Zendesk CEO: AI Customer Service Agents Are Ready For Primetime

Channel: Alex Kantrowitz

Published at: 2025-03-26

YouTube video id: -araPrJQodA

Source: https://www.youtube.com/watch?v=-araPrJQodA

If you're like me, you've been wondering
how much of the conversation around AI
agents is hype versus actual reality.
Today, we cut through the noise with
Zenesk CEO Tom Egimire as the company
launches its AI powered Zenesk
resolution platform in a conversation
today brought to you by Zenesk. Tom,
great to see you. Welcome to the show.
Great to be seeing you as well, Alex.
All right, so you are working on AI
agents. Uh it's a term that we've been
hearing all over the place from almost
every AI company and every nonAI
company. Uh everybody's been talking
about agents. Um the one thing we're
trying to figure out on this channel is
what's real and what's not and it seems
like there's a lot that's not. Um but
rarely do we get to speak with someone
who's actually developing the technology
ground up and that's you. So just give
us a sense as to what the lay of the
land is right now. um where we stand
with AI agents, what's real, and what is
hype. So, I think AI, you know, I'm old
enough to remember when 20 years ago
there's just going to be this AI
revolution with machine learning and
predictive analytics. I hate to say it,
but old school AI. And I think there was
a lot of hype there, but there wasn't
much delivery. I will tell you overall,
I think large language models are
actually changing the game. You know I I
believe in the old adage uh that you
know technology uh impacts in the short
term we overestimate in the long term we
underestimate. I think that's what's
going to happen with large language
models for customer service in
particular. U we are seeing AI agents
really transform our particularly our
business to consumer customers. We're
seeing some people get 60 70 80% what we
call automated resolution. So a consumer
comes into their business, they can
actually solve the problem with an AI
agent 60 70 80% of the time. We see B2B
businesses lower like 20 30 40%. Um what
the hype is probably is I see a lot of
people go out to companies and say we
can solve 80 90% of your interactions
with your customers. Not solve them but
I mean solve your customers problems in
a matter of we'll implement in a day.
we'll have we'll be have you up to 80%
in a matter of days. It's all going to
be good and you're going to have no
mistakes. And so I think that is the
hype. I think there's still some hard
work behind uh getting people to 60 70
80% even if they're a business to
consumer uh company and 20 30 40% if
they're a businessto business. But um I
think um the hype is real overall. AI
agents can solve consumers problems. I
think we're going to get into there's a
little bit of a hype right now about you
having your own personal bot uh
contacting a company. I don't think
that's working really really well yet,
but it's going to come. And so I think
the hype is actually justified and we're
seeing uh the impacts with our customers
right now. Okay. So let me talk to you
about this for a minute because we've
been hearing about automation. There's
been robotic process automation before
there was AI agents. Um there have been
automated customer service chat bots for
a long time. So I I'd love to just pause
here for a minute to for you to talk
about what the difference is between
let's say 3 or four years ago and now
and then even more than that what are
the type of ramping up uh capabilities
you've seen from AI generative AI
companies recently that's allowed you to
be able to do more than you have been
able to do in the past. Yeah. So I think
I think a couple things are different
first um agentic AI buzzword. Okay, I
recognize that. But I think bots are
actually able to reason now where in the
past 3 or four years ago, you would have
an AI bot, but behind the covers it was
like 95% rules-based. If X do Y,
decision trees, things like that. And so
people would, you know, triumph it. Hey,
we got a really great AI agent. It
really or AI bot. It really was
rules-based you know was 90 95% of the
underlying technology with Agentic AI
and with the latest large language
models you are really able to have take
a input into information uh take the
large language model do some post
training and it can get the right answer
just an incredible amount of the time
and it can reason and so I think that's
one of the differences. The second
difference is you're able to personalize
them so easily right now. Um I can't use
the uh pop star but think of a major pop
star uh and she was uh uh she was
releasing an album uh and um she wanted
to have an experience that um the u the
uh bot uh the AI agent was going to be a
little more in her tone uh when she was
interacting with her fans and we were
able to personalize that you know in a
way that the fans had just a truly
fantastic experience like that they were
talking to the artist and I don't think
You could have done that personalization
really easily three or four years ago
and I don't think you could get to the
personalization of a company that they
wanted to have really a tone with the
brand. I know we have an options three
like default options. Do you want a
professional? Do you want casual? Or do
you want some kind of hybrid and and and
do you want to go in different languages
with the customer? There's just a lot of
personalization that's easy to turn on.
