Did Klarna Really Automate 700 Jobs With AI? — With Sebastian Siemiatkowski

Channel: Alex Kantrowitz

Published at: 2024-07-17

YouTube video id: 34P1XLXmEUI

Source: https://www.youtube.com/watch?v=34P1XLXmEUI

let's talk with the CEO of Clara one of
the most enthusiastic adopters of
generative AI about how the technology
is being applied in practice and whether
it can really do the work of 700
customer service rubs that's coming up
right after this welcome to Big
technology podcast a show for cool eded
Nuance conversation of the tech world
and Beyond we're joined today by
Sebastian shimanovsky the CEO and
co-founder of Clara which is a fintech
company that specializes in buy now pay
later but also has its own shopping app
and it's also a company that's been
leading the charge in implementing
generative AI one of open ai's very
early Partners Sebastian welcome to the
show great to see you thanks for having
me it's great to be here so let's just
start off with this um stat that you
guys put out a couple months ago about
how you have effectively built AI That's
capable of doing the work of 700
customer service reps listeners of the
show know I have a St stance on this
which is that when I see these numbers
from company I generally think that they
have haven't actually replaced that many
people with AI um here's like one
example right like IBM they had an
announcement in August 2023 saying that
they're going to replace nearly 8,000
jobs with AI but also as they're going
through a layoff and look maybe this is
a case where you've actually been able
to and I think your wording is pretty
interesting and we'll get into it maybe
this is a case where you actually been
able to hand off the work of 700 uh
people to AI but also like you guys did
do a layoff of 700 people right
beforeand hand and so I always wonder
like is this is this like replacing
people with AI or is it something a
little bit different once you get back
into the details so you tell me like did
you hand off the work of the 700 people
directly to AI or is it something
different that we should know about
right so the layoff of like the
comparison to the 700 layoff is actually
a misquote by News Magazine online it's
not accurate it was 2 years ago when we
had to uh you know change um the amount
of instest M we were doing we had to
make layoffs and it just happened to be
that the numbers are fairly similar but
it's just coincidence so um so that's a
separate thing but taking that aside to
answer your question um
the you one one should think about this
way right like I think it's almost
better to give the like a little bit of
the story in the context um when AI came
along we started a lot of initiatives
internally to explore the capabilities
of it and we were quite free internally
in the sense that we said look you know
it doesn't need to be like core business
that you go after so we build a lot of
different things somewhere that are more
related to our business and some that we
would expect other companies to build uh
that aren't necessarily core to what CLA
does and one of the teams that happened
to be very successful internally was a
team that started creating basically a
kind of co-pilot for customer service to
resolve um disputes and disputes is one
of the like more tricky thing in our
customer service world because you
collect data from a merchant from a
consumer you have to decide
based on that data you know one claims
that they didn't receive the package the
other one claims that they sent the
package and you have to decide whether
the customer should you know keep the
money or not or whether you should
persist in trying to have them pay for
something right and so it's kind of a
difficult it's almost like a mini Court
decision that you have to decide on and
so uh this team managed to do this in a
very very good way so they managed to
create a co-pilot the co-pilot started
helping these customer service agents
collect this information from both
merchants and consumers started applying
a more methodical approach to assessing
these errands and then also providing
decision support in like what should we
do on this specific occasion so that was
kind of the beginning and the disputes
at us had always been like there was a
backlog of like 30 days it's a quite
complex matter it's always frustrating
to us because consumers wants to get
answers very quickly and we need to
collect a lot of information so it takes
a little bit too long time etc etc once
they took the co-pilot live first that
in itself basically meant that in a few
weeks our backlog was down to zero which
was unheard of and we even had this fun
internal slack message where an engineer
is like we're out of errands send us
more errands we want to run it on a
co-pilot right like so it was at least
an indication and then we task that team
to say hey would you be would you want
to try to actually build a full service
customer service kind of agent right
based on uh what they have done and then
they worked on that for another six
months now to your point when you didn't
think did we really replace uh 700 or
not um what has happened is that like I
think if you look at CLA you know if you
look at American companies a lot of them
will have fairly Advanced ivrs right so
you would call in press one for this
press two for that and then they make
col I uh so like you know you know when
you call a customer service support
number it will be like press one press
two whatever they will have these like
fairly you know and we all hate them
we're not too happy about them they're
always like a little bit annoying
because you want you just want to call
to you want to talk to the human but the
truth is these systems they do resolve
quite a lot of customer service errands
in that they start presenting you with
facts and you're going to hang up and
not talk to the agent because you kind
of got what you needed but it's a little
bit annoying because you want to talk to
a human so CLA was not very advanced in
that there were other there are other
companies that are more advanced in
building out such kind of services you
know some semi-automated Services which
requires you to collect a little bit of
information and present some information
um and all companies have been doing
this in a way to kind of reduce the
number of errands that agents actually
deal with on a day-to-day basis right
like it's Prett standard procedure so
one have to take that into account when
you think about the potential savings
that we had because we didn't have as
much of advanced such systems it was a
little bit of a lower threshold for us
to achieve this accomplishment right um
in addition to that however uh what was
clear is that like when we started
expose this chat AI agent to customers
and they had the opportunity to interact
with it as an alternative to a human
agent um people the customer
satisfaction on that was equal to a
human agent in many cases and so uh that
allowed us to say well we should then
scale and propose to more customers to
use this as an alternative and to be
fair like all product development all
things that you've been doing to improve
your app or whatever I mean partially
you want customers to be able to selfs
serve and serve themselves so any
product Improvement partially has an
implication on you know reducing number
of varant to customer service right if
you have a really bad app
um people will call you more often if
you have a good app people will call you
