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Connor Jeffers, CEO of Aptitude 8, interviews marketing, sales, and customer success leaders about how they are using artificial intelligence to innovate, optimize, and scale their go-to-market operations.
Connor: you guys are doing it and
doing it at a level that most folks are
still trying to figure out one on one.
If you're looking for.
What does great look like?
I think what you just described is
absolutely the definition of that.
Lindsay: Would it be helpful to
dig into some of these specific?
Connor: Yeah, absolutely.
Hello and welcome to go to market with
AI, a podcast for sales, marketing,
and customer success leaders using
AI to scale their growth operations.
I'm your host, Conor Jeffers.
And in today's episode, we are talking
to Lindsay Rothlisberger, director
of revenue operations at Zapier and
previously at Unidays and Oracle.
With over 16 years of experience in
marketing and a passion for transforming
ideas into impactful strategies, Lindsay
brings a unique blend of strategic
foresight, People centric leadership
and a relentless pursuit of driving
business impacts or optimized customer
experiences and operational excellence.
We talk about real AI use cases.
Zapier, how Rev Ops is uniquely
equipped to drive AI and organizations
and all of the incredible work
that Lindsay and her team is doing.
I think this will be our best one yet.
Let's listen it.
Connor: Lindsay, hello and welcome.
Lindsay: Hi, thanks for having me.
I'm so excited to be here.
Connor: I'm so happy to have you.
you are my capstone,
headliner, end of season one.
person.
And I think the combination of RevOps
and Zapier and your illustrious
career is extremely interesting.
And I feel like I got off of
the highway and took a hard
right into entrepreneurship.
And, had I not done that you are
in one of the couple of dream job
categories that I would put up there.
Lindsay: Oh, yeah.
Wow.
That's quite the compliment.
Yeah, I feel very, very lucky to
have sort of fallen where I am today.
It's been, I think also at the cusp
of like, I feel like I started my
career when marketing automation
was really like a new thing.
So to see it evolve, like toward AI
now is like just really exciting.
Connor: Well, I'm curious to sort
of just start from like, If you're
marketing automation into RevOps now
more into AI generally, which I think in
hindsight is like, oh, that makes sense.
But at the time, these are all brand
new things of sort of what's the
path that you charted background to
where and what you're working on now?
And then we can talk about
all of the cool, amazing
things that you're doing that.
Get us so interested in learning about.
Lindsay: Yeah, that sounds great.
started my career in event
marketing and demand gen which
during that time, I was always just tended
to be very interested into both, like why
we were doing the marketing activities
that we were, and then how could we
do them better and more efficiently?
So that led me into a marketing ops role
where I got to figure out like the how
of how to make these marketing campaigns
much more personalized in these much
more scaled great customer experiences,
which was sort of new at the time.
and really interesting for me.
I'm particularly excited
about RevOps though.
I kind of made the transition from
marking up ops into RevOps here at
Zapier actually a couple of years ago.
And I found that RevOps really
combines like everything that I love.
it's allowed me to be
able to find these like.
Disparate go to market motions across our
go to market org and figure out how to
connect them, make them more efficient,
use automation and AI and workflows to
really like grow and scale efficiently.
and in a way that is like.
It creates a great customer experience,
which I think is also really interesting.
I'll just like plug, I think
RevOps is becoming just such a
critical function in organizations.
Because we have this really unique
view across the entire customer
experience, as I mentioned, and then
also like this understanding of how
everything works under the hood that
nobody else in an organization has.
And that gives us such a leg up.
When it comes to being able to contribute
to strategic decisions for the business.
So I just think that's a really
interesting specialization that
I've sort of found myself in and
being at Zapier at a place that.
Like services RevOps orgs like us has
also been just really cool because
I get to sort of like connect to the
customer in a really unique way as well.
and sort of like provide product feedback
and learn, based on the use cases that we
have and what might work for customers.
So that's been a dream really.
Connor: Yeah.
How were you the first RevOps
official person at Zapier?
Lindsay: Yeah.
So I was the first marketing ops hire
Connor: Oh, really?
That's wild.
Okay.
Lindsay: And we did not have
a sales team at the time.
We've been product led
for a really long time.
So marketing ops at Zapier was more
oriented around like the personalization
at scale and like, how do we use marketing
and our product to, to fuel growth.
