June 25, 2025 • 50 mins
Summary:
In this episode of Insurance Unplugged, host Lisa Wardlaw interviews Margeaux Giles, CEO and founder of Iris InsureTech. They discuss the challenges and nuances of building operational systems in the insurance industry, emphasizing the importance of transparency, trust, and effective data management.
Margeaux shares her journey from being a producer to a tech founder, highlighting the need for authentic transformation in the industry. The conversation delves into the complexities of understanding customer needs, the pitfalls of traditional data storage systems, and the innovative approaches Iris is taking to embed trust and verifiability in their technology. In this conversation, they discuss the evolving landscape of the insurance industry, focusing on the integration of technology, the importance of trust, and the challenges of implementing AI.
They explore the need for real-time decision-making and the generational mindset required for long-term success in the industry. The discussion highlights the risks associated with misusing technology terms and the importance of transparency in data management.
Takeaways:
Margeaux Giles has nearly 20 years of experience in the insurance industry.
Building operational systems requires understanding the real needs of the industry.
Transparency and trust are critical in the insurance value chain.
Many founders struggle to move from vision to execution.
Data processing is essential for effective insurance operations.
Traditional systems often serve as mere data repositories.
Iris InsureTech focuses on embedding trust at the data level.
Operational processing is different from data storage.
The insurance industry needs to evolve beyond outdated practices.
Effective data management can lead to better underwriting and risk management. We don't trust you.
The upside for a broker is massive.
We need to understand how to make business decisions in real time.
If you want to go fast and break things, be honest about your tech journey.
Stop saying that you're using a genetic AI.
Sound Bites:
"I am the epitome of the hot seat."
"What do you really need?"
"How do we understand that person?"
"It's a balancing act."
"You have to have firepower underneath that."
"That is not operational processing."
"You can't be human centric, transparent."
"We don't trust you."
"The upside for a broker is massive."
"If you want to go fast and break things..."
Keywords:
Insurance, InsureTech, Margeaux Giles, operational systems, transparency, data management, trust, technology, insurance industry, SaaS, insurance, technology, AI, real-time decision making, trust, risk management, innovation, data transparency, generational planning, brokerages
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:01):
Insurance. Unplugged in the hot seat where
the complex world of insurance is laid bare.
Hosted by Lisa Wardfall, this podcast promises an unfiltered
glimpse into the industry like never before.
Each episode invites you to listen in on the candid
conversations that usually happen behind closed boardroom
doors. From deep dives with industry
(00:22):
leaders and thought leaders to innovative discussions with
minds shaping the future of insurance, we bring the most
genuine talks directly to your ears.
Our guests take the hot seat alongside me to explore the
inner workings, challenges and triumphs of the insurance world.
If you've ever wondered what goes on in the shadows of the
insurance industry, from the boardroom banter to the behind
(00:44):
the scenes strategies, this is your chance for a front row
seat. Prepare for.
Unguarded, enlightening and engaging discussions.
That cover every. Angle of Insurance presented in
a way that's both insightful andaccessible.
Welcome to the conversation. Welcome to Insurance Unplugged
in the hot Seat with Lisa Wardbaugh.
(01:06):
Welcome to today's episode of Insurance Unplugged, proudly
sponsored by Iris and Suretech, your gateway to the future of
insurance distribution. At Iris, we harness the power of
generative AI to revolutionize data processing and decision
making across the distribution spectrum.
Our platform integrates Gen. AI to provide not just insights,
(01:26):
but actionable intelligence, configurable workflows, and
dynamic form generation, all underpinned by continuous data
quality management. Discover how Iris is pioneering
smarter, more efficient operations in the insurance
industry, paving the way for a new era of distribution
excellence. Let's dive into how Jen AI is
transforming the landscape of insurance distribution today on
(01:50):
Insurance Unplugged. Welcome to another episode of
Insurance Unplugged. I'm your host, Leisa Wardlaw.
And this week I like, feel like we need drum rolls.
No, no, no. I like Margaret Iles joining me,
CEO and founder, Irish and Shirtak.
And I'm going to let her like she's no stranger to the show,
but I want her to kind of introduce herself once again.
(02:12):
For those of you who may not, I've heard her intro or may not
know her deep level journey. But really what I wanted is
first lead in was saying like Margot and I are not here to do
a highlight reel. She and I are really going to
break down. I'll call the deep nuance of
what it's actually like to buildan operational system, not for
(02:35):
demos, not for, I'll call it, you know, point solutions, but
what it's really like and and I'll call it like her, her
candor and survivorship through this, it is huge.
So, so welcome to the hot seat. Margot, I feel like you've been
living in the hot seat going on this.
Mountain I am telling me of the hot seat I.
Think so if you don't mind introducing yourself.
(02:58):
I think like if people have listened to us previously,
they've heard your journey that maybe just a high level intro
and synopsis of what you set outto build so that they can
understand for context, the questions and the follow-ups.
I'm going to kind of put you through on this season finale.
Sure, awesome. Margo Giles, Founder and CEO of
Iris and Suretech. I have been in insurance for
(03:21):
almost 20 years. So I did not start my journey on
the tech side. I started in the seat.
I have always lived on the distribution side of the house
in PNC insurance. So, you know, writing mid to
large market commercial, being aproducer and then being an owner
and then going through M&A and now I'm coming through M&A
again. Always living in that world, you
(03:44):
know, so running agencies and brokerages, dabbling in Mgas and
MG US. But yeah, I mean an insurance
girl through and through. And I kind of got thrown into an
OPS role as my career progressedand, you know, being younger,
the younger side of a COO in insurance.
I think I was 29 when I took over the first insurance agency,
(04:05):
so very familiar with technologyin my personal life.
And then I would come to work and it was just such this huge
disconnect between what I was able to do outside of insurance
and what the software and the systems were allowing me to do
inside my own business. And I felt the friction of that
and the disparity between those two things was just way too much
to ignore. And so I went on a very long
(04:26):
journey of learning by trial anderror and by fire how to how to
build tech, what it really means, how to do it internally
in my own teams, and then how toscale that out, you know, into a
real SAS company. And, and that whole journey has
probably been about 8 years. So yeah, I definitely am battle
worn at this point. I feel very, very confident that
(04:46):
I can answer a lot of questions you're probably going to ask me
about how this process unfolds. Yeah, yeah.
And and just to kind of like recap because I think people are
like, well, like I responded it,but we like went through a
journey. We heard so many voices like
over the season and prior seasons, right.
And I just wanted to kind of like connect the dots for
people, right? So just recently we had Kohli
Perry on last week. If it's not auditable, it's just
(05:08):
a toy, you know, audit as thoughyou're being subpoenaed designed
for that. We had Casey Kempton, who you
sat on stage with her Margo lastyear at ITI, you know, and she
really got underneath the policyof predict and prevent without
back end design. And Andrew Morse treating Jen AI
as a teammate, not a bolt on thecore theme underneath us was
(05:29):
really real transformation. Like authentic transformation
means that you have to face these financial truce, system
lags and infrastructure failures, not just adding AI to
sales slides, right? Like you and I see that a lot.
And, and to me, clearly, I know this because of my work with
you. Iris sits underneath every one
of these themes and how? You.
(05:51):
Really went beyond selling a vision.
So Margaret, now I want to get to the like, right?
Like you and I had this conversation a lot, right?
Like most founders have to sell vision, but most founders stop
there. Few survive the execution layer,
which is why we see, I'll call that high velocity against that
Cliff that you and I started a couple seasons ago talking about
(06:13):
that 3,000,000 ARR Cliff. Why we see so few people push
into the execution layer and even fewer can build something
that the industry, you know, isn't yet asking for but needs.
Right. Because like a lot of people
would say we'll build for what'sasked for.
But it's like, yeah, but you know, again, we'll quote handy
to forward. They would have asked for a
(06:33):
better horse, you know, like andwe hear that a lot, especially
in the distribution. They're asking for a better
horse. I mean, realistically living in
the seat of sales and marketing,but that's what they're saying.
But that's actually not what they mean.
And it's very difficult to get underneath to your point as what
are you? What do you really need?
You're describing a symptom, notthe root cause.
(06:54):
That's tough. So how do you, so let's let's
take that one because you know, you and I talk about this all
the time and, and it's difficultbecause you got the lenses,
you've got the customer lens, you've got the BC lens, you've
got your funding lens, you've got your product lens, you've
got like, how do I market this? How do I like, like, like the
dimensionality of all the platesyou have to spend literally in
(07:15):
parallel on, I'll call it quicksand, right?
Which is your timeline and get get to market and you've got to
have something that can be usable.
So what? Let's step back in your voice.
What do you think the industry actually wants to change?
Or how do you give them what they think they want?
(07:38):
Is there a but there's a reality.
It's like, well, I still have tohave a car that drives even
though I might want a new radio in the car.
Like. How do you how do you
compartmentalize that and balance it, Margaret, because I
think that's. Huge for other founders to hear.
