AI Priorities That Actually Move Revenue

Headlines scream about AI every day, but the real story is quieter: the teams winning with AI aren’t chasing shiny tools, they’re rebuilding how revenue work gets done. We sat down with Dan Morgese, Director of Content Strategy and Research at Gong, to unpack the new State of AI report and reveal what separates impact from noise. The report pairs a survey of 3,000 director-plus leaders with Gong Labs analysis of 7.1 million closed opportunities, giving us both market sentiment and inside-the-workflow evidence.

What stood out first is a mindset shift: productivity just jumped to the number one growth lever, reframed from time saved to revenue per rep. That changes everything. Instead of using AI to draft more emails, top teams use it to guide seller actions, expose deal risk, and align coaching with what actually moves win rates, cycle time, and ASP. Depth of adoption beats breadth—leaders who treat AI as a core driver of strategy, not a sidecar, see stronger commercial outcomes across the board.

We also dig into the underappreciated frontier: forecasting, strategic planning, and initiative tracking. Adoption for these systemic use cases surged as teams realized forecasting improves when you combine call intelligence, pipeline dynamics, and engagement signals. Planning gets smarter when AI informs territory design and compensation scenarios. And tracking initiatives in the wild lets leaders see whether new messaging lands with customers and whether it moves revenue, closing the loop from strategy to impact.

Trust inevitably comes up. Sixty-seven percent of leaders say they trust AI, but the smarter framing is trust in data. Domain-specific systems that capture reality—conversations, signals, and activity—beat manual CRM fields when accuracy and explainability matter. With AI quickly becoming table stakes, the advantage shifts from “Are you using AI?” to “Are you using it well?” If you’re ready to move beyond pilots, this conversation offers a blueprint: pick systemic use cases, build depth, measure what matters, and let revenue per rep be your scoreboard.

If this resonated, follow the show, share it with a colleague who owns forecast or RevOps, and leave a quick review so more revenue leaders can find it.

Transcript

Seth Marrs: 0:03

Hello everyone and welcome back to the Innovative Revenue Leader Podcast. So we have breaking news. Gong just released its state of AI report, and I'm fortunate to have Dan Regacy, Director of Content Strategy and Research, joining me to explore it. So a little bit about Dan. Dan has been in his role at Gong for over four years. Prior to that, he was at series decisions and then at Forrester, where he really hung his expertise around creating driving insights from benchmark studies, going through and really understanding. Like him and I work together on customer studies. How do you understand what's going on with sales drugs when they report information? So I know the value of the work that he's done. Gong's really fortunate to have him. I mean I'm excited to have him on our show today. Dan, welcome.

SPEAKER_00: 0:50

Thanks for having me, Stat. It's good to get the band back together, as you said. We talked about adjacent topics, I would say, and some of the things we'll we'll get into today uh hundreds of times, probably at nausea with with uh clients in our uh in our previous live.

Seth Marrs: 1:04

So uh Yeah, yeah, yeah, yeah. So I'm really looking forward to this. So just to kind of start out on an over, can you just provide an overview of the report, kind of what you were thinking when you're when you're putting it together and the things that you were looking for in building it?

SPEAKER_00: 1:17

Yeah, for sure. So obviously um it's it's 2025 and we can't go a day or an hour without talking about AI and the impact that it's having on on businesses at large, more specifically, obviously, for Gong. That's that's the the revenue organization. Um and so Gong, we actually celebrated, I think, technically our like 10th birthday uh as a company uh last month. And the interesting thing is Gong's been an AI company, given the nature of call transcription using LLMs to analyze and and turn this contextual unstructured data into usable formats using um, you know, foundational AI layers, I would say. Um and so obviously the boom, it's it's the hot topic. I think everyone's trying to wrap their arms around it. Um, there's a ton of research out there. I think one of the gaps that that I was seeing as just a consumer of a lot of this research and trying to better understand this space is um we know that adoption is surging, but we really want to understand what separates a great AI deployment that's really driving bottom line impact and revenue performance from like a lackluster one. Everyone has immense pressure to figure out how they're going to implement AI. So we wanted to kind of, to the best of our ability, start to tease out what exactly separates those those best in class AI deployments from from the rest.

Seth Marrs: 2:33

Awesome. Awesome. And you got you you hit it at this a little bit. Can you talk a little bit more? I mean, this isn't your typical survey, right? Like you do a survey, but you also combine that in and with what's actually happening. And that's something that that you and I have had discussions around. It's like that it the traditional survey is kind of becoming less focused on, and there's more focus on what's actually happening because tools like Gong and others that are using AI to understand what's going on in calls can do that. And you did this in a report in this report. Can you talk a little bit more about it?