Finally, Alex, I'd say it's easy to get
up to speed. In the past, you would do a
lot of customization and there are six,
nine, 12month pro projects because you'd
have to take all this data in. You'd
have to go put all these decision trees,
these rules, you'd have the bot on top
of this. You know, we can get a bot or
an AI agent up and um running in a
matter of days um with a lot of
personalization. So, I think it's a lot
easier right now to go implement um and
to get a high resolution rate. So, for
all those reasons, there is some hype.
Don't don't don't don't don't uh don't
misunderstand me and I don't want to be
too uh on top of the hype cycle, but
we're just seeing like really really
strong um uh automation rates and strong
impacts on our customers. And we are
going from I think that you mentioned
we're going from uh if then statements
basically to a broader range of
possibilities and you're going to see
more powerful stuff come out because of
that. And we just had a conversation
with the people that built the new Alexa
Plus here on the channel and they are
also saying yeah we we basically I mean
my reporting said it was a a you know if
entry they want to talk about the new
technology they're now moving to
something that leaves uh a much more
broad space of possibility open to their
new assistant. Now there's an
interesting component to that which is
you go from uh deterministic technology
where it says you know if this happens
then this should happen. If I say, you
know, uh, a word, and I won't say it
because it will, that, not that a word,
the a word that summons this assistant,
you know, turn on the, um, the lights,
it knows, okay, I'll turn on the lights
and the lights go on. But when you're
living in the world of large language
models, it's there's so much more that's
left open to chance or probability. Uh,
you're in more, as they call it,
probabilistic space. So, I don't know.
We we still don't we still haven't seen
Alexa Plus. Maybe it'll be come out by
the time this this interview hits. Uh
but I don't know how you do that in
business. Uh because you really in a
business uh you're kind of risking a lot
if you start to leave that open. So how
does that work? Yeah. So we we take um
you know some off-the-shelf large
language models and we actually have um
18 billion interactions that are um that
are anonymized uh in a database with
people rating customer uh interactions a
positive or negative. Real simple thumbs
up, thumbs down. And we do a lot of
post-training on our models, the basic
models after the fact to try to get
their probability to your point, win
rate, their resolution rate as high as
possible. And what's interesting um you
know when a when a bot makes a pro an
error they do make errors it's a
hallucination when a human makes an
error you know it's a mistake and what
we find right now is on our next
generation agentic AI bot um we have a
lower error rate than a human being uh
from a you know contact center support
center answering like for like inquiries
and so when we talk to our customers we
say look there are going to be some
errors 100% it is going to happen. Um,
just like a human being, no matter how
much you train them, how much you work
with them are going to have some errors.
But what we're finding generally is you
can serve your customers at a a much
lower cost. you can get their your
customer satisfaction up because if you
have a personalized bot that can
instantaneously respond versus waiting
online for 5 minutes or 10 minutes for
someone to respond to your email, your
message, your chat, your web form, your
phone call, you're generally more
satisfied and um the error rate is
actually lower. Okay? And so there's
some things that you can uh protect
yourself that you could hardcode in
still like hey I don't want to share any
personally or take in any personally
identifiable information to the bot. You
could have a like a hardcode rule to
protect some things particularly for
regulated industries. But overall bots
are quicker, they cost less uh and they
have better customer satisfaction. So
you know I think it's a win-winwin if
you implement them correctly. Yeah.
Yeah. And there were some early examples
that were floating around the internet
of I don't I don't think it was Zenesk,
but like companies setting up C bot
customer service and them like giving
them 50% discounts on a Mercedes. I'm
making this up, but it's directionally
along the lines of what it was. But what
you're saying is now you can actually
set some rules in to make sure that
stuff doesn't happen. Yeah, I think you
can you can lower the error rate. I just
just like if you train your human agents
really really well, you could still have
errors. And it's kind of funny some of
the things uh it's a competitor. So, I
hate to give them a pass, but I will.