And then it, since adding in sales.
We hired a couple of sales ops folks,
a CS ops person, and we made the
decision to centralize into a unified
RevOps org, which just works so well
for our business model because sales
cycles can be quick subscription based
products, like the tight coordination.
For Ops is just more important
in a lot of ways than like
the stakeholder coordination.
Connor: Sure.
Sure.
Sure.
Lindsay: Yeah.
Connor: And does RevOps at Zapier
own like service marketing?
Say what's the breadth of like the
RevOps department, the RevOps function?
Lindsay: Yes.
So we own audit, but all of the day
to day automation and operations
process workflows for go to market.
And that includes marketing,
sales and customer success.
And then functionally.
We own strategic insights and analytics,
so we have sort of a specialization there.
And then sales enablement, which is
also CS enablement and and tooling
and technology and integrations.
So the sort of like platform
of our operations teams.
Connor: So for anyone listening what
Lindsey is describing is the modern
best in class RevOps organization.
And it's so funny because I
could sit here and ask you
about RevOps is happier all day.
and that's very much my
background and my love.
But I think while we are here for AI and
GTM, I think what's super interesting is.
I, I was just even this morning on the
RevOps bootcamp with HubSpot Academy
talking about for people who are like
getting into RevOps the first time
and trying to build that department
and trying to build that organization.
And it is so interesting to go from that
conversation this morning, which is very
one on one to you guys are doing it and
doing it at a level that most folks are
still trying to figure out one on one.
and if you're looking for.
What does great look like?
I think what you just described is
absolutely the definition of that.
Which maybe gives you the jumping
point for the next of how AI
is coming into GTM at large and
RevOps driving that at Zapier.
Lindsay: Yeah.
No, absolutely.
I think AI.
Is going to really transform the
platforms, the tooling, the product
landscape over the next several years.
I think.
Longer term, what we'll start to see
is these, platforms and solutions that
are so much more easy to customize
for your unique business needs.
Being at the forefront of like the new
tooling and tech for go to market is is
a really great place to be right now.
But I think like even just in the
short term, AI is going to make
go to market teams, just like.
10X more efficient and effective.
When you think about qualification,
campaign development, like intent signals.
In the short term, there's
just so much that you can do
I think to harness AI today, that
then is going to make you really
well positioned as this, like
transformation happens over time.
I also think you called out something
really interesting that I wanted
to highlight too, which is that
RevOps and AI, like, I hate using
jargon like synergy, but like,
Connor: It's true.
I agree.
Lindsay: pair, like, literally when it
comes to excelling at both personalization
and efficiency, like, that's exactly
what we're trying to do in RevOps
and like AI is, I going to be such a
powerful way to like 10x those outcomes.
Connor: I think to your point, something
that you mentioned around having a.
holistic view of the entirety
of that customer journey and all
of the teams in the processes.
I think one of the things that ends
up happening with Bad or just sort of
phase one deployments of some of the AI
tooling is there's so much that happens
in our world that just goes through like
the human being litmus test of a filter
that no one documents and no one writes
down because no one needs to check.
Like, it's just very obvious.
Like, oh, that's wrong.
And there is no check on that with AI.
And so you have organizations
where They want to deploy this into
part of the function, and then it
rolls to the next department, the
next function that's downstream.
And they're like, what on earth is this?
Now that you just ran this through
AI, like, this is terrible.
And I think as a result, RevOps is
really uniquely positioned to say, these
are the actual areas of opportunity
versus this is just the thing that
maybe hurts me the most, or I have
the least capacity to and RevOps can
probably deploy that more effectively
than other groups might be able to.
Lindsay: Yeah, like my mantra is
RevOps is closest to the problems
like RevOps is closest to the
problems around how things work.
And even just at Zapier, like our RevOps
team being being so close to like the AI
implementations, we've started leading
these internal AI strategy forums where
we actually share out like learnings
from AI with the rest of the business.
And I think that's been a really
useful way to like get feedback and
buy in and ideas, but then also be
like the facilitators of the change.
Connor: who's involved in that?
As a peer, is that like you,
someone volunteered and said, Hey,
we think this is important and
we're going to go and lead this.
And it's kind of like a center of
excellence or a working group or like,
what's the level of formalness to that?
Lindsay: Yeah.
