053 - Rethinking Customer Data: From CRM Chaos to Customer Outcome Maps w/ Tim Gosnell, CEO, CommonThread

Tim Gosnell: What I do know is this is bigger than the E comm change. I know that, like, there's no question in my mind that this piece is fundamentally exponentially larger. M. It's so dramatically different that I don't know that we will ever have a clear idea for at least for 100 years what it's doing for us. And at that point, I don't even know if the revolution will be over.

Chris Fanchi: That spark of innovation. It's just waiting for the perfect combination of tools and talent to bring it to life. Welcome to Pitch Build Scale. I'm your host, Chris Fanchi, a recovering startup founder, veteran SaaS marketer turned dev tech enthusiast and growth specialist. Each episode we connect with the builders, the visionaries, and the problem solvers who are redefining what's possible, possible with today's technology and pioneering the websites, apps and software products of tomorrow. These are the conversations that transform how we build, how we sell, and how we scale technology businesses in a world where the tech evolves by the minute. Ready to stay ahead of the curve. This is Pitch Build Scale. Hello and welcome to today's episode. We have a very exciting guest. For those of you just joining the podcast, we are Big North Marketing and specialize in helping DevShop and SaaS companies scale their revenue. So if you want to learn how to take your SaaS or dev business to the next level, make sure to stick around. And if you're interested in a free growth consultation, just drop us the word growth in the DMs. We're really excited to have today's guest, Tim Gosnell, founder of Common Thread AI. He's a Rev Ops expert, a data whisperer, and a straight shooter when it comes to fixing CRM chaos and turning customer data into revenue outcomes. Tim, welcome to the show.

Tim Gosnell: Thanks for having me.

Chris Fanchi: So I like to start with kind of the. The basics and. And your background. What first got you into technology and software? Going all the way back to. To the beginning.

Tim Gosnell: Oh, my goodness. Uh, you're going to date me here.

Chris Fanchi: Um.

Tim Gosnell: Um. So first personal computer I ever saw was in 1980, and that was when one of my friend's dads brought it home. It was an Atari and it was Atari computer, not an Atari console. Right, Right. I hadn't even seen an Atari console at that point. And they had this game where you ran around a dungeon with a sword.

Chris Fanchi: Right.

Tim Gosnell: It was totally rudimentary, you know, pixelated graphics, that sort of thing. And I was fascinated by it. Just, I mean, no, I'd never seen anything like it. And literally no one had anything like that. I can't describe the number of hours I wanted to play on that thing, but it was at a friend's house, so it wasn't really in the cards for me. Then it kind of goes into. I, um, had another friend who wound another Atari computer, a much more advanced one a number of years later. And he had programming books. So we sat down and there was actually a game in the book. And we worked on copying that game in and saving it and running it. It never worked. Right. It is what it is. I didn't understand anything about what I was doing. But we'll call that the very first introduction to what programming and software truly as what computers were. Still didn't understand what you could do with them at that point. And then that just grew. You know, I became part of the BBS, uh, World some point. Most people probably don't even know what that is. That's the pre Internet Internet, right. Where we would call into another person's computer and we could leave messages for people and they could come get them asynchronously. Right. So kind of fun. And you could download and upload things and all that. So that was interesting. And then after that, you know, Internet came along, Right. I got back from a year, uh, abroad in Germany and my brother had literally printed off the entire HTML manual. Uh, that there was an HTML manual to print off was kind of tell, right. Um, so we sat down and we ended up building a website for the Colorado Avalanche. Before the Colorado Avalanche had a website. Say it like that. Right? So we did that. We managed that and updated it for, I don't know, like a year. And it just got to the place where it was like annoying. So I got, we did away with it. At any rate, at some point I went looking for a job and it was during the dot com boom and they were like, you know how to build web technology? I can get you a job doing that. That's kind of where the whole ball of whack got started professionally. But I'd been in the computer space for a really long time before that and written tons of stuff.

Chris Fanchi: So how does your background then take us to common thread, walk us through your care, career progression to get there?