There was a, you know, airline company
that had an issue with a bot to one of
our competitors. When they went into the
details and figured it out, it sounds
like what it was actually was a
documentation error on the company that
the bot took information in. And so,
again, this is why documentation,
knowledge, we call something a knowledge
graph that we have, you know, inputting
information. It's really really
important to have accurate information
and to scrub that information because um
you know the bot does reason the or the
AI agent reasons the AI agent gets
personalized the AI agent uh takes in
information but the the bot is only as
good as the information it's taken in.
Okay. Well, justice for the bot. This
was clearly a human error where they
uploaded the wrong stuff. And I want to
publicly apologize for defaming that bot
and hopefully uh it doesn't take legal
action against me. So I I hope so too,
Alex. And and again, I think it's gonna
be cool with stuff like Alexa, and I'm
worried I have Alexa right next to me
that it's going to turn on here, but
Alexa talking to uh an AI agent that's,
you know, powered by Zenesk. I really
think that's going to come over the next
six or 12 months, and we're going to
have some really, really cool use cases
where the consumer is there using their
own bot to interact with a company's AI
agent and it's just going to really uh
take a lot of friction out of the
system. And so, I'm kind of excited when
we see that. We've got, you know, we've
got examples of that, but on scale, I
think it's coming with, you know, Amazon
and Apple and Google and everyone else
coming out with these personal
assistants. I'm definitely looking
forward to bottobot communication. And I
do want them to develop their own
language just to be be booping the
information back and forth and ditch
natural language. Uh, maybe we Whenever
I hear that, I think of Star Wars, by
the way. I think of Star Wars, the same
kind of thing. C3PO was on to something,
right? Everyone made fun of him for
beeping, but actually turns out we're
the inefficient ones. Exactly. Was it C
or R2-D2? Anyway, my knowledge is
getting rusty. Um, so but but this is
also coming at a at a moment where you
have big news around this topic, which
is that you're launching the Zenesk
resolution platform. This is brand new
and there's lots of news in there uh in
terms of how you're going to uh enable
people to build agents and serve your
customers with agents. And I think um
like again as we started out with the
beginning there's a lot of people
philosophizing about Agentic AI buzzword
it might be but again as you said you're
doing it so tell us a little bit and
it's great to have you here as you
announce it. Tell us what is coming
today. Sure. So we're uh announcing um
at our customer event uh March 25th to
27th uh in Las Vegas our global relate
event that we're uh launching what we
call in our resolution platform. Um we
have a couple kinds of points of view
here. One that AI for service is unique.
It must h handle to our earlier part
conversation unpredictability. It's high
stakes a lot of times and a lot of times
it's a customer that's interacting
that's having an issue. So you really
need to think about when an AI agent
handles something when a human agent
handles something to solve the
customer's problem. And we've built the
resolution platform platform to solve
problems not just manage tickets. So
it's integrating AI automation and h
human expertise for real outcomes. Um
I'll talk a little bit uh at relate
about how one of our competitors is
charging by per interaction. And
companies and customers don't care if
they have an interaction, they care if
they get their problems solved. And so
we're really pushing this resolution
platform and the whole premise behind it
and how we've built it is how do we go
help companies solve their customers
problems or their employees problems or
their uh their business problems. And so
that's what we're doing. So um other
people are looking at isolated AI
agents. We're delivering an intelligent
coordinated AI and human network that's
wellrained. It's like a you know a
well-trained search and rescue team.
It's not going to replace human agents.
We think we have got a point of view
that there's always going to be human
agents. It's going to enhance them and
it's going to take off a lot of the low
um low difficult task off their table
that um the the resolution platform can
go solve. All right. So, can you just
walk us through because we'd love to
hear the practical examples. How would a
customer basically use this platform?
