Seth Marrs: 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: 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: 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: 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: 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: 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: 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: Right.
Seth Marrs: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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.