Tim Gosnell: Well, I could go through all that, but it's rather boring. It's uh, a lot of software and development and all of that. But let's go with how do you get to. You know, I answered this question yesterday, by the way. How do you get to interested in customer Outcomes and AI from that humble beginning, if you want to call it humble, and that's really this. I've always wanted to start a company. I always. That was always part of my thing. I always thought that that would be a challenge that I wanted to accomplish. I never wanted to make that a space of let's just do it to do it. I'm not into just making money. I actually like providing value. And I made my software engineering career on bleeding edge technology. So I was looking at AI, uh, I was looking at data, all these things kind of before everything. So, like, uh, the Web two thing happened. I was in it before it was even called Web2. Right. And Web3, blockchain and AI kind of emerged as, uh, your options for the new emerging technology. Um, I decided I wasn't going to delve into blockchain in no small part because I don't really love encryption. It's not really my passion. Right. But algorithmic processing of data is more. MySpace. I've done stuff where I've captured all sorts of data and looking at the patterns and that always fascinating. And then there's this first startup we did. Well, first startup we did in this AI space, I should say it like that is was a company that processed Personas to help you sell better. Right. So we'd look at who you were selling to and we would crunch that down. Using AI to get you a solid understanding of which Personas work for you. I still own that software. It's still baked in the cake with Common Thread. That company was taken out by bad customer relationships. M. And bad customer relationships being we didn't know what we were doing. Right. I was a software engineer. I was more focused on the engineering, less about the customer outcomes, less about how we maintain that, less about. I'm always been one of those people, like, if I'm buying something from you, I just kind of. I keep buying it from you until it's not good. And then I move on. And I don't tend to get upset about what I've paid you. That's just me. And, you know, humans have this lovely thing we do where we assume everybody is like us.

Chris Fanchi: Yeah.

Tim Gosnell: Regardless of whether it's true or not. Right. Um, so there's that. And then we ended up making somebody unhappy and they wiped out pretty much all of our cash flow one month, which meant we couldn't pay our bills.

Chris Fanchi: Wow.

Tim Gosnell: And then I said, hmm, this looks like a problem. Let's do something about this. How many other companies have this problem? And how many other Companies have varieties of this problem that they're not actually addressing and they're just assuming it's working or if we want to take it to where we talk about now, they know it's a problem and they don't know what to do about it. And so they sit in the latent pain space, which is, yeah, we know it's a problem, don't have any idea what to do about it. So we're going to ignore it and we're just going to, uh, address it in other ways. Let's increase sales to cover our revenue losses, right. That sort of thing. So we just. I just not in that space. I don't love that. I'm also not a. I like to say this is a linear process of thinking, right? Which is I'm losing customers, so I need more, right? Not, uh, I'm losing customers. Why are we losing customers? What can I do about that? And how do I have to invest my time, my energy, my money to make that a better situation? And let's do that intelligently. Let's not just start throwing time and energy at it. Let's look at where we get the most return. So that's where common thread comes from is this space of let's manage that.

Chris Fanchi: So how does it go about doing that? Can you break that down a little more?

Tim Gosnell: Well, so the first thing that we look at is your data, your customer data, right? And most companies don't have a plan around this. We know that because we've had lots of these conversations, lots and lots of these conversations. What happens is people are the data pipeline. And what I mean by that is somebody tends to click export over here, bring it into Excel or Google Sheets or whatever. Maybe they do some work there, maybe they don't. Maybe they're only looking at it, right? And that moment, they're acting as a scorer, they're looking at it to see does it meet whatever expectations I have, right. And then if it does, they say, oh, well, I'm going to import it over here. This could be as easy as exporting a lead list from one place, taking a look at it, make sure it's what you want, and importing it into your cr. Mhm. And eventually that person gets tired of doing that work and they buy something like Zapier. Really, really normal use case. Really, really. I love Zapier. I love that it moves stuff around for you. I love that it works really well. The thing is, when we start building a process around, this piece comes from point A through Zapier. To point B. Zapier is not providing any additional processing there at the moment. They're thinking about it. You can tell that because they just introduced tables. They are absolutely thinking about it. And that piece is super useful still. It's transferring from point A to point B, which is to say we have a data pipeline. And a pipeline that goes from point A to point B is basically water going from your faucet into the drain. Right? Right. What does the transformation look like? If we want dishwater, we need to add SOAP to it. Right. So we need that plug. We need to have a place where it can sit and think about it. Right. So what do we want to do with that? So in this space for us, we talk about data hygiene and we talk about intentionality with what you're doing. So if you are getting data from your web forms and it's a username, uh, maybe it's a name, maybe it's not, and an email because you've gated content and somebody is giving you stuff, or some people are spoofing it. Regardless of what your validation is, I have a way around it. I can easily, uh, show you ways around all of these things. So that's one of those people are like, that's good. I've got all this validation. I'm like, yeah, yeah. Anyway, point being, we are looking at what does a transformation look like? And so now we're talking about real software engineering stuff, which we tend to call ETL or ELT in our space. Right. Extract, load, transform, extract, transform, load. Whichever way you want to talk about this. Um, um, it's kind of moving in one direction or another. People have opinions. I don't really care. I'm agnostic. So long as the end results get us where we want to go. Call it what you want to call it. Anyway, point being, we are looking at data pipeline and we're talking about data, customer data. And you see this in RevOps teams, when they get advanced, they start talking about putting data in a data warehouse, not in the CRM. And that is what I would refer to as, uh, the kind of the evolution of point A to point B, which is you now have a staging area and a actual formalized pipeline. Right. So that's where we're headed there. That's what that's. That's kind of the first place we look. Do you have your mind on this? Do you have a strategy around it? Do you know how you're executing that strategy? Do you know how you're grading your result? What are you looking at that's number one. Once you are there, you can actually understand how good your data is. Right. You can start looking at do we have the data to do the things we want to do. So when we're talking about customer outcomes and reducing churn, that's the one I like to pick on these days. But we talk about customer lifetime value, average contract value, profitability growth, all those things are outcomes that companies are interested in.

