Value-First Platform: AI Data Readiness - Jan 13, 2026

[00:05] **Introduction** Chris Carolan: Good afternoon and happy Tuesday, LinkedIn friends, Value First Nation. Welcome to uh our first episode of Value First Platform AI Data Readiness in 2026. Uh, here with Trisha. How are you doing?

[00:31] **Catching Up** Trisha Merriam: I'm good. Here we are. Feels, feels like I've been missing you. It's been a few weeks, but I'm glad to see your face. Chris Carolan: Uh, well, I appreciate that. And I feel the same way. And, um, I got together with uh with Nico once over the holiday. And it's so interesting. Like, we kind of build, build these relationships. I'm always a big advocate of like one of the like immeasurable things that content collaboration does is build relationships in ways that um it's hard to like recreate that any other way. And so you build friends and, you know, part of the space that we work in, everybody's digital. But when you tell, like, when I tell Isabelle that I'm, like, it's New Year's Eve, and I'm gonna, I'm gonna hang, I'm gonna, I got a call with Nico today. Uh, that's like, I thought you weren't working. Uh, I'm not, it's not work. Can I just like hang out with my friends? Uh, sorry, my friends are digital, so I can take them anywhere. But, um, yeah, so, I, I do, I'm excited to get back to this content schedule, if for the only reason of, you know, getting to hang out with wonderful people like yourself. Trisha Merriam: Same. I love listening to you and Nico talk about stuff. Chris Carolan: Oh, we appreciate that. And, you know, that's that's why we're here on all the shows that I'm doing right now. Because it's definitely, we're in a moment of like show and tell. Like see, seeing smart individuals, tech-savvy individuals, still find ways to to, um, kind of excuse themselves from thinking that AI can be valuable, that it's ready. Like all these excuses come up in terms of um, you know, why we we haven't like dove in yet and why we haven't tried it for ourselves, why we haven't experienced the value of it. Uh, I was on a client call last week. Um, talking about the possibility of like a new AI role for for this person in the organization who's used to managing systems and like there's things coming up like, like AI Ops and just AI Native. Like, all these questions and, but each organization needs somebody to kind of own like, we, we need to leverage AI. Like, our organization needs to start getting value out of AI. And if we just leave it up to everybody, like it's, it doesn't get done. Similar to how like, you know, you might have HubSpot in your organization, but the marketing person is using it, the salesperson is using it. It's like, and everybody's using it differently. Nobody's actually in charge of the success of HubSpot. And it creates a lot of issues. And this is a person who uh, I've known to like understand the value of AI, use it, use it a lot in terms of his personal kind of work in and in and out of work. But the moment we started talking about it in terms of like production environment, day to day, he was like, oh, I it's, it's hard to imagine. Like that scenario where it's in play, it's implemented, everybody on the team is using it every day, and he said, it's just, I can't see it. It's not there yet. Right? And, and to our point today, we are in the process of creating unified customer view for him and the organization and guess why AI is not ready and you can't leverage it, because your data's not together. Like. So it is very hard to see it and feel it in practice in those moments, but that's where it's like the human hubris comes in and it's like, oh, AI must not be ready. Like no, the capability jump that we need right now is humans. Like and their ability to to understand what needs to be done and then understand how to use AI. So, we're gonna go hard on that. Trisha Merriam: Would you also say it's a willingness to give up some control? Chris Carolan: Certainly. Yep. Um embrace uncertainty. And uh that's I think we mentioned on on Sunday. Uh, spent some time with Nico and George. Like one of the first things I did once I started to really understand like the possibilities is I asked AI, because I like to break down status quos and the way it's always been done in software. And whenever we make decisions like we keep it simple because humans can't handle a level of complexity. Like, I wanted to take a step back and say, well, now AI's here. Like and and one of the conversations specifically was smart, smart part numbering versus dumb part numbering. Right? Um, are you familiar with that, that, that concept? Trisha Merriam: No. Chris Carolan: Right? Trisha Merriam: No. I mean, does that essentially have to do with having a naming convention for part numbers? Chris Carolan: Yeah. So, like, if it's basically a code. Like, um, if this shirt like is, is uh for sale and you can tell by the, by the part number that it's yellow, it's long sleeve, like there's codes. Either there's a Codex that everybody has to understand and read or it just says yellow shirt, like and that's it. So that's smart in that you can read it and understand what it is versus dumb is like, this shirt's 1001, next shirt's 1002. Right? And often you want to default to smart because it, it becomes easier to understand and