Yeah, it's so I think at the core, and I'm going to go like
existential, I'm going to stay away from tech for a minute
because I think there's always atechnical solution.
(08:00):
But the harder part is trying tofigure out like to your point,
what is it that you're actually,what do you need?
What are you asking for? How do you bridge that, that gap
in insurance? I think especially right now
with our climate, what distributors in particular and
carriers, what we're really asking for is the, you know,
transparency in a lot of ways. There is an inherent distrust
(08:25):
amongst all the parties in an insurance value chain.
If we're going to be candid about what's actually going on,
customers, insurance people, they're frustrated, rising
rates, you know, increasing economic and weather related,
you know, anomalies like like everybody's under stress.
And I think at the end of the chain, people feel the as though
(08:45):
insurance is not transparent. They don't understand what
they're buying. They don't think the coverage is
what they thought when they purchased it.
Every time you have to get in a claim, you realize all the red
tape that you didn't understand because you're not a lawyer as
an actual insured. And you and I have both.
I've recently had to go through the claims process.
You had a terrible claims process.
And we're in insurance and it's still difficult for us to
navigate. So I can't imagine somebody who
(09:07):
genuinely isn't entrenched trying to figure out why these
things are happening. So get the very base level,
everyone in insurance wants to insure a risk correctly, make it
profitable, and have transparency so that their
customers don't have this intrinsic feeling like they're
being ripped off. Like at the core, that's what we
all want and people are asking for that.
(09:27):
So they want to be able to see their customer.
They want to be able to have thecustomer data, and then they
want to be able to utilize that.They want to know what's right
and it's correct and it's traceable for underwriting
reasons. And then they want to be able to
use that information in their daily operational activities,
whether they're binding policiesor servicing post bind, right?
Like all of these things kind ofdrill back into did you sell the
(09:50):
right policy to the right personat the right time with the right
coverage. And that is the the core of what
we're all looking for, not the tech solution, but the core of
what we're looking to to build. I think what's interesting about
that, and I'm just going to kindof weave in here is because I
think to your point, everyone's after that, like, right.
Like I think in general, you know, we're an industry built on
(10:12):
an intangible promise, trust andcreation of trust and
transparency. I, I don't think there's many
people would say they're not trying to do that.
But then it's kind of interesting is because like,
what line did you have to cross that others weren't tackling to
get that? Because I think I call it like
the treadmill or or you could call it like like a revolving
(10:35):
door. People all start with the kind
of core business objective aligned in principle.
But I think it's like we go nowhere.
You know, I heard you say AP is to nowhere, the revolving door,
the treadmill, whatever. How did you decide and what
line? So how did you decide to cross
the line and what line did you decide to cross to kind of, if
(10:58):
you will stop the performative nature of what people were doing
to achieve that transparency andtrust?
Yeah, For us it was the human inthe center design.
I think that that led everything.
So if we were really going to beable to deliver auditable to
your point, immutable like a verifiable information to all
(11:20):
the parties on the chains. I think all of us realize that
there's a data exchange problem at the heart of this.
The next layer up is great, now we have the systems and the
data. How the hell do we give it?
How do we exchange it between all of these disparate parties
and all these different interested parties that take
place in a policy? So I think for us it was, well,
we have to agree to tackle the first step, which is can you
(11:41):
even get that data in a place where it matters?
And to us, it was, I need a singular view of a person,
whether they're participating ina corporation, whether they're
stand alone, whether they're in a household or marriage, you
know, however they're participating in our
relationship. How do we understand that
(12:02):
person? And then how do we get all of
that data surrounding that person to give us a really
cohesive view of what not just their value proposition is, but
what is their risk? Like what, what are they?
What are they actually doing? And how do we like actually
cover the risk at a much broaderlevel?
So for us to do that like, and if we get into the tech side,
(12:24):
like rebuilding a boring basic relational database, like it's
just like every other company has ever done, it just it wasn't
going to garner the results thatwe were actually looking for.
And I think for us, it was goingWeb 3 and looking at components
like blockchain to try to figureout like it, you know, is there
(12:44):
something there that we have notexplored before?
And we know other companies aren't exploring, at least not
yet. And how do we actually make that
a reality? And so was it.
That was a very big, big jump. I think that that's so pivotal
because, and clearly I've had the, you know, the, the specific
perspective on this of being very much in the trenches with
you on this. So I have a depth that maybe
(13:05):
our, our listeners don't have, but it was so easy to just
performatively do what others did and try to do it
incrementally better. And and I would also say
incrementally better Margo, likeyou could have done what other
people did before and you could have put a prettier UI on it.
You could have made it feel prettier and more 2025 ish right
(13:30):
like. I mean this this decade, yes.
Like this this century, No, I'm joking.
But but, and you know what's really interesting, and I call
that like the marketing or the performative nature of it.
By the way, there's nothing wrong with that.
Like if you want, if people wantto go do that, like that's cool.
But that is the treadmill, and that's the revolving.
Well, it hits a it hits a wall. At least we've seen it over and
(13:52):
over and over. Again, how did you know?
Because what what I think is so hard is most people the inertia
of VCs and I'm not criticizing any VCs, right?
Get to market, get revenue, you know, put training wheels on it,
get it out there, get it used. That is the like the undertow of
(14:14):
the current you're swimming in with money and funding, right?
And then there's the like, I'll call it like the oh shit moment.
I could just do what everybody else is doing and make it a
little bit better and probably sell it by the way.
And then there's like the whole reckoning which says yes, but
I'm not going to actually achieve what's necessary.
And I think holding all of that in in in essence, in congruency
(14:40):
with the tension is something that most founders can't do,
Margaret like they they have so like really difficult.
How do you do that? A lot of whatever you choose to
decompress or insert adjectives.Well, it's, it's a, it's a
(15:01):
balancing act. So one, I think we had the
luxury of building a lot when weweren't VC backed.
So I made a lot of mistakes withmy own money, frankly, right
early on in my own agencies and our own brokerages.
Like we like we hit that wall with this human in the center
and verifiable data and how are we going to actually like show
(15:22):
all of these relationships and how are we going to get the data
in a place that's usable? We did that early on with what
the platforms that everyone elseis building on and I won't say
them Salesforce, but like we everybody's been down this road
and we I hit. That wall and let's throw some a
genic AI in there and make it even sooner like Aster to hit
the wall. Like the crash dummies hitting
(15:42):
the wall in the car. Whatever buzzword you want to
put in there, the underlying operating system could not
handle like. It just, it could not handle the
level, first of all, of of data that insurance companies
actually have. Like if they were to represent
their data digitally, which mostof them are not full stop.
Like I don't know what the percentage is, but I can
guarantee you it's pretty low, especially on the distribution
(16:04):
side as to the totality of the data they have versus what they
ever actually digitized and, andis usable is like obscenely low.
So in that scenario, it's like if, if you're really going to
put all the data in and you're going to digitize and you're
going to utilize AI and you're going to share data across
multiple entities, like you haveto have firepower underneath
that that will support that or you will break it.
(16:26):
And I think early on in our agencies, even the sizes we
were, which we are not a large agency, you know, we're not,
we're not the marshes and aeons and like the huge agencies of
the world. And I hit those compute
processing ceilings. So I can't even imagine what,
you know, a large carrier or broker is going to hit in that
scenario. It wasn't cost effective and it
(16:46):
wasn't going to work. So for us, it was like, OK, we
built and failed a lot in private.
And so I was very candid with our investors when we took VC
money that this was not we, we had no plans to flip this
business in three years. This was not a widget that we
were going to build up with no back end and then exit to one of
(17:08):
our competitors. That was not the plan.
It will never be the plan. And so having money invested
that understands that I think was really, really critical for
us. So talk to me about the next
layer before we get into, because I want to go into kind
of some of the provability points of Iris.
But before that, there's also, so it's kind of inverting the
(17:29):
data model customers at the center and and I would assume
that most naive listeners may think, oh, that's what CRM does
you. Know you know what I mean?
We. All have to go there, but OK,
we've heard we've heard those words before.
So I want to go to another layerfor for you which I know Iris.
Does it wasn't. Just customer at the center,
it's customer at the center and with customer at the center, the
(17:53):
ability to do all of the operational processing.
Yeah. And I know that that's
incredibly like an afterthought for most people that are
thinking like, whatever, I've got HubSpot or I've got this CRM
tool or I've got that CRM tool and I've got like a database.
I'm like, OK, like a database doesn't process aesthetic.
So, so like that operational processing.
(18:13):
And yeah, I mean, you heard me talk about this a bazillion
times. I was like, oh, well, my
reinsurance background was so familial when I went into the
distribution background because clearly we do policy movement
and we do these things which aredeeply operationally intense.
How did you combine that and howdid you think about that as you
(18:35):
were building this with, you know, your API?
Like because then I want to get into your like, why do AP is go
nowhere? Like, you know, like how did you
think about that and what's so important?
And I think he gets back to the data exchange, but I'll turn it
over to you to kind of bring thereaders in on that, listeners in
on that. It's also an actual operating
(18:55):
system. It's not just a storage
database. Right.