Yeah, a customer would use this platform
say um we what we do is we come in uh
for our larger customers. Uh for our
smaller customers um we will go real
quick analyze their ticket data or their
customer interaction data and we say we
think there's an opportunity to automate
40% of those interactions. Okay. And we
think we can go do that with you in two
or three months or two or three weeks or
two or three days depending on the
complexity. And um and so we're pushing
some things in products to let them know
what we think's going to happen. Like we
think we can go automate 40% of
resolutions. We think this is going to
lower your cost to serve by X. And we
think it's going to take your customer
satisfaction up from depending on if
you're doing seesat uh top box from a 4
to 4.4, if you're doing MPS from a 60 to
67. And so we talk about that with them.
And then what they do is we go in
through um you know an implementation
with them in an adoption phase to go get
that. We usually do an AB testing to
show them um on the um on the on the
platform that we are getting what we
promised okay through an AB test and
they are getting the customer
satisfaction. One of the biggest worries
we see with our customers is I believe
you can go automate X percentage of my
interactions. I'm I'm worried that
customer satisfaction is going to go
down and um companies love the loyalty
loop. They love making sure that they
have happy customers and that's the
biggest worry. So a lot of times we go
in and show through AB testing that
customer satisfaction for the similar
kind of interactions is actually going
to go up and once we able to do that the
the roll out the adoption usually goes
pretty quick. That's on the AI agent
side. On the human side, uh we've got a
co-pilot solution that integrates with
the AI agent solution. And so for those
kind of interactions you want to go take
to your humans, it's a really uh easy
implementation. It's part of the Zenesk
platform. You turn it on. Your human
agents are getting assistant. They're
getting suggestions. Hey, this is the
ticket came in. This is the reply we're
recommending for you. Would you like to
accept it? Would you like to change the
tone of voice uh that you're replying?
Would you like to add something? would
you like to say this is wrong and we
need to go suggest a different reply and
so that's what we go in and talk to our
customers about to really really
generally for everyone an easy
implementation but the hardest thing is
getting people over the hump that this
is going to not degragate um uh um
customer satisfaction. You said in your
example that you can go in and say
there's 40% of customer interactions
that can be automated. uh where did that
number come from and do you think that
this is going to be like an average
experience with the company? Yeah, so
what we do is we get permissions from
our uh customers and we take a look at
their interaction data. So we use an
algorithm uh that looks at all their
interaction uh data. So uh we do about
we process about five billion do five
billion tickets or five five billion
interactions a year almost. And so a
company will have a subset of that. We
look at the data and we say based upon
what we know, we have over 10,000 AI
customers right now. And those 10,000
customers, we think this password reset,
we think this return, we think these 17
use cases can be automated. And we tell
them this is 3% of your interactions
with your customers. This is four, this
is seven. And there's going to be some
of those that are going to be really
complex that are ultimately going to
humans. We break all that down and we
say these are the interaction types.
these are the percentage of those
interactions or use cases that we think
we can automate and we give them that
the data and then we say hey we've got
10,000 customers around the world using
our uh resolution platform and you look
like two or 30 hund of those customers
and these are kind of resolution rates
that they're getting from doing this
automation. So it's not a um it's not a
you know wet your thumb put it in the
air and kind of guess this is based on
uh old school uh a little bit uh
algorithms machine learning to predict
what kind of resolution rates our
customers are going to get and again it
gets into vertical the size of the
company are they B2B B TOC what region
are they in you know it's going to be
different in Germany and even within
Germany it's going to be different
between Bavaria and u maybe um the
Berlin you know region
And so we get
all
of them.
Okay, it's coming in as for this type of
uh request, let's give them a human or
it's coming in from this type of
request, let's give them robots. And
then uh I imagine the customers are able
to if they need uh get to a human if the
robot's not able to solve their issue. I
mean for me I just type agent agent
agent when I need a person and
eventually that works 50% of the times I
would say. So how's that going to work?
Yeah. So we we try to design uh some
some companies just accept hey we want
to go automate all this. Others will say
we want to automate this unless it's
Alex. Alex is one of our VIP customers.
So you can do customer. Oh that's
interesting. Yeah. And Alex no matter
what even if uh we could do an
instantaneous password reset for Alex.
We want to give him VIP service. So we
connect Alex with a human being you know
immediately. Okay. So you can put into
those kind of um you can put into how
you c you configure the system those
kind of um you know segmentation of
customers you can put in um if the you
know if it goes too long you can go to a
human. We again recommend we don't think
we think a 100% of interactions are
going to be AI related meaning uh it's
either an AI agent or it's a human agent
using AI but we think about 80% will be
automated within the next 3 to 5 years
leaving 20% of those interactions still
to go to the human being and so we think
it's important to have that whole
resolution platform that looks at
automation in the human experience other
people are doing um you know
disconnected things so if you've got
this great AI agent that's really not
tied to your human, you're going to get
frustrated, Alex, even before you go
agent, agent, agent, agent. You might
have a conversation with the AI agent.