you can move faster and you don't have to like build a system to understand everything else about it. But once you add complex layers and there's 50 different attributes and that's where you either have a a super long, smart, smart part number or a super like uh, codified, like codex. Like this character means this, this character means this, this character means this, right? And when I was going through that process, I was like, wait a second, all the reasons that we choose dumb part number I'm pretty sure you can handle, right, Claude? And it was like, yep. And that was a conversation about, okay, what are humans good at? What is AI good at? And I think if any that's a great place for anybody to start in terms of trying to figure out like where the value like can be for you. Um, because then you can do things like uh, and the other habit I'm, I'm starting to build right now is like if I, if I get a spreadsheet and it's just like crazy, like bunch of tabs, like, you know there's value in the data, but the work to get the value out is just immediately like, oh, I don't know if I wanna do this right now. Like. And whenever I have that reaction now, I'm trying to say, wait, have I asked AI to do it yet? And it's like every like and now it's done in two minutes. Instead of the situation where it's like, you know the thing would be valuable, but you put it off. And the farther it goes off, the like we just don't get those things done. Um, and that's another thing like if you can kind of hack your way into like whenever you have those moments of man, this thing is gonna be really hard. Like and I think that's the difference between AI Native and and not, right? Like, and I'm trying to work hard in in Q1 of this year. So all the content, I'm sorry, might be repetitive, but like unified customer view, AI Native, like value path, like if we can rally around these principles. That's what I mean by AI Native and not AI first, right? Which the outcome is similar. But I'm trying to avoid like oh, if AI is first, then humans can't be like first. Right? It causes this conversation about, oh, we're gonna be humans first. Well, you need to be prioritizing AI. Like sometimes, right? So that's why I'm distinction between AI first and AI Native. AI Native versus let's say industrial Native. I've, I've gotta do some work with Claude to figure out what's the opposite, but this industrial age mindset where when you see AI, you're asking it to do things like write better emails or this thing that we've always done, help me do that faster, more of it, whatever, versus AI Native is maybe I don't have to send emails anymore. Maybe it can be completely hands off. Maybe I can do this other thing instead. Um and I think that can be a great filter for people too, because there's so much noise. And the reason that we're here and we're gonna be relentless on the content, is there's so much use case information out there right now about how you should be getting value out of AI. But when you don't have your own data house in order or your people aren't trained, like, it can be the exact same business in the exact same position. We did this, got a bunch of value. That doesn't mean I'm anywhere close to being able to do the same thing. Right? And so if you can look at that like and say, is that an AI Native business or not? Are we an AI Native business or not? It like changes the way you even can look at things like HubSpot and the way you can can use it. Um, so uh, I'm fired up in 2026, Trisha. Trisha Merriam: I am too. I am too. Chris Carolan: Uh, would you say AI Native, okay, so one of the things that quite frankly shocked me, was when you built the Value First website using Claude instead of using Claude to tell HubSpot how to build it. Like I was just like, what are you doing? What are you, wait, you can do that? Wait, what are you doing? Why would you do that? Um, so, when I think about like, uh, embracing giving up control and like doing it the way that we've always done it, that's kind of an example of what I'm thinking of is you really have to relook at even how you're, even what systems you're leveraging and how you're leveraging them, uh, because everything is really under question. Chris Carolan: Yeah, and it needs to be, right? Like, in the same moments I'm like, if, oh, it's gonna be hard. Like if you can take a step and take, take a step back, like, if it is hard, should we even be trying to do that thing anymore? I talk to Ryan Ginsberg and Casey a lot of like, I'm I'm making an assumption at this point. Like if there's a problem and you go to reporting or to workflows, it's almost always tech debt. Like that you're building out in the moment. And if you can say like that hard thing like is there an easier way to do that? Often in HubSpot it's using the data model, like a little bit, a little bit differently. Right? And now you're not in this space, building, building, building, which has been interesting to watch, like I mean that's the community of SaaS. Like we build products to try and solve, solve problems, but most the problem that's where it's a human capability layer. Like that's a gap right now. You can't build products to help like to get a team to trust like AI. Um, so that's where I'm like so we talk about AI data readiness. Like what is