Ep. 141 – Measuring AI ROI and Turning Adoption into Business Outcomes with Denis Scott - Part 1

General Episode Description:

In this episode of Selling Intelligence, Denis Scott, Chief Marketing Officer at Laradin, joins Mark Petruzzi and Alan Rudolph to explore one of the biggest questions facing business leaders today: how do you measure whether AI is actually delivering business value?

Drawing from his experience leading marketing at companies like OpenTable, Lyft, SurveyMonkey, and Fanatics, Denis explains why AI adoption is no longer enough. Organizations now need visibility into how AI is being used, where it creates measurable outcomes, and where it introduces unnecessary complexity. The conversation explores the challenges of measuring AI investments, hidden AI costs across organizations, and how executives can connect AI usage directly to business performance.

What You’ll Learn:

• Why AI adoption alone is no longer a meaningful success metric
• How organizations can connect AI usage to measurable business outcomes
• The hidden AI costs most companies fail to track
• Why measuring AI proficiency matters more than measuring adoption
• How AI improves workflows when applied to the right business processes
• Common mistakes organizations make when introducing AI into daily work
• Why executive teams need visibility into enterprise wide AI usage
• How AI measurement helps boards, CFOs, and executives make better investment decisions

Key Topics:

• AI measurement and ROI
• Enterprise AI adoption
• AI proficiency versus adoption
• Hidden AI costs
• AI workflow optimization
• Marketing leadership
• Executive decision making
• AI governance
• Business outcomes
• AI productivity measurement
• Board reporting
• Enterprise technology strategy

Guest Spotlight: Denis Scott

Denis Scott is the Chief Marketing Officer at Laradin, an AI execution intelligence company focused on helping organizations measure AI adoption, productivity, and business impact. Prior to Laradin, Denis held senior marketing leadership roles at OpenTable, Lyft, SurveyMonkey, Fanatics Betting and Gaming, CharterUp, and other leading technology companies, where he built data driven marketing organizations and enterprise growth strategies.

Resources & Mentions:

• Laradin
• Comscore
• OpenTable
• Lyft
• SurveyMonkey
• Fanatics Betting and Gaming
• CharterUp
• AI execution intelligence
• AI ROI

🎧 Listen now and follow Selling Intelligence for more conversations with today’s leading revenue executives, AI innovators, and GTM leaders. Subscribe wherever you get your podcasts.

Mark Petruzzi (00:00)

Welcome to Selling Intelligence. I'm Mark Petruzzi.

Alan Rudolph (00:03)

And I'm Alan Rudolph.

Mark Petruzzi (00:04)

Every company we work with has the same exact story right now. Budget is improved, tools rolled out, adoptions declared a win in some sort of a board deck, and almost nobody can tell you with real data whether any of it has moved the business at all.

Alan Rudolph (00:22)

Our guest built his career on the marketing side of some of the biggest consumer brands in tech, and now he's on the other side of that exact problem. Dennis Scott spent over 20 years leading marketing at Open Table, Lyft, SurveyMonkey, and Fanatics Betting and Gaming, and most recently served as CMO of Charter Up. He's now CMO of Laradin, an AI execution intelligence company built by the team that created the original.

Digital Measurement Infrastructure at ComScore. Dennis, welcome to Selling Intelligence.

Denis Scott (00:56)

Hi everybody and Alan and Mark, thank you so much for having me. Excited to be here.

Mark Petruzzi (01:00)

Excellent. So we'll jump right into topic one. it and it's entitled From Consumer Marketplaces to AI Measurement, Dennis's Plant Path to Laradin. So, Dennis, you spent your career building brands people trust. they trust the the those companies with their money and their time. Companies like Open Table, Lyft, SurveyMonkey.

What did you see that pulled you towards an enterprise AI measurement company at this point in your career?

Denis Scott (01:27)

Yeah, I think I've had the fortune of working for some great brands throughout my career, which has been an incredible experience. But one of the things that, as a marketing leader, you're always accountable for is really the the measurement, given that you're you're usually one of the largest line items on a PL. And as we all know, this AI explosion three years ago, none of us were really living in this world. And now

It's sort of the topic of the day every day, every and so as a marketing leader, one of the problems I saw was this is really hard to quantify and understand. And how do we how do we figure this out? And so when I learned and heard about Laridan and the team that was running it, I almost ran to it living here in the Bay Area, being surrounded by this AI challenge and opportunity.

To know that somebody is actually solving how do I think about this? How do I understand this? And how do I communicate this to leadership, to boards, to investors?