You don't get total resolution. You want
to have that all that data, that rich u
conversation passed on to the human
agent so they're not asking the same 15
questions again. You want them to start
where the AI agent left off. And we
think that's one of the advantages of
the of the Zenesk resolution platform.
We realize it's not going to be all
automated and you've got to have these
tight links. And even just as
importantly, we figure out, we have
something called quality assurance um
that figures out looks at 100% of
interactions, whether it's an AI agent
or human agent. And we find out what
interactions the humans are doing
better. And then we try to train the AI
agents with that information. We
actually find out what the AI agents are
doing better. And for those, you know,
you still want to go to the humans for
some of those VIPs. And we try to change
train the humans to do better as well.
So you have to have this kind of
virtuous loop between human and AI
agents and our resolution platform. Tom,
first of all, if you're able to get the
human that picks up the line to be
queued into what I've spoken with the
agent about, uh, you've done us all
humanity a big service because all too
often in customer service, you go
through the same steps again. You put
the same account in. So if you're able
to make that more seamless, uh, not just
myself, but I'm sure half, you know,
half the planet, if not more, will be
will be thrilled about this. And then
you've brought up the the co-pilot a
couple of times. So what is that going
to look like in a customer service
agents dashboard? And then we're going
to move on to jobs because the questions
about what will happen people jobs are
starting to percolate. But I want to
know what it looks like in the
dashboard. Old school human agent would
get a um you know information from a
customer whether it came in in a
message, a WhatsApp, uh Apple business
chat, an email, a web form, you name it.
Okay? And then they would craft their
own reply and they'd have some canned
replies on likely interactions um that
they could, you know, like cut and paste
basically. Okay, that's kind of old
school human agent uh you know
resolutions. What happens now is if you
have even on a voice bot, but I'll we'll
put voice the side any kind of digital
interaction the platform should be
suggesting a response for you. So it's
not you pulling as a human agent, okay,
data or response into a response. It's
actually here's the suggested response.
We looked at 17 similar customer
interactions. This is the response that
got the best uh response. Do again, do
you want to personalize it anymore? Do
you want to change the tone? Do you want
to accept it? Do you want to edit it?
And so it's kind of changing the human
agent experience uh experience from a
lot of times creating things on their
own or you know pulling information in
to more of an editor where they're
deciding hey this is the right response.
this maybe it's not the right response
because um the um the co-pilot does not
res realize how upset you are Alex
because you had to type in agent 55
times to get to the human agent and
you've got a really big issue and so
then they can take over and edit and you
know profusely apologize for that
experience where maybe the suggested
response wasn't there. So, we really
think it's changing the human agent to
higher level tasks to more of an editor
to more uh less of a creator uh and able
to respond more quickly and more
accurately to customers. Yeah, look, I
promise I'm not that guy who's uh
getting mad and slamming agent. But
anyway, I always have a lot I always
have a lot of empathy for agents and I
say, "Hey, I'm I'm especially on the
phone. If I'm on the phone, I'm not
frustrated with you. I realize you're
doing your job. It's one of the most
difficult jobs in the world because you
don't get people to call and say you're
doing a great job. Uh but I disagree
with your policy or I disagree with the
outcome that I'm getting on, you know,
this particular service. Yeah. No,
definitely it is uh without a doubt
among I would say top 10 hardest jobs uh
in the world to be able to sit and
sometimes have to take take it and you
know deal with with customers that are
not happy. Um they call people call
customer support lines often because
they need an issue resolved and um and
yeah I mean that's if you're doing that
all day long that is that is tough tough
work. Yeah. Most of those jobs have 50
to 100% turnover a year in a lot of
companies and so really really high
turnover because to your point it's a
really difficult job. It's stressful.
You need to learn a lot and it doesn't
pay that uh well in a lot of situations.
And so we think like co-pilot AI is
going to help people have a you know
better job that's higher paying
hopefully and uh it's going to allow
them to have better job satisfaction
because they're able to you know answer
customers uh interactions better. But
will they have a job because you
mentioned that you want to what did you
say 80% of these interactions automated.