Well, Zapier has really truly embraced
AI across our entire organization,
like even within our product itself.
So we have some really Smart folks
who've been working on AI projects
on the product and engineering side.
And then we have senior leaders
involved in those forums as well.
So like senior leaders in go to
market and then across other AI areas.
and so it's them.
And then it's a few members of
my team or whoever's working
on an interesting AI project.
And it allows us to like, share
ideas, get feedback share resources,
because all of this is so new.
It's allowed us to like, just
to have a little an element of
collaboration from like different
specializations across the org.
Connor: I think a lot of
organizations get stuck in this.
We don't know how to start.
We don't know how to get people engaged.
We don't know how to educate folks.
And it sounds like you've really
leaned into, we're going to
be that center of education.
We're going to really,
Share that information.
How are you identifying
what to show people
and
Connor: what gets them excited, what
gets them engaged and from sort of this
AI center of excellence that rolls to
the rest of the organization that they
latch on to, they're excited about.
And they, it kind of makes it click
versus being maybe scary or hard?
Lindsay: So the first step was
creating ample space for this.
So in our RevOps team, we are busy people.
We have a lot going on.
Connor: You haven't solved
all of the operational
Lindsay: No, like, as we're
still struggling with reactive
versus how do we be proactive?
How do we
Connor: Always, always
Lindsay: these, like, needs coming
down the pike and being 10 steps
ahead, but we really, really had
to be intentional about carving
out time in our week to be able to.
experiment with AI, whether it be
building a workflow that uses an
AI step to unlock a new use case.
So we even had an AI hack
week at Zapier where everybody could
sort of set aside a whole week and just
play with these new tools and learn.
And so setting aside time is
the first step to explore.
The second thing is actually
being able to show results.
I think Is really what drives the buy-in
cross-functionally when we can show, and
I have some specific use cases we'll walk
through later, but when you can show that
you've literally opened up sales capacity
by two demos, a rep per week with this
one use case a lot of people internally
start listening and want to make sure that
you're supported and that you have the
resources you need to double down there.
So I think once you latch on and
you find something that works, like.
Going deep with those types of use cases
and really pushing the boundaries to
show those like very tangible results.
Connor: I feel like something
that everyone forgets when
new stuff comes along.
And I just thought that answer did
such a excellent job crystallizing
this, where it's like, well,
how do I get everyone to care?
And how do I do it?
You're like, we'll show them
that there's results that they
care about and then they'll care.
And it's the same as all things forever.
And it may be a very new mechanism
of doing it, but I think the
process by there is the same and.
I think that is extremely actionable.
I'm eager to jump into like what
you guys have been working on
and what you're seeing in the GTM
org and the more tactical stuff
that you guys are deploying.
And even the we're opening rep
capacity alone is super interesting,
but what's the coolest stuff
that you guys are working on?
Lindsay: yeah.
So I 1st kind of want to break down
into, like, the different sort of,
like, categories of problems that we're
solving with AI, because I think it
helps sort of crystallize and I can show
exactly sort of where we're focusing.
So when you think about, I, there are
a few different ways to implement it.
So you have sort of this use case where
you're using AI as like a concierge
or a co pilot where you have someone,
like you have chat GVT, or you have
some tool next to you assisting you
as you do some tasks, like creating
marketing content, writing blogs, like
developing new strategies or templates.
So I think the concierge use
case is very interesting.
But I think where you start to see AI
being even more powerful is when you get
into using it to analyze information.
So pulling out trends, pulling out
insights, like you actually don't
have to be a data scientist anymore.
If in RevOps, you have a strong
business acumen and you can think
strategically about what problems are
we trying to solve as a business to
ask the data, the right questions.
The technical skills become a little less.
Important, which I think is
really interesting opportunity.
in RevOps And then our
main group of use cases.
So we've got the concierge.
We've got analyzing information at the
main use case and where we're finding
the most immediate value is around what
I, I don't know another way to sum it
up other than synthesizing information.
So you're taking information across
several sources or inputs, and then using
it to using AI to create outputs that
mean Faster access to key info for your
reps and then much better personalization.
And the the way that we're using AI
Zapier is mostly utilizing our own tool.
So Zapier, so like using OpenAI and
Zapier together in our workflows.
But I also would be remiss if I don't
mention Similar to this vein of analyzing
information and synthesizing information.