Chris Fanchi: Right.

Tim Gosnell: We can say we're not there with just getting customer data in place. We need to understand this on a deeper level. And I offer you Customer journeys. And how do we get to customer journeys? Well, we need to understand the events on a timeline. Uh, events on a timeline are touch points. Right. Touch uh, points are comprised of people, messages and delivery mechanisms. So in this case, Riverside is our delivery mechanism. You and I are the people and what we're talking about is our messaging. And there might be more than one message in this conversation. Right. That's pretty typical, at least in a conversation. In a marketing post, it's usually one message. In dms, it's usually one message. So we can like break this down. Right. And we can use LLMs to do that stuff if we want to do that. So Chat GPT is really good at taking a transcript and saying what are all the main points? And saying these are the messages. Right. And then what is the actual example? So we can use that for this space if we want. Right. And we can assemble touch points based upon the people that are involved, the messages and the delivery mechanism, A channel, media, whichever way you want to phrase that. So now we have touch points. We have touch points on a timeline because we can look at all of them in order and then we can look at outcomes. Right. Because we now have a timeline we can look at. Where are we at? What's the relationship look like? Well, that's fun. And actually if we're doing this well, and we have this agentic AI set up to capture all of this data, we can actually do this for every customer in our customer base all the time, in real time, right?

Chris Fanchi: Yeah.

Tim Gosnell: So most people do customer journey work when things go wrong.

Chris Fanchi: Mhm.

Tim Gosnell: That's just the thing. I've been there. Most people have been there. If you've run a company or you've been in the triage or customer support place, you've been there, right?

Chris Fanchi: Yeah.

Tim Gosnell: And how long does that take? Three weeks, a month? Two months sometimes. Right. Like that's everybody getting into, let's get all the emails, let's run through the Whole process of collecting everything that ever happened with this person. Well, this is not an ROI generating activity, which is why people tend to put it only in the triage space. But if you can get it all the time, why wouldn't you? And when you do that, we can start looking at what we refer to as the tier beyond the customer journeys, which I call the customer outcome map. Uh, which is when we take all of your customer journeys and we put them on a map and we map them against outcomes. So if we're talking about churn, we're talking about retention and churn, those could be positive and negative and mapping which touch points lead to which places and what are the variables in there? You notice I just talked about a lot of variables. Lots of people, lots of messages, lots of channels, all of that going on. Companies have many, many teammates who interact with customers and they deliver many varieties of messages a lot of times based upon what customers are asking for. And we are not particularly good at understanding that these things are happening as humans in other humans space. So this thing that I'm talking about actually gives you deep understanding, which is a translation of the word insights. I use deep understanding these days because everybody talks about insights, but most people don't actually know what it means. Right. They're like, I want insights. And I'm like, uh, a deep understanding is what we're looking for.

Chris Fanchi: Yeah, yeah, that's, that's much more clear.

Tim Gosnell: Yeah. And so that's what I'm talking about. The customer outcome map allows us to actually say what percentage in the profitability are we looking for? What types of growth are we looking for? What types of customer lifetime value are we looking for? All of these questions become things we can start to deeply understand.

Chris Fanchi: Mhm. Yeah, yeah, I can see that having value in a lot of different aspects of a business.

Tim Gosnell: Yeah, yeah. And that is once this became clear that we could make this happen, I knew I had to do it. Right. It's not because, oh look, it can make me a gajillion dollars. That's what a lot of, I know a lot of people in my MBA courses were like that how do I make money? It's not that this is so compelling in my opinion, because it's about how we manage our relationships. How do we create positive impacts in the world as opposed to negative ones.