Alan Rudolph (02:26)

Awesome. great intro, Dennis. So Laridan's entire pitch is built on radical honesty. Love that comment about what's

Denis Scott (02:34)

Right.

Alan Rudolph (02:35)

actually happening within a company, within a company from an AI spend standpoint. And that's not always flattering, as we know in today's marketplace. So, how is marketing that kind of product different from marketing some of the journeys that you've been on more of a consumer brand?

Denis Scott (02:52)

Yeah, I steal from that old adage in marketing, half of my marketing is working, I just don't know which half. And so I think we should we should always be open to the idea that not everything is working. And you know, as a measurement platform and identifying opportunities for organizations through workflows, and we can get in deeper into that, I think it's actually important to share the good and the bad.

Right, or I would say the good and the opportunity, right? And it is different than marketing a consumer brand because often a consumer brand, there's this this almost one-to-one relationship. Whereas in an enterprise type motion, you're you're solving across these large organizations, right? And those those opportunities that you're identifying.

we are a very horizontal product working across all functions. You may not even know where those opportunities lie or where things are are going really well. And so I think you just are are talking about a one-to-many relationship and how we can help solve a f a, sort of organizational wide opportunity slash challenge, and so I think that's where.

In a consumer brand, you could be talking to to people that fit your constituents, right? whatever it is.

Alan Rudolph (04:05)

Mm-hmm.

Denis Scott (04:05)

when I worked at Clorox, I worked on STP. It's like our our our target set was 18 to 34 year old males, And so

Mark Petruzzi (04:11)

Mm-hmm.

Denis Scott (04:12)

that was that one-to-one relationship. Whereas now it's like, hey, you're talking to senior executives about, yeah, how do we understand this AI investment and what are the business outcomes we're seeing from it? So it's there's still a personal element to it, but it's a

much larger solved in some ways.

Alan Rudolph (04:28)

Got it.

Mark Petruzzi (04:28)

Yeah, and Dennis, what's interesting is when w as I've learned about Laradin over the last couple months, it strikes me as a company that is building that kind of consumer relationship just with executives rather than all consumers. So, and the way I look at it, especially having the the fact that I'm on two boards, any board director

should be building their relationship with with Laradin because in that standpoint, these are all the things that at the board level and the senior executive level, these this is all the data you need to know, especially about something AI. And by the way, I think I'd like to take that quote you just made about marketing and make it about AI. because same thing, I know my

Denis Scott (05:14)

Right. Same thing.

Mark Petruzzi (05:16)

AI is working. I just, I just don't know which half.

So and and every company, every single company, big and small, is going through that same challenge right now.

Denis Scott (05:26)

it's interesting, Mark. You sit in the right spot here for who our our target audience is, which is boards are asking these questions, investors are asking these questions. if you look at earnings calls a year ago, tokens didn't come up that often. Now that's skyrocketed as far as questions go,

Alan Rudolph (05:43)

Right.

Denis Scott (05:44)

But if you think about it, right, th I'm gonna make a silly statement, but if I used to say, hey, on an earnings call and I

was fortunate enough to prep a lot of these and work side by side and be on these calls because marketing was such a large investment, right? So let's say marketing you're spending fifty million dollars, naturally they would say, Hey, what are you getting for that investment? How is that working? Right. Those are the right questions to ask. We often now find that in AI, it's like, hey, you're spending $50 million, what are you getting for that? And it's very hard to answer.

I don't blame people. This is what we're solving, but but boards are asking those questions, investors are asking those questions, CFOs are asking those questions, And that's really what we're building and where our customers see the value is, shoot, I'm getting that question from my board and I don't know how to answer it. Right now I'm using surveys and asking people, and I think I know what people are using, and it's really kind of it's very, very challenging, to answer that.

Mark Petruzzi (06:39)

Yeah. And it has to

Alan Rudolph (06:39)

Yeah.

Mark Petruzzi (06:40)

be and it has to be so much more precise than that for it to really work. Meaning the the surveys, the feeling the feelings, the a lot of companies, even some big ones that I work with, are looking at wow, they just wanna get AI in the hands of their their employee base. It's not about that. It's it's it takes a lot more precision. So Dennis, you're a CMO of a company who

you measure marketing and really every other function from an AI productivity standpoint. Does that change how you think about proving your own team's impact internally?