less often right so there's always going
to be that now the difference was when
we got this customer service AI agent to
reach a level where it actually served a
lot of errands and on a satisfaction
level equal to what human agents Mo many
times did and we took it live the number
of errands that our human agents needed
to deal with that was removed in a
single day was the equivalent of what
700 people used to do manually before
right so that is actually true and that
has led to a saving now in our case we
don't hire these people ourselves we use
customer service companies and so these
agents would go on and do other jobs for
other companies in the shorter term
because these companies deploy hundreds
of thousands of people so when CLA has
less errands somebody else will have
more errands and they will go and work
on them instead right but we still
wanted to share this metric because we
felt look directionally speaking if this
continues it will obviously have
implications on the number of custom
service jobs that exist in the wider
economy right we still thought it was a
very worthwhile statistic to share so
did you then reduce so you Outsource
your customer service did you then
reduce sort of the headcount that you
get from these Outsource vendors by 700
yes or like what so 700 less people from
these companies and like fulltime
equival we on average would have like a
about 3,000 it depends the have to get
remember also like our just like Amazon
we have much more transactions around
Christmas because we're very online you
know so there will always be variations
in these numbers but like on the average
you would look at like two to 3,000
agents and that was removed by about 700
when this when this AI chat agent went
live which meant that it's actually a
reduction in cost for us as well for
paying these customer service companies
that we you know we basically are
suppliers we reduce the spending with
about $40 million on an annual basis
what did these vendors say to you when
you were able to make this happen well
they were not very happy because we
tweeted about it and a few of them had
very severe implications on their market
cap because like one of them lost like
over a billion dollars in market cap on
the on the stock exchange now that was
definitely not our intent and we felt a
bit embarrassed about it that was not
what we were trying to to accomplish by
sharing this statistic but uh no but
they I think that H you know they are
you know some obviously were you know
they were first they were asking like
where are all the errands that was the
first thing cuz they were surprised
obviously to see that dramatic shift in
number of erand that we were shipping to
them uh but then there was also like I
mean since we Shar the statistic you
know some of them are more like well
what what else could we do for you and
how could we grow the relationship some
of them are more keen to learn and
understand how did we do this because
they're trying to offer similar services
to their customers and so forth so
there's been like a mix of reactions
right so give me an example of like what
an AI customer service bot would be
doing that a human customer service bot
would have done previously
well it could be very specifically it
could be like more simple errands like
you know hey I want to find out can I
delay the payment on this transaction
because I don't want to pay now I want
to pay next week right like and
previously customer service may have
instructed that individual on like where
in the app you go and do that or I may
even help you prolong it and give you a
new due date on this specific uh payment
right in this case the AI would
basically show like basically serve you
directly in the app the button to click
and
delay that right so that would be a good
example of something that uh you know
nowadays would be handled by the AI as
opposed to the human agent one thing
that we also observed in this which I
think is worth adding in this is that
and I think most of us have had these
experiences when you interact with human
agents over chat right many times and
you know companies will always say that
we try to avoid this but it still
happens and it's a fairly General
applicable practice is these agents will
have five six chat conversations going
on at the same on time and we we always
as customers we experience that because
we write something and then we're like
why are you not answering immediately
like give me an answer right like and
but at the since I've also sat on the
other side as a customer service agent I
also know that like that's also normal
because maybe you Alex are pinging me
something and then you like you get a
phone call or something so you're not
writing anything so I'm not going to sit
and wait for Alex you know I'm going to
go and have a few other conversations
simultaneously it's like it makes sense
because otherwise it would be very very
inefficient however we experienced that
now the difference with AI is that we
don't need to do it that way so what you
see is the I think the biggest
difference is that in general when a
human started a conversation with
another human to resolve a single task
took on average 14 minutes right uh just
because of those delays that happened
because people aren't really actively
talking to each other all the time
somebody say oh let me go and check and
some you know there a delay Etc and here
uh the resolution time went for 14
minutes to 2 minutes and that is because
you get instantaneously response from
the AI instead right and like the AI is
kind of focused on your conversation so
to speak um and so that is I think one
of the biggest differences and also
something that the C that drives up the
customer satisfaction because you feel
it's more immediate right uh but it
doesn't mean obviously that all errands
are you know can be answered by the AI
today there's obviously still a lot of
things that humans deal with because
they're more complex and more difficult
and there's also a huge amount of
customers the first thing they write
when we expose the AI agent to them is
Agent right that's the first thing a lot
of people have had so much bad
experiences with these AI Bots that they
just want to talk to human right so like
that's also another thing that we see a
lot of yeah because I I gave it a shot I
was in your app yesterday and I wrote
just I want to refund I want to refund
for my order and the bot writes back I
understand you're looking to get a
refund to assist you better this is what
you have to do one exit this chat two go
to the customer service section under
settings three uh select the purchase
you need help with and once you've done
that you can proceed with the steps you
need for your refund so effectively it's
not like the AI is necessarily going out
and accomplishing it for me but it's
directing me to the place that I need to
go to finish this action and that's a
very common thing so what you'll see is
that what you know one of the things
where we were actually a little bit
lucky when we compare because we talked
to a lot of companies that are trying to
do similar things right and so CL was a
little bit Lucky in the sense that like
before it already like a few years ago
we had this vision for our customer
service had nothing to do with AI at
that point in time it was just that like
if you for example ask like you did in
that chat for a specific action rather
than just inform you of where to do it
in the app we would actually serve like