Chris Fanchi: Mhm.

Tim Gosnell: Right. So that's really what I'm interested in. That's why I'm so fascinated by this.

Chris Fanchi: Yeah. It's not just about being able to craft the perfect marketing message and know the exact audience you're talking to. But it's also deeply understanding your clients and your current customer base and seeing where their needs are and being able to anticipate that. It can help with product management, can help with a lot of different aspects of the business.

Tim Gosnell: And, I mean, we don't talk about this much, but what if you met your customer face to face for the first time and they gave you a hug? Right? Like, in that. Like, that kind of gives me chills just thinking about it. But these are the spaces that we connect as humans. Right. And how do we do that? By actually getting intentional with what we're creating.

Chris Fanchi: Yeah, yeah, that's.

Tim Gosnell: And to be able to provide a piece of AI that can push the ball down that road, I have to do it. Have to do it.

Chris Fanchi: Yeah. It's going to make your. Your support team so much better. It's. You're going to build that customer loyalty and you're going to have fans out there talking about your. Your product. Right.

Tim Gosnell: And that was, you know, it was an interesting thing for us because we understood that evangelism and champions and all those things can come out of this really, really easily. M. Nobody wants to buy that. Ah. So for us, then, this is a really normal thing in founders and. Right. We have this. We want to change the world in a positive way. We want to talk about what we can do for you. Right. Except that's not your situation. You're not in a situation of I want fans. Right. You're in a situation of my marketing's not working. Right. Or my customers are churning. Right. Or I want. The conversion rates for my sales team are not where I'd like them to be. And I have a mandate from management to make that better. Right. Those are the normal places that we go. And so we've had to take all of that positive language and say, uh, yes, we can go there, but let's talk about churn. Let's talk about profitability. Right. Let's talk about the things you, like, worry about.

Chris Fanchi: Yeah, exactly. So what kind of businesses are you seeing adopt your technology?

Tim Gosnell: Well, that's an interesting question. The primary space that we're playing right now are, uh, actually Revops companies, which is to say companies that do consulting for RevOps.

Chris Fanchi: Yeah.

Tim Gosnell: Right. So maybe somebody wants to migrate their customer tracking to HubSpot. That's a Rev Ops company. Right. Those people are like, oh, your stuff makes sense to us. I almost don't even have to sell it. They're like, oh, yeah, that makes sense. I got it you're going to allow us to scale because you do things way faster than we do them. And that's literally, you could say that's a zapier synchry natan make problem. Except those tools are not AI'd, even though they've bolted AI into them. Like as an example, our mapping for fields is done by AI. So when you upload a data set into our system, we look at that and we map it automatically. This is basically the babelfish data. Babelfish to use a uh, old reference.

Chris Fanchi: Right.

Tim Gosnell: And it's something I love.

Chris Fanchi: Right.

Tim Gosnell: Like this I don't have to spend time mapping fields. Fantastic.

Chris Fanchi: So you're seeing these revops companies using it for their clients to engage with their clients, improve their clients outcomes.

Tim Gosnell: Yeah. So they, they use it for that. Another place that we're watching that happen is uh, lead aggregators. Right. And not lead providers. So not lead generators, not lead list provider places, but people who buy those lists to assemble them together for other people and resell them. So yeah, it's an interesting. That's.

Chris Fanchi: Yeah. How does that work exactly? What are they, how are they benefiting from. From.

Tim Gosnell: Well, so uh, they get to do things like understand how well the lead lists they're buying are performing for their customers. Right. So that's a space and they don't have to do that work themselves. So that's a space where our tool actually helps. We're looking at outcomes, we're looking at the different data sources. Because what we're actually doing here, one of the pieces that when we talk about the data pipeline that kind of skipped over here is the gaps in between your tools are really where everything is going off the rails.

Chris Fanchi: Mhm.

Tim Gosnell: Right. So if you're getting a lead list over here from one place and you're getting another one from here and another one from here. So A, B and C. Right. And then it's all going into your CRM. Well, in your CRM, how do you know which ones of these is working? Right. And are you providing good value? Are they providing good value for what you're investing? That sort of thing. And that's just when we look at sales and then if we take it to the next step, which is how did those customers do long term expectations get set first engagement. So we have to do that, we have to understand how that's happening and whether that's working for us or not. So that's, that's part of the equation.

Chris Fanchi: Okay, interesting. So why do you think the, the product hasn't resonated directly with those end user clients just yet.