Denis Scott (07:16)

Yeah, it's a really interesting question. I mean, you kind of it's one of those the expression like you should be drinking your own water, your own champagne, whatever expression you want to use. So, we are an AI first company and we it's AI native built. really AI adoption across our company is a hundred percent, but we all use it. It's how you use it and how proficient you are. And that's part of what we get into at.

Alan Rudolph (07:40)

Okay.

Denis Scott (07:41)

can we help you understand how people are using AI, not just are they adopting it? Right. And so at a company where I'm at, I myself even as a CMO, I'm very hands-on in AI, I'm able to do a lot without having to ask other teams or other people. And I think that's how we approach a lot of what we do. Now it doesn't

It doesn't replace the human element of measuring quality or understanding is that as a marketer on brand or not on brand and how do we word this? I kind of have a general rule of thumb. If it's under 500 words, write it myself. just because I want to get that voice and tone right. Sure, I use AI sometimes on longer pieces to to help get me started. And then I always go in with that human touch. But yeah, I think in in the type of company we work in, which I think

Is honestly how a lot of companies are going to operate and look

Alan Rudolph (08:32)

Hmm.

Denis Scott (08:32)

as they progress. Maybe we're a little bit ahead because we live and breathe this every day. but I think that's how companies will operate. And I actually think it's pretty exciting because what people end up doing is I spend my time doing the things I want to do rather than some of the things that used

Alan Rudolph (08:47)

The same.

Denis Scott (08:48)

to be the the pain to try to get to. Right.

Alan Rudolph (08:51)

Right.

Denis Scott (08:52)

so you're actually taking humans and you're saying,

You're doing the most important task in spending your time there rather than some of these other tasks that AI can just speed up and help you with.

Alan Rudolph (09:01)

Yeah. Great great feedback, Dennis. So let let's move on to a another topic, so relevant today, which is again, where where does Larin play? How do we measure? What kind of data is available to us? It's a fascinating statistic in terms of AI spend last year, six hundred plus billion dollars, Just a phenomenal number. and again, it all comes back to, when I think about how do we drive business value.

Right.

How do how does how do our customers, how do our clients, whether it's AGS, Larada, et cetera, how do they drive business value? So Larada's own research in terms of the research I've been doing found that 72% of enterprises say AI adoption has become their biggest challenge, Every C level executive, They want to do AI. How do I measure it? And obviously, 600 plus billion, if I'm not getting value, then it's ultimately hurting the bottom line.

So walk us through what it actually looks like inside a company. What are leaders getting wrong?

Denis Scott (09:57)

Yeah, great question, Alan. I think what's really interesting is if we rolled back the clocks to a year ago, AI adoption was the topic of the day. And it was almost the sort of this binary question, yes or no? Are you using it? Are you adopting it? Yes. Okay, well,

Alan Rudolph (10:11)

Right, right.

Denis Scott (10:11)

great. That that adds to the value of your organization, Or no, it's like, you guys better learn how to do this, right? And so I think there was this almost mad rush to tell.

as a leader, to tell your teams or to tell your organization we have to adopt AI, we have to do it. And I even lived in that world where it was was a mandate. Everybody has to be using AI, But that that tells you how at that time sort of almost uneducated were in what you could actually do with that kind of notion of like you have to use

Alan Rudolph (10:39)

Hmm. Yep.

Denis Scott (10:40)

it because there's a cost behind it, right? And that's exactly what you're speaking to with that investment. And

In anything you do in business, when you have a large number like that, naturally the question is going to follow. And this is why Laridin exists. I mean, and Jim and Russ have really built their career around this. When you're throwing a lot of money at things, it doesn't mean it's right or wrong, but you're going to have to measure it and tell that story and understand it. And that's really what we've built. And so I think the challenge for leaders was we rush so fast to say, our team's using it yes or no.

Alan Rudolph (11:14)

Mm-hmm.

Denis Scott (11:14)

And now

that we're a bit more sophisticated and understand these tools and quite honestly have seen examples, particularly this May, where

companies had spent their whole budget by April that we all know, and or somebody was spending five million dollars a day because they were gamifying, AI adoption and token spend. Well, all of a sudden it hit us of like, well, what what is the value? What is the business outcome? And I think

That's the number one question now, which is is a great question and probably the most

Mark Petruzzi (11:37)

Yeah.

Denis Scott (11:41)

important question that we get from organizations and senior execs and quite honestly boards as well is not only what is my spend and how do I make sure I'm getting that Because that is actually pretty complex to even get to that number,

Alan Rudolph (11:53)

Mm.