You've got all of these really
interesting and intent tools on the
market that I think are going to get
so much better and more sophisticated
around outbound and demand gen at scale.
So, think, no more spam outreach.
Like, I think we're going to get much
better at, like, reaching people at
the right time with the right message.
So there are a lot of really interesting
tools on the market that I think are
going to be pretty groundbreaking when it
comes to like sales efficiency and doing
effective demand gen and sales outreach.
Would it be helpful to dig
into some of these specific?
Connor: Yeah, absolutely.
I think what I'm so excited about
is especially in talking to you is I
think previous episodes of the show
have been Talking to folks who are
building AI companies or AI products.
And so we get into like a lot of product
we get really, and then maybe like
conceptual and some arm cherry stuff.
And the tactical area that you guys
are hands on doing real things is so
interesting and I'll give the plug because
I think you did it very subtly, which is
if anyone has not checked out Zapier's
AI functionality, it is probably the
most go to one that everybody references
as, have you seen this of being able to.
Just write what you want to do and
get automated actions out of it.
It is pretty mind blowing.
And so I'm really excited to hear the
actual stuff that you guys are working on.
Lindsay: yeah.
So our RevOps team I might've
mentioned this before.
I think it's around 50 percent of
our Zapier workflows now utilize
some form of an AI component.
I think being as happier has given
us this unique leg up because we
have the ability to actually use AI
in our workflows, which I think is
like, just can be extremely powerful.
So the first use case
that I want to highlight.
is lead routing in particular.
So we have a contact
sales form on our website.
We get a fair amount of
inbound traffic which is great.
And so we, you know, typical
form, we're able to collect
the information that we need.
And we have this hypothesis
where we thought that maybe the
free text portion of the form.
Could do a better job of getting
customers to where they needed to go
than the traditional drop down menu.
Connor: select a handful of things.
Like what's your use case that
where are you trying to go?
All of those pieces.
Lindsay: Exactly.
Exactly.
So we set up a test where we
looked at the inbound free text
information coming in from customers.
And we were able to correctly identify a
pretty large volume of support inquiries.
People who just needed technical
support with Zapier that weren't
really meant for sales requests, but
they needed to get to the right place.
Like
Connor: Yeah.
And they're hitting
whatever form they can.
Cause they can, they're just like,
can someone please get back to
Lindsay: We want to have
them have a good experience.
And also it filled up.
It's also not great for our sales
team because those aren't the right
conversations for them to be having.
Connor: Sales reps love getting non leads
Lindsay: yeah,
Connor: lead rotation.
It's their
Lindsay: exactly.
So they were very happy with
us when we figured out that.
And now this is, this form is a
hundred percent powered by AI to
identify these unique inquiries
and get them to the right place.
So we've been able to correctly
route our customers more successfully
than when we have them self select.
So that's an interesting use
case there, but I would say
another really powerful one.
And I kind of framed this up as.
Go to market alignment type use
cases, but also customer experience.
So, as you know, like when you work in
RevOps, you are responsible for making
sure that customers have a seamless
experience across marketing sales and CS.
So when a deal closes and then the
CS person or the account manager
takes over, like there's a lot
of context that they're missing.
And so our refs were spending a lot
of time kind of walking CS through.
through like the use cases or the
things discussed during the deal.
So what we do is we, this is one of
this is a really powerful workflow
for us because it saves about
two to three hours of like prep
time between those two functions.
We take all of the notes and deal
information and we summarize GonCon
transcripts of like what the customer is
interested in, and we use Zapier to like.
Connect to those sources, have an AI step
that summarizes the customer experience
up until this point, and we and and
then it automates a handoff to customer
success of customer success sort of
has all these notes and information and
so they're ready to, like, really kick
off a positive working relationship
with the customer off the bat.
Plus it saves a lot of time internally.
So, that's another use case that we love.
And we do something similar
for product as well.
So when we're having,
Connor: like, we, you haven't even
told me what this is yet, but this
is one that I'm so excited about
because I feel like AI is going to
be the world's best product manager.
Because instead of.
Sales told me this, I'm going
to interview these people.
It's aggregate all of the
information and kick it back.
Lindsay: yeah, you nailed it.