Tim Gosnell: Probably our messaging. Right. It's, it has a lot to do with. Well, we talk about data. We talk about data. So we are currently running campaigns at uh, revenue operators. Right. That's what we're working on right now to dial that language in. But typically things don't work because you haven't matched your market's desire with your message. Right. And that's what we're working on right now. We know it's there. Like we know it's there. We circled all of this. We went through like literally every permutation on our Personas saying, hmm, Hm. Is it these people? Is it these people? The answer was it's all of them.

Chris Fanchi: Right.

Tim Gosnell: It's not any one flavor here. It was how we were presenting what we were talking about, which was two in our head and two about. So we would do things like let's not talk about data because you do this mind game with yourself. Right. Well they must not understand what we're talking about. So let's translate it to, into something that. And then they don't know what the hell you're talking about.

Chris Fanchi: Yeah.

Tim Gosnell: So let's not do that. Let's just define what we're talking about. So one of the reasons, like I throw it deep understanding in for insights is let's just actually define words. So when I talk about data hygiene, we now talk about, we have nine different points that we talk about in data hygiene. Right. And I draw very, very clear connections from what's going on in your data to why, uh, we call it data hygiene and why we call that bad data or janky data or whatever other words we use. Right. In that space. We want to make sure that these dots are getting connected.

Chris Fanchi: Right.

Tim Gosnell: And you just go with what people understand, go into their space, hit the situation.

Chris Fanchi: Yeah, interesting. So talking a little bit more about AI and the current environment, you know, you've been in technology for 25, 30 years now. You've gone through the highs and the lows, the new thing that's going to change everything. Where do you see AI in that cycle? Do you feel like this is uh, another boom that will eventually slow down and just become the norm. Do you see this as truly transformative?

Tim Gosnell: So yes, this is not an either or. It is more transformative than the E Comm. Change it like the impacts that this is going to have. So let's go back to Amazon. Started in the 90s, right. No one could have predicted what Amazon would become. No one could have predicted at this point, at that point what Amazon would do to the E commerce space. Right. No one could have predicted what Netflix was going to do.

Chris Fanchi: Sure. Yeah.

Tim Gosnell: And I used Netflix back then. Back then they were competing with Blockbuster. Does anybody even remember Blockbusters?

Chris Fanchi: I think as a meme more than anything. Yeah.

Tim Gosnell: Like we were all in that era worried that Blockbuster was going to put everybody else out of business and be the only provider of movies.

Chris Fanchi: Right.

Tim Gosnell: This is not a thing. So my point is like we don't know what the long term impacts of AI are. And I will also throw in here, this is also like asking what's the long term impact of having a brand new digging apparatus? If we replace shovels with something that's totally amazing, what does it do for us? No idea. Can we now dig to the center of the planet? I don't know. Right. Like, uh, we can't answer that question. So I don't try to sooth say that. What I do know is this is bigger than the E Comm change. I know that, like there's no question in my mind that this piece is fundamentally exponentially larger. M. I mean exponentially larger. It's so dramatically different that I don't know that we will ever have a clear idea for at least for a hundred years what it's doing for us. And at that point I don't even know if the revolution will be over. Right. That's how I. And the reason I say this is I'm talking about, when I talk about the customer outcome map, I'm talking about something that's over the horizon. Um, right. Most people can't even understand this idea. Like if you were to say, hey, I'm going to build a customer outcome map, they'd be like, what? I don't get what that means. Right. And then when I explain it to people, they're like, okay, that makes some sense. You can do that. They always start there. Right. And then uh, the next question usually, well, for business leaders is can I tie my expense, my costs into that? A hundred percent? I can tie your costs into that. I can tell you what you're spending where and how it's generating money on the other side. And in marketing, for people who are no good at marketing, who make a lot of money on it, that's scary. Uh, right. So there's that. So it's going to. This piece that we're talking about in that space is going to cauterize bs and that's frightening for people who don't actually provide value. M so Tools. We have a tool that can get us over the horizon. Well, once we're over the horizon, what is over the horizon then? So this is like taking our minds and trying to say, how do we even know what the next tiers are when we can barely see beyond the current ones?

Chris Fanchi: Yeah, yeah.

Tim Gosnell: And, um, why I say this, this piece, and it's about time saving. Let's go there. Like, that's why this is such a critical piece of understanding. When I finally understood that AI is not about building things for you, it's about keep allowing you to get to, uh, the place you've never been able to get to because it was too expensive in time, energy and or money to get there. So now we're past that. What else can we do?

Chris Fanchi: Yeah. And we may not even know yet what we can do because we, we've never been past that point.