So I think people because let's just, I'm going to stick in in
distribution because this is where I, I live.
So we have something called an agency management system, an AMS
or BMS depends on where you are in the world or where you're
listening. And so those words are really
deceiving because it sounds likean opera, like it's, it's named
(19:15):
as though it's an operational system, but what it actually is
is a filing cabinet. It is a data repository.
It is a data repository that wasalways built to be a data
repository. And the moaning of the UI it
like that's what it was for. It was meant to, I have this
file and I need to digitally type into a screen the
(19:36):
information from this file so that I can store it here
digitally in a filing cabinet. That is if you take the words
AMS or BMS out of the equation, that is what those systems do.
And that's great because you know, in the nineties, 80s and
90s, like we need, that's what we needed.
That's what we had. But unfortunately a, a dummy
black box data storage software is not built and will never be
(20:01):
built to run policy movements toto do long like long tail
processing. That's actually, if you believe
insurance, that's another separate system.
That's your oracles and your sapiens like those are big
processing platforms. And so if you were outside of
insurance, you would have. And the crazy part is I think
(20:21):
people look at distribution as asales organization and there and
we are sales organizations, but actually we're so much more
highly complex because we don't own the asset or the premium
that is the policy, right? Like it's not our premium to
collect. It's not ours to to run risk
against, right? But we still need to to be
(20:43):
completely tied to every one of those movements as a broker,
Like if you're really going to do a good job as a broker, you
have to understand when a policycancels, why it's cancelling?
Is it for non payment? Is there material
misrepresentation? Like those are policy movements
endorsing cancelling. And the systems that are
(21:03):
databases do not have the ability to actually run the
process. Now they can record the process
once it's happened. So if, if, if you're an AMS user
or BMS user, you're very familiar with something happened
somewhere else and then I go into my repository and I tell
the repository that something has just happened.
That is not operational processing.
(21:25):
That is record keeping. There is a big difference
between the two. And I don't think there's enough
education for you're trying for a lot of these bigger brokers
too. They're trying to scale to
operational processing like a carrier or reinsurer would need,
but they're still using data repositories and they, they
(21:45):
don't understand why they can't get there.
Well, you're not using the rightsoftware.
Like, full stop, you're not well.
Let's take that then, like because I think this is such a
natural segue. Iris embeds trust because you
started with like, what's the Holy Grail that you were solving
for, like transparency and trust, and you chose to embed it
at the actual data level. So you don't just repository
(22:07):
data, you're actually natively embedding trust.
You know, we could talk about whatever I'll call all the, all
the ways you achieve that in thelike technology world.
So let's put that over in stage,right?
But at the end of the day, the way you achieve that is over
here. But what you're actually doing
is you're embedding trust and logic at the like, in like at
(22:27):
the substrate level, every single data object is, you know,
individually like provable, so verifiable.
Exchangeable, right? That is a massive shift in
infrastructure software from what from what we have today.
Exactly. So when you did that, I, I would
say, why did you do that? What are you opening up with
(22:48):
that to your point about like, hey, I've already broken this.
I've already seen the crash or the tidal wave that maybe other
people can't see. And then what does that open up
for Iris and Iris users? And then what breaks?
Like what's the tsunami coming for people that are trying to
still operate at this? I'll call it data space storage
level that you articulated, you know, filing cabinet.
(23:10):
I love that word. It's a filing cabinet.
I mean, that's what it is. Everybody's familiar.
Yeah. A lot of it is you are a company
that relies on, you know, your ability to prove and to be
trustworthy. And if you, your entire systems
are not able to be audited, likethink about the absurdity of
that. Like my whole company, my
(23:33):
billion dollar company depends on my ability to collect the
right data and then transmit that data effectively to all the
other parties so that we can then find a policy and and I can
have my, my cover my you know, right.
And like you don't, your system just physically won't do it.
So how can you possibly be humancentric, transparent?
(23:55):
How can you be good at underwriting and risk
management? And you don't even like, you
have no tools and no ability to do that.
To me, that was just such a likethe chasm was so wide there.
So for, for, for us, it was a couple things.
One, I mean, anybody who's ever owned an insurance company who
has been on the, the gone to thealtar at least and had to have
(24:17):
an E&O claim. And most of us have had large
ones. And sometimes very, you know,
frequently depending on the typeof insurance you rate and you
get audited or you get audited or you get subpoenaed, man, like
think about that process and what we're going through
currently and how that looks andour inability to as brokers and
distribution partners leverage any kind of buying power against
(24:41):
a Swiss Re or a Munich Re when we're talking about Reno
exposure. And like we have no leg to stand
on. And you being from the
reinsurance side, I think the sentiment is, is, and I don't
know if it's right or not, but it's certainly justifiable.
Brokers don't have the sophistication nor the
reliability of data. So what happens by the time it
(25:02):
comes to you on the reinsurance side, Lisa, you guys are
verifying, you're reverifying itlike there's billions of dollars
in that process. I was.
At a lunch one day recently, andI said to this very large broker
sitting across the tape for me, you realize that like 80 to 90%
of your commercial day that you submit is ignored and we go get
our own data. And he literally, Margaret
(25:22):
looked at me. He's like, you're lying.
And I'm like, there were two other reinsurers that worked at
my peer competitors, like at thetable with me, we were sitting
in a triangle kind of. And they're like, no, she's like
not lying. And he was like, he was like.
Why are we collecting it? There was like this.
Awakening Margot that he was like what?
Like he couldn't believe it. He could not believe it.
And I'm like, yeah, we go get our own.
(25:43):
We ignore, replace, extend, backfill, verify, you know all
the things because we have our we've just kind of gotten into
like a self-serve data mode. But to your point, it's about
trust and verifiability and we're like, well, we'll just.
We don't trust you. I mean, the, the bottom line is
we don't trust you. And you know, it's like, well,
yeah, I mean that that's the reality of the situation we're
(26:04):
in. But think about as if I'm a
large broker and I'm looking formy competitive edge and and my
value proposition and I want to be a a partner.
So let's say that I want to develop new programs.
I've got a lot. I want to take the data that I
have and I want to be just as reliable as my carrier, my
reinsurer partners and in order,maybe I want to take those
(26:26):
programs directly to my reinsure.
Maybe I want to start my own reinsure, like these are real
things, your own risk. Appetite network or what?
Whatever. I mean, there's so many risk
solutions now. I agree with you.
I think the problem is, and I think a lot of big brokerages
are finding this out and as it'smoving down to mid market
brokerages, you are woefully, woefully unprepared to step into
(26:49):
that realm. Even though the the return on
investment is huge. The upside for a broker to be
able to do these things with their data is massive.
The downside is if you like we makes our, we make ourselves
obsolete. So Lisa, if you and every
carrier in the world does have to reverify everything we're
sending you, then at what point do we like work ourselves out of
(27:12):
a job because we're unwilling todevelop technology?
Like at what point do you guys, and we've seen this, we've seen
this over the last five years, say we'll find a different
distribution channel, one that is reliable that we don't need
to pay 30% to because we're not only repaying you, but now
we're, now we got to go redo everything that we're paying you
to do to have. And, and it's a, it's a
contentious back and forth between distribution carrier and
(27:36):
reinsurer in the, in the broker space because it's been
disjointed, because there hasn'tbeen somebody to come in and,
and say, we're going to lead thepack here and we're going to do
these hard things. We're not here to just turn and
burn and make a dollar off of brokers, right?
No one's LED that pack. So there's no buying power in
the broker space and they haven't been able to unify
behind any, any one piece of technology.
(27:58):
And unfortunately, the ones thatare in the market do not have a
financial interest because why, why would anybody push the
technology forward when we're fine doing what we've been doing
for 30 years? It's a really, it's a quagmire
and it was one that I think nobody that doesn't intimately
understand insurance could possibly walk in and understand
(28:18):
and then build against. And even those in insurance
don't necessarily understand thelevel of technology available
and or needed to actually solve that problem.
So it's it's a hard space to be in.
You know, for me, like seeing, seeing so much of what you've
done, the next level to me is like, it's that how did you
create the data and how do you create the transparency and the
(28:39):
trust? It's also how do you create, and
I'll use the word dynamic, but how do you not do period and
close? Like how do you get what we
would call more event driven, even if you're not 100% event
native at the outset out becauseyou know, whatever, there's
still things like downloads, there's still things like you've
got to go to a portal and. We're batching some things too,
(29:02):
I mean. Let's call it hybrid mode right
now because you know you can't build for something that the
other side isn't meeting you at either.
Correct, correct. But like why is it such a
difference to not have somethinglike a period and batch like
like financial engine like like when you designed Iris?
(29:22):
It's really like producing like I can see in real time my
margin, I can see my profitability, my producers can
see that. How is that different from like
this concept of like type in allmy transactions?