Denis Scott (11:54)

but then what are the business outcomes I'm seeing from that spend? Right. And that's the piece I think.

we kind of hear those questions in that order, like, I don't really fully understand this. And then, but by the way, what am I getting for that? And so that's where w Laridan is really built to connect those dots. But that question didn't exist a year ago, We almost we almost bought ourselves a year by checking the box. Yes, we're using AI, right? And now the question naturally has come is like, okay, but what are you getting for that? Right. And I think budget

Alan Rudolph (12:21)

Yeah. Yeah. Yeah.

Denis Scott (12:22)

season is probably a very interesting time to be having this conversation because

Mark Petruzzi (12:27)

Okay.

Denis Scott (12:27)

When

you go in to the board and say, I need thirty percent more token usage next year, every board member is gonna say why? And if you can't answer that, that's gonna be a tricky conversation to have, So

Alan Rudolph (12:38)

Hmm. Got

Mark Petruzzi (12:39)

Yeah,

Alan Rudolph (12:40)

it.

Mark Petruzzi (12:40)

you

know. And that's one of the reasons why at AGS we have built set of offerings that really is built around AI to ROI. we use that term and I'll give a little credit to a close friend of mine for for naming his podcast after that. you know, and I should be clear, he he he came up with the podcast first, so I guess I've we've run with it from there. His name is Ray Reich.

He has been an incredible CEO and leader in the benchmarking space, starting more on the finance side, more on the like private equity metrics that private equity firms are looking at before they acquire a company. and he's now moved moved very deeply into the AI space as well. So at the end of the day, what we say at AGS is AGS amplifies like

I'm sorry, AI amplifies whatever motion, whatever process you have, whether it's in go to market or other functional teams, whatever you've built already, it's going to amplify it. And that's going to give you either a good or bad, meaning these there's lots of poor processes out there that are being amplified amplified every single day. So how does that match the pattern that you're seeing in Laridan's data?

is AI adoption exposing execution problems that were already there? And is that happen often? not as often. What are you seeing out there?

Denis Scott (14:07)

Yeah, we measure millions of of kind of moments, when you think about our customer base, we have tens of thousands of of users on our platform, And so the

Alan Rudolph (14:17)

Mm-hmm.

Denis Scott (14:18)

when you think about workflows and how AI is being used, and think about your own personal experience, how often you're you're touching an AI tool, it's it's for a lot of people, it's almost daily. but really there's some interesting patterns that we see. One is

this is one of the things we like to measure is again, not just adoption, but proficiency. And it's it's proprietary how we've done that and scored that, but essentially it's looking at the types of things you're using AI for, So what are the prompts? And one of the the challenges we see is folks you are using AI, but they're using it in a way that isn't necessarily, more productive. So I'll give you an example.

We have seen where, back in the day you used to write an email and maybe you would reread it before you send it and you make a few edits and you would send it. Now we see at times people are taking every email they're writing, dropping it into AI. It's not getting the voice right, it's not getting the the tone right, it's adding dashes, it's doing things you don't want. And that email now has has gone into this AI world where maybe there's 15 takes, 20 takes to get this email right. Whereas like

Alan Rudolph (15:23)

Holy

cow.

Denis Scott (15:24)

You didn't need AI in that

instance, You just kind of added a bottleneck to the process, whereas there are other things where I think about a go to market motion with an enterprise sales team. It's like the ability to take something like a gong call, digest that, move that into a sales force. Like now what teams are doing is actually preparing better for the next call because AI is doing some of that, call it task.

type work and

moving data around and summarizing, and there are even AI tools where it's like, okay, now I'm gonna, I'm gonna mirror the client and you can have a practice run. Right. So in that sense, it's actually amplifying it because it's back to what were talking about. It's like actually the salespeople are spending their time doing the most important task, which is prepping for this call and understanding their customer and what the needs of that customer are. Right. So

There's positives and negatives, especially in a

Alan Rudolph (16:15)

Yeah.

Denis Scott (16:16)

go-to-market motion, that we see. What our tool actually does that's interesting as well is we will identify within your workflows, one of the most interesting is when AI is there and when AI is not within the exact same workflow. And often what you see is AI is is a helpful tool or you're able to do more complex things. It doesn't always mean it's faster, but but the outcomes are better. but there's also opportunities where we identify.

You've added AI into this workflow and it's really not helping. It's causing a lot more friction. and so

Alan Rudolph (16:45)

Mm-hmm. Right. Right.

