Chris and Tricia dive deep into what “AI data readiness” actually means, why most teams aren’t ready, and how to fix the human and technical gaps that are holding organizations back.
In this episode of Value First Platform, Chris and Tricia unpack one of the most overlooked truths about AI: the technology can only be as good as the data that feeds it. Despite all the excitement around AI, most companies haven’t done the groundwork to prepare their data—and it’s why so many early experiments fail.
Together, they explore why recorded conversations are the missing link between human context and machine intelligence, how sales and marketing teams misunderstand “clean” data, and what it will take to make systems (and people) genuinely ready for AI.
What “AI data readiness” really means — and why it’s more than just clean spreadsheets.
Why recorded conversations are the richest form of context for both AI and humans.
How to make the business case for data cleanup and enrichment that resonates with sales leaders.
Why traditional lead scoring and CRM activity metrics are failing in an AI-powered world.
How to bridge the data divide between sales, marketing, and customer success by focusing on shared context.
Practical starting points for leaders who want to prepare their organizations for AI without boiling the ocean.
(00:00) Introduction — Why “data readiness” is suddenly on everyone’s mind
(01:20) The painful truth: Everyone’s talking about AI, but no one is fixing their data
(03:12) The missing link: Why recorded conversations hold the real buyer context
(06:47) The human challenge: Why even smart sales teams resist data hygiene
(10:35) The mindset shift: From demographic data to contextual data
(14:00) The paradigm change: AI can’t help you if it doesn’t know what matters
(18:12) Signals vs. scores: Why intent matters more than points
(22:04) Invisible blockers: What sales leaders don’t see in their pipeline data
(27:56) Reframing AI readiness: From “clean” to “collaborative” data systems
(33:18) Claude’s definition of AI readiness: Trust-based collaboration between humans, AI, and data
(39:42) The leadership gap: Why most execs don’t realize their data sucks
(45:01) The fix: Unifying call transcripts, CRM data, and human context
(49:15) The takeaway: Start now—because AI won’t save you from bad data later
HubSpot CRM – the “system of record” most teams underuse
Claude – used live to define “AI data readiness”
Google Meet native recordings – how real context gets captured automatically
Value Path Framework – Chris’s approach to standardizing human context
Loop Marketing Model – connecting buyer signals across the funnel
Chris:
Good afternoon, Value First Nation LinkedIn friends. Welcome to another episode of Value First Platform. Today we're talking about data readiness with AI, for AI, because of AI, all of the things here with Tricia Merriam. How are you doing, Tricia?
Tricia:
I'm great. Very much because of AI. And boy, do I love the sound of Value First Nation. That has a ring to it, doesn't it?
Chris:
I think so, but I might be biased. Why is this on your mind today?
Tricia:
It's on my mind today because despite how excited every organization is about AI, I don't see any of them doing anything to address their data so that they can use AI. And that combined with The little experiments that I have seen so far not going so well because their data is like they're not getting great results from AI because their data isn't really in shape to support it.
I don't have that many clients. I'm just a single person. I'm just a single person doing a job here. But none of my clients, they all record their calls, every single one of them. And HubSpot often has links to the recordings, but they never have the call transcript data.
And it is just very top of mind for me that having industry and job title and location is no longer good enough to do the job. And everybody I think is still in the mindset of like, that's enriched data. It's not good enough anymore.
And I'm starting to ask a lot of people like you, what are the most compelling arguments we can give to organizations to help them understand that right now, between now and the end of the year, they should be focusing on enriching and cleaning up their data so that in twenty twenty six they can go wild?
Chris:
Yeah, that's a tough one. I can tell you this is the why behind most everything that I'm doing right now value first, it's why we had the data summit. But it sucks that we're in a place where we have to define things like what clean data means, and what enriching your data means. Right?
Like the two things that you suggested people should be doing. And especially with when you add on the nuance of We want to get more out of either HubSpot, which has been the case forever, or AI.
I view it as our system of record needs good data to enable both AI and humans and customers. And since all the focus is usually on customers, especially if we're talking about sales land, Everything inside the CRM is very internally focused to get our process done.