And I'm going to be like, like, why is that so important to run
the business? Because how like our world is
not, we're not making decisions by quarter anymore like or by
(29:45):
year, right? Like it's funny actually,
because I'm, I'm here right now.We're, I'm recording this with
you and I'm in San Francisco, I'm here in Silicon Valley and I
am meeting with investors. We, we recently had an investor
from South Korea and I'm meetingwith a bunch of Asian investors
that are in insurance. So you know, MSNAD and Sampo,
there's a, there's a bunch of really great Asian insurers and
(30:05):
I've been having discussions that that I have found just so
starkly different from how we think of things.
Other countries, other investor types, they talk about
investment in terms of generations.
They're not thinking what is this quarter's numbers.
I mean everybody is right, but the rest of the world is
(30:26):
thinking in generational like what are we investing in now
that's going to do return in 10 years.
To me that's something that I see in the on the broker side in
the mid market, these great legacy mid market providers that
you know that have built something that are continuing to
do M&A right these really and they have this great opportunity
now to kind of keep going. Those people are thinking in
(30:48):
generation, they're thinking about perpetuation plans and how
they're moving things from their, you know, to their
children and all of that. And so those thought process are
a lot longer. But if you move into some of the
larger brokers or the really small ones, it's always this
short term planning of we got tohit this quarter's numbers and
this year and like we'll we think about next year once a
(31:09):
year. It's a difference in mind and
mindset shift. But the ones that are thinking
in the longer term generational planning, they're winning right
now. They're the ones that are
winning all the M&A deals, right, Because nobody wants to
be a part of a turn and burn. Everybody wants to be a part of
something. And when you think about what it
takes to plan like that, it likeyou need to understand how to
(31:30):
make business decisions in real time.
You need to understand when a market is shifting, You need to
understand when a when an insurer is moving or reinsurer
is moving. And you cannot do that if you're
doing a one year annual planningon what next year will be.
You need more information, you need the ability to pivot, you
need the ability to quickly disseminate from the top down
(31:51):
and initiative. So maybe this year we thought we
were going to go after AG, right?
Because this is like a really great market and blah, blah,
blah. And then boom, all the sudden
immigration happens and now, Oh my God, AG is actually falling
apart. Now.
How do we take this initiative that we plan for, and how do we
move it quickly? You can't do any of that when
you don't even get to the numbers until 90 days after
(32:12):
something happens. Like how are you actually going
to be nimble enough to respond to a world that is in real time?
Yeah. And I think part of the
fascinating thing for me when I started, you know, working like
in distribution with you and started understanding, I think
everyone thinks they have that capability and, and the way they
define it is, well, we'll send out an e-mail to all of our
(32:34):
producers. And I'm like, what?
They don't know, they don't havethe ability to.
It's like a leak, you know, likethat automatic faucet shut off
that shuts the water off and it prevents the leak from getting
like immediately. I think of that with capacity
and wrist placement, right? You've got to be able to switch
your flow immediately, and sadly, humans aren't conditioned
(32:57):
to just read an e-mail and always do with e-mail says if
they can go in and do whatever they used to do, they're going
to keep doing it. Yeah, it's like pattern, they're
like trained, they got muscle memory, right.
You're breaking that muscle memory.
And it's really important that like I cannot stress like if you
talk about like we've talked about what's coming after this,
like we lay this foundation layer and you get this real time
(33:18):
and you get this verifiable data.
Like what does that mean to you as a business?
Yes, it's nice. You're, you're going to have
pivot, you're going to have the initiation, the, you know, all
of that's going to happen. But what does it mean long term?
Well, if I let's let's talk about AI for a minute, because
that's what everybody's talking about, even though I hate
talking about it. Like.
It's just, it's over. It's there's a lot.
(33:39):
But if I am a big broker and I now have my board comes down and
says, Hey, everyone's using AI, like, right, this is the, this
is the buzzword and all the the boardrooms we want to deploy AI
and you don't have verifiable data to deploy AI on top of or
you don't have an operational process to insert AI into.
(33:59):
How are you putting? Yeah, like where does AI live on
your data? Like in your filing cabinet?
Like what is the best AI can do with a filing cabinet filled
with broken, wrong, dirty files?I mean, what is the best hope
you can get on lift? I think you're seeing this right
now. I mean, you and I have been in
so many of these big broker rooms and they're and they're
(34:21):
like, we got a pilot. We got a pilot, but nothing
rolls out and nothing's returning the investment.
So these executives and boards are like, well, AI's doesn't
work. It's like you're trying to
deploy something on top of something that it is never going
to. It's never been built for it
can't support and it also can't feed.
And we're looking into the future now with Eno, where
(34:42):
you're going to train. This is happening right now.
People are going to start training these LLMS on this
really bad, broken, non processable data and they're
going to start getting into big trouble.
That right, you're going to yourEno is going to go like, so it's
going to be who can figure this out versus who can't.
And it's not an easy problem to solve.
And if I had a dollar for every time someone was like, well,
(35:03):
let's go to GPT and I put my into it and I'm like, OK, but
like, cool. And that's great for writing an
e-mail. But like, I'm talking about
operational processing data at speed and at volume.
And how is your LLM going to respond?
And how are you training it? There's no concept of what is
actually needed to make that successful.
(35:24):
Well, there's no. And just to amplify that, anyone
and I will say anyone that has ever been involved in any level
of any enterprise grade technology like you've worked at
a carrier or you've worked at a corporation or you've worked at
a reinsured or whatever. We never used ACRM system Margo
(35:46):
without connecting it to some sort of master data management
or data harmonization layers like we had data governance.
So if you've been removed from that and you've got your data in
this, I'll call it CRM only database, only universe, and you
haven't connected that throwing AI on top of that is by
(36:07):
definition asking it to hallucinate.
You're like inviting it to a drug.
Yeah. You're you're training it to
hallucinate at that point. Yeah.
Like that's what you're trainingit to do.
I mean, even the organizations that have had decades of
governance, they're still not easy to implement operational
AI, to your point, but they're getting there.
(36:28):
And so I think that this disconnect of I can just throw a
ChatGPT on top of my database and everything's fine.
To your point, that's naive. But what I find even more
interesting is that we're not even proving that.
And then we're like, hey, let's add some agents in there and go
full bore on a jet. Ticket.
But yeah, let's, let's go, yeah,let's do that real fast and at
(36:52):
scale, if we can even get it up and running.
Just nobody has new business talking about a genetic AI if
they're still operating on a database with no master data and
no intelligence later. My favorite part about that is
you can't even do agent to agentcommunication vis a via and AI
API. So OK, so let's let's go back to
the world of API. You got to put all kinds of
(37:13):
layers in between that. There's certain things can't
even do API to API communicationthat's not event based.
Now we're gonna go into we're doing agentic AI and if anybody
listens to this, your people aretelling you they're doing
agentic AI and all you have is at best API calls hashtag no,
because an an agent has to talk in an agent to agent.
(37:33):
So there's the Google agent to agent, there's the manage
control plane and we are only seeing very sophisticated
companies start to road map out that.
But Margo, the amount of distribution people that I hear
saying they're doing agentic AI and I'm like, they're not.
I mean, and there's nothing wrong like if you can rig up
something that that works like I'm like more, I've done it 100
(37:57):
times, like more power to you, but you need to understand the
repercussions of what you're potentially building.
And again, I, I think about there's a couple instances that
I've been a part of where this isn't even a genic just API.
When an API call, when you're pushing 8000 quotes through an
API and it's coding and IC codesand AIC codes incorrectly and
(38:21):
you just wrote 4000 policies that now all have material
misrepresentation, right? Yeah.
And a carrier accepted. That was literally like a bot,
by the way. Yeah, yeah, yeah.
That's a. Bot that's not a genic right.
So like you're talking about even taking that and then a
carrier can't, doesn't even realize it until 120 days when
they're doing their post audit, which is also happening by
humans going through looking at things.
(38:41):
And that now all of a sudden we've got millions and hundreds
of millions potentially and justcomplete utter chaos.
I think about that. And we don't even have a genic
yet. Like we're not even talking
about a genic. And I think about the
repercussions of that. And so I think it's, again, I
think it's really naive in two ways.
One, to say that we're at a place that that's even
(39:04):
deployable because I don't thinkthat we are.
And two, that even if you could rig something up that you
understand what the E&O repercussions are of that build,
they're massive. I agree with you.
So let's get into like the inside the build.
We'll keep it short. But you know, I think a lot of
times we we talk to founders andit's like, you know, it was hard
(39:25):
triumph success, but it comes with a huge cost.
So one of my favorite things to reflect on is like what almost
broke you and what did you learnfrom being on the edge of
failure? I mean, you've never had a
safety net in anything you've ever done.
Why haven't you walked away? What is your pulse point that
(39:46):
keeps you on the edge? I don't see anybody else doing
this. I don't see it in my peer
groups. I don't see it when I'm on
stage. I don't see it when I'm behind
the boardrooms with these executives.
Like I, I see the the problem islarge in scale.
It is very difficult to solve. And I see a lot of people come
(40:07):
in and enter, and then I see them realize that it's going to
take literally. Every ounce of self preservation
just going out the window to even attempt it.