Tim Gosnell: Right, exactly. So this is kind of like saying if you look at the evolution of metallurgy. Right. Is this kind of the same process that we're talking about? At first we had to figure out that you could actually melt metal out of ore and then you could cast that into something. And that's just metal casting. And there's plenty of other pieces in metallurgy. So we're not talking about alloys, we're not talking about forging, we're not talking about any of the other pieces that you can do to make this work. Yeah, like that. We've, uh, like over hundreds and thousands of years have developed chunks of technology those was continually the next horizon, next horizon. Once we knew that you could make steel, then what can we do with it? Right. So that's what's, uh, what we're about to see is this. And it's going to come rapidly. And that's the part that's going to scare everyone, because humans hate rapid change because we're not used to it. Right.

Chris Fanchi: How rapid do you think it is? You get very varying opinions. Some people say we're a year away from super intelligence. Some people say, you know, 10 years, could be 20, 50 before things really ramp up. What do you think the timeline looks like?

Tim Gosnell: Well, let me say it like this. My workflow from three years ago looks nothing like my workflow today. Like, nothing like it. And that is thanks to AI. So if that is the first tier completely transforms your work process. Right. Then we're going to do that again. Right. As the AI models improve and as we. And this is the thing that I've noticed with most people who are getting the results out of AI Right now they're actually able to say this is what I'm looking for. And uh, one of the things we struggled with in the beginning was we would say, we would. We did AI Personas. And people would say well what do you do with it? We do AI Personas. That's what you do with it. Right. Like there's, there's no next thing. It's that your Personas are always up to date now you don't have to go. And it's. That was hard for people to wrap their minds around like this. So it's not doing anything. Like it's not gonna make my sales go through the roof. No. It's a time saver. When's the last time you updated your Personas as an example? Right. And they wouldn't wanna answer these questions. Cause they still. They're looking at. I want. We were in this era of sales, technology enablement, tools. Right where they wanted the way they were being sold. Is when you buy my stuff, your sales are gonna go through the roof. Mhm. And nobody believes that anymore. Thank God. Right. Uh, now we wanna know what do you do for me? And if I say to you, well now you don't have to move data around. Let's start with that. And I can actually quantify how much money that saves you and how much time it saves you. Now it starts to be a little bit more like real value.

Chris Fanchi: Yeah.

Tim Gosnell: And so the reason I say all of those things is simply for us to get our minds around. How fast is it coming? It's already here. Next question for me is not. I don't care about the gen General Artificial intelligence like that. That's not a thing. Uh, for me, I don't truly subscribe to the idea that there is one artificial intelligence Score sheet.

Chris Fanchi: M. Yeah.

Tim Gosnell: And let's go with. And the reason I say this, I've said this to my team too because I get have had this conversation many times with them. They're like, oh, open away. AI Released a new thing and they're talking about Gen A. I'm like great. How does it do the task that we want it to do? M. Right. And do we have to spend a lot of time training it to do that? And if I have to spend a lot of time training it to do that, isn't it? Doesn't this start to sound a lot like hiring somebody?

Chris Fanchi: Yeah.

Tim Gosnell: Okay. So if we build a system that's already skilled, that is specialized now, you don't have to train it. This is where I think we're actually going to see a lot of niching in, in the AI space. Right. We're going to start to see. It's not just about the LLM and ChatGPT or Claude or Notebook or any of those things. They're all fantastic, by the way. I love them all. And you know, I've had Windsurf, which is Claude, under the hood. Although you can switch them all. Build lots of code for us. M. We've been down that road. I get it. I uh, totally am all about it. The thing is, and so that code generator is not perfect. In fact, if you're not an expert in how to build a platform and you're asking it to help you build a platform, it will screw it up. Yeah, right. Pretty hardcore.

Chris Fanchi: Definitely. Yes.

Tim Gosnell: So what are we really talking about? Do we. Are we looking for general artificial intelligence or what? Or am I looking for simply some way to get my work done more quickly? I m don't. I mean, we're going to build the general artificial intelligence. The androids are coming. They're on their way. We already know that. Like, yeah, Boston Dynamics has had robots that have been there for a long time. You combine that with AI, like this is here. We're now talking about the Android dot of aliens. Right? That's coming.

Chris Fanchi: Yeah, Yeah.

Tim Gosnell: I don't know when that's coming. I don't know that it's coming in my lifetime. Right. Like they might be a little too real in terms of looking like a human, but I think that that's like nigh.

Chris Fanchi: Yeah.

Tim Gosnell: So how long, what does it take to skin a bot? To look like a human and provided enough context. And this is what you're seeing right now. I don't know if you've noticed this. Context engineering.