And then all the customer engagement part, the live conversations is where the real context happens, right? Which is why it would be lovely to have those calls, like getting into the portal. That's really the only way we're going to solve this problem, in my opinion.
but what what do you think you're running into uh exactly when like people don't want to or maybe aren't prioritizing like what's missing as far as uh why they don't feel it's more important to get the data in there to get the calls in there
Tricia:
I lack at this point in time, a really compelling sales strategy. I need a sales message for this is why you need to do it. And maybe we could develop that together today. I don't know, but I'm, I'm, you know, I feel like a salesperson that's trying to close and I can't close because I'm not helping them understand why they should want this.
Like I can see how it can benefit them, but I'm failing to really help them understand why they need it and the problems it's going to solve for them.
And I kind of wonder too, is this something that we can sort of solve at a high level for a lot of people? Or do you think it is going to be very specific for every, like, and unique to every single organization? Are there any generalities that we can use here?
Chris:
I'm going to be bespoke. I think there's a way to standardize. It's just everything that we're doing right now is trying to figure this out because it's just a different paradigm. And I think sales specifically is that place where it's like, You know, if you get the call on the schedule and then you get the next call on the schedule and then you close the deal, it's like, okay, system was there or wasn't like we made the money. I was successful at my job.
Like it's the one place in my opinion that you can get away with just doing nothing in the inside of the system. Right. On a, on a very like one-off kind of basis. which makes it easy to be like, okay, what you're describing is too hard. And I don't want to do that right now.
Because I think what's missing is the understanding of how important context is, right? Even though we inherently know it and we inherently respect it when we're trying to hand something to somebody else because i'm going to be on vacation or i want i really am invested in this deal and i need you to help out with it so i'm just going to unload like everything i know because i know how important it is for you to know all that right and unless there's that forcing function there it's so easy to just not share everything and wait until the moment you're on a call or the need for context arises, that humans are very good at putting the pieces together in the moment.
But when we're trying to get a system in place that can help us do that, it's a different story. So the context that we're not used to tracking is that of the buyer, in my opinion. And that's what we're looking at here.
So value path is something I've been harping on as I think the way that we can standardize across every vertical, every buying situation. Because we're talking about humans.
And just like AI is working from a natural language perspective, because it's easy to talk to, it's easy to have conversations. When we use words that most people can identify with what the definitions, right, like, these all mean something.
Uh, I mean, all four of these mean something outside of business, like anywhere in your life, like you could stop somebody and they could give you a reasonable definition of any of these words. Right.
Try to do that with lead MQL, SQL, all of the other internal terms that we come up with. And it just leads down this road of like these conversations that just gets so complex where it's like, you know what? I'm good right now. I don't need to. to do this extra work to get my call transcript in if I'm the one that's using the context anyways, and that's what you're telling me I'm supposed to be doing. Right.
So this is how I think we get there. I know it's a bit of a long walk. But if we want to be successful with AI, it needs all of the context, the humanity of the context, right?
Because like you said, job title, industry, I was talking about this with George yesterday on Value First Humans, it doesn't tell you anything about that human being, right?
No. Meanwhile, these calls that you record can tell you just about everything, right? about what that even cares about, what's really on their mind, who they're working around, right? So I do agree with the goal of data capture at all costs.
I imagine HubSpot will continue to get better at working with other call recording vendors to make sure that stuff does get in. I can tell you I've been enjoying using the native recording tools with Google Meet and HubSpot. connected with that stuff is automatically coming in.
But this is a message that trying to get out there right now, because I do think it's, it's a different paradigm. And if we can't look at it from this perspective, it is really hard sell to do something differently, like get your call transcripts in there, because the old ways of of humans through this internally dehumanizing process where we just care about demographics and scores, we don't need call transcripts to support that motion.
It's when we actually care about are we creating value? Is there true engagement coming from the human being? Are they just checking boxes right now? like, are we just the third person and some kind of, you know, RFP process and we're, we're, we're, we're missing it because we're so excited to work the deal and the opportunity that we're not actually listening to their answers.
That the whole, the old way, in my opinion, like misses that misses all that. Right.