You already know this use case, basically,
In a product led org, it becomes so
much more important for product to
get the feedback back from customers
because we're using our product to
fuel a large portion of our growth.
And so those feedback loops are crucial
and that's another really unique
role that a RevOps team can play
that I think often gets overlooked
is how do you also like align.
Go to market and product teams.
And so we take Gong transcripts.
Again, we do a lot with a call
transcripts and we Analyze those.
We use an AI step in his app to look at
all of the product feedback, mentions
of new products, things the customer
got stuck on or didn't like, and we
summarize that and we send it off.
We aggregate it, send it off to
the appropriate product manager
based on the piece of product
that's mentioned.
Yeah.
So it's
Connor: whole extra routing workflow
on top of those individual pieces.
Lindsay: Exactly.
Exactly.
So that's completely automated.
And then reps and product managers
don't have to sort of go back and forth.
Let me get on a call.
And of course, our product managers
still talk to customers, but this can
even help them pinpoint like which
customers they should be talking to.
Connor: Yeah.
Instead of like the age old slack
of, does anyone know a customer that.
Insert here and hoping
someone gets back to you.
And then when you talk to that
person, it's maybe the right people
to even be on the phone with,
Lindsay: Exactly.
Exactly.
I have a lot more.
I can keep going.
Connor: give me another one.
And
we'll talk about other
Lindsay: cool.
Okay.
So the, this next one, sales
efficiency is a huge focus for rev ups.
So we're always looking for ways.
How can we make sure sales is spending
the right time on the right things, and
we can minimize admin work and we can
just make their lives so much easier.
So there's a few things that we do around
sales efficiency that we use AI for.
So.
Again, with call transcripts, we can
actually take data from those calls
and use that to populate our CRM.
Think about things like additional
stakeholders that the person might've
mentioned on the call, their budget,
their timeline, things we might ask
reps to manually take note of and
like add into deal notes, et cetera.
We can automate that with AI call
prep guides and post call follow ups.
That was one of the first use cases,
the most obvious low hanging fruit
where our reps are having to check
a lot of sources to prep for a call.
They might want to look at the
details of the support tickets.
This customer has had
or their product usage.
They might want to check the
web to look at what What types
of things this company might be
interested in based on what they do.
And so what we do is we use AI and Zapier
tables to combine all that information
into a call prep guide for the day
so they can go in and they can see,
this is the customer I'm talking to.
Here's the synopsis of this information
that's going to inform my call for them.
Connor: and is that in And structure of
that is essentially data intake across.
Recording support systems, a whole
bunch of other sort of data sources
and then combining all of that.
And then is the output a doc?
Like, where is the rep consuming
that particular piece of information?
Lindsay: We've sort of
gone back and forth.
This has been iterative.
We used to have it in a note in
HubSpot and I think we do still
have it there but we also created a
Zapier table that the reps prefer.
They want to see their list that, they
want to see things in a certain way.
Connor: Everything goes back to like, can
we put that in this spreadsheet though?
Lindsay: Yeah, but at
least it's a Zapier table.
And so, so yeah, right now I think we're
in a table where there's a column for like
company use cases, support tech tickets.
And then we also have that
for the post call follow up.
I think the post call follow up is
actually a note in HubSpot where
it gives them a suggested email
template suggested next steps.
Steps based on the call
transcript as well.
And that's been.
Really, really useful for them to
be able to like, have that info at
their fingertips and just save time.
the last use case I want to
talk about is my favorite.
So, and this is our
newest, our newest one.
So like one of the problems, so we have
one sales enablement person and he's
wonderful but he only has so much time.
And then we have, our sales
managers are super busy as well.
and coaching in sales is just.
So important and consistency around,
like, the narrative for sales.
So, our sales enablement team member,
what he actually did is he he created
this method of taking call transcripts.
So Gong transcripts then using AI.
And he wrote this prompt that
looks at our sales methodology, how
well the rep is doing discovery.
Negotiations, objection handling,
and he creates a database of
feedback for each rep that then
the sales manager can reference.
So then the sales manager doesn't have to
go through every call, every transcript.
And it's actually a huge
result right out of the gate.
We literally launched this a couple
of weeks ago and we're seeing we've
actually 3xed the amount of feedback that.
Managers are providing to their reps and
we've already seen like the consistency
in ours Zapier pitch just improve.
Connor: I think you're totally right.