And I see the I see them, I see their face turn and I see them
give up. And I cannot imagine knowing
what I know like and being so passionate about.
(40:28):
I know how to fix this problem. Like we have the knowledge we
have the path forward. We have the technology like it
exists in the world to to have all of those things and then to
not be able to get this industryto where it will.
It's going to go there at some point like it there is no other
path it has to or it will fail and and to walk away.
(40:51):
I like. I can't even imagine a scenario
where unless I was forced to, I would never.
I could never. Yeah, I think that's such a such
a strong part of this journey that we often don't talk about
because I think, I think a lot of people and I'm not
disrespecting any founders, but they feel a calling for an exit
or until it's hard. Lisa, listen, I'm AVC startup
(41:15):
like if you don't think that that has been like kicked around
and talked about and all like itit you have to because one of
the things that I think I buckedthe system on and I get so much
shit for it online, you'll see it everywhere.
It's irises vaporware. It's never going to launch is
the idea that I'm not building towards ARR in a sense so that I
(41:38):
can flip this company right and there's this I call it like the
demo. I call it like, like every
founder gets to a point where they build towards a demo.
Yeah, so they'll go because everybody's like, oh, I don't
understand. I want to see the system.
Let me get and play with you. Like, well, it's an operational
system. So like, good luck.
I mean, it's it's triggering operations across, you know, 42
(41:59):
different platforms. But yeah, let's set up demo
work. But but you have to here's.
A sandbox. For that, here's a.
Sandbox, by the way, everything in the sandbox is completely
fake, right? So like it's a, it's like a, a,
it's like a, like a safety net or like a baby blanket.
You're like, I got my demo works.
I feel really good about this. It's like the everything in
(42:20):
there is just completely fake and it's all being triggered and
hard coded and it means nothing.It's like a simulator, yeah.
Yeah, yeah. I mean, but which is OK, like
if, if and you know, we'll we'reon the path of building the
simulator. So because it is difficult for
people to understand, a lot of people are visual, so they need
to see this, what this process means to them and that there's
no shade there. But that was not what I was
(42:43):
going to spend our limited resources and money on, building
a baby blanket for people like that.
It doesn't prove anything to. I think for us that was we take
a lot of heat for that. But I stand by my decision.
And we have customers that are on the platform that have access
to the system and they're clickety clacking in there right
now. But it doesn't mean that, you
know, we spend a million or $2,000,000 in an irresponsible
(43:06):
way to make people who probably weren't going to buy the
software in the 1st place feel better about the software.
I mean, I think it's interesting.
It's interesting juxtaposition because there are very, very,
very large enterprise platforms that do not have an environment
that you get access to or you get to go play with.
I mean once you're post, you know, if you're in an
implementation you get UAT or you get sandboxed, that's
(43:28):
normal. You don't.
Get anything for you pay like inmost ways, let's take Oracle ERP
or work day or let's just use something like that.
So we don't like throw anybody else under the bus.
That's it. Like you sign a multi year
contract, you get an environmentright like this, but it's really
hard to be, I'd say a, a, you know, pre series, a founded
(43:48):
startup and say that. But again, that comes back to
you're building an operational system, not a widget.
And I think widgets can have sandboxes.
I think operational systems don't.
So I think that's really interesting that you've had to
hold that tension. Oh, it's really difficult.
I mean, it's it's very difficult.
And I think look rightfully so, because especially in the broker
(44:10):
side, so many people have come to this market, built something
that is really not stable or really doesn't have anything
behind it, has sold it for a couple years and then flipped it
only to leave a whole bunch of people that have invested in it
and spent time in it, you know, with nothing.
And they're back, right back to where they started.
So, and I was the victim of thatas well.
(44:32):
So like I, I fully understand where it's coming from, but I
think the, the understanding from our market needs to be when
you're building something that has never existed before, never
existed before and you're going to market with it.
It's not going to be for everyone, but it'll be 5-6 years
before virus is big enough wherewe can go deploy it and you
(44:55):
know. Crossing the chasm right like.
Crossing the chasm exactly. And I, we cannot do that out of
order, as you say, we cannot. So if you're you know, if that's
the if you're looking to be someone who is not first in,
then don't look for first in software.
But then you have to understand that you are not going to get
first in results like that's thewhole part of being the leader
(45:17):
of the cutting edge and and being that kind of personality
is it does not come without riskinherently, yes, there's risk
there, but you cannot expect extraordinary things if you are
not willing to be extraordinary.So if if you are the kind of
company that wants to be to have100 companies go before you,
that's fine. They're plenty.
(45:38):
There's lots of successful companies that do that.
But you are not going to get theresults of the 1st 100 companies
got as buyers. I you got to wrap your brain
around what that means for you. I love that.
Well, let's. Go into, I mean, you and I
could, I think we can do a wholeother podcast just on Can I
cross the chasm out of order? Maybe we should do that one for
like 1 of the holiday weeks to get a lot of attention.
(46:00):
I love talking through these things with you and in a format
where other people can hear themand benefit from your journey.
Let's go into my final call to action question for you, which
you know, I ask every guest, butit would be like not an end of
an episode if we didn't have this question.
Say, Margo, with everything you've said, with the journey
you've been on, with the momentsof tension that you have to
(46:23):
hold, with the market the way itis today, with the actual
problem you're trying to solve, which is this kind of reckoning
with capital, trust, human in the center, actually honoring
and delivering to our customers.What is the thing that you wish
everyone listening would start doing, stop doing, and continue
(46:43):
to do? OK, let's see.
This one always stumps me. I think what I would advocate
everyone to start doing is to, to start being honest about
where they fall in the technology journey and what
their, you know, threshold and capacity is for risk.
And then understanding that as acompany and then applying your
(47:06):
Technology Strategy to that. Meaning if you want to go fast
and break things, then you need to be looking for companies that
want to go fast and break things.
If you are not that company, then you you got to stop being
upset when you try to buy a product from a company that that
is, that's my first thing. So like start really being
honest and self reflective aboutwhere you are in your tech
(47:27):
journey and being able to own that with precision.
I would stop using oh God, this drives me this.
I would stop saying that you're using a genic AI because you
have a handful of automations that are running sequenced.
That is not a genic AI. Please, please.
And I'm, I'm not saying that to be rude.
I'm saying that you, we need to understand the difference and
(47:48):
the severity and the difference of cost and you know, exposure
to what we're actually talking about when we say agentic.
I think it's amazing that you people are even talking about
it, that we weren't even talkingabout generative AI two years
ago. Now we're all the way into
agentic. A is I think it's wonderful, but
let's stop misusing the term because it's not helping us move
forward in the actual deploymentof that tool.
(48:11):
And then continue, I'm going to continue to advocate that the
insurance distribution channel is worth the time, effort,
energy. It is a valuable part of our
value chain. I think it might end up being
the most valuable part of the value chain at the end of the
day since we hold the relationship with the consumer
and that is our strongest asset.And I would continue advocating
(48:34):
that we deserve the kind of technology that other players
have and that our size and our current education level should
not preclude us from being involved in the discussion
moving forward. I love that.
Well, thank you, first of all, so much for being a guest today.
Thank you for your grace under fire in the hot seat.
As always. For anybody that doesn't know or
(48:57):
follow Margo on LinkedIn, you can follow Margo Giles on
LinkedIn. You can also follow Iris and
Suretech on LinkedIn. She and the team do just an
amazing job of I would say like no nonsense, truth telling,
keeping it real education, constant education like of of
(49:18):
the industry and, and lots of I would say webinars, awards,
participation. I mean, you can just feel the
energy whenever I look at all ofthe posts and activity going on
on your social channels. So thank you so much for being a
guest today, Margo. Thank you, Lisa.
And to all of our listeners, as always, stay curious, stay
(49:39):
informed, and stay plugged in. Thank you so much for being a
guest. Today's episode of Insurance
Unplugged, the AI and distribution series, is proudly
sponsored by Iris and Suretech, your gateway to the future of
insurance distribution. Iris harnesses the power of
generative AI to transform data processing and decision making
(50:00):
across the distribution landscape.
The Iris platform integrates AI driven decision engines, dynamic
form generation and configurableworkflows, all underpinned by
continuous data quality management.
Discover how Iris is powering smarter operations and more
efficient distribution with cutting edge AI setting a new
(50:21):
standard of excellence across the entire industry.
Insurance. Unplugged in the hot seat where
the complex world of insurance is laid bare.
Hosted by Lisa Wardfall, this podcast promises an unfiltered
glimpse into the industry like never before.
Each episode invites you to listen in on the candid
conversations that usually happen behind closed boardroom
doors. From deep dives with industry
(00:22):
leaders and thought leaders to innovative discussions with
minds shaping the future of insurance, we bring the most
genuine talks directly to your ears.
Our guests take the hot seat alongside me to explore the
inner workings, challenges and triumphs of the insurance world.
If you've ever wondered what goes on in the shadows of the
insurance industry, from the boardroom banter to the behind
(00:44):
the scenes strategies, this is your chance for a front row
seat. Prepare for.