So I wanted to, share this i'm not going to read all of it but the true the truth of the matter is there's a very humans are complex beings hopefully that's not a surprise to anybody the buying process is very not linear and none of our systems to solve for it ai can help us solve for it right but we have to give it the guard rails and this is what those guard rails look like understanding that humans that are in learning mode right there's a different set of data that you you care about related to what's going on with them like is your content hitting and and so much of this as i worked here at this with you know casey hawkins clement robat george uh danielle urban like we're all trying to figure this out because there's an understanding like
The difference between these two audience and researchers, one hundred percent intent. Right. And it's it's like explicit intent, not buyer intent, not the intent data that we are hopeful that somebody just came to the pricing page five times. So that means that they have buyer intent. Right.
Are they actually doing research? Right. Because at this point, as they change, they care about different things. And if we're not there to serve those different things, we might as well just be caring about company and company size, industry, things like that.
As you get to hand raiser, a different set of data. And that continues throughout this whole journey that, again, it's a scale and a depth that humans can't do like on their own.
So things like getting call transcripts in and like can, can very easily seem like busy work that marketers have asked for in the past where it's like, oh, just get all the data and we're going to give you these great reports and you'll have all these, just so much clarity over, over what's happening, which just never, like that hasn't, hasn't worked out. Right.
Most people scoring is really bad because we don't, we're not taking into account the whole human on either side, in my opinion, but definitely on the buyer side of what's actually going on.
And why are we getting surprised by lost deals? Um, by the difference in opinion of lead quality between marketing and sales. Right.
Um, so I don't think this helps you at all. we're not there yet we're not there yet but it's like how do we get good at this story of like can we even get them to acknowledge that they don't like the way it works right now that's a good start I don't know.
Tricia:
I don't know. I don't know. If I went to them and said, what do you not like about the way it works right now? And if I asked a salesperson, I think the number one thing I would hear is I'm not getting enough leads.
And then, okay, so let's think this through. Is that actually the problem or is that you're not getting the right types of leads? I.e. people who really want to talk to you, who are open to what you have to say.
which is why maybe all this context matters.
Chris:
Yep. And that's why ultimately we're tying a lot of this to, at least in other content, to the loop marketing. I think this is at the heart of what the marketing playbook is trying to enable, right? Like, are we clear on this on both sides? Like we're expressing who we are effectively. We're allowing the buyer to express who they are effectively.
A lot of this at the end of the day is the difference between signals and scoring.
Because I would want, like, I mean, I enjoy it. And I know most salespeople enjoy it when you get onto the call and somehow they know you already, they've seen your content, they've interacted with you or somebody, I mean, in probably most cases, like, hey, I talked to this person, and they worked with you before. So now, like, there's already trust built before you can get on the call.
Right? Like, instead of us making up scores, are there any signals that we can work off of to, to hone in on those people and not just get lucky?
Right. And I think that's where we want to get to the place where sales takes a more active role in either the marketing content or the business development on LinkedIn so that they're around when signals are happening.
Right. Because then they're going to learn. or we're going to learn together, like tailoring the message, how to do that at scale, who we should be working with in the market, and learning.
Ultimately, this is supercharged with the call transcripts and things like that.
Tricia:
Well, I think tailor, amplify, and evolve are all so much impacted by what we learn from both good and bad examples of calls.
Chris:
Yeah. So maybe it's a place. So it sounds like we've got work to do, in terms of just getting people to clearly understand the pain and confirm that they have pain outside of I mean, this is why I don't like leads in general as a concept.
It's very easy to just say, and almost every sales guy I've ever met, like we'll say I could use more leads, like every single one of them.
Um, and this concept of like the right leads or easier leads or like they, if they like they're willing to go through a certain amount of pain in their daily activity if they know what the outcome is going to be versus trying to save them time or quote unquote make things easier or faster or right as soon as uncertainty gets gets added as an element especially in terms of ai Uh, they, they check out, they, they, they sign out of that plan, you know, pretty quickly.
Um, which I understand why, because they've, they've got a process in place, whether it's documented or not, that has been getting them closed one deals has been giving them a paycheck that has, has been working in some regards.
Um, so. Do you know if the use cases that you're thinking about, do they do closed one or closed loss debriefs?
Tricia:
I don't have a lot of visibility into that right now, but I believe that they do.