Chris Fanchi: Mhm.

Tim Gosnell: Right. It's shifting from prompt engineering to conduct engineering. And why? Because as I'll use Windsurf as an example, when you don't tell Windsurf what you're doing, it will totally denude everything that was working to accomplish just the task you told it to get done now.

Chris Fanchi: Right, yeah.

Tim Gosnell: And you see that in ChatGPT too.

Chris Fanchi: Yeah, absolutely.

Tim Gosnell: Yeah. All of them, yeah. So that's what I'm getting at. So where does that context come? Once the context chains are there, we'll start to see this in this other stuff start to happen in real spades. And for me, this is like I've always said this. We have AI tools that are running constantly, that can rebuild your Personas or do the message analysis against, you know, your Channels against your Personas. You always need the multiple point of points of view, by the way. You need things to work against. That's just part of it. Right. One of the reasons Chat GPT fails when you say, hey, build me a marketing message is because it doesn't have anything to work against. Right. You have to provide that thing to work on.

Chris Fanchi: Yeah.

Tim Gosnell: Right. And so that piece is there. And what we're looking at is when you're in that space, that's the context.

Chris Fanchi: Right.

Tim Gosnell: And so the specialization, this thing that I'm talking about is all that. So building an Android to walk around and act like a human, be able to open doors, that's its context in the AI space.

Chris Fanchi: Yeah, Right, Right.

Tim Gosnell: So that will be the next wave. And then you build all these pieces and you put them together. This is where agentic AI comes in.

Chris Fanchi: Right.

Tim Gosnell: And what is agentic AI? From my point of view, it's AI operating system, which is basically what your brain is. Only we can hold context. So will it compete with us eventually? A hundred percent. Is it going to kill us? No, it won't care. It literally won't care. Just like. Just like if there was another group of humans or aliens living on some other planet and on the other side of the galaxy doing their thing, it's going to be doing its thing. Won't care. It doesn't have. You know, that's the thing. We like to assign our own fears to things. Projection is a human trait, not a computer trait. Yeah, yeah, yeah, yeah.

Chris Fanchi: I, I think that's. That's very interesting. Interesting perspective.

Tim Gosnell: I mean, I'm sure somebody disagrees with it. Don't get me wrong.

Chris Fanchi: Oh, yeah, you'll hear all over the place. I think yours is very grounded. I think it's wise take.

Tim Gosnell: Well, I mean, when you really look at it and we don't operate from a place of, uh, it's going to kill me or it's going to take all of my stuff. My survival is at risk. Right. When we don't operate from that space and we recognize that it's going to happen no matter what happens, you can't regulate this away. Like I said, it's already here. Right. Like you can't stop an engineer, a software engineer, from downloading a model and working on it. Can't regulate that. If you want to regulate that, you might as well just give up on technology in the United States. Just throw it away. We're done. We're walking away. We're going to go back to not having computers because that's really what we're talking about.

Chris Fanchi: Yeah.

Tim Gosnell: So let's not do that. Right. At least I make my living on that. I'm not particularly interested in making that happen. I know lots of other people when we're on a computer, we're talking like, this is really not practical, a practical set of reaction. So how do we approach this from that grounded space? Yeah, let's go there and let's approach it. Like I said, I had a conversation with somebody the other day. They were like, is AI, uh, going to kill us? I'm like, when has math ever killed you? And they're like, what? I'm like, what do you think AI is?

Chris Fanchi: Yeah. Just have to hope that cooler heads prevail. But that doesn't seem to be the, the way our world is running right now.

Tim Gosnell: You never know. I think there are plenty of people out there who are. And if you can't actually get to the space that I'm talking from, well, I can hold an example. Right. Best I can do. I can't control, uh, everybody else. I'm not interested in that work. Not, not fun for me, not where I want to be. But I can offer these thoughts and this space and say, this is the way to approach it. And, you know, the spiritual community freaks out about AI too. Except when we think about everything comes from divinity.

Chris Fanchi: Hmm.

Tim Gosnell: You know? Yeah, yeah. We can attach that there too, if we want.

Chris Fanchi: Yeah, no, that's a, that's a whole nother conversation. Right.

Tim Gosnell: I know, I know.

Chris Fanchi: Trust me.

Tim Gosnell: I've had it many times. And a Buddhist, uh, giving me a Lyft ride kind, uh, of attacked me for sitting in both spaces because I'm very spiritual. And he was like, I don't understand you. And I'm like, I'm not asking you to understand me. I'm just me. Yeah, yeah, right. Uh, do what I can. Provide some perspective. If I can offer my point of view, offer the value I deliver, that's the way I approach this.