So I I guess like companies aren't
thrilled necessarily with the way
they're doing customer support and
customer experience because people are
just bogged down handling handling basic
queries and they don't take get a chance
to take a lot of time to spend with
their customers. So this might free the
existing customer service reps to spend
more time with people. However, there's
going to be companies out there that
will be like we can probably get the
basics done with robots and I anticipate
there will be layoffs. So what does the
job outlook look like from your point of
view here? So a couple things. One is uh
we've seen the amount of interactions
and we do some surveys on this double
the last two to three years. Okay. And
this is for a couple reasons and we
think it's going to continue. U number
one depending on what survey you look at
uh there's between about 13 and 18% of
the world economy's digital economy. and
you still have, you know, 82 to 88% that
is bricks and mortar. That's going to
still that's going to continue to move
more e-commerce. The more e-commerce
happens, the more online or voice
interactions that happen um just over
time. And so we think uh that's one
trend. Second trend is we've seen with
other technology uh changes when you
lower the barriers to entry, when you
lower friction between customers and
companies, interactions spike. So we
have a point of view that interactions
are going to go up between 3 and 5x the
next 3 to 5 years. And so what we think
is going to happen are interactions are
going to go massively up. We're going to
help companies automate 80% of them. And
you're going to have about the same
amount of human beings that you had
before in the human agent role because
of this, you know, uh bricks and morted
e-commerce shift, lower uh friction,
more um more um more uh instantaneous
responses. And what we've seen with a
lot of our companies is they want to get
even better service. And so they realize
when it goes to that 20%. So those that
uh companies that have got to 80% or
plus automation rates, they've actually
usually kept their customer service
flattish because that last 20% is
usually really a really iate customer or
a customer that has um a really really
complex or really really important
interaction. And if you nail that, a lot
of times you're going to have a customer
for life with a customer for the next 5
to 10 years. And so what we've seen so
far is people flattened out hiring just
to be clear on customer service uh human
beings or slightly up. But what they're
doing uh is um really making sure that
those people are editors more highly
skilled and really really about the
customer experience and try to drive
customer experience through the roof.
And you might have higher retention
because instead of people waiting on the
phone for 10 minutes and you being
pressured to solve a result, solve a
case in 30 seconds, maybe the companies
will say, you know what, take five
minutes, talk it through. They'll be
happier. It'll be a better interaction
for the customer. Alex, you're spot on.
There's um in the, you know, phone
contact center world, there's a the term
called average handle time. And there
was always how can you get AHT down?
We're starting to see a difference.
Exactly what you're saying is hey let's
talk about the resolution and do we have
a positive resolution for the customer.
Okay that's why again why we're
launching the resolution platform less
about AHT and more about because you
have a little more capacity if you do
the automation right more about customer
satisfaction more about the loyalty
loop. Yeah. And you've written a little
bit about Sebastian Simonowsk's uh plan
to purge customer service at um at CLA
and you said that's uh not exactly the
right way to go about things. We've had
them on and I think they did have to
roll back a little bit in terms of their
all allin on AI moment. Yeah. You know,
I I love when people are like um I don't
know Sebastian personally, but like I
really respect Clara, you know, and what
he's done. um you know with the afterpay
market just like created a market
himself. I think absolutely fantastic
what uh Sebastian Clark have done. you
know, he made some bold predictions
about um what you could do uh from an AI
perspective and um you know, he he said
he did it a lot on his own. And what I
tell our customers, if you want to go
spend, you know, tens of millions of
dollars upfront and you know, tens of
millions or five plus million a year on
perfecting that. You want to take some
engineers and not every company has
engineers like Clara has engineers,
software engineers to go design this.
You could probably get a decent customer
service solution. I I don't think it's
going to keep up because we've got
thousands of engineers making sure that
we're delivering the best for our
companies, but you could do it. Um, but
I think what he rolled back a little bit
was um there is still the human element
at the end of the day where automation
can go solve a lot of things, but it
can't solve everything. And so, you
know, what we look one of our, you know,
missions um is that we want to
democratize AI. Like I said, there's a
really small percentage of companies can
do what CLA did because you have to have
the the money, the technical knowledge,
the software engineers, you know, etc.
to go do this on yourself working with
Open AI and uh you know, on a really
really or you know are um anthropic or
someone else on a close basis for the
99% of customers that aren't able to do
that, Zenesk wants to democratize this
so they can get a lot of use out of AI
on the resolution platform. will
automate a ton, but we've got a strong
point of view that humans beings are the
customers or the employees at the end of
the day. And you know, you might start
out like we talked about before, Alex,
your personal bot talking to uh a
company's AI agent. Uh if you don't get
resolution, your bot might go to a human
agent. Ultimately, it's going to be back
to the human going to the human on a
really, really difficult problem. And we
always say the customer is always human.