That would be where I'd start in terms of what's actually not working or working. I'm always trying to get people to use the data that they have. because it's real, first of all. It's not hopeful, like buyer intent and Zoom info data.
And that's often a much easier place to start. It's the best place to start with AI, because you can define the parameters and you know what it means for your business.
I wonder if you, let's see. I mean, there is a world, right, where if it has all the call transcript data that you could match up whatever the engine or like sales workspace is telling you about, okay, here's your deals that are in trouble right now versus actually looking at the call transcripts and saying, look at the call transcripts and through that lens, tell me where I have a problem.
It would be interesting to see what that, I don't know.
And they have to do it in order for it to even be a thing to show them that it's a thing.
I was heartened when I heard an example from an SCR this week who built, he built some sort of thing using AI to look at call transcripts from their team that is doing cold outreach.
And so he, I think he runs it once a week, once a month, whatever, but they look at their call transcripts specifically from their cold outreach activities.
And they took the initiative to do this. And I said, well, that's fantastic. What are you learning from that?
He said, essentially like my coworker that does most, he's a wizard. That's what I learned from this. Like, He learned how good he is and effective he is. I think what he hasn't learned yet is specifically what is the magic of how he's so effective.
But even that is a first step. Getting them to start using AI and trust the data that they're getting back.
Chris:
Yes. Because that just came up here. So I just asked Claude. cause I'm kind of at a loss too. And this actually came up with Nico this morning.
Like there's so many problems. Like I often talk about like the ability to let go of the things that we've never been good at, like data at scale, data cleanliness.
Tricia:
Oh honey, you get to be my age. You become so happy to get rid of those things.
Chris:
One would think. But when it means getting rid of all the KPIs that you know and love, people don't know what to do.
Even if they like the idea, all the usual things are broken, like open rates and all this stuff. It's like, okay, prospecting agent's going to do all that for us. Then what are we measuring?
And I think we need to get, this is where the trust comes into play. We've got to ask AI for help. To say, look at the thousand emails, look at the, like the ten call transcripts, the hundred call transcripts.
What does tell us how this new world works? Like, what should we be looking for? What are you finding?
Like data management at scale and data understanding at scale is what we've never been good at without buying a set. biasing it significantly.
So I just asked Claude, what does AI data readiness even mean?
Most organizations treat AI data readiness as a technical checklist, clean data. And I would, again, I think you ask ten different organizations what that phrase means, you'll get ten different answers.
But this is industrial age thinking, and we're in an AI native world.
This is huge. And I think when you can make these assumptions, AI is just another tool.
This is no different from the CRM we haven't been using for twenty years. Right? It's like, oh, I'm still going to be fine as a salesperson if I don't have my CRM data right.
Because it's just other people that care. I'm still getting I'm still closing deals, right?
And if that's all it is, then why do you need the transcripts again?
So let's see if we like this answer.
What AI data readiness actually means.
The capacity for your data systems to participate in authentic trust-based collaboration with AI agents. not about making data clean enough for AI to process.
It's about creating the conditions where AI can recognize what matters.
Your data structure reveals intent, not just records. The relationships between your data points tell a story about how value actually flows through your organization.
Um, like these are words that everybody wants more clarity on, right? Like intent. That's why they're buying the data. I would hope that's why they're buying buyer intent data.
Cause they want data that suggests people want to buy stuff. But, uh, when it's just the purchasing person coming to check the website out, like, I don't know if that's buyer intent.
Number two, humans can verify AI reasoning. When AI makes a recommendation or surfaces a pattern, the underlying data trail is transparent enough that humans can quickly validate or challenge it.
Number three, systems can evolve together. Your data architecture doesn't break when AI discovers new patterns. or when human needs shift. It's designed for emergence, not just execution.
And trust compounds over time. Each interaction between humans, AI, and data creates better context for the next interaction.
The system learns what good looks like for your specific organizational reality.
That last one is like the opposite of the reality of the last twenty years when you think about marketing and sales misalignment.
It's like each interaction between marketing and sales and data creates worse context for the next interaction, like more distrust.
We as the system learn what bad looks like and then we don't get better. We just stop using the system.
Oh, man. Is there anything in here that you think anybody would like, I want, I'll listen to a little bit more.