SPEAKER_00: 3:06

Yeah, so this is this is the second time we've we've taken this uh I would call like hybrid methodology approach where we took survey survey data, right? So um we worked with panel providers um to source uh over 3,000. So we actually uh increased the end count significantly this year is like 5x what we did last year. Um so we talked to 3,000 director plus uh revenue leaders um through an anonymous survey. Again, nothing proprietary about that. Anyone with budget can go out and ask questions. Hopefully they're asking the right questions. I like to think we we take a unique angle at asking those questions, but I think the real game changer for us and and and what we're really proud of here at Gong, honestly, one of the things that four years, uh, you know, four and a half years ago when I was making a decision to to uh you know at my next career move is this thing, Gong Labs, right? And so uh for those who don't know, Gong Labs is a content series where we analyze the hundreds of millions and sometimes billions of sales interactions and all of the data that's captured across Gong users. And so for this report in particular, we look at 7.1 million sales opportunities that were closed in in 2025 to understand how is AI leveraged. Uh, and and so it's it's a good blend, I think, using both the survey-based and the Gong users, because obviously the the Gong data is narrowly focused, right? And so we're gonna get an understanding of maybe some early adopters of AI, right? Like maybe maybe more of those cutting-edge companies. And and again, it's a it's a biased sample because they're specifically using our tech. Um the the survey provides a nice market perspective of understanding at large what are kind of like the priorities, challenges, and ways that folks are starting to think about this. Again, if we were to try to identify the AI adoption numbers by the Gong, it's gonna be, you know, really um, you know, it's gonna be 100% essentially, right? Like whether or not people realize it, they're using AI. And so it provides a nice balance of of uh of both.

Seth Marrs: 4:56

Yeah, so you mesh them together to be able to find you can see it in the report, and some of the that I love the way we'll and we'll talk through some of these things as we go through by hey, here's what they're saying, and here's what we're actually seeing. It's it's pretty cool.

SPEAKER_00: 5:08

Right.

Seth Marrs: 5:08

So one thing I was surprised about is like productivity is the number one area for sales organizations. Like, Dan, I I don't really see sales leaders as the productivity focused type people. So I was really surprised to see it jump all the way to first. Is this, I mean, is this just the narrative that that they're just regurgitating the narrative that everyone's saying? Because the last year, most people have been focused on productivity with AI. So it kind of feels like to be in the boat to say that you do AI. I have to do productivity. But yeah, like how do you how do you see that? Because I wouldn't have expected it, and you've seen it. Like Gardner did a study around what sales leaders care about, and it wasn't productivity, it was like the last thing on the list. Like, how do you see that? Why do you think that happened?

SPEAKER_00: 5:56

Yeah, I I think um, I think a couple of things. So you mentioned it, right? Everyone, I I think Gardner to to to mention them again, the uh earlier this year in the beginning of 2025, they said 87% of revenue leaders had board-level mandates to somehow implement AI. And so AI and productivity go hand in hand. And so if I'm responsible for somehow figuring this AI strategy out, I'm going to tell myself that productivity is probably the needle I want to move the most. The what stood out to me is not necessarily like I I think I think exactly to your point, maybe productivity isn't the main focus for a lot of revenue leaders. I would say RevOps and the sales ops folks obviously are like, oh, we think about process uh and and uh tech uh efficiencies and improvements and how can we reduce friction across our our our our sales cycles. Um so so there, I would say they're focused. But when we looked again, we uh I think uh we even took a look at by persona, and there wasn't much difference. And so this year, um, year over year, productivity moved from a number four ranked growth priority to the number one. And the way we think about productivity is there's a finite number of levers essentially as leaders we can pull to drive revenue growth. We can introduce new products to market, we can uh, you know, uh update our pricing and packaging to essentially charge more, and that's gonna hopefully increase revenue floors. We can go after new buyers, new markets, new verticals, um, et cetera. And so again, very shocking to see that this ranked as number one. And I think, and my hope is as a former analyst that was really focused on sales productivity, is that leaders are starting to treat this rather than like an always on, yes, we care about increasing productivity and kind of just like it's an always-on thing, but it really never gets the focus or attention that maybe it's going to get in 2026 and beyond, is that they give this productivity initiative and saying, yes, we're going to grow by increasing the output of our existing team, our existing resources to drive revenue, that should get as much focus as a really shiny, exciting new product that they're launching to market, or this really bullish strategy to go after an entire new industry or an entire new buying center for their product, right? Like those are things that are constantly on dashboards. Can this productivity initiative be tracked, monitored, measured, and adapted as much as like what I would say those more strategic initiatives that we've seen historically from the revenue organization?