Unguarded, enlightening and engaging discussions.
That cover every. Angle of Insurance presented in
a way that's both insightful andaccessible.
Welcome to the conversation. Welcome to Insurance Unplugged
in the hot Seat with Lisa Wardbaugh.
(01:06):
Welcome to today's episode of Insurance Unplugged, proudly
sponsored by Iris and Suretech, your gateway to the future of
insurance distribution. At Iris, we harness the power of
generative AI to revolutionize data processing and decision
making across the distribution spectrum.
Our platform integrates Gen. AI to provide not just insights,
(01:26):
but actionable intelligence, configurable workflows, and
dynamic form generation, all underpinned by continuous data
quality management. Discover how Iris is pioneering
smarter, more efficient operations in the insurance
industry, paving the way for a new era of distribution
excellence. Let's dive into how Jen AI is
transforming the landscape of insurance distribution today on
(01:50):
Insurance Unplugged. Welcome to another episode of
Insurance Unplugged. I'm your host, Leisa Wardlaw.
And this week I like, feel like we need drum rolls.
No, no, no. I like Margaret Iles joining me,
CEO and founder, Irish and Shirtak.
And I'm going to let her like she's no stranger to the show,
but I want her to kind of introduce herself once again.
(02:12):
For those of you who may not, I've heard her intro or may not
know her deep level journey. But really what I wanted is
first lead in was saying like Margot and I are not here to do
a highlight reel. She and I are really going to
break down. I'll call the deep nuance of
what it's actually like to buildan operational system, not for
(02:35):
demos, not for, I'll call it, you know, point solutions, but
what it's really like and and I'll call it like her, her
candor and survivorship through this, it is huge.
So, so welcome to the hot seat. Margot, I feel like you've been
living in the hot seat going on this.
Mountain I am telling me of the hot seat I.
Think so if you don't mind introducing yourself.
(02:58):
I think like if people have listened to us previously,
they've heard your journey that maybe just a high level intro
and synopsis of what you set outto build so that they can
understand for context, the questions and the follow-ups.
I'm going to kind of put you through on this season finale.
Sure, awesome. Margo Giles, Founder and CEO of
Iris and Suretech. I have been in insurance for
(03:21):
almost 20 years. So I did not start my journey on
the tech side. I started in the seat.
I have always lived on the distribution side of the house
in PNC insurance. So, you know, writing mid to
large market commercial, being aproducer and then being an owner
and then going through M&A and now I'm coming through M&A
again. Always living in that world, you
(03:44):
know, so running agencies and brokerages, dabbling in Mgas and
MG US. But yeah, I mean an insurance
girl through and through. And I kind of got thrown into an
OPS role as my career progressedand, you know, being younger,
the younger side of a COO in insurance.
I think I was 29 when I took over the first insurance agency,
(04:05):
so very familiar with technologyin my personal life.
And then I would come to work and it was just such this huge
disconnect between what I was able to do outside of insurance
and what the software and the systems were allowing me to do
inside my own business. And I felt the friction of that
and the disparity between those two things was just way too much
to ignore. And so I went on a very long
(04:26):
journey of learning by trial anderror and by fire how to how to
build tech, what it really means, how to do it internally
in my own teams, and then how toscale that out, you know, into a
real SAS company. And, and that whole journey has
probably been about 8 years. So yeah, I definitely am battle
worn at this point. I feel very, very confident that
(04:46):
I can answer a lot of questions you're probably going to ask me
about how this process unfolds. Yeah, yeah.
And and just to kind of like recap because I think people are
like, well, like I responded it,but we like went through a
journey. We heard so many voices like
over the season and prior seasons, right.
And I just wanted to kind of like connect the dots for
people, right? So just recently we had Kohli
Perry on last week. If it's not auditable, it's just
(05:08):
a toy, you know, audit as thoughyou're being subpoenaed designed
for that. We had Casey Kempton, who you
sat on stage with her Margo lastyear at ITI, you know, and she
really got underneath the policyof predict and prevent without
back end design. And Andrew Morse treating Jen AI
as a teammate, not a bolt on thecore theme underneath us was
(05:29):
really real transformation. Like authentic transformation
means that you have to face these financial truce, system
lags and infrastructure failures, not just adding AI to
sales slides, right? Like you and I see that a lot.
And, and to me, clearly, I know this because of my work with
you. Iris sits underneath every one
of these themes and how? You.
(05:51):
Really went beyond selling a vision.
So Margaret, now I want to get to the like, right?
Like you and I had this conversation a lot, right?
Like most founders have to sell vision, but most founders stop
there. Few survive the execution layer,
which is why we see, I'll call that high velocity against that
Cliff that you and I started a couple seasons ago talking about
(06:13):
that 3,000,000 ARR Cliff. Why we see so few people push
into the execution layer and even fewer can build something
that the industry, you know, isn't yet asking for but needs.
Right. Because like a lot of people
would say we'll build for what'sasked for.
But it's like, yeah, but you know, again, we'll quote handy
to forward. They would have asked for a
(06:33):
better horse, you know, like andwe hear that a lot, especially
in the distribution. They're asking for a better
horse. I mean, realistically living in
the seat of sales and marketing,but that's what they're saying.
But that's actually not what they mean.
And it's very difficult to get underneath to your point as what
are you? What do you really need?
You're describing a symptom, notthe root cause.
(06:54):
That's tough. So how do you, so let's let's
take that one because you know, you and I talk about this all
the time and, and it's difficultbecause you got the lenses,
you've got the customer lens, you've got the BC lens, you've
got your funding lens, you've got your product lens, you've
got like, how do I market this? How do I like, like, like the
dimensionality of all the platesyou have to spend literally in
(07:15):
parallel on, I'll call it quicksand, right?
Which is your timeline and get get to market and you've got to
have something that can be usable.
So what? Let's step back in your voice.
What do you think the industry actually wants to change?
Or how do you give them what they think they want?
(07:38):
Is there a but there's a reality.
It's like, well, I still have tohave a car that drives even
though I might want a new radio in the car.
Like. How do you how do you
compartmentalize that and balance it, Margaret, because I
think that's. Huge for other founders to hear.
Yeah, it's so I think at the core, and I'm going to go like
existential, I'm going to stay away from tech for a minute
because I think there's always atechnical solution.
(08:00):
But the harder part is trying tofigure out like to your point,
what is it that you're actually,what do you need?
What are you asking for? How do you bridge that, that gap
in insurance? I think especially right now
with our climate, what distributors in particular and
carriers, what we're really asking for is the, you know,
transparency in a lot of ways. There is an inherent distrust
(08:25):
amongst all the parties in an insurance value chain.
If we're going to be candid about what's actually going on,
customers, insurance people, they're frustrated, rising
rates, you know, increasing economic and weather related,
you know, anomalies like like everybody's under stress.
And I think at the end of the chain, people feel the as though
(08:45):
insurance is not transparent. They don't understand what
they're buying. They don't think the coverage is
what they thought when they purchased it.
Every time you have to get in a claim, you realize all the red
tape that you didn't understand because you're not a lawyer as
an actual insured. And you and I have both.
I've recently had to go through the claims process.
You had a terrible claims process.
And we're in insurance and it's still difficult for us to
navigate. So I can't imagine somebody who
(09:07):
genuinely isn't entrenched trying to figure out why these
things are happening. So get the very base level,
everyone in insurance wants to insure a risk correctly, make it
profitable, and have transparency so that their
customers don't have this intrinsic feeling like they're
being ripped off. Like at the core, that's what we
all want and people are asking for that.
(09:27):
So they want to be able to see their customer.
They want to be able to have thecustomer data, and then they
want to be able to utilize that.They want to know what's right
and it's correct and it's traceable for underwriting
reasons. And then they want to be able to
use that information in their daily operational activities,
whether they're binding policiesor servicing post bind, right?
Like all of these things kind ofdrill back into did you sell the
(09:50):
right policy to the right personat the right time with the right
coverage. And that is the the core of what
we're all looking for, not the tech solution, but the core of
what we're looking to to build. I think what's interesting about
that, and I'm just going to kindof weave in here is because I
think to your point, everyone's after that, like, right.
Like I think in general, you know, we're an industry built on
(10:12):
an intangible promise, trust andcreation of trust and
transparency. I, I don't think there's many
people would say they're not trying to do that.
But then it's kind of interesting is because like,
what line did you have to cross that others weren't tackling to
get that? Because I think I call it like
the treadmill or or you could call it like like a revolving
(10:35):
door. People all start with the kind
of core business objective aligned in principle.
But I think it's like we go nowhere.
You know, I heard you say AP is to nowhere, the revolving door,
the treadmill, whatever. How did you decide and what
line? So how did you decide to cross
the line and what line did you decide to cross to kind of, if
(10:58):
you will stop the performative nature of what people were doing
to achieve that transparency andtrust?
Yeah, For us it was the human inthe center design.
I think that that led everything.