Denis Scott (16:47)

that's the honesty element of our our platform and tool and the feedback we want to give to customers. We tried to make everything look great. you we're not being honest with customers, And so that's really an interesting thing about AI: is where is it, where is it really having an impact for the positive and where is it actually creating?

More friction or being a bottleneck and not actually producing great value.

Alan Rudolph (17:07)

Right.

I love that comment, Dennis, about the dashes in an email. Like we've all been there, right? No way. Did who

Denis Scott (17:12)

Reha.

Alan Rudolph (17:14)

who wrote this email and why are those dashes there? So anyway Exactly.

Denis Scott (17:17)

Exactly. Who am I talking to?

Alan Rudolph (17:20)

Is it is it Claude ChatGBT or is it actually the person I want to talk to? So every CFO I've worked with inside a portfolio company

good news, they can tell you exactly how much money they've spent on AI tools by month, by quarter, by year, whatever time period they want to look at. But we're not there yet in terms of the return, Coming back to that comment about the business outcome. Why is that gap so much wider for AI than other technology investments, whether it was ERP, CRM, marketing automation, though those initiatives all had very well defined

Charters, project plan, scope, and ROI. AI AI we're struggling with. And so why is that data succinctly?

Denis Scott (18:00)

I think there's two factors. One is the speed of which we all adopted AI, So got ahead of ourselves and we all started using it. And then the CFO would get a bill, And go, wow. Okay.

Alan Rudolph (18:07)

Yep. sh darn.

Denis Scott (18:12)

there are also, I will just say there are some hidden costs to AI that they're we find this almost every time with customers. We have had customers where, they say, Hey, we're using these tools and it's kind of in the tens to twenties, right?

Alan Rudolph (18:25)

Only.

Denis Scott (18:25)

And then we actually implement our solution and we can see all the tools being used and it's often in the hundreds, And part of that that is happening is is naturally, if I'm on a marketing team and I decide to use a product, let's say like Canva, and I

Alan Rudolph (18:39)

Mm-hmm.

Denis Scott (18:40)

just grab a license for myself so I can edit some photography or videos, I may just swipe my credit card and expense it like a travel expense, like a meal,

And so that's really hard for a CFO to completely understand what are all the tools and where is all my hidden cost on AI that I don't even technically get a bill for. It looks like 20 bucks and marketing has somebody doing this, So that that that's part of the complexity of it. But this the second thing is exactly what you were saying, Alan, which is well, what are these teams doing and did that What were those outcomes? And that's why we built our workflows product connected to that token usage, right? Which is

What did the team use it for? Where was AI within this workflow? And we can see within those workflows, going back to our example of a sales team, you can watch that process of prepping for a call. It's like, where was was a tool introduced? And then, was that call effective, successful? Right. That's where it starts

Alan Rudolph (19:33)

Mm-hmm.

Denis Scott (19:34)

to come into, I'm starting to really understand the outputs of how how teams are doing this. Right. And I'll give you an example from

from the marketing side of things, a kind of hidden cost in marketing. And we've we've had a couple of customers come to us and say, I can't fully understand why my marketing team is spending so much in in token usage. Well, one of the heaviest things you can do in token usage is video creation and editing. Why? Because it's so heavy on the data side of things. And you know, you used to outsource this sometimes to to third party companies that would do a lot of the editing for you. And now

Some of the teams are trying to do it in-house, themselves. But what they don't realize is is that AI cost of what they're doing. Now, you could argue the business outcome is good because I didn't have to outsource it, or geez, this token. And that's kind of the decision making process we're hearing a lot of organizations make on these different workflows and what teams are using what AI and even what version they're using, That that really plays into it. I mean, the

That is where the complexity starts to add up, Which

is, well, what version of Claude are they using when they did that?

Alan Rudolph (20:37)

Right.

Denis Scott (20:38)

most CFOs don't have all of that data because it changes

Alan Rudolph (20:41)

Granular.

Denis Scott (20:41)

all the time. I mean, these models change honestly every week. And, if you were to go across Claude versus OpenAI versus Gemini versus codex, that they're all kind of priced differently and it evolves, right? And

Alan Rudolph (20:53)

Mm. Yeah.

Denis Scott (20:54)

There's discounts, there's not dis it's really hard. and so that's why we we create that view for customers to really understand that.

Alan Rudolph (21:00)

Thank

you, Mark.