a small widget that allowed you to do it
directly in the chat thread right so as
I said like you know we don't have that
we don't have those widgets for all
actions so the actions that you happen
to ask for we don't have that widget for
but there are other actions that we had
such widgets for and so what we see now
with a lot of other companies trying to
catch up with this idea is that they
don't have widges for anything like it's
always been you know customer service
interacting with Alex and then going and
doing that in a separate guy or you know
a separate uh software right in our case
we did had already a few quite a quite a
few of such uh widgets that we could
serve into that thread that would allow
you to do it and that is partially what
has allowed us to you know to get this
going a at at a bigger but to your point
you will always be able to find things
that like you know are yet not working
or not yet at that level and so forth
right so uh and that's why I said I also
made a comparison with ivr if had a very
Advanced such you know press one press
two system then you know the difference
between what we did and uh the outcomes
would have been not as great as 700 it
would still have been something but it
would not been at the same level yeah I
definitely hate those systems so yeah
anyway that's a subject for another
conversation let me ask you this there
have been some problems with these
customer service chat Bots that they've
hallucinated sometimes I mean I think
like one of the examples that I've been
given was that um an auto sales bot was
like someone basically you know uh
conversed with it and then convinced it
to give them a car for like half price
and then the dealer kind of had to owner
uh had to honor that decision have have
you have you had any of these issues
with your Bot saluting or like pulling
the wrong data and so how have you
navigated that not not the wrong data
right so you have to be very I mean they
obviously have to put very strict
standards into what like what is
accessible not accessible to the a
application itself uh so there you can
you can control that but it has
definitely hallucinated and it has
answered incorrectly H but to us the way
we think about that is that like you
have to make a compar you have to also
recognize the fact that humans don't
necessarily hallucinate uh hopefully uh
but they they also do errors and they
will also answer incorrectly so what we
simply do is we read a lot of these
transcripts on a continuous basis and we
do continuous quality checks to ensure
that the error rate is not higher for
the AI chatbot than it is for our human
agents and if we see that they are at
least on par then we think that's an
acceptable outcome but it would be you
know in we could never like promise that
it never makes errors just like you
can't promise that your human agents
won't make errors because our human
agents unfortunately also make errors
right like so it's just about making
sure that there are not you know as
substantial bigger amount of errors that
the AI is doing than the human agents
are doing it's kind of like The Logical
self-driving car threshold which we'll
never see but if like you can kill less
people than human drivers you should
probably roll out the self-driving
Cars specifical one because but but the
the the the difficult thing for
self-driving cars obviously is that
human lives are Stakes so the acceptance
within Society for those mistakes will
probably be at a very different level in
our case it's a little bit more fine if
it makes a mistake right it's still
Financial Services it's not like you
know some music app or whatever so like
you still have a different level of
compliance and we're a bank we're fully
regulated so we have a lot of things
that we need to live up to but obviously
it's at least is not life's at stake
right like in that sense so I was
speaking with somebody about this move
and they basically said listen like if
Clara was able to figure out a way to
automate customer service this way I
think you've had the last public number
is 2.3 million conversations and 2third
of your customer service chats happen
with the bot basically they're like if
this was working as well as clar claims
it they wouldn't be talking about it
because they have this like uh Edge over
the competition and why give that up in
public so I put that question to you why
talk about it
uh for two reasons one is that like you
know I've
been um I've been part of a a
controversial industry for quite a while
called buy now pay later and people have
criticized buy now pay later and I also
see I see why but I also see strengths
to what B nowator offers to the market
compared to credit cards and the
traditional Banks and so H my learning
from going through that of being first
like a hailed amazing tech company doing
awesome things and then starting being
criticized for some of the also risks
and you know challenges associated with
I mean if you provide credit it's still
credit right like you can provide better
credit or Worse credit but it's still
credit and people will have opinions
about credit and my learning from that
is that it's helpful to be proactive
that it actually is better to kind of
lean forward and and and share things on
a proactive basis rather than kind of
have them you know um appear later right
so so that's one reason my learning from
that help made me believe that I think
it's better of us to can of be proactive
and share these statistics and so forth
I also feel partially like you know that
I sometimes feel that the you
know when when I see so much noise and
discussions about AI this and that or
whatever I actually feel that we have a
more kind of a moral responsibility to
share that we are actually seeing Real
Results and that it's actually having
implications on society today and hope
to encourage people specifically Pol
politicians in society to actually
treating this as a serious change that's
coming and start thinking proactively
about how to do that so that's more like
on a human level and then finally the
third thing obviously is also
self-promotion for sure like you know
that's definitely also play right like
so obviously what we've seen as a
consequence of sharing this is that more
of the AI startups wants to work with
clana because we're regarded as you know
a thought leader or somebody doing
something exciting in the space we see
more people want to work here you know
Etc so there's like obviously also that
aspect right but but it's a combination
of those three so throughout this
conversation you've hedged a couple
times about how powerful this stuff is
because saying that if you had like a
better phone tree for instance it might
not have helped you save
700 uh you know uh employees or not
employees 700 workers time so and you're
not you said there'd be some impact but
you're not 100% sure what the impact
would be uh that being said is this
stuff actually all that powerful or did
it just kind of help you paper over like
a a different problem like how should we
because I'm thinking like all right so
we should think about the implications
for society but like is this the moment
or is this like a little bit less
powerful than it seems given the
problems that existed
beforehand so I think um I want to make
sure I understand your question
correctly but I look I think it's a very
good question in the sense
that you know it's always like how how
how much is hype and how much is real is
that the question basic right well yeah