So if we were really going to beable to deliver auditable to
your point, immutable like a verifiable information to all
(11:20):
the parties on the chains. I think all of us realize that
there's a data exchange problem at the heart of this.
The next layer up is great, now we have the systems and the
data. How the hell do we give it?
How do we exchange it between all of these disparate parties
and all these different interested parties that take
place in a policy? So I think for us it was, well,
we have to agree to tackle the first step, which is can you
(11:41):
even get that data in a place where it matters?
And to us, it was, I need a singular view of a person,
whether they're participating ina corporation, whether they're
stand alone, whether they're in a household or marriage, you
know, however they're participating in our
relationship. How do we understand that
(12:02):
person? And then how do we get all of
that data surrounding that person to give us a really
cohesive view of what not just their value proposition is, but
what is their risk? Like what, what are they?
What are they actually doing? And how do we like actually
cover the risk at a much broaderlevel?
So for us to do that like, and if we get into the tech side,
(12:24):
like rebuilding a boring basic relational database, like it's
just like every other company has ever done, it just it wasn't
going to garner the results thatwe were actually looking for.
And I think for us, it was goingWeb 3 and looking at components
like blockchain to try to figureout like it, you know, is there
(12:44):
something there that we have notexplored before?
And we know other companies aren't exploring, at least not
yet. And how do we actually make that
a reality? And so was it.
That was a very big, big jump. I think that that's so pivotal
because, and clearly I've had the, you know, the, the specific
perspective on this of being very much in the trenches with
you on this. So I have a depth that maybe
(13:05):
our, our listeners don't have, but it was so easy to just
performatively do what others did and try to do it
incrementally better. And and I would also say
incrementally better Margo, likeyou could have done what other
people did before and you could have put a prettier UI on it.
You could have made it feel prettier and more 2025 ish right
(13:30):
like. I mean this this decade, yes.
Like this this century, No, I'm joking.
But but, and you know what's really interesting, and I call
that like the marketing or the performative nature of it.
By the way, there's nothing wrong with that.
Like if you want, if people wantto go do that, like that's cool.
But that is the treadmill, and that's the revolving.
Well, it hits a it hits a wall. At least we've seen it over and
(13:52):
over and over. Again, how did you know?
Because what what I think is so hard is most people the inertia
of VCs and I'm not criticizing any VCs, right?
Get to market, get revenue, you know, put training wheels on it,
get it out there, get it used. That is the like the undertow of
(14:14):
the current you're swimming in with money and funding, right?
And then there's the like, I'll call it like the oh shit moment.
I could just do what everybody else is doing and make it a
little bit better and probably sell it by the way.
And then there's like the whole reckoning which says yes, but
I'm not going to actually achieve what's necessary.
And I think holding all of that in in in essence, in congruency
(14:40):
with the tension is something that most founders can't do,
Margaret like they they have so like really difficult.
How do you do that? A lot of whatever you choose to
decompress or insert adjectives.Well, it's, it's a, it's a
(15:01):
balancing act. So one, I think we had the
luxury of building a lot when weweren't VC backed.
So I made a lot of mistakes withmy own money, frankly, right
early on in my own agencies and our own brokerages.
Like we like we hit that wall with this human in the center
and verifiable data and how are we going to actually like show
(15:22):
all of these relationships and how are we going to get the data
in a place that's usable? We did that early on with what
the platforms that everyone elseis building on and I won't say
them Salesforce, but like we everybody's been down this road
and we I hit. That wall and let's throw some a
genic AI in there and make it even sooner like Aster to hit
the wall. Like the crash dummies hitting
(15:42):
the wall in the car. Whatever buzzword you want to
put in there, the underlying operating system could not
handle like. It just, it could not handle the
level, first of all, of of data that insurance companies
actually have. Like if they were to represent
their data digitally, which mostof them are not full stop.
Like I don't know what the percentage is, but I can
guarantee you it's pretty low, especially on the distribution
(16:04):
side as to the totality of the data they have versus what they
ever actually digitized and, andis usable is like obscenely low.
So in that scenario, it's like if, if you're really going to
put all the data in and you're going to digitize and you're
going to utilize AI and you're going to share data across
multiple entities, like you haveto have firepower underneath
that that will support that or you will break it.
(16:26):
And I think early on in our agencies, even the sizes we
were, which we are not a large agency, you know, we're not,
we're not the marshes and aeons and like the huge agencies of
the world. And I hit those compute
processing ceilings. So I can't even imagine what,
you know, a large carrier or broker is going to hit in that
scenario. It wasn't cost effective and it
(16:46):
wasn't going to work. So for us, it was like, OK, we
built and failed a lot in private.
And so I was very candid with our investors when we took VC
money that this was not we, we had no plans to flip this
business in three years. This was not a widget that we
were going to build up with no back end and then exit to one of
(17:08):
our competitors. That was not the plan.
It will never be the plan. And so having money invested
that understands that I think was really, really critical for
us. So talk to me about the next
layer before we get into, because I want to go into kind
of some of the provability points of Iris.
But before that, there's also, so it's kind of inverting the
(17:29):
data model customers at the center and and I would assume
that most naive listeners may think, oh, that's what CRM does
you. Know you know what I mean?
We. All have to go there, but OK,
we've heard we've heard those words before.
So I want to go to another layerfor for you which I know Iris.
Does it wasn't. Just customer at the center,
it's customer at the center and with customer at the center, the
(17:53):
ability to do all of the operational processing.
Yeah. And I know that that's
incredibly like an afterthought for most people that are
thinking like, whatever, I've got HubSpot or I've got this CRM
tool or I've got that CRM tool and I've got like a database.
I'm like, OK, like a database doesn't process aesthetic.
So, so like that operational processing.
(18:13):
And yeah, I mean, you heard me talk about this a bazillion
times. I was like, oh, well, my
reinsurance background was so familial when I went into the
distribution background because clearly we do policy movement
and we do these things which aredeeply operationally intense.
How did you combine that and howdid you think about that as you
(18:35):
were building this with, you know, your API?
Like because then I want to get into your like, why do AP is go
nowhere? Like, you know, like how did you
think about that and what's so important?
And I think he gets back to the data exchange, but I'll turn it
over to you to kind of bring thereaders in on that, listeners in
on that. It's also an actual operating
(18:55):
system. It's not just a storage
database. Right.
So I think people because let's just, I'm going to stick in in
distribution because this is where I, I live.
So we have something called an agency management system, an AMS
or BMS depends on where you are in the world or where you're
listening. And so those words are really
deceiving because it sounds likean opera, like it's, it's named
(19:15):
as though it's an operational system, but what it actually is
is a filing cabinet. It is a data repository.
It is a data repository that wasalways built to be a data
repository. And the moaning of the UI it
like that's what it was for. It was meant to, I have this
file and I need to digitally type into a screen the
(19:36):
information from this file so that I can store it here
digitally in a filing cabinet. That is if you take the words
AMS or BMS out of the equation, that is what those systems do.
And that's great because you know, in the nineties, 80s and
90s, like we need, that's what we needed.
That's what we had. But unfortunately a, a dummy
black box data storage software is not built and will never be
(20:01):
built to run policy movements toto do long like long tail
processing. That's actually, if you believe
insurance, that's another separate system.
That's your oracles and your sapiens like those are big
processing platforms. And so if you were outside of
insurance, you would have. And the crazy part is I think
(20:21):
people look at distribution as asales organization and there and
we are sales organizations, but actually we're so much more
highly complex because we don't own the asset or the premium
that is the policy, right? Like it's not our premium to
collect. It's not ours to to run risk
against, right? But we still need to to be
(20:43):
completely tied to every one of those movements as a broker,
Like if you're really going to do a good job as a broker, you
have to understand when a policycancels, why it's cancelling?
Is it for non payment? Is there material
misrepresentation? Like those are policy movements
endorsing cancelling. And the systems that are
(21:03):
databases do not have the ability to actually run the
process. Now they can record the process
once it's happened. So if, if, if you're an AMS user
or BMS user, you're very familiar with something happened
somewhere else and then I go into my repository and I tell
the repository that something has just happened.
That is not operational processing.
(21:25):
That is record keeping. There is a big difference
between the two. And I don't think there's enough
education for you're trying for a lot of these bigger brokers
too. They're trying to scale to
operational processing like a carrier or reinsurer would need,
but they're still using data repositories and they, they
(21:45):
don't understand why they can't get there.
Well, you're not using the rightsoftware.
Like, full stop, you're not well.
Let's take that then, like because I think this is such a
natural segue. Iris embeds trust because you
started with like, what's the Holy Grail that you were solving
for, like transparency and trust, and you chose to embed it
at the actual data level. So you don't just repository
(22:07):
data, you're actually natively embedding trust.
You know, we could talk about whatever I'll call all the, all
the ways you achieve that in thelike technology world.
So let's put that over in stage,right?
But at the end of the day, the way you achieve that is over
here. But what you're actually doing
is you're embedding trust and logic at the like, in like at
(22:27):
the substrate level, every single data object is, you know,
individually like provable, so verifiable.