that? What does that even mean? Right? So trying to find ways to pick goals where it's like, oh, you know what that means? Right? Like unified customer view is an example. So, I would love to walk you through the unified customer view playbook that is currently on the valuefirstteam.com website. So if you wanna go to the website and then share your screen. Trisha Merriam: I was already there. Oh, I'll go to the homepage. That's what I'll do. Chris Carolan: All right. Um, so, if you click on content and if anybody's interested, like Trisha mentioned, um, didn't build this in HubSpot. Like in October I was a ride or die HubSpot websites, websites guy. From November to Holy January. Trisha Merriam: Right. Chris Carolan: November 8th is when Nico unlocked my brain. Um and this platform, I'm referring to it as, like has been being built out since then. Right? And this is very specific wide use case in mind and that I've gotten good at showing up all the time to have long conversations, what I've never been good at is repurposing the content, developing other forms of content to help educate uh, on these, on these concepts. And now with this platform and the reason it was easy to move away from HubSpot is that every idea, now that this platform exists, every idea can just immediately become a digital experience. And once it's there, now it can easily become a video even. Started, there's a way to code videos like into, it's, it's wild. But every, every weakness like I've had in the past of, because I don't like doing on demand video for whatever reason, I can't get myself to do some of that. And that's happening a lot right now. There's a new beta with HubSpot where uh, exactly what I asked for, the video team has provided in terms of recording videos from HubSpot. Right? And the cycle keeps happening where it's like, all right, I'm gonna, I've got a reason to go back into HubSpot and and like give it the old college try. And I went and I recorded, you know, one video. It's okay, it's like early beta, right? And then three days later, Ryan Ginsberg gives me this link to Remotion uh, that is like uses React to code a video into existence. And that solves for my on demand video like thing that I want to be doing, because I also used 11 Labs for to do a voice clone and it's on the money. Um in terms of how good it is. Trisha Merriam: You have to do a plug here. If you're doing voice cloning, you should also be looking at Inworld. Chris Carolan: Okay. I mean I'm I'm sold though. Like for me, I'm sold like. Uh and that's where all of the work I've done, like content wise, I did one series in March of last year, which was the first 11 episodes of Value First. Where it was the only time I've ever scripted the entire series. Right? It was just me talking at the basically keynote style. And because I could immediate, I could give over two hours of of those, of that audio to 11 Labs, it took 15 minutes to to import it and then a couple hours for it to come back and I like I could believe the results. It still like felt really awesome though. Trisha Merriam: Yeah. Chris Carolan: Um so, the ability to recreate and repurpose and try any way possible to start to get these messages through is how I kind of see this this platform. So, on that note, um, we see here the four, the four views, right? That I'm gonna be hammering over the next, you know, few months. Uh, because it is something I found that people who don't really care about HubSpot and how it works, like business leaders often, these are goals and actually measurable goals, not in the same way. We talk about measuring goals. It's a yes or no answer for each of these. It's something that they can get behind because it's very obvious. Um in terms of like what it would mean, like, unified customer view. I don't have to explain those words. Right? An example of the power of that in Sprocketier, uh this came up. And he said, I feel like unified data context is, is yesterday's master data management. Right? Um And my point is like unified data is like I don't have to explain that as much as I might have to explain master data. Um so all of this is like kind of revolving around just using simpler language to to get alignment. Um uh so scroll up if you would. And so that is the hack I'm gonna try and use in terms of to get to AI data readiness, can we just get to unified customer view? And now our, because we have human data readiness now, AI will also like uh be able to leverage your data. So under content, go to playbooks. Trisha Merriam: Oh, this is new. Chris Carolan: Let's click on unified customer view. Trisha Merriam: And so Trisha's never seen this before. No, this is cool. I love a playbook. Chris Carolan: Yep. And again, this is the kind of thing like when I first made this with Claude, it was a markdown doc. And I had no plans of putting it on a website. But then like one of the unlocks was when Casey like built her holiday office checklist and we were just like, okay, I wonder if I could just put that on the site. I was like, yep, and it's interactive and we'll save for you. Working on the the the ability to save it like into the Value First HubSpot so you can save and come back to it later and um. So, before you begin, this playbook will guide you through configuring HubSpot for complete