Seth Marrs: 8:18

Yeah, it should be like right, you can drive growth through the team you have, and you eliminate a lot of the problems that you would have with ramping and all the other things that you go through if you can drive if you could accomplish that. So yeah, that that makes sense. What one of the interesting, because I mean you and I had a discussion about this, and I kind of pressed on it and said, you know, hey, it's this, did you just over did you like over-leverage ops people when you're doing the report? You actually ran that and found that the it's this was not a over-leveraging or a scope. Actual sales leaders, when you ran it just for sales leaders, it was the same thing. So it's real, it's a real focus.

SPEAKER_00: 8:56

It's real. And even, I mean, if you read not, let's say like not non-niche specific. I know we're we're laser focused on on revenue teams and read all of the great publications and research that are coming out specifically for you know our our kind of immediate industry here. But if you look at the the big B2C brands and the folks making headlines when you turn on the news, right? Everyone is focused on this metric. So, you know, um in the revenue organization, we're looking at revenue driven per rep as like this new productivity metric. We don't care about time savings anymore. We want to know what is the impact of that time savings on the bottom line. But even Amazon, you know, unfortunately, you hear like these these shocking headlines of a 14k reduction in in headcount. Yeah, but the second line or the second paragraph of that article is typically in in hopes to increase the revenue driven per FTE, right? And so I think it's just be it's it's it's uh AI is is kind of putting a spotlight on the fact that we need to do more with either less or what what we have existing uh in our business. It's no longer to grow revenue 20%. I'm gonna add 20% more reps to to go and sell our widgets, right? So um probably not probably not a shock, but again, it was it was really interesting to see that you know uh last year we saw after you know a two really difficult years in 2022 and 2023, um a lot of churn, a lot of retention problems were issues. For instance, folks said, Hey, I want to um grow revenue through existing customer cross-sell upsell, right? And really get like retention back on and focus on the existing install base. Um, so again, productivity, um, again, not a new priority, but one that's really getting the spotlight, I think, uh, in the next 12 months or so.

Seth Marrs: 10:33

Yeah, and not an easy one to implement because there is embedded like sales leaders are experienced, they're used to the game of I need more headcount to drive more revenue, and this changes that game. So it's it's encouraging that they're that they're doing that. One of the other things in there, like you talked about 96% of people said they're gonna use AI in 2026. I think we're at a point now where using AI is no longer who cares, right? If you're not, you're kind of a dinosaur and you need to figure out a way to use it. The the one thing that you talked about is that you said if they're using it as a core driver, those are the companies that grow the most. Now, is that causation? Because is it just that if I'm a okay, like talk a little bit more around the the causation versus correlation around this? Because if I'm a super advanced company, I'm future forward, I'm working, I'm doing a lot of really good stuff. Of course I'm gonna use AI because that's the thing I need to use. Is it I'm just having good companies just are smart about doing this, or are they actually using it to drive results?

SPEAKER_00: 11:36

Yeah, I I I think it's both. So as the the marketer of a revenue AI company, I would love to say, no, they're flipping the switch on AI and they're just coming in that top performing cohort. Um, but no, to your example. So when you talk about a core driver of strategy, the the the way that that particular question in the survey was framed was uh around basically depth of adoption, or like if you want to think about like AI maturity, right? Are you just uh experimenting with pilots still? Have you uh deployed or implemented AI uh across one functional area within revenue, multiple teams, or has it really become like the North Star of a lot of the process changes that you're basically, are you re-engineering your entire go-to-market team, your processes, your technology and tools around do like basically increasing productivity with AI, right? And so for that small percentage that that fall within that most mature cohort, they did see um, I think we saw um significant lift in uh revenue performance. And then we also saw what we called um our commercial impact score, which is a calculated score essentially identifying 11 different KPIs. Have they increased win rates? Have they reduced uh deal cycle duration? Have they um, you know, improved average deal size or ASP? So basically all of the metrics that are on an executive revenue leader's dashboard, are we seeing those move in the right direction? Hopefully, as a direct result of the investments we're making around AI driven productivity. Um, and so again, uh I think the story here is that we saw depth of adoption was actually better than breadth of adoption, right? So Yeah, that makes sense. I think we we might, if we have time, we'll probably get into like the domain specific versus general speci uh general purpose solutions. But what we're seeing is that um for those revenue teams who view AI as like the core strategy, I think another correlating factor here, and again, I say correlate, like it's not I I definitely think it's correlation, not not not cause. Um, what we see is that year over year, so we've been tracking AI adoption across revenue teams. It was 26% in 2023. That jumped up to 48% currently using, and this year it was 87%. And then to get to that 96 year reference, there's another 9% planning to roll it out in the next 12 months. And so we have like this 80 to 85% year over year lift in AI adoption. So it's going like crazy. What we saw is that 2023 was very much like the year of experiments. Like, okay, ChatGPT came onto the scene and caused a big splash in November of 2022. 2023 was like, oh, this is cool. I can write sales emails with it. Last year we saw that for those like early adopters, it was a significant competitive advantage. What we looked at was basically what was their go-to-market efficiency. So, based on their spend across marketing and sales, how much growth were they able to drive? And there was a direct correlation between um those leveraging uh AI across the go-to-market functions and um their magic number, essentially, right? So, how much growth am I generating but based on that go-to-market spend? Um, this year, it's like everyone's using it. It's an expectation, it's no longer as much of a competitive advantage. So honestly, if we were to run that same kind of correlation analysis, I think it's gonna be much flatter next year because everyone's gonna be using AI. We might not see like the has and the have nots in terms of returns on growth, assuming that all other things being equal, you know, across the deployments that that they actually have.