Tricia:
I can understand why this might be a good idea for us. I mean, I want you to send that text to me, by the way.
I might start out I'm having a conversation with a salesperson tomorrow and SDR on this topic.
Um, I want to put this in front of him and say, what is this? Which piece of this grabs you?
My instinct is that it's not here, right? It's, it's here for me, but I don't know if this captures the, if this just excites sales in the same way that it excites me.
Chris:
Yeah. Right. And I think it's largely because they don't know what to get excited about when it comes to AI.
They know what to get upset about, right? Hallucinations.
Tricia:
Can you ask Claude why should sales be excited by this?
Chris:
Sure. Why should sales be excited? Get excited about AI data readiness. Is that a fair way to put it?
And while he says that, I love it when I ask questions like that. He's already answered it, and I just haven't read through the whole thing.
But everybody gets upset about hallucinations, right? AI is going to hallucinate if it can't do this.
If it doesn't know what matters, it's going to come up with its own ideas. Maybe you get lucky, maybe you don't.
Everybody hates black boxes. They talk about black box, I don't know what's going on. Right?
This, this one covers that. You know, these last few, like I don't know if we're ever going to solve marketing and sales alignment.
That's what this would do. And that's where I know marketers care a lot about it sometimes, but I haven't heard a lot of salespeople complain about it.
Uh, so that's why it's like, can we just have a customer team? Like we just not have these silos anymore is my approach to some of these things.
Like let's just be done with that effort.
Like why are we continuing to bang our head into the wall?
Like, this is a statement, for example, that I think most salespeople can intelligently respond to. Maybe not this last word.
But where's their value in the process? Where are we creating value? Even if it's just value exchange, does the customer know?
At which point in the process do you know that the customer thinks what's happening is valuable or not?
Which again, brings us back to value first.
That's just the foundational principle, the first principle, in my opinion.
Why sales teams should get excited about AI data readiness.
Phantom sales teams are drowning in phantom signals. They can't remember what happened in the last conversation without digging through notes.
Spending thirty percent of their time just figuring out where people actually are. Broken handoffs. Invisible blockers.
This is the one we had a great conversation yesterday about the hero stage, which is about empowering all the people in the buying process, really.
Are you helping them build conviction or is the internal champion on their own to get the buy in? inside the organization, because when they are and you haven't given them what they need, it just takes one CIO or CTO or CFO to be like, Oh, some random thing happened over here.
And I think that's a terrible idea. So we're not doing that. Like, and it's complex to, to decide. I don't think it's complex to decide to want to deal with that proactively. It's complex to actually deal with it. you know, proactively.
But again, we're here with AI. We're here with HubSpot. We're here with all these tools that could help you do it.
Right. Should you decide to do so?
What does it actually give sales teams?
That first bullet, I just feel like you're going to love that.
This lead scored eighty five points, meaningless number.
Just feel like that's something I've heard from you so many times.
Tricia:
Yeah. I mean, majority of sales people, I think view lead scores in this way. They don't know what it means. They're doing prospecting around the scores. why would they be doing that if the scores actually like were meant something like if they wanted more leads that were scored they would work with the marketing team to like figure out scoring and like how do we make scores better which again is another part of this like paradigm shift when you move to signals before they hand raise it's like how do we
Are we even giving them opportunities to build scores? Like, why are we not seeing people before they raise their hand?
Why are we having to make so many assumptions that because they watched this video and read this article and hit the pricing page and the security page, and we're having to make a hundred percent assumption that they have That is raising their hand instead of actively doing it.
Right. Sales people in the sales team, I think are, they're actively, their radar's always on. Like whether they're at in-person events, they're on LinkedIn, they're always looking for those signals.
And they've never been, they've never had a system they could rely on to like go put it there. So it just sticks here.
Right. Sometimes they act on it immediately. Sometimes they don't. They're very unlikely to take that feedback back to the organization and figure out, how can we get ten more of these? This was a good exchange. This led to an easy deal.
Instead, it's more like, oh, yeah, marketing attribution, huh? Well, actually, I just talked to this person yesterday. They told me exactly what the blocker was. We saw who's on the call, and that's why they're closing today.
Don't tell me it was because they watched a video two weeks ago.