I mean to be more direct you've said
that you'd see less of an impact if you
had your phone tree built out a little
bit more before you turn this over to Ai
and I'm like okay so like how much of
this is actually just like papering over
like a preexisting problem that is a
good question look I I've I'm I'm sorry
I wish I could give you like a great
answer to that question but I haven't
worked in another company and I can't
really make that comparison I've tried
to talk to other entrepreneurs and I I
don't want to mention them by name
because I'm not sure whether they want
to share the details but I have talked
to others who have for example come much
further in automation using non- AI so
to speak before like ivrs and Sy like
and where and also who has been tougher
negotiators with their customer service
suppliers and hence their cost per
errand was low lower because they had
better prices than we had from a scale
perspective and their savings by moving
to AI was more Limited in ours right so
there is definitely an element to that
but it's very hard it's very hard to
answer that right because it's so
Company by company specific my belief
though if you ask me is that like I
don't know like maybe 70% is AI and 30%
is automation or 5050 yeah but I still
think it is that much actually that's
still my belief right and and then the
other thing is like even when I talk to
some companies that were really good at
like had really low customer service
cost really good quality and really high
level of automation even before AI I
still feel that like I am super happy
that we did this because this is just
the first version of this right like
we've learned so much from the
implementation and it's not like we just
launched it and now we're letting it be
right we're continuously improving on it
making it better and better so I think
that like even though maybe the first
iteration would only have been a
substantial Improvement compared to some
other companies if you give it one or
two more years it will definitely be an
improvement versus what any other
company's doing with general just
computers autom and so forth right so I
think it's a little like that's a more I
mean maybe it's boring it's not the
headline catching answer but I think
it's a more real answer we want to
unpack the Nuance of this stuff so yeah
yeah yeah yeah but I think it it that's
I think that's the reality of things
that's that's why I just want to lean in
I just want to learn using the
technology as much as possible but um
you know it's still going to take some
time for it to fully mature right and
this extends beyond your customer
service department it's also in your
marketing department um let's see you
you've tweeted our in-house marketing
team is half the size it was last year
but it is producing more so I mean you
said I think ai ai is used for 80% of
all copyrighting within the company I I
struggle to believe that could be that
good but I'm curious if you could tell
us a little bit more about how AI is
working within your marketing division
and is it taking on responsibilities of
full workers like it is within customer
service or is it more enhancing the
impact the marketers are having so CLA
is active in over 20 countries which
means that we cover Over 20 Languages
and that in itself like you can imagine
like and I think everyone on the call as
well would like agree like translations
have basically been nailed right like if
you look at like not only chat GP and
open AI but also if you look at
companies like DL the translation
quality is extremely high so if you
think about our copy you have to
remember that a lot of those copy people
were actually you know also per forming
copy in different languages and so forth
and so that in itself is just a massive
reduction in number of work labor hours
just to translate from one to another
and so forth I think in addition to that
a lot of before you go on I want to ask
you as a as a CEO are you are you
comfortable allowing an AI translation
and marketing copy to go in front of an
audience without having a human take a
look at it it depends on what copy it is
and where it sits right so if you think
about like it's actually quite
interesting because we're a bank for
example if we Market our credit card
there are very strict requirements on
the copy and you know how that is
expressed right but there's also other
things in our app like we we have for
example descriptions of products that
you can buy from our merchants or you
have things that are of lower level of
sensitivity right so the thing is that
you have to basically structure your
information internally in a way where
you start separating an understanding
that some things will have higher levels
of requirements and some things will
have lower and and and the point with
80% is obviously that's focusing on the
areas where it's less you know critical
if the translation isn't perfect but
there's a lot of human review still of
the translations themselves but it's a
little bit like you can have ai write
the code but you're still going to
review the code many times from a human
previously you just had to have both the
human to write the code and to review it
right so I think that but so that again
is a reduction in in work effort but it
is still not like a full removal then
what we've seen is also that AI becomes
very powerful when you split it in
multiple assistants doing different jobs
so if you for example have one AI bot
write the copy and then another one
review it according to rules then you
actually make that also even better
higher quality and so forth so you see a
lot of the latest AI that we see and at
least apply within CL is that you
actually ask the AI to pretend to be
different roles one is the reviewer and
another one is the writer and then they
kind of interact with each other as like
a multi-agent team and that actually
gives even better quality outcome so but
but to your point obviously if it's like
Credit Card promotions you're still
going to have humans review that and
look at that right and it doesn't seem
like AI can do some of like the core
functions of marketing right speak with
a group that has something to Market
understand their objectives bring it to
the creative agency with a brief go back
and forth find a good midpoint and then
ah you think so well look I I just
there's been some really cool things
I've done so like I I'll give you one
example right we um one of the first AI
applications we actually built
internally was and this was again just
like an idea that we just did it it's
not CLA as core business but one of the
it's just a concept that we wanted to
test it was that when you do these CL of
classical Employee Engagement service
right like which all companies do like
how happy are you working at clana or
how happy you working at me or you know
whatever and you say like one on a five
and like how happy are you with the
office how happy are you with your
salary how happy you know whatever how
happy are you with team do you trust
your colleagues Etc so a lot of a lot of
companies do these surveys that you you
collect data you know people are
supposed to say by on scale one to five
this and that you know whatever and then
you kind of synthesize that information
you try to you know analyze it interpret
it put some kind of report you know Etc
and spread that and so forth right it's
very typical company doing these things
so we said to our like wow you know what
wouldn't it be happy to like wouldn't it