Chris Fanchi: That's great. I appreciate you sharing that. That's. That's really interesting. I love to get different perspectives, and yours is definitely, uh, an interesting one to add to the conversation.

Tim Gosnell: I mean, I get the same feedback a lot. So it's this space that I understand that a lot of people can't quite put their mind on yet. Yeah, but it's coming whether you want it to or not. Just like Amazon came, whether we really thought it was going to or not.

Chris Fanchi: Yeah. Yeah. I'm sure a lot of the big box stores wished Amazon never came and they've had to adapt.

Tim Gosnell: Well, I mean, like, I just saw Macy's shut down, uh, one of their big stores near where, one of the places that I spent time. Right. And I guarantee you they never thought that was going to happen.

Chris Fanchi: Yeah.

Tim Gosnell: And they put that in. They were like, we did our research, we did everything. We always do. It always worked.

Chris Fanchi: Yeah. Yeah, that's right.

Tim Gosnell: Moving on. That's how it works. We've got to evolve. That's what we do.

Chris Fanchi: Yeah, absolutely. Well, let's, let's close out with this. What are you angling for with Common Thread over the next year? What is your, your goal, your, your big win that you hope to achieve in the next year?

Tim Gosnell: So for me, it's watching my team, like, really start to see the fruits of their labor.

Chris Fanchi: Right.

Tim Gosnell: That's me, that's my personal, like, objective here. They've put so much time and energy into it that seeing them be rewarded for it is truly remarkable. And that's happening. It's just, I want it to get to that level where they're like, holy crap. Like, that's, that's where I want, I want their minds there. Right. That's the most rewarding for me. So from a leadership perspective, in Common Thread, what I'm really looking for is to walk through. We have a, um, what we call a data pyramid. It's our implementation of what a customer data framework looks like. Right. And getting people to wrap their minds around that and starting to get them to understand that you can accomplish these things. Right. Regardless of whether they're doing it with us or not. I don't really care. It's about, let's take it to the next level. If, if I can see our message start to resonate in a space of, you can have the customer outcome maps, you can understand how the ROI is happening and you can understand where the, uh, where things are coming off the tracks, where the wheels are coming off the tracks. And you know, you can do that if you just. Let's stop talking about data driven. Let's stop talking about data informed. Let's start talking about, let's build a data, customer data strategy, a foundation, that framework, and actually start looking at this as, uh, the way we do business, not something we're bolting on. That's what I'm really after. That's a win for me. Will that be next year? I don't know. It seems like the big ask for one year, but we ask for big things for Christmas all the time, so that would be a big Christmas in July. If this, if we had this conversation next year and it was like that.

Chris Fanchi: Yeah, absolutely. Well, that's. Always want to reach for that. The North Star.

Tim Gosnell: Whether you're Bhags are a thing for a reason. Right? Like, um, I always talk about that with my team. Let's set a bhag. I know we don't know how to get there. That's the point. That doesn't mean I don't want smart goals, too. See, this is the thing. People think they're in between. Like, you have to pick one or the other. I'm not like that. Like, set the really, really big ones and then let's bring it in.

Chris Fanchi: Yeah, okay, that makes sense.

Tim Gosnell: And not money. It's not about money. It's about. Let's shift. Let's put a reed in the river for the current of the way people think about this. Right. And if that, uh, reed grows into 6 and 8 and 15 and 20, pretty soon we have a new riverbank.

Chris Fanchi: Yeah, exactly. Right. Well, Tim, this has been a, uh, fantastic conversation. Thank you so much for coming on the show. Where can our listeners find you? Learn more about your work and common thread.

Tim Gosnell: Well, you know, hit me up on LinkedIn. That's always a thing. I'm. Some people call me Tim. That's my vanity, uh, handle, which thrills me to no end, by the way. Um, you can always hit me there. You can always find us. Ah. @commonthread.AI happy to have a conversation. Literally, for me, it's about the conversations around this space, and I give that stuff away all the time. Like, let's talk about the frameworks, let's talk about the strategy, let's talk about my tiers, my pyramid, or any of that stuff. Can have it.

Chris Fanchi: Fantastic. Thanks to everyone for tuning in and listening. Be sure to subscribe, leave us a review, help us be found, and as always, keep pitching, keep building, and keep scaling.

Tim Gosnell: Cheers, Chris.

Chris Fanchi: Thanks for listening to this episode of Pitch Build Scale. Be sure to subscribe on your favorite podcast provider so you never miss an episode, and we'll see you next time.