And I think that the coaching
element is going to be so powerful.
And I think what people in every role.
Really crave and want is help me get
better and teach me what I don't know.
And too often I mean, always managers
are slammed overwhelmed and the ones that
are the best are nights and weekends.
I'll listen to call recordings
and I'll try to get back to you
on some stuff when I have time.
And I think synthesizing that and doing
that real time coaching element is so
powerful to make people more effective.
Lindsay: Yeah, it's this one
has been particularly exciting
just because it's saved time and
it's improved rep performance.
Like, what a win.
What a result.
The other thing I just want to
call out to is, like, all of these
use cases that I went through,
they were not done by 1 person on
the team who's like an AI expert.
This was really across the entire team,
a lot of different people experimenting
with a lot of different things, trial
and error and some folks not technical
at all, like, and able to implement
these solutions pretty much autonomously.
Connor: What do you think the
key is that a unique attitude?
Is it a unique environment?
Is it that you guys have
the tools exposed to people?
I feel like that's where a lot
of organizations get stuck.
Is there like, how do we start?
Do we need to go higher ahead of AI?
and you're basically suggesting like,
no, you don't need to do that at all.
And do you think there's anything unique
about Zapier about where you, what
you're doing that enables you to do that?
And is it a hard or is it a soft
thing that other people can embrace?
Lindsay: Great question.
So at first I was Not convinced that
everybody on the team would be able to
pick this up out the gate and run with it.
I guess what I thought initially was
that what we would do is we'd have one
or two sort of AI evangelists on the team
who are just super skilled at this and
can go just focus on the AI workflows.
And I don't think that's
the right approach.
I think that the right approach is
inspiring your team to focus on what
problems are we trying to solve.
And AI as a tool to getting
there more effectively.
And so that mindset and building
that muscle again, of like, I have
this tool at my fingertips, I know
how to use it like at a high level.
Yeah.
I have to like tinker a little bit to
kind of figure it out at first, but
knowing what the goals are, like our
enablement manager, like he was like,
I got to get managers coaching more.
I got to get managers coaching more.
I've got to improve consistency.
What are the ways I can do that?
And AI becomes a very natural Easy
solution to be able to go solve that.
So I think not thinking of AI as sort of
this like separate entity that we've got
to go figure out, but like, what are the
problems that we're solving in RevOps?
And how do we really
leverage this new technology?
Connor: I think that's incredible.
And I think something that you said,
which is a phrase that I repeat constantly
all the time, which is what is the
problem that we're trying to solve?
And.
You can get very far
with just that phrase.
And I think that combining that
with a framework on where is AI
going to give us a superpower on
actually solving this problem.
Is there anything that you found that
isn't a fit or something that you
guys have tried to do that didn't
work or areas that you are like, don't
apply AI to this particular thing?
Lindsay: Yeah.
So we did a lot of experimentation
around, can we use AI to generate
automated marketing emails?
And we kind of got it there, but it,
we did a lot of experimentation, like
we were a little nervous, like if these
emails are going out to customers, like,
are they going to be consistent enough?
Is the AI going to go rogue?
So I would say.
What we were leaning in heavily in that
direction early on, because it felt like
the most obvious thing, like, how do
we generate marketing content at scale?
And so I would say it was like,
okay, but I didn't find that, like,
the emails that we sent that were
AI created did a whole lot of a
better job than just our most simple.
Segmentation could do.
And so we've really started to, when
we, I think the first thing was that
call prep use case, and when we really
started to see like, Oh, we increased rep
capacity pretty significantly with this.
And then we started to lean a lot
more into the efficiency use cases.
And like, I think what's really
interesting about revenue facing
or revenue driving teams and
customer facing teams is like.
Not only does it really matter from
a revenue perspective, how much
time we're spending on these things,
because of course you want to be
focused on revenue generating stuff.
But secondly, they are the
front lines with the customer.
And so they're almost embodying
like that experience for a customer.
So if we can arm them with all
of this information that has just
had a much bigger impact for us.
At least.
In the short term and for now.
Connor: if I was to summarize that back
to you it would be focus on ways to drive
efficiency and enable your human folks
to be more impactful versus automating
away the humanity from the interaction.
Lindsay: Yeah.
Yes, I think so.
I think so.
I think that touch point is crucial.