customer visibility. Before we start, let's make sure you have everything you need. Trisha Merriam: You want me to do this now? Chris Carolan: Yeah. Uh, if you wouldn't mind like talking through these things, um if they make sense. Um, I mean, do we need to talk through them? Trisha Merriam: I mean if they're obvious. really self-explanatory to me. Chris Carolan: Right. Good. That's the, that's the idea. Um, I will highlight that often when starting like that first checkbox, right? That you checked?. When people come, like especially to Profoundly or even in several conversations, like you say, we need help with HubSpot, clean up our HubSpot, optimize HubSpot. And then you ask them what HubSpot they have. And they cannot answer the question without going to look, right? It's like, oh we use tickets so we must have service up like. Nope, that's not how that works. Um, so literally trying to like just not take anything for granted. So often like permission structures, uh, are the reason you can and can't get help or the reason like your team isn't using it the way that you expect them to. Um, So handle the basics on on page one here. Ooh. Team member identified. Trisha Merriam: I mean, gotta stop here. identify the person. There's no person identified. Yeah. Oh, that's you consultant. You're supposed to do this. No, I'm not using your unified views daily. Right? Chris Carolan: No. Trisha Merriam: No. That has to be your organizational champion, right? The one, the one who buys into this view, I think is the same person as team member identified. Chris Carolan: Right. And here's where like the nuances depending on how you use HubSpot and who's using HubSpot, every individual using HubSpot needs a unified view when they're trying to do their job, right? So this is where part of what I'm trying to do this month is like validate like how, how this playbook runs. Um But like at least one person who actually new needs a unified view. Right? Because often, Trisha Merriam: Oh. Right? Like So this should be one other person who needs the view. Chris Carolan: Um, no, I think it's it's use. And this is where like an admin can't decide, like it's hard for an admin to decide this on behalf of the sales rep. If you're not like asking them about what they need to see as they reach out to customers as they do follow ups, right? And a couple times it happened last year where it was clear that HubSpot wasn't actually business critical for any process at all. It was just capturing random data from emails. And that's where you get to really quickly with a question like this. Oh, nobody like actually needs it to get their job done. Right? So I think finding these like little yes no, like no, we don't even have anybody who needs this goal that we think is a good idea, like that's problem number one because that like leads to less engagement during any kind of process and HubSpot's not for me. And I've learned to kind of try and be empathetic to, if it, if it, if HubSpot's not required to get their job done, we should not be blaming them for not using HubSpot. Right? Um, no matter how much time it thinks we can save them. Uh, yeah. So, teams aligned, agreement on which customer facing teams need visibility, uh, are they in HubSpot or not? Um this is interesting. They all need visibility. Uh, and this gets to the source of truth conversation. Um like let's say you have a team that's marketing in HubSpot, sales is in Salesforce, customer success is in Gainsight. Everybody likes their system. Trisha Merriam: Product is in Linear. Chris Carolan: Yep. And that's where I'm glad you brought that one, because the, the first three are the customer like facing org. And it would be nice to have them all in HubSpot. Um But if you don't, they all need the unified customer view, which means all three systems need to be tightly integrated and accepting the fact that okay, you work out of this system, that means the unified customer view needs to be here, but it can't possibly be the same view as it is in HubSpot because they're not the same systems. Right? And what a recent story is, like so in those situations like, okay, we need one source of truth. Like first of all that's the wrong path to get on. Like the whole system needs to be considered the source of truth. And then whoever's using it wherever it's their source of truth. But instead, we create like data lakes in like Snowflake, for example. But then since we haven't solved for this alignment, teams will cherry pick the data that they want to go into that place, which defeats the purpose of trying to create like the unified view, because inevitably like there's two reasons we we we leave data out. And the first innocent reason is we don't know the other team needs it. The second one not so innocent, I don't trust the other team to see that data and not do things I don't want them to do. Um, very common between uh marketing and sales and between sales and service. Um so agreement on which teams need visibility. Uh, systems documented. Man, we're not in good shape here, Trisha. Like which systems are currently holding customer data, even informal ones? Trisha Merriam: Gosh, all of them. And probably a few you're not even aware of. Chris Carolan: Yep. Um And to help ease the uh the shock of what it takes to do unified