be fun to like it's still like so much
of at least to me when I look at such
emplo engagement service what I really
care the most about is the comments
where people have written free text
because it's so much more interesting
like you know what does it mean to say
one to five this and that it could mean
so many different things for different
people and stuff like that when I read
the comments at least a little bit more
concrete and I get some like input on
like what do people think or whatever
right so what we decided to do is we
created we built our own internal in
interviewer so we let the AI interview
our employes in a session like ask these
questions and synthesize and have a
conversation with emplo it's not to
replace human managers because you know
in the end your manager is the one who's
responsible for you being happy at work
and all the stuff right but it's an it's
an additional um additional tool on top
of that to provide some additional
insights and understanding of what's
going on in the organization and the
funny thing is that it worked extremely
well our employees liked it it was very
thoughtful and we felt compared to a
standard survey it gave us much more
interesting outcomes and understanding
of what was going on in the organization
that again doesn't replace the
management that is still the the primary
objective but like it still is much of
much higher quality so to your point can
you use the same for customers can you
survey your customers and collect and
synthesize information yes we've seen
that work as well it actually becomes
much more interesting so again like it
doesn't replace all of these things but
what I feel with AI with people forget
is if you go to Ai and you ask a general
question right like whatever give me a
great way to work with marketing you get
a very general answer it's like reading
[ __ ] corporate management literature
like it's a you know it's little bit
like good to Great hire the best thank
you but how the hell do I find them what
interview questions do I ask like I get
it it's important to hire the best
people what does that mean in practice
right the interesting thing is though if
you take Ai and you ask something much
more concrete which is like
imagine the front of a house what would
I expect to see at the front of a house
it will answer very correctly it will
say probably a few windows and a door
right and so a lot of what people I
think Miss is that if you if you take
any work task whatever work task we as
humans do and you derive it down in very
very small concrete steps that are very
specific then actually AI is quite
effective at performing those and and
most of our more complex work task are
really consisting of very small specific
work task that are combined together and
when you start thinking about it that
way you can actually make it do a lot of
things like but you have to think about
it in that kind of step and right so
yeah so I actually think you could do
more than you than one may think right
right but these are all tasks and and
it's yet been able to effectively
orchestrate and maybe that's coming but
it's not been able to orchestrate like
you can't tell me that you would sort of
trust like the core of what your Market
marketing department does to a machine
yet cuz it's involved I mean even if
they have great you know great surveys
that they can send to like different
divisions about like what they need to
do like the the that ultimate end where
you're positioning and figuring out what
the message is and what the benefit is
to your target like y that is something
that humans still need synthesizing of
information I agree with you at some
level you know humans still obviously
outperform AI a lot the other thing that
I believe humans really outperform AI on
is creativity um in my opinion and uh
it's a little bit in a way a little bit
different with it depends if you look at
copy or text or if you look at images
because images are more fun in a way
like you have seen you know AI dream up
some pretty cool images right that like
you would like who I fun to see an
artist that would have dreamed up that
crazy image right it was pretty cool so
I think image is a bit different but if
you look at copy one thing that I really
see challenge in how the llms work is
they work towards the average because
that's how they constructed so that
means that they're constantly all the
answers are pushing through the general
answer and like I think everyone will
know that like if you invest in the
stock market on the average you're going
to see average returns but if you in
invest with a very strict contrarian own
idea you may lose a lot but you may also
win a lot and I think that like the same
applies to your point to copy is that
like if I want some amaz amazing copy
that you know articulates what clana
does that's different I would not rely
on chip P to do that I would rely on a
human because it a human has a bigger is
much better at kind of being far-fetched
and do something crazy and out there and
different and and so forth why the llm
is just pushing it to the average all
the time and the average doesn't sell
right yeah and you you've also been out
front talking about how like you're
using generative uh imagery versus um
images from let's say uh you know I
don't know stock image companies and but
it's also like I'm looking at your your
app right now I'm holding this up for
listeners it says like shop at Amazon
with like a bunch of boxes and now it's
about to scroll to top pet deals with
like actual specific information about
the deals you have with Chewy and then
talking about the right season with Yeti
this isn't stuff that machines are able
to handle yet maybe there's an image
or so tell me the truth tell me the
truth yeah yeah I don't know if the
images you showed where AI generate I
can't tell you that sorry but I can't
they look like General stock stock
images that you would get versus like
the AI gen okay sorry go ahead but I
can't so I can't tell you for those
specific ones that you showed on the
show but what I've seen internally us do
right is that you currently the problem
is if you just go to like one of those
tools uh you know uh any of those image
generating tools and you just write like
give me a to your point like give me a
PR picture of a guy with box images or
whatever they're not going to be on
brand tonality they're not going to look
the way CLA wants them to look they will
be a little bit odd there will be tons
of issues and to your point you won't be
able to apply them but what we have seen
that we can do is we can uh basically
again put this through a mechanism of a
number of things so we can first text
prompt an image we can then take the
same image as an input and move the
image so it looks more on brand we can
then make a assessment and we basically
then move them like through a factory
basically of that and the outcome
picture you have at the end of that are
actually usable directly in the app and
are being applied now again would I do
that for my you know Super Bowl campaign
no uh would I do that for category
pictures in the app that are just there
to say hey we have shoes we have this
yes that we're already doing and
applying so you can see that your uh
that is definitely feasible but you need
to set up this production you know uh
environment where you basically almost
like a workflow you take it through
multiple models and multiple things to
get to the Quality levels that you're