Like companies are human.
I found that,
Connor: the good
Lindsay: yeah, the good
companies are human.
Like, I wouldn't want to
take that element away.
At least not right now, who knows what
I'll, I mean, AI is moving so quickly.
It's really hard to make guesses right
now, but, it's definitely helped us
improve customer experiences overall.
Connor: That's amazing.
In terms of sort of the areas where,
is there anything that's in the,
like we Don't aren't touching this.
we're, this is in the,
like, do not AI category.
is there anything that is in that
versus maybe things that we've
experimented with and failed?
and is there something that
maybe is in that category that
you don't think should be there?
Lindsay: That's a really good question.
I would say at Zapier, we're sort
of like, let's shoot for the moon.
Let's try it.
let's go for it.
Let's see what happens.
Let's take some risks.
Think in the tech industry and at
Zapier in particular taking risks,
making, big swings, creative bets.
What I will say is I think early on.
We probably weren't taking big
enough bets, like, I think early
on we were, incorporating AI sort
of a little bit here, a little bit
there, but we weren't thinking of
it as like, how do we 3x 3x sales?
Capacity, like, and I think we've
sort of evolved toward, let's be a
little bit more focused now that we've
found some wins and we found some
areas where we're having an impact and
let's like go all in and make that.
Workflow or that process like even better.
So I would say for us,
nothing is really off limits.
We're, we're trying it all and seeing
what works and learning as we go.
Connor: So, so if I was to, and
I'm going to start, I could.
Ask you about all of this all day.
and you guys are bleeding edge.
So I describe not only, I think the RebOps
organization is absolutely the best in
class structure and the things that you
guys are doing are very bleeding edge.
And I, I say that from seeing a lot
of stuff and thinking it's pretty
sophisticated and a lot of what
you're describing is outclassing that.
So nothing but admiration for.
The work that you're doing and I think
key takeaways that I would grab from
this would be surprisingly bigger
goals, try to solve bigger problems
which is the first time that I've heard
that in these conversations, which I
think makes it extremely interesting.
We have the concierge use case, the
trends and insights use case, the
synthesizing of information use case.
The coaching use case is sort of the core
ones that maybe to look into and evaluate.
And maybe the last thing that I would ask
you with in terms of a closing thought
for anybody is you guys are bleeding edge.
I think you've given amazing
tactical things for people who have
a foundation in place for anyone
who is on the starting line and is
saying, this is really exciting.
I'm extremely inspired
by what you're doing.
And, We don't even really
know where to start.
What's step one?
Lindsay: Step one just Do it like,
if you have access to chat, there's
a lot you can do with data analytics.
If you have access to
their more premium plans.
And then when it comes to automation,
like Zapier has a free plan.
You can start playing around with it.
I would say like, you really
just have to lean in there.
I think the thing that's a little
bit tricky is there's not a whole
lot of content out there, right?
There's not like a whole lot
of courses on this stuff.
there's not a ton of materials,
but I think there actually is.
There are some now, like if you.
Search these RevOps
communities and the internet.
There are lots of materials and
videos that can kind of help you
get started and walk through things.
Zapier has a great Zapier community
and Zapier learn resource too,
if you're looking to explore
with Zapier in particular.
But I think, yeah, just try it.
I think in RevOps, we're
such tinkerers, right?
Like we love new tools.
Like we're the perfect people to
like, kind of just roll up our
sleeves and see what we can make.
Connor: Love it.
I think if you're in RevOps, take
this as Lindsey's call to action.
and for, last question for me is,
when, how, you guys have only been
doing AI stuff for the, I mean, we're
talking maybe a year and a half max?
Lindsay: Yeah, I would say our
first use case we implemented was
probably just about a year ago.
Yeah.
Connor: So take all of the progress.
Lindsay, what you guys have done
in the last year is incredible.
And it makes me really excited.
I'm leaving this conversation with
a whole laundry list of stuff.
I'm like, Oh man, we gotta go do that.
And across the board.
And thank you so, so much for
joining us, sharing your insights
and sharing what you guys are doing.
It is incredibly amazing.
Lindsay: Thank you.
Thank you so much.
It's been so much fun talking about this.
So I really appreciate you having me.
Connor: Absolutely.
I hope to catch up with you more soon.
Lindsay: Definitely.