Exchangeable, right? That is a massive shift in
infrastructure software from what from what we have today.
Exactly. So when you did that, I, I would
say, why did you do that? What are you opening up with
(22:48):
that to your point about like, hey, I've already broken this.
I've already seen the crash or the tidal wave that maybe other
people can't see. And then what does that open up
for Iris and Iris users? And then what breaks?
Like what's the tsunami coming for people that are trying to
still operate at this? I'll call it data space storage
level that you articulated, you know, filing cabinet.
(23:10):
I love that word. It's a filing cabinet.
I mean, that's what it is. Everybody's familiar.
Yeah. A lot of it is you are a company
that relies on, you know, your ability to prove and to be
trustworthy. And if you, your entire systems
are not able to be audited, likethink about the absurdity of
that. Like my whole company, my
(23:33):
billion dollar company depends on my ability to collect the
right data and then transmit that data effectively to all the
other parties so that we can then find a policy and and I can
have my, my cover my you know, right.
And like you don't, your system just physically won't do it.
So how can you possibly be humancentric, transparent?
(23:55):
How can you be good at underwriting and risk
management? And you don't even like, you
have no tools and no ability to do that.
To me, that was just such a likethe chasm was so wide there.
So for, for, for us, it was a couple things.
One, I mean, anybody who's ever owned an insurance company who
has been on the, the gone to thealtar at least and had to have
(24:17):
an E&O claim. And most of us have had large
ones. And sometimes very, you know,
frequently depending on the typeof insurance you rate and you
get audited or you get audited or you get subpoenaed, man, like
think about that process and what we're going through
currently and how that looks andour inability to as brokers and
distribution partners leverage any kind of buying power against
(24:41):
a Swiss Re or a Munich Re when we're talking about Reno
exposure. And like we have no leg to stand
on. And you being from the
reinsurance side, I think the sentiment is, is, and I don't
know if it's right or not, but it's certainly justifiable.
Brokers don't have the sophistication nor the
reliability of data. So what happens by the time it
(25:02):
comes to you on the reinsurance side, Lisa, you guys are
verifying, you're reverifying itlike there's billions of dollars
in that process. I was.
At a lunch one day recently, andI said to this very large broker
sitting across the tape for me, you realize that like 80 to 90%
of your commercial day that you submit is ignored and we go get
our own data. And he literally, Margaret
(25:22):
looked at me. He's like, you're lying.
And I'm like, there were two other reinsurers that worked at
my peer competitors, like at thetable with me, we were sitting
in a triangle kind of. And they're like, no, she's like
not lying. And he was like, he was like.
Why are we collecting it? There was like this.
Awakening Margot that he was like what?
Like he couldn't believe it. He could not believe it.
And I'm like, yeah, we go get our own.
(25:43):
We ignore, replace, extend, backfill, verify, you know all
the things because we have our we've just kind of gotten into
like a self-serve data mode. But to your point, it's about
trust and verifiability and we're like, well, we'll just.
We don't trust you. I mean, the, the bottom line is
we don't trust you. And you know, it's like, well,
yeah, I mean that that's the reality of the situation we're
(26:04):
in. But think about as if I'm a
large broker and I'm looking formy competitive edge and and my
value proposition and I want to be a a partner.
So let's say that I want to develop new programs.
I've got a lot. I want to take the data that I
have and I want to be just as reliable as my carrier, my
reinsurer partners and in order,maybe I want to take those
(26:26):
programs directly to my reinsure.
Maybe I want to start my own reinsure, like these are real
things, your own risk. Appetite network or what?
Whatever. I mean, there's so many risk
solutions now. I agree with you.
I think the problem is, and I think a lot of big brokerages
are finding this out and as it'smoving down to mid market
brokerages, you are woefully, woefully unprepared to step into
(26:49):
that realm. Even though the the return on
investment is huge. The upside for a broker to be
able to do these things with their data is massive.
The downside is if you like we makes our, we make ourselves
obsolete. So Lisa, if you and every
carrier in the world does have to reverify everything we're
sending you, then at what point do we like work ourselves out of
(27:12):
a job because we're unwilling todevelop technology?
Like at what point do you guys, and we've seen this, we've seen
this over the last five years, say we'll find a different
distribution channel, one that is reliable that we don't need
to pay 30% to because we're not only repaying you, but now
we're, now we got to go redo everything that we're paying you
to do to have. And, and it's a, it's a
contentious back and forth between distribution carrier and
(27:36):
reinsurer in the, in the broker space because it's been
disjointed, because there hasn'tbeen somebody to come in and,
and say, we're going to lead thepack here and we're going to do
these hard things. We're not here to just turn and
burn and make a dollar off of brokers, right?
No one's LED that pack. So there's no buying power in
the broker space and they haven't been able to unify
behind any, any one piece of technology.
(27:58):
And unfortunately, the ones thatare in the market do not have a
financial interest because why, why would anybody push the
technology forward when we're fine doing what we've been doing
for 30 years? It's a really, it's a quagmire
and it was one that I think nobody that doesn't intimately
understand insurance could possibly walk in and understand
(28:18):
and then build against. And even those in insurance
don't necessarily understand thelevel of technology available
and or needed to actually solve that problem.
So it's it's a hard space to be in.
You know, for me, like seeing, seeing so much of what you've
done, the next level to me is like, it's that how did you
create the data and how do you create the transparency and the
(28:39):
trust? It's also how do you create, and
I'll use the word dynamic, but how do you not do period and
close? Like how do you get what we
would call more event driven, even if you're not 100% event
native at the outset out becauseyou know, whatever, there's
still things like downloads, there's still things like you've
got to go to a portal and. We're batching some things too,
(29:02):
I mean. Let's call it hybrid mode right
now because you know you can't build for something that the
other side isn't meeting you at either.
Correct, correct. But like why is it such a
difference to not have somethinglike a period and batch like
like financial engine like like when you designed Iris?
(29:22):
It's really like producing like I can see in real time my
margin, I can see my profitability, my producers can
see that. How is that different from like
this concept of like type in allmy transactions?
And I'm going to be like, like, why is that so important to run
the business? Because how like our world is
not, we're not making decisions by quarter anymore like or by
(29:45):
year, right? Like it's funny actually,
because I'm, I'm here right now.We're, I'm recording this with
you and I'm in San Francisco, I'm here in Silicon Valley and I
am meeting with investors. We, we recently had an investor
from South Korea and I'm meetingwith a bunch of Asian investors
that are in insurance. So you know, MSNAD and Sampo,
there's a, there's a bunch of really great Asian insurers and
(30:05):
I've been having discussions that that I have found just so
starkly different from how we think of things.
Other countries, other investor types, they talk about
investment in terms of generations.
They're not thinking what is this quarter's numbers.
I mean everybody is right, but the rest of the world is
(30:26):
thinking in generational like what are we investing in now
that's going to do return in 10 years.
To me that's something that I see in the on the broker side in
the mid market, these great legacy mid market providers that
you know that have built something that are continuing to
do M&A right these really and they have this great opportunity
now to kind of keep going. Those people are thinking in
(30:48):
generation, they're thinking about perpetuation plans and how
they're moving things from their, you know, to their
children and all of that. And so those thought process are
a lot longer. But if you move into some of the
larger brokers or the really small ones, it's always this
short term planning of we got tohit this quarter's numbers and
this year and like we'll we think about next year once a
(31:09):
year. It's a difference in mind and
mindset shift. But the ones that are thinking
in the longer term generational planning, they're winning right
now. They're the ones that are
winning all the M&A deals, right, Because nobody wants to
be a part of a turn and burn. Everybody wants to be a part of
something. And when you think about what it
takes to plan like that, it likeyou need to understand how to
(31:30):
make business decisions in real time.
You need to understand when a market is shifting, You need to
understand when a when an insurer is moving or reinsurer
is moving. And you cannot do that if you're
doing a one year annual planningon what next year will be.
You need more information, you need the ability to pivot, you
need the ability to quickly disseminate from the top down
(31:51):
and initiative. So maybe this year we thought we
were going to go after AG, right?
Because this is like a really great market and blah, blah,
blah. And then boom, all the sudden
immigration happens and now, Oh my God, AG is actually falling
apart. Now.
How do we take this initiative that we plan for, and how do we
move it quickly? You can't do any of that when
you don't even get to the numbers until 90 days after
(32:12):
something happens. Like how are you actually going
to be nimble enough to respond to a world that is in real time?
Yeah. And I think part of the
fascinating thing for me when I started, you know, working like
in distribution with you and started understanding, I think
everyone thinks they have that capability and, and the way they
define it is, well, we'll send out an e-mail to all of our
(32:34):
producers. And I'm like, what?
They don't know, they don't havethe ability to.
It's like a leak, you know, likethat automatic faucet shut off
that shuts the water off and it prevents the leak from getting
like immediately. I think of that with capacity
and wrist placement, right? You've got to be able to switch
your flow immediately, and sadly, humans aren't conditioned
(32:57):
to just read an e-mail and always do