looking for and then finally on the
product experience so people are going
to be conversing with Clara I guess
through your customer service agent but
you have a vision to make that a much
deeper experience where you're like
looking through different sites and
trying to shop and there's effectively a
shopping assistant right there with you
yeah I think look it's actually not only
shopping in the sense like actually this
started when you know as as you may know
I've been uh you know running CL for 20
years next year we've been a long period
of time and we have pivoted kind of
direction of the company but in 15 so
about 10 years ago um we were pivoting
away from being a B2B to more of a b2c
company and at that point of time we
asked ourselves like okay where is
financial Industries going and partially
this was because already in 11 we
acquired a company in Germany called
sefor and they had been they had built
not a base but they had built basically
an a an application that did a very
simple thing in Germany to transfer
money between bank accounts was a
horrible experience the banking apps
were horrible right it was just like
horrible a lot of clicks a lot of dat it
was just like a bad exp they're still
pretty bad but sorry it's still pretty
so what they had done they had said look
they had basically built a plaid likee
experience where they said Alex give me
your banking credentials your password
and your login and there was basically a
macro it was B basically like the old
Apple macros that we used to have on the
Apple Computers back in the days was
basically a macro they logged in on your
banking account on your behalf and they
scripted to send that payment on your
behalf so you didn't have to go through
the ugly goey of the bank and when I saw
that to me it was very inspirational
because I felt like wow you know what
like amazing can you imagine if you had
like this digital assistant that kind of
did this thing on your behalf so you
didn't have to interact with these
horrible goes and experiences of other
companies and it would just do this on
my behalf like wouldn't that be great
and you have to remember like that's not
a small business we process we do about
$100 billion do worth of volume we do 30
billion volume on debit through that
solution through basically scripting
this doesn't work that way today it's
more API based but the point is that for
many years we basically did millions of
payments transactions through scripting
on you know on banks um G right and so
it so it worked you can put it to
production now at that point of time
it's not AI so if like if one of the
banks changed the guy there would be a
phone call down in Gees and in Frankfurt
where the team sits was like hey it's
broken you need to fix the script and
somebody would you know wake up in the
night and fix it because the bank had
changed the guy right like so it's like
robotic process automation yeah exactly
right and you will see uipath and some
other companies have come you know to
some degree on that on that
so some of that has already been done
but it inspired us to believe that like
okay what's the future of financial
services well the future of financial
services you wake up in the morning your
computer tells you have I analyzed your
Mortage I realize I could save you $10
by switching from Bank a to bank B the
only thing you need to do is to say yes
to execute on that change and so like
that to us became the direction of where
Financial indust is going it also means
a reduction in excess profits because a
lot of the banking profits are generated
due to the fact that the switching costs
are so high and we're not willing to
switch and so the competitive pressure
is actually lower than it's perceived to
be and so that became a conviction of
vir is already in 15 and ever since
we've been trying to build services in
that direction um now you know we've
come some part of it we haven't you know
nailed it but that's the direction that
we would add now when we then saw Chachi
P we felt like oh my God this is
probably going to happen a little bit
faster than we had previously envisioned
uh but it's a bit like self-driving cars
I personally believe the self-driving
cars at some point in time will happen
but I don't know when uh my bet is my
daughter is now uh 10 and I have always
said I don't think she's going to get a
driver's license but that's eight years
out right so there's still like there's
still some time and maybe I'll be wrong
but that's been my bet on it and I think
it's like a little bit of similar here
right like so um and that's that's what
we're trying to do whether it's a
shopping assistant or a digital
Financial assistant but it's somebody
who's helping you save time save money
and make you in more control of your
finances okay so your company is also
one of the most enthusiastic adopters of
chat PT Enterprise um I want to talk
about that on the other side of this
break about how so many of your
employees are in these tools every day
and whether that's helping and whether
that's sustainable all right we'll be
back right after
this and we're back here on big
technology podcast with Sebastian shimi
and Kowski he's the CEO of Clara and the
co-founder of the company we've been
talking about AI in the workplace and
Sebastian your company is also using uh
chat PT Enterprise with I maybe the most
enthusiasm of any company uh uh in the
world so these are just some stats you
put out 90% of your employees are using
generative AI tools powered by open AI
daily um Communications marketing and
legal are using adoption are using it
with adoption rates of 93 88 and 86%
respectively uh you're also seeing a
wide variety of additional use cases
from building software to streamlining
customer services uh service which we've
talked about okay first question for you
your 90% uh daily active use on chat PT
Enterprise has that gone down since you
released those numbers or does it remain
consistent I don't know I haven't
checked but I I would I would believe
that it's consistent what are the people
using it for you know it's tons of
things on day-to-day stuff like you know
help me draft this help me review this
have me check at that text um we have
also built a lot of U what we internally
refer to as uh ccts so this could
basically be like different assessment
tools like if you want to check you know
is this text good enough for this
purpose uh as an example actually if you
want to text uh you want to test some
copy to see if it's a you know is it um
correct from a legal point of
perspective is it following the policies
and routines that we have stuff like
that so there's tons of different things
um that people are using it for with
that said though you know you know when
when like when Bitcoin came along and
and these Technologies like I tried that
as well I personally didn't find the
technology to solve a real problem in my
opinion I didn't see how it was going to
help my mom prefer using clana over
something else like I may be wrong you
know maybe it turns out that you know
blockchain is the amazing technology
that will disrupt the whole world but I
wasn't convinced when when I tried this
uh CH for the first time I got very uh
personally convinced that like this is a
technology that will have real time
applications and since then we've been
encouraging everyone in company to learn
and apply it and learn how to use use it
in a productive way with that said
though like you know I have days when I