Seth Marrs: 14:56

In this study, the the best of the best are taking advantage, they found unique ways to add value by being narrowly focused, more task-focused. I identify a use case, I apply it to a problem I have in the business, and I get I get results. You're thinking in 2026 that's gonna reverse and it's gonna be the laggards, the people who don't do it are gonna be the ones that get left behind. So if you're not using it, you're gonna have more problems. Whereas today, the the people who have worked ahead of it are the ones that are taking share, getting an advantage from it that others aren't.

SPEAKER_00: 15:26

Yeah, the marketer me, I I love alliteration, right? And so I'm premium says like experiment in 2023 to edge, right? Your competitive advantage in 2024. Now expectation, essential, imperative, whatever word you want to use in in in 2025 and beyond. So um, I think we're gonna see kind of like less of a competitive advantage based on AI just because it's gonna be so uh commonplace across teams. And we're seeing that, right? 96% plan plan to use it. So don't know what those 4% are doing, but maybe that very unique, uh unique products that they're selling.

Seth Marrs: 15:58

Yeah, unique unique use cases. So the the most insightful part of the report for me was that the use cases you showed across every single one of them went up significantly. Uh in particular, I was interested to see that forecasting and planning use cases went up because that's uh that hasn't been traditionally one where people have thought about AI and and and helping in the daily work of those and the utilization of it. Can you talk a little bit more around why you think that's happening now? It sounds like there's been and you could see it, like there's some unique use cases that have emerged that go past the traditional forecast cadences that you would see in a business.

SPEAKER_00: 16:36

Yeah, for sure. So it's actually really, I think uh I don't remember the exact day, but it was like around, I know it was the week before Thanksgiving last year on stage. We were in Dana Point, California at our Gong Celebrate event. Uh, and I paul I was uh lucky enough to be able to get on stage and present some of the findings from last year's report, very similar to the report we conducted this year. And we showed um for the for the same uh metric you're you're you're talking about, which is revenue AI use case adoption. Um I had, I remember um two two different bar charts, right? One was what I called the automation use cases of like how can we speed things up using AI, right? So content generation, can I generate sales emails? Can I um, you know, uh automate note taking, data entry, right? All of those like administrative type, like basically low-value tests for sellers, how can we speed them up, offload them or delegate them to AI, take them off their plate. We saw pretty good substantial adoption across those. And I kind of like to think of it as like the low-hanging fruit for a lot of these cool tools that that teams are looking at. The other side of the equation, which we called like the intelligence layer, and again, there's a marketing spin to this and a bit of a show to it. But those um those are what I would say are the more transformational back, like RevOps has to get involved. These are like the process changes and very much more like an executive priority rather than just you know giving a tool to your to your sales rep and say, Hey, go wild and crazy. Like they're not gonna be able to make this change as an individual. These are like transformational use cases. And I call these intelligence because hopefully, if if done properly, they're gonna make your team not just more efficient in terms of time savings, but more successful, right? And so um things like uh the prioritization and guidance, so guiding a seller's next best action with the intelligence that they need to move a deal forward, um, automating um or or I'm sorry, uh, you know, to your point, the forecasting one. We saw I think a 50% year-over-year lift in terms of number of yeah. Um, and so it's it's really encouraging to see, not just because Gong has an AI forecasting product, but um, folks are understanding that to transform and really generate the productivity that they're after, they need these more transformational strategic use cases of AI. Um, to your point, like the strategic planning use case. And um, what we gave some examples just so that folks had context as to like what we're asking. We're talking about, you know, compensation um design and and planning. We were talking about territory mapping there. And so, like, how can you start to use AI to automate or get better insight into the strategies that you should be actually like putting together for the business? Uh, and then the last one that saw another significant lift was the tracking of strategic initiatives using AI. So that's understanding what field adoption looks like for these big bets that we're making for a business. Is it working? Is it resonating with our customers? And then finally, are we able to attribute those changes and the the kind of dials that we've turned to actually having an impact on revenue? Um, so all of those things, one, it was like my call to action on stage last year. So it was very exciting to see that like I'm not gonna take credit for, but it's good, it's good to see that we're on the right track. Um and then the cool, the really cool thing is when we looked at again that commercial impact score, the top five percent. So basically the the folks saying yes, AI is doing all of these great things for my business, we're significantly more likely to have those what we call strategic use cases of forecasting of of the planning, of the tracking strategic initiatives across uh the business. So it's definitely having a impact. Um, not just these like fancy low-hanging fruit use cases of writing having AI write my sales or my outbound emails. Um, you know, actually re-engineering and and and reprocessing the business around how how AI can make us better.