customer view, um, it's not new, right? Unified customer view as a concept. And it actually exists at various points in time in the organization. It's just usually like day before a board meeting where collecting all the data from everywhere, systems, spreadsheets, emails uh and heads, brains of the people. And then one person is collecting all the slides together and then in that moment in time, we have a unified customer view. But obviously it's out of date like immediately. And so we're doing it. Like we have to have, people have to have this to do their jobs effectively and to communicate like customer state. So, that's where even informal ones most often brains of the people that are are working with customers. Um, and time available. Like again like the consultants and coaches cannot do this for you. Do you have time to do the work? Trisha Merriam: This is already feeling like a huge hurdle. Chris Carolan: Yep, this is why you need, you do need help. Uh. Trisha Merriam: This box is hard. Chris Carolan: Right? Trisha Merriam: First box is really deep, this one hard. Chris Carolan: Right. I think it's no surprise I like where this is going, because what we've seen and like the gut reaction is to build often tech debt, solve for these things. Um or that the, the person that you're asking for help can just come in, do a systems audit without you involved and then give you an action plan and then they're gonna clean it, somehow they're gonna clean up the data too because we all have fancy technology, we can just export everything, clean it and then bring it back in and now we're gonna have a clean like. No, that never works ever. Um So these commitments, I think, like organizational readiness is, is exactly right. Um let's see what we got under data readiness. Confirm you have the baseline data needed to build on. Contact records exist even if incomplete. Trisha Merriam: These do have this and this. That's pretty easy. Chris Carolan: Yep. Test cases identified at least 10 good customer records to use for testing. Um, yeah, like when we start from a place of let's get the Zoom Info data like process cleaned up, like let's fix this problem that's being created by data that has not created any value for the organization up to this point, but we think it will. Meanwhile there's this boatload of like customer data of things that have actually happened, like black and white data that we can use to start to develop and actually create a view where it's like, okay, this is true. Like starting to build a trusted view from from from the Git Go is is important. Trisha Merriam: These test cases, are they supposed to be examples? Oh, you just, it says right there good customer records. So we're not looking for examples of where the data is not unified already. Chris Carolan: Correct. Trisha Merriam: Okay. Yeah. We're trying to start on some kind of known ground. And that's where usually there is. Like so good could even mean like an incomplete contact record that you know Trisha is right around the corner and can come in and complete it like in three minutes, because all the data's in the head. Like sometimes it's a combination of those things. Somebody who knows how to get it, good. Um, I think is probably where a lot of people are. That last one's probably a curve ball for most. Um so I'll probably work on this language. Uh Claude likes to make a distinction between native and custom and object library stuff as as if that's something that users care about and they don't. Trisha Merriam: They don't. Chris Carolan: Um So this probably needs to be like customization versus versus not and just understanding the data model is probably better worded as a data model documentation or something. Trisha Merriam: Okay. So you think having a data model documented is part of the data readiness step? Chris Carolan: Yes. Yeah, at least current state and also like the gets to the customer view like future state. You're gonna have to do some data model, have some data model fun. For sure. Um, and it could start, it's probably starting from a place of like deals. Like, okay, we use that. What do we use it for? Well, it's 15 stages and it starts in sales. And then like we need this other team to do some stuff. So we've got a task stage. And then it comes back to sales and then we need to manage our post sales process somehow. So that's the last five stages. And all of a sudden like you can't document what the deal is actually used for, who owns it. Like all these things. Um, uh, last section here. All right. You can still continue but you may encounter blockers. If you haven't verified all requirements, we recommend addressing any unchecked items before proceeding. Fairly simple, I think. I don't know. Trisha Merriam: I I'm with you on this. I understand, I understand the request. But woof, this is a piece that most people when they see playbook, they think, oh, here's something I can start right now today. And I think uh the well, I mean, I don't know any other way that you would do it, but one of the worst things to be faced with when you're ready to get started on something right away is to find out that you're not ready to get started. Hate that. Yeah. But it is what it is. Chris Carolan: Yeah. Like, so what do we do instead? We try to find the, the quick wins