Again, this is something that I think people could understand about why this would be valuable, but it's very hard to comprehend how you could get it from your system.
Right? Which is where it requires like, are we giving humans that we work with a chance to keep us, to give us real time truth? So like, tell us what their story is.
Because we're active in the right spots where they are, like we go to where they are. Instead, we're usually forcing signals by giving them a call, doing things on our time.
Pattern recognition they can't possibly do manually. I don't know if sales reps would like this. I know sales managers and the leadership team definitely want this kind of stuff.
Elimination of bullshit busy work. Thank you, Claude.
with AI data readiness, you see, this is the part where they are willing to do busy work, if they know it will help them get the deal. But like, manually logging every activity. They're not doing that.
Right? So it's not, it's already gone, it doesn't need to be eliminated. So we have to sell them on the value of logging every activity, which is kind of what we're talking about here.
Writing the same email, forty seven different ways. Again, it's not something I've heard them complain about. Much. even though they should not want to do it.
Guessing which researchers to prioritize, like let's pretend this leads in general.
Do you hear that as a concern? Looking at these four bullet points, logging, writing emails, guessing what to prioritize and attending pipeline review meetings.
If I am pretending to be a salesperson, none of those are really landing with me. Some of the other stuff did such as above, uh, the, like the blocker bullet where like this thing, invisible blockers.
Chris:
Yeah. I think invisible blockers, it showed up twice, is something that maybe if you talk about it in terms of it's beyond your human capacity to even find this because you can't spend the amount of manual hours to get to this that AI can do so quickly and easily and excellently.
Right. And usually I'd say This, this one hits when it's bad, like, but also when it's, when these are good, it's what handles things like invisible blockers, because you've got somebody else on the team that has done that thing before.
And they tell you to watch out for this, you know, role, or they worked with that company in this division and this is what happened. So maybe that applies to your situation.
Right. Where you're actually, so if you can know what's real, like again, the whole story, these are, honestly, this is the worst offender in terms of when these meetings suck, like they don't get replaced by anything either.
Everybody's like, oh, it's not a good use of our time. So we just end up communicating less and then we never reveal invisible blockers.
The real opportunity for that last bullet item that you have highlighted, I think it's not eliminating bullshit busy work. It's helping the sales leaders understand how they're actually doing.
I think that bullet is most important to sales leaders, helping to trust that what's in the pipeline is real. That's one of the number one pain points that I've seen with the leaders is that even when they have groomed and led and established processes and done pipeline reviews consistently, there is often still a high amount of uncertainty over what's in the pipeline.
And if you can give them more confidence, that's huge.
Tricia:
Yep. And again, that's a spot traditionally Like, and this is where they know they can do this. So they just do it. Like they will call the salesperson. Just have a direct unplanned call. Like, I see this, I hear this, like, tell me what's going on, like, give me the story.
And then everything happens verbally. Right. And then the sales leaders got to put, like, put it all in their spreadsheet or all in their deck and then it gets transformed many ways to tell the context.
And then that systems can't learn from that. Our teams don't learn.
Um, yeah. Uh, authentic competitive advantage. Uh, no. They say that this is one of those things that is like in the business book.
And people say like, yeah, we, we care about that. I've never heard about it.
I've never seen this hit. I only see people compare about competitors to make sure they're not falling behind or they're avoiding their like fear of missing out, like, or just feeling good about what the other person's doing.
But I mean, this is right now, just like taking the digital experience or customer experience seriously. AI is another thing.
Like if you just take half a step forward for your organization, you can start crushing, providing dramatically different experiences for the customers internally and externally.
Um, It's just a mindset shift. And I'd say behind all of this, most of the KPIs, especially activity KPIs that teams have to abide by, don't necessarily support AI data readiness.
Like, did you make enough calls today? Did you send enough emails? Like, did you book enough meetings? Right. They're just very surface level metrics without the context.
That's why a lot of what we're talking about in value for scoring is turning that into a health metric. Like it is on the customer success side of the fence.
Like if we talk our contact scores, our deal scores and our company scores, if that was all just health of the relationship Like that's what it all meant.
It gets harder to screw that up, in my opinion. Again, it's harder to trust it if you're not using AI to tell the whole story. But now you can actually know the whole story.