don't use it at all right like I have
days when I use it much more and and
sometimes my I have more successful use
case and sometimes I have less but I but
what I want to do is encourage the
company to learn because a lot of things
have actually are day-to-day use case
that actually very helpful um but it's
also like by applying in learning that
we explore and get to know what are the
limitations why is it not working what
do we need to improve um and to us we
realize
that it can either be not working
because the technolog is flawed and not
good enough or it can be not working
because we are not set up to use it in a
way that uh makes it productive and many
times we realize it's the latter so
we're changing a lot of our fundamental
processes of how to work and so forth to
make sure that they also helps us and
can make us more productive and some of
these use cases are quite interesting so
this is from another article about your
about what you're doing uh you said you
use generative AI namely open AI chat
GPT are the communications team uses it
to evaluate whether press articles
written about the company are positive
or negative I I see it do you really
need an AI to tell you that can't you
just kind of tell it by reading it well
you could but I'm actually really proud
of our Communications team they're so
efficient um we you have to remember
like clona uh as a company today you
would probably read you know you would
have about
40 articles written every day and it's
not only about the sentiment analysis of
those it's actually even a little bit
more detailed than that it's very common
I would say about I think we've analyzed
it about 20% of the articles that are
written about us com contain factual
errors they're Incorrect and this has
meant that our communication teams calls
and tries to correct those errors right
it could be anything from where the
headquarters of the company is to
statistics that are wrong or whatever um
and so and we call or email the
newspapers to try to correct this to
make sure that it's correct right that's
a lot of manual work right that team
would much rather be out there and like
you know pitching a new story or
building relationship with journalist or
doing something else than doing that
manual work and so like having it assess
those articles and identify those uh
errors and then even sometimes draft an
email to the journalist to ask them to
correct it it's like a nice it's a nice
thing to avoid that manual work and
spend time on something else wait the AI
can actually pick out the errors in
stories or is it just the sentiment do
you just check the negative St very
relevant Point Alex that's actually
exactly a very interesting thing because
one of the key learnings we had the last
12 months is that there is an old rule
in uh data scientists which is [ __ ] in
[ __ ] out and that still applies in an AI
world as well if you feed the AI with a
lot of noise and incorrect information
you cannot expect it to be able to
answer such things so in order for it to
assess whether the information in the
articles is accurate you also need to
have a very strict data set that says
what is accurate information about the
company and you need to have that in a
in a in a good solid place and one of
the things that we've seen is that CLA
has been historically using a ton of
Enterprise systems different Erp systems
which actually silos and puts more noise
to the internal information so we have
our or charts in workday we have our
clients in Salesforce we have you know
um our suppliers in m files we have tons
of different pieces of information
spread out on Preparatory Data Systems
with different structures and so forth
and that's hurting the ability for us to
use that information in a standardized
way uh to make AI work better and so
part of what we've been doing in
parallel with this is we realize that we
need to standardize and centralize our
information about the company what we're
doing because then both humans actually
can make more productive work as well as
as um as AI on top of that both Ai and
like if if there's too many silos of
information in the company if things are
not transparent not open it makes it
harder and that's true both for AI as
well as that right I think that like
that's why I wasn't too convinced when
people were like oh look I have a PDF
reader and AI can read a PDF and answer
questions about like yeah but you know
the problem is in many companies you
have too much information and too much
inconsistent information and too much
duplicative information so you have to
think about both both how do you improve
the information that you have as well as
like how do you then use that to do
things like this which is you know check
whether the data is correct or not in
the article yeah there was a Wall Street
Journal article talking about how
companies were like really struggling to
get this to work when they try to get AI
to pull data it would pull data from
like the wrong year and I think the key
takeway from that is that really that it
just has to be uh the data has to be
clean for these things to work and and
structured to to some extent you're also
your lawyers are also using uh Chad GPT
to write first drafts of contracts and
that's cutting the hours it's taking to
draft a contract I mean that to me seems
like an a loow hanging fruit type of
area where like you you have your
lawyers like effectively draft the first
draft and then they can do like some of
the stuff that you're like actually
paying for them for them for versus
having them write up boilerplate
contracts well I think that's true again
coming back to like low hanging fruit
and you know more difficult things there
will definitely be tasks that people
could have like you know instead of
drafting this maybe you should have just
shared a draft internally so you didn't
rewrite it every time right did you
really need to you know start from
scratch every time and now just because
AI is there people are applying it and
like oh I'm saving that time but you
might as well just share the draft like
obviously there are use cases like that
obviously there are things like that as
well happening where it's just like this
could also have been done by just a
little bit more standardization and like
simplification or sharing information
internally um so it's a combination
obviously but it it helps provoke the
idea right it helps accelerate and then
uh as long as I see the business
implications and the business results
doesn't matter me too much uh you know
how those are accomplished in that sense
right okay I want to end with a question
about uh the state of buy now pay later
like we've talked a lot about AI but I
would be remiss if I had the CEO of
Clara on the show and didn't ask a
little bit about uh buy now pay later so
by now pay later was obviously like a
darling of the fintech industry and of
tech Apple tried it uh your valuation
was in the what 45.6 billion in
2021 uh but then went down 85% to 6.7
billion uh a year later and this was the
CNBC headline clarent evaluation plunges
85% to 6.7 billion as buy now pay later
hype Fades um so I'm just curious to
hear two things first of all from you uh
how has it been navigating this sort of
like up and down of the industry and
then what do you and then secondly what
is the future of byy now pay later given
that yes Apple's out and it seems like
this thing that used to be in the
spotlight is now uh moving out of it so
what should we think about when we think