Seth Marrs: 20:15

You know, the cool thing about that is like I I interviewed, uh, I did another interview around this, and uh with a different vendor, like completely separate, different research report, very similar result. The only difference was they called it systemic versus seller. So systemic meaning broader business, and then seller versus and and in that report, 80% of the business leaders wanted the systemic stuff. It's so you're seeing something completely separate, the same type things coming saying help me with the with the wider business objectives. Interesting. So there's always one stat in a report that I always a little skeptical about. So in this one, it was that 67% of people trust AI. So I just want to press on that one a little. Are you saying that if I if I go in and go to chat GPT and say, What's my business strategy? That the answer that comes out of that, that I'm gonna trust that and use that to guide my business. Like, how do you think about when you say trust AI? Like, is it trust but verify? Or is it like the do you feel like this is like they actually just trust the answer in move on because that's scary to me.

SPEAKER_00: 21:22

Yeah, so this um, so yeah, we we asked what your level of trust was, and so 67 two-thirds of of revenue leaders that we surveyed said yes, they they um they either trust or fully trust, right? And so we we looked at the top two of the Likert scale questions in in terms of that. And so um the interesting thing is I think we had uh uh playing a Monday morning quarterback, I think we could have been better about the wording or or even broken this out into elements. Do I trust the actual LLM that I'm based on? Do I trust the underlying data, right? And be and the reason I bring this up, I was actually I was speaking at a uh an industry event uh in in in New York City on Wednesday, and I was at a table afterwards and and it was just a panel, but I I I presented that statistic of the 67, and they go, Do they do they truly trust the I could see trusting the AI? My reservation a lot of times is the data that's underlying and and and the AI is learn you know learning from. And I said, that's a great point. And I wish we we separated that out. Like, what's the trust in the data? And I think for a lot of organizations, last year, the much of our research was around getting your data strategy in a place where you're even ready to to start using a lot of these cool technologies and tools. Um, and so yeah, so what our what our data says based on the way we ask the question is two-thirds of leaders and then 69% of um leaders actually say that they they now regularly use AI as an input in in critical business decision making. And so um, again, if it's if it's one point of data that they're triangulating, I totally think honestly that number should probably be even higher. But um I think the you know that that that person that was sitting next to me at the at the table the other night made a really good point that the data is really where where things come into place. And I think, and again, being the marketer at Gong, I think that's why we are seeing that like domain-specific solutions that are able to understand the context of all this unstructured data and put it together in a usable way within the context of the workflows in the business, are correlating with stronger results than maybe some more general purpose solutions. Um, so yeah, do we trust the way the LLM is actually interpreting and and and uses the data? And then most importantly, do we do we trust that the data we have is accurate? If it's manually input CRM data that I'm driving my AI off of, I should probably be a lot more skeptical than 67% of people actually just saying, like, yes, I I trust those. Got it, got it.

Seth Marrs: 23:49

Cool. Dan, thanks so much for taking the time. It's great to have you on. Um, yeah, I really, really love the report.

SPEAKER_00: 23:56

Yeah, thanks for having me. It's always a pleasure, Seth. Uh we did a lot of fun work together and uh it's awesome to get to uh to work together.