They stop losing deals they should win.
How do they, can you give me anything else besides like need more leads as a pain that you're hearing from?
Tricia:
Well, we just talked about sales leaders not actually knowing how, like how good their pipeline forecast is.
I've seen that just sort of destroy teams periodically.
And that lack of confidence in there, like once the leader starts feeling that pressure from his leaders and his trust is undermined, then his trust in the team is undermined.
And that puts sales in a really shaky position for a good long time. So it's a big one.
But I'm going to take that when you send me that. But Claude spit out, I'm going to take that. I'm having a meeting with an SDR tomorrow to try to talk about this is like, how do we make this matter to sales?
What's, what's, and I'm going to show it to him and say like, what of the things on here are really resonating with you? That would be like, oh, hell yeah. Sign me up.
Chris:
Okay, I'll share it with you. And I'll let you know.
I'll share one more thing. This is a narrative I'm working out because this is where I start with leaders.
Because most of this is getting leaders, in my opinion, to use the right words to hold their organization accountable to massive change management and transformation.
And so starting here, do we agree that this is good, that we want this? Usually that's a resounding yes.
OK, like right away, call data, historical context, informing current decisions.
Like the pipeline, the pipeline's never been good, never been understood because we have not joined real revenue with forecasted sales and marketing revenue. They're always in different systems.
And we play this game that we think we can make up sales velocity metrics that aren't based on the data it needs to be based on, right?
So real-time revenue visibility as an example.
Again, if you give this unified context to AI, it's going to smash any of the poor attempts that we've made to try and make forecasting matter.
And if you've ever been on any of these review calls with sales teams, it's like, yeah, it's eighty percent, but they just said this yesterday, so I don't know.
And that changes it from like green to yellow in the spreadsheet.
AI can't work with that just like your human management can't work with that.
That leads to unified business context, like everything in one place.
And usually we think we're starting here. I think we're enabling teams by giving them tools and automation And we don't like, we don't even get this part. Right.
So like we give them the tools and then they got to log into like four different systems to understand the life of that customer.
Um, so again, these are four goals that should matter to every single business imaginable. And they probably don't know that they could even achieve it.
So starting here, just like, OK, let's not worry about the systems or our processes. Can we agree that this would be a good thing to happen for the business?
So I'm working on that right now in terms of connecting that to the value path, to the data model.
And how do you even put yourself in a position to say, see these ten calls transcripts, guys? Here's things we missed here and here and here.
And this is exactly how it impacted our success or our failure in this deal.
So there's some clay for you. I'll share the stuff from Claude. But I think this is what we're going to rally around for this series.
Moving forward, we do need to get very good at this narrative, I think. And I think we'll get there.
It helps that the other ways aren't working.
It's hard to defend the old ways. And there's not a lot of alternatives other than, oh, we're just going to apply AI.
It's like, that's not working out so well either.
Tricia:
So, yeah, I think there's a huge gap in understanding for most people that the data they have in their portal right now is too limited and not cleanly structured in a way to actually support them to do anything with AI.
Like they think they could just plug it in and start going and they, So anyway, that's the next big challenge for me is to help them understand you have a lot of work to do.
And let's get it done right now. Let's make this top priority. Like I said, so you could just cook in twenty twenty six.
Chris:
I like that plan. I know I convinced you of that plan. And I think we'll convince others to.
Again, I don't know. I haven't heard of a lot of other. Options for people.
We know that everybody is acutely aware right now that their data sucks.
That seems to be apparent. They just have no idea how to make it not suck.
Tricia:
Well, I don't even know if everybody realizes their data sucks. If you're the HubSpot admin, you realize it. But I'm not sure the business leaders know.
And I would say I don't think most of them do know.
I've been seeing it through, I mean, it's not directly, but when they say, we need to get more out of HubSpot and, or AI is not working for us.
Like, I mean, you got to connect the dots with the data, but they're feeling the pains at least.
They might not be referring to it as a data problem, but they're feeling the pains of their data being bad in some ways.
Chris:
So we're going to be here weekly deciding on a new day soon. But yeah, we really want to get to the bottom of this and help you guys get to the bottom of this too.
So until next week, thanks so much, Trisha.
Tricia:
Thank you.