And uh we think um you know CLA got a
lot of things right but we think what
they got wrong is uh you know they for a
little bit they thought they could
automate everything and where you know
maybe forgetting a little bit that the
customer is always human. One
interesting thing that Sebastian told me
in our interview was that he uh actually
was able to do as much automation as he
was because his customer service wasn't
built in the right way and there were a
lot of phone trees that were built the
wrong way and therefore it was easier to
automate it with bots and maybe if you
just build in the right way in the first
place you won't have to resort to this
and and I think that's one of the like
technology technical debt for the last
20 years um people buil built these
logic Tre's, you know, phone and IVR,
you know, interactive voice response
system that that just became spaghetti
code. And so I think there's an
opportunity where people have got to
refresh. And what I always recommend to
customers is if you're just going to
have the same process, the same people
and do it the same way, a technology
upgrade is not going to really help you.
You really got to go reimagine
processes, people, your customers, how
you want to serve them better. And so I
think a lot of places are in a similar
situation like Clara. grew up really
fast um and they had kind of, you know,
these um old school decision trees and
old school workflows. It's really really
easy to have a best-in-class um customer
service platform right now. And we think
Zenesk can help customers do that
because uh you know we like to think
that we're easy to implement, easy to
use, and easy to value. And we're
democratizing AI uh for the thousands
and tens of thousands and hundreds of
thousands and millions of customers that
um you know don't have the knowhow to do
it themselves. Yeah. On those phone
trees, I'm just hitting zero. So I think
I'm bad. I think I'm just concluding
that I'm the most annoying person. Alex,
I'm I'm old enough to remember where I
thought those phone trees were a little
cool where uh the first before the web
you could call and get your bank
balance. And I thought going you'd have
to go to the bank. That was really cool.
But those those IVRs, phone trees very
quickly outlive their usefulness. I'm
with you. All right, we are about out of
time. I would just love to hear before
we leave state of Zenesk because you've
had a very interesting recent history
and just give us a view of uh what the
company's up to and where you're
heading. Yeah, we're we're pretty
excited. Um we just passed our we have
about 100,000 customers overall and we
just passed uh having 10,000 customers
on AI. Um we're excited that you know
the first act of Zenesk was
revolutioning um uh revolutionizing
customer service help desk uh you know
kind of CRM customer relationship
management for service and we think
we're in this second phase now of
disrupting the marketplace again uh and
uh disrupting both employee service and
customer service and uh as you just
mentioned there's a lot of legacy out
there and um if you want a beautifully
simple really powerful solution. Um, we
think Zenesk is the right way to go.
We've really fundamentally shifted the
company the last two years. Like I said,
we've gone from zero to 10,000 AI
customers. We think we're going to have
over 20,000 AI customers by the end of
the year. Um, we've done some really
interesting acquisitions. Um, M&A has
been a big organic growth, but M&A has
been a really, really key part of that
growth strategy. And, um, we're really
excited about being on the forefront of
this agentic AI revolution. There might
be some hype along the way, but um you
know in our last quarter we saw u I'll
just give you a range between 20 and 30%
of our bookings being AI related uh and
you know versus zero two years ago. So
kind of getting to back to your first
point. It's not hype. It's uh and I'm
using bookings as a proxy for uh
customer satisfaction. Our customers
wouldn't be buying from us unless they
really adopted these great solutions and
they got a lot of value from it. Well,
the Zenesk resolution platform is out
today. If people are interested in
learning more, where can they find more
information? Uh, they can go to
zenesk.com um or uh, you know, don't
hesitate to uh go get on our website.
Um, you know, we've just rolled out. We
were one of those companies that
automate it too much as well. And so
now, if you need to, you can get click
to call uh you can get a human being uh
after you go through our AI agent, which
we think is going to solve your problem.
Go to zenes.com and we'd love to go
interact with you. Good stuff. Well,
Tom, great to see you and thanks so much
for joining the show. Thanks a lot,
Alex. Really appreciate your time. Thank
you so much, Tom. And thanks to
everybody for watching. will be back on
the channel with another interview