about this
service yeah so I mean first on
navigating up and downs like I you know
I think to some degree I benefit from
the fact I've been doing this for 20
years and as much as like this up and
down was maybe the most media publicized
and I've never been in that like spot ey
you know or like as visible as this I've
gone through a lot of up and downs with
the company both valuation wise as well
as anything else right so I think that
like it was obviously very tough and I
was sad and I was you know very you know
stressed by by that the public traded
companies that we are often compared to
like PayPal or you know a square block
or whatever they had the same 85% drop
in the stock market during the same time
right so we weren't singled out in that
sense but that was a general fintech and
Tech kind of uh reduction in in stock
price but still because we're private
company you know it became such a bigger
you know news and and and then obviously
also at the same point of time as
investor sentiment changed and we were
at that point of time unprofitable we
had to make you know very tough
decisions that are very you know uh you
you don't like making which was a
reduction in staff and stuff like that
which is very um challenging to go
through
um but at the same point of time I feel
like you know we have to do what we
think is right for the company and for
the the emploees that are still here and
our shareholders and our customers and
so forth so I think we we did the right
thing and now we are a profitable
company again which we actually know
people don't know this but CL was was
profitable from 2005 to 2018 so we had a
history of kind of running this a little
bit differently than most tech companies
just like burn money we have been
profitable but then when we came to the
us we invested heavily and that meant
that we were lost for some years um now
on the um on the other topic on bopat it
to answer that you have to like first
Define what is bopat because at at the
core what it is to me is um we have had
a credit card industry which basically
Works in a way where you swipe your card
you get your monthly statement with all
of your transactions and then you're
encouraged to revolve uh and if you do
so you start building depbt and that
Dept earns the money a lot of earns the
bank a lot of money right uh buy now pay
later the difference with it the way I
Define it is that it's interest free and
it's installment based so you take a
single transaction you don't pay
installment on interest on it and you
pay it down in installments you can
offer that on a card as well right like
you can re rethink your credit card to
make it work that way you don't have to
offer evolving you can do installments
on a credit card right so um but the
concept of Interest free in installments
is to me a healthier credit concept than
revolving um in addition to that the
difference with bat also in cl's cases
we're our own network right so we are
the equivalent of American Express in
the sense that we're third party Network
we have stripe and addan and other PSPs
and acquirers that offer clown as a
payment method side by side with Visa
Mastercard but we have a direct consumer
relationship just like AMX so um the
network means that the fee that the
merchant pays is not through Visa as
they do on Visa but directly to us and
this means that there are less middlemen
uh in between uh the merchant fee and
the actual income to us as an issuer of
giving you as a customer this oper offer
allows us to offer an interest free
product which Merchants still are paying
for on equal parts with a credit card
which actually in also charges on the
consumer side so if you look at the
total cost of payments in the US and you
look at both what the bank is earning by
interest on your revolving plus the
merchant fees it's actually a crazy $5.5
on a $10000 spec
and we earn less we do about2 and half
per $100 spend so we do much less but we
also have much less cost right we're
coming from a different cost profile
we're a small we're lean ftech we're not
a big Bank Etc so you're accepting a
lower Revenue per customer with the
benefit of providing a slightly more uh
a better product for consumers from a
credit perspective now it's still credit
I always say that look I'm fighting CR
I'm fighting fire with fire so I'm not
saying like credit still has this issues
and so forth but in a future state if
people want to use an installment
product with zero interest over a credit
card I think that's a better outcome you
should use debit and then you
occasionally use a buy out pay letter as
opposed to using a credit card for all
of your spending I think that's a better
thing and I always make the comparison
back in the days your card used to have
press one for debit and press two for
credit but the banks removed that
because you wouldn't build as big of a
balance on a monthly basis if some of
your transactions went on debit so by
having all of your transactions on
credit you were more likely to evolve
and then the banks make more money the
average outstanding credit card balance
is $5,000 the average outstanding
balance on CL is 50 so it's a huge
difference right so I think it's like
it's a but but there was a McKinzie
study that said that in the US there
about 20% of the consumers are what they
call self-aware avoiders these are
customers who are like really looking
for this kind of product as opposed to a
credit card with massive bonus points or
you know other services that are more
interesting for them or heavy revolvers
and stuff like that so this is not the
full population necessarily but I think
there's a a good amount of people that
prefer this kind of products and see
value to it versus other options yeah
okay this is really the last one and
it's quick as you start to put more AI
into your business do you expect to
increase headcount or decrease headcount
from here well I I you know you didn't
ask about that but actually one another
announcement that we' made is that we
haven't been hiring since September uh
and since uh we as manych companies have
a natural attrition rate where about you
know 20% leave on an annual basis so
people stay about 5 years which is kind
of typical for tech companies this means
that we are shrinking so we used to be
about 4,500 we're now
3,700 and we are basically shrinking on
average about 70 people per month and um
and we've doing that because we don't
want to do layoffs um so we are actively
managing down because we see we can do
uh more with less people at the same
point of time in order to create you
know some benefit for for employees in
this we have said that like and been
very clear about it um which is that our
total employee cost will go down but our
cost per employee will go up right so
that is basically a commitment to our
employees that like they will benefit
from this in seeing higher salaries and
more Equity shared with them which is
what we' done we just did that a few
weeks ago now we distributed a lot of
equity to our employees so we're saying
that like for the people that want to
stay and participate in this there's an
upside to them as well in the fact that
we're doing this but we are shrinking
and we will continue shrink uh as a
company we'll be much less but doing
much more fascinating step Sebastian
thanks so much for coming on great
speaking with you thank you Alex thanks
you for having me all right everybody
thanks so much for listening and we'll
see you next time on big technology
podcast