Mark Petruzzi (21:01)

Good good stuff. yeah, let's finish up episode one here with topic three. and that is Inside Scout, what enterprise AI usage data actually reveals. So when a leadership team sees the organizational AI usage data for the first time, what's the thing that surprises them most, other than they have token spend? That always surprises them, and that's

Denis Scott (21:26)

Right.

Mark Petruzzi (21:27)

usually not good news.

And then really what are the gems that they're finding in in the the having that data for the first time as well?

Denis Scott (21:34)

Yeah, I think there's two things that really jump out at me. One is, and again, this speaks to the horizontal nature of what we've built, is I think a lot of leadership teams think about AI and often naturally think about engineers and developers, And they're the ones using AI, coding has changed. and that is a very true story. And those teams are often your heavy users.

But when you look horizontally across teams, it's people teams, it's marketing teams, it's finance

teams, and then

Alan Rudolph (22:01)

Mm-hmm.

Denis Scott (22:03)

starting to to really understand, geez, AI is really being used across my organizations, And so I think that's one of the eye-openers is is really what their true adoption is. The second thing that we often see with the adoption piece is we score that proficiency. And we do that based on how it's being used and also within.

within these workflows, how that how effective it is. And you will actually find there are teams that you didn't think about outside of engineering that are are quite advanced at times. And then you'll have the opposite, which is is more the challenge is like, geez, that team really doesn't know how to use this tool, which is is fair quite honestly. They they've never really been enabled or learned or how do I do this? And they're using it because they were told they had to, right. And so

That often becomes some of our first conversations. And then the second piece that really jumps out as well is just and we spoke to this a little bit earlier of like I had no idea this many tools were being used. And that's probably the eye-opener. I think most organizations and most customers we talk to think they have a, and they do have a relatively good grasp of the heavy usage tools. It's the kind of long tail, like if you think about

search queries back in the day. It's like, yeah, I understand really kind of those big terms, but it's like all these other long tail terms that are ending up driving things to my site. We see that with AI. What is what is the long tail of AI tools across your business? and often those are unauthorized and that that's okay. Like we're not saying that's a good or bad thing. We just want to identify it. And those might be some of your most

Proficient users are the ones actually adding tools because they're keeping up with the the newest platforms or or what they could be doing differently in their roles. And so we don't want to discourage that. We just want to identify it because organizations don't see that.

Alan Rudolph (23:51)

Got it. So as we talk about the gap between the teams that are super AI efficient, And fluent in terms of how to which tools to pick, how to use tools, et cetera, versus the teams that, they log in because they check a box, they need to say they're AI efficient. How big is that gap in practice? What separates the two? Like can can the tool actually, help us

digest and sort of that A where are we in that trend line in terms of AI fluency?

Denis Scott (24:21)

Yeah, we just actually, it's a great question, Alan. We just actually were looking at data around engineering, because we've had engineers on our platform the longest. So that's kind of the longest

Alan Rudolph (24:31)

Mm-hmm.

Denis Scott (24:32)

we can look at. And overall we've seen engineering output basically increase one and a half times over what it used to be, right? And

Alan Rudolph (24:39)

Okay.

Denis Scott (24:40)

this is this is based around adopted good code.

And what we see is kind of our P90 users, who we score as the highest, most proficient users, are actually driving that. We've seen them actually get more effective and efficient at a two and a half times clip, And so what happens with that is well, if you're doing that math, actually our lowest quartile, the folks that are are sort of the least proficient in in how we score it, actually haven't moved at all. Now you would have thought the whole curve moved up.

But it we've kind of talked about. I don't know if it's the right phrase, but the idea is like the rich are getting richer, like the top

Mark Petruzzi (25:14)

So

Denis Scott (25:15)

P90 group is actually getting more and more effective and really understanding these tools. And, as the new models come out, they're knowing when to use it, when not, All of those aspects. Whereas that sort of bottom quartile hasn't moved at all over time. And so

Alan Rudolph (25:30)

Yeah. Got it.

Denis Scott (25:31)

that

That gets back to this kind of enablement idea, Like how are we training teams and how do we take our best, most effective AI champions and have them sort of train and teach the others in the org?

Alan Rudolph (25:42)

Got it.

Mark Petruzzi (25:42)

what Dennis just described about measuring AI impact is exactly the conversation we're having inside our P advisory practice every day and our our AI to ROI group as well. So and that's tying AI adoption directly to EBITDA multiple.

Multiples at exit. This allows companies to take in board ready results. There's no long-term commitment required to start from an AGS perspective. And learn more at get-ags.com slash P advisory. And look for us next week for part two with Dennis.

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