and these like this checklist of things that we can get done. Meanwhile, you three months later, we don't have any idea what value it's, it's happening. We know emails are going out. Like, we checked those boxes. We got leads coming in. But nobody's in like in charge of the situation, right? To make sure that work that we did. Um So, I think uh a challenge for everyone in 2026 is like, can you sit down and get this stuff done like first? Like this is why I say things like HubSpot's not the problem. Like AI is not the problem. Like so many teams like if that first one's a blocker like I'm sorry, we need to handle that one first. Uh, I think there's a bridge to be like in a lot of people offer this as services, like to be that person, that can embed it in the org. Uh, pretty hard to make that sustainable. But works when that person can understand the business like over time. But that's why like it needs to be somebody on the inside. And I think there's moments like this where you start to find ways to leverage AI to free the person up in ways that they can spend time on being this person. Um, so what do you think? We got we got a chance here? Trisha Merriam: We got a chance. I mean these steps right here, it feels, feels daunting just because you need to take a big step back and it means that you have to have organizational alignment around the importance of a unified customer view. If you're not starting with that, it's really hard to identify the people who are gonna be using it and have alignment over which teams are gonna be using it. But if you have that alignment to begin with, these become pretty, I mean this is not a big task completing this section. But you have to start with convincing the org that this is what you need, which is the biggest task of all. Chris Carolan: Yeah. So what I'm feeling and uh is that we need some some links and resources on how to get those things done. Um probably somewhere in here. Trisha Merriam: Um, you have, well HubSpot has like a data model map, right? Chris Carolan: Yep. So I think it's if if this is what I think it is, I think you could just put a link or instructions on how to find that within HubSpot. But what if they haven't implemented it yet? Um, then there's a data model designer on this this platform. Trisha Merriam: There's a couple spots. I know. I I found that earlier today. Data model designer right here. Chris Carolan: Yep. I was gonna ask you on our call today, Chris, how do you help visualize a data model for a company that doesn't have HubSpot yet? And I was poking around here and look what I found. It's like you anticipated every need. Chris Carolan: Um, and in the at the end of the day, I'm just like building stuff that I need uh to help and it's where I'm happy that you saw that. Um, I'm not sure does this, I'm hopeful that this button works. Trisha Merriam: Did it, did it right? Chris Carolan: Probably ask you to log in. It asked me, it sent me a link, which I haven't had a chance to open yet. Chris Carolan: Yeah. Um, yeah. So in the meantime, if you go to uh learn to Education Hub and uh at the top, actually in the in the nav under implementation, on data model examples. And scroll down. Uh so you can see the value first data model here. And so this um takes you through the objects involved, the pipelines involved, the properties. But you're on the right track of going to the data model designer. Trisha Merriam: I know that there's a I know that there's a thing that you have in here already that shows you the actual like it's a visual of how all the objects are relate together. How do you get there from here? Chris Carolan: Um, so go, go back a couple times. Uh, one more time. Main site. Uh, under help. Click on services. A little bit of a long walk, sorry. Uh scroll down to the tier three services. Uh so implement at the bottom of this. Click on uh view all Tier three services. And scroll down and in the middle of this page somewhere or actually you gotta click on that one. At the top one. Which one? This one? spot customer value platform. Yep. Scroll down and it's in the middle of the page here somewhere. Trisha Merriam: Here. That one. Trisha Merriam: I for some reason when we were looking at like the existing data models, I thought it was gonna open like this view of the example models. Chris Carolan: Yeah, I started to do that, but then uh, yeah, all this is helpful feedback. That does it do anything if you click on it? Yeah, there we go. The thing on the right shows up and Again, this is where that I'd like to get to. If you don't have your data model documented, there you go. Like what should we use in a deal for? It's right here. Full description, what this enables, common configurations. Right? So um this will probably be coming up in the playbook. Um But do you think this is a valuable activity to help people get AI data ready? Trisha Merriam: I I think it is and I think it's also very eye-opening in terms of people want to start. Awesome. Helping really understand what AI data readiness means. Uh and I'm also gonna use the, I'm gonna use the tool this week, the data model designer and I'll let you know what I think of that. Chris Carolan: Sounds good. Chris Carolan: So until next week, uh thanks so much Trisha. See you next time. Trisha Merriam: See you. Yeah.