then when I'm actually speaking to customers, they're doing the complete opposite. And they're kind of, okay, maybe they've cut a few legacy tools, but they're running AI initiatives and they're spending money on an AI tool to automate procurement and an AI tool to automate account research. And there's tons of use cases up in the air at the moment. How do you think we're recording this in December 2025? How are you encouraging your customers to think about the landscape and all of this choice and all these new toys that are available to them?

Speaker A: It's a really good. And typically when we come to our customers or prospects, we're coming from the conversational intelligence lens, right? And so they have. This is no knock to Gong, but to be fair, a lot of companies just use Gong as a very expensive call recording tool. They don't use like the different types of solutions that they have, like Gong Engage and things like that. And a lot of the customers that we run into or prospects we run into are just using Gong for that regard, just a recording tool. And so they don't sink into their CRM, they don't sink into their slack. And so they have all of these disconnected kind of data silos or products, and they're not working together. And so that's why we typically come in and say, hey, if you don't want to go with Spiky, that's fine, but you need to start thinking of consolidating into kind of one either solution or a tech stack that works together as kind of a cohesive brain. And so that way there's no gaps really in anywhere within the sales cycle. Everything is kind of working together to kind of push a deal further to closing or capturing that incremental revenue that you're looking for for the next quarter. And so what Spiky's doing is, like I said, we sit on top of that tech stack, ingest the Gong recordings, ingest the CRM data, uh, ingest your emails, ingest your slack messages to understand, hey, what's the health of this account? Like, what's the next step? What's your best rep doing and how do you take that behavior and apply it to this account? So all of these different types of insights. We're making that actionable through the real time intelligence layer, where at a click of a button someone can just execute that insight and say, hey, I want my reps now from moving forward. Every discovery call, I need them to top of your tech stack. And ingesting that data to have something more actionable is going to really what help you get a lot more ROI from your, your tech, your existing tech stack. Yeah.

Speaker B: Ah, and I remember one of the challenges that maybe that kind of previous generation of companies have had was without access to the core data source, things like forecasting, predictability, you know, they were trying to do just based off historical data of what was in the CRM. But the past can be a good predictor of the future, but it was never quite that accurate and they can never quite crack that reliable forecasting accuracy. So I think having this data source now, given that adoption is increasing, kind of feels like the fuel or the kind of gold of the future for go to market technology. Would you agree?

Speaker A: Yeah, yeah, definitely.

Speaker B: So if you think about 2026 trends, right, buyers are uh, awash with all of these different point solutions that are experimenting. That consolidation you talked about hadn't yet happened. CRM is probably still going to be here to stay by the sounds of things. What are some of the main use cases that you're seeing work? Because we've heard loads of investment and you mentioned it yourself, that's gone into AI experiments that have failed. McKinsey, uh, or MIT recently did a report, I think it was 87% of enterprise AI experiments have failed, have failed. Where are you seeing success? Like what are you seeing as maybe the two or three use cases where wow, you know, AI is really helping solve a use case.

Speaker A: Yeah. So I'll just talk about kind of like what we've been solving and really helping our customers out with. Well, I'll say one role playing, I'll give, you know, a shout out on that. Uh, a lot of large, large amount of enterprise companies, they actually value role playing if they have the time to do it with their teams. And so for an AI, uh, to kind of model a prospect and kind of do role play with the person, I've seen a lot of value created off of that because you can just basically improve yourself, you know, on your, on your own time rather than having to have a manager pull time from his calendar, from his kind of, you know, calendar or a director, whoever. So that's, that's helped a lot. I've seen that one.

Speaker B: Willie, what have you what have you seen the kind of uh, usage or engagement beyond the first couple of interactions on that? Because I completely agree, like in theory it sounds great. The business case is super clear. You're basically replacing all of this human time for scalable AI roleplay time. But I'm then also thinking about the reality of uh, I think about role plays in my own sales career. You know, they're important, but you kind of hate doing them. How likely is it that we're going to get salespeople to come back off their own accord and run role plays with a, than an avatar or an AI?

Speaker A: Yeah, so that's, that's the thing, like role playing is great in kind of theory, but like once you start to do it, a lot of reps, like they kind of dread it and they may like practice versus reality is very different sometimes. Right. You can get a complete wild card as a prospect and won't be, you know, anywhere near kind of the parameters and boundaries you set up with an AI role player. Right. And so that's kind of like where we took that differentiated approach. And so what Spiky's doing is we're coming with the approach of real time insights. So imagine a world where you're on a call with a prospect and you have basically a side panel where it tells you what to say as soon as a prospect is giving answers. So kind of what we've built here is we have a three kind of tab approach. So one is kind of like communications tabs where it tells you, hey, here's how the call is going. So far you've talked about pricing. The prospect really hasn't given much pushback there. You're doing good. Hey, you haven't set up that next meeting. You know, let's start talking about that a little bit. Or even your, your um, your methodology, if you're running like a Bant or a Medpic or whatever methodology you're running, did you adhere to these? Are uh, you close to finishing that out by the end of the call. And then our latest feature that we just came out with, which is um, our whisper is basically listening to everything that the prospect is saying and is giving you the most optimal answer to respond back to. So if a prospect for example, says, yeah, we're already going with competitor X or we're evaluating competitor X, how do you guys differentiate in that regard? It will basically pull from your data sources that you have within your company and give you the best answer to approach that. And so that's a lot more effective because you're changing behavior on the spot rather than having to go back and practice and then adhere to it in an actual call without any crutches. So think of this as kind of like riding a bike. You have your training wheels and then once you become a killer rep, you don't need those training wheels anymore. And so that's kind of what we're in the game of.

Speaker B: Yeah, I mean it sounds incredible. Technology will, it sounds like you're essentially building an AI rep. Yeah.

Speaker A: Eventually it's going to get there, I think.

Speaker B: How far away do you think we are from that?

Speaker A: I think that an AI rep will only come into kind of maybe the discovery stage after that. A human needs to be involved because these relationships, especially within the SaaS industry, run deep. And you're never going to be dealing with an AI and an AI is going to be selling to throughout the whole sales cycle. Right. You need someone involved, you need a person person to take you out to dinner to, you know, smooth a little bit, things like that. So I honestly think always a human element is going to be involved in the sales cycle. But potentially maybe review. Removing stage one and two is where an AI, full AI can come in.

Speaker B: Do you think that's true for all deal sizes? You know, if you got a smaller, uh, transactional cell? I mean, I know I've bought uh, software where I kind of wish I didn't have to, uh, speed to the breath.

Speaker A: Yeah, yeah. Uh, no, I think the larger the deal is. Yeah. The harder it'll be to have fully AI on it. But of course, especially the B2C side of things, that could probably be easier. Instead of talking to like a rep through a chatbot, it'll be an AI. I've seen also others that are coming out with products that basically you talk to an AI person on like a video tool and they kind of like demo you the product. So that's like been one stage that I've seen where it's like you're getting demoed by an AI person. And so yeah, I think the smaller deals, it'll be a lot easier to be able to do that because it's just something that's quick and it's a small deal that needs to just get through rather than a, um, multimillion deer that needs multiple, you know, multi threading and multiple decision makers on it.

Speaker B: Yeah, makes complete sense. So the humans are going to end up being kind of concentrated on the enterprise deals. We're going to have your spiky AI rep handle the rest for us. AI Roleplay you mentioned. That's one case. Any others that you're seeing is working

Speaker A: really well from the conversational side of things. Those would be probably like the two or three that I've seen of course, like on the kind of prospecting side of things, enrichment and stuff like that. I've seen a lot of AI uh working in that space but yeah, more tapped into kind of like the conversational side of things. And those are be the two or three use cases that I see the most of.

Speaker B: It's such a fascinating time in the space. I mean it feels like this year has really been a kind of explosion of opportunity and obviously buyers are also really excited about, about the potential heading into 2026. Like what are your thoughts on um, where you know, how are buyers going to be approaching their options when it comes to technology? You think it's basically going to be stick with maybe two of the three, two or three of the kind of long standing trusted brands and then look to kind of work with AI natives for, for the rest.

Speaker A: I think it's going to be a mix to be honest. Like we've been disrupting the space quite a bit and like again we've sat on top of gong like we work pretty well with them. So the incumbents can still be there while the AI uh, kind of natives come in and carve out a small portion of the budget from the tech stack there. And then it's really up to the AI natives to see how much market they want to take once they're kind of of, you know, nudge their way in a little bit. And it's all about kind of like how fast you innovate because the incumbents, they'll catch up quickly. They'll, they'll catch word of what things are being worked on and what new innovative solutions are coming out and they'll quickly come and spin off a solution that can do that. I think it's in the next year it's all about kind of how you find yourself kind of carving out a small portion of that budget, working with the incumbents and then once that happens, quickly iterating on how you can basically either replace the incumbent or even work side by side even better with them, um, hopefully for acquisition or some other exit.

Speaker B: Yeah, and we are literally seeing that happen in real time.

Speaker A: Right.

Speaker B: You spoke about the kind of AI role play, but I've seen other much bigger, uh, players now kind of talking or even releasing kind of early versions of that. It feels like the kind of bigger guys are kind of like Trying to keep, trying to keep up. In some ways this is awesome. I guess just to kind of wrap up, this has been an amazing conversation thinking about where things are headed. I mean, if you're open to sharing, I love to hear what's kind of one thing that you're most excited about heading into 2026? The future of the go to market tech stack. When it comes to what you're building at Spiky, what's one bet or opportunity that you're most excited by?

Speaker A: I would say just real time intelligence. Just like we couldn't imagine selling without a CRM M today I want to think about how do we get that kind of same notion or thinking around the notion of real time intelligence and how, huh, we can't sell without that Today I've tried it firsthand with our product and sometimes it feels like it's an itch that you can't scratch if it's not there. Like I. It really helps a lot during the calls and so I want that same feeling kind of being resonated with a lot of our prospects or customers that we're talking with, hopefully in 2020 March.

Speaker B: That's awesome. Um, hey, look, really looking forward to seeing how that evolves and uh, the AI reps. It feels like us humans are just going to be the kind of mouthpiece. We're going to have the AI doing, uh, all the thinking for us.

Speaker A: Yeah, right. Yeah, I think it's um, it's important not to do that though too because there's one company that I was talking to their, their founder and yeah, they're an AI, uh, company, of course. And he actually said that there's no on Fridays, it's no AI Fridays where you can't use ChatGPT, can't use anything. Just use your free thinking brain form and, and work. And so I think we need to also develop a world where we do that as well, where it's just like we don't completely rely on AI and just become kind of like a machine and just, you know, still have our human elements to us.

Speaker B: Yeah. Wow. I wonder how much productivity drops on those Fridays.

Speaker A: Yeah, I know.

Speaker B: Woody. This has been another amazing episode for folks listening in to this who want reach out, continue the conversation around go to market tech stack. Assume LinkedIn's a good place for them to do that.

Speaker A: Yeah, yeah, absolutely.

Speaker B: Awesome. Willie, thanks for coming on the show, man. Looking forward to catching uh up in SF very soon.

Speaker A: Yeah, absolutely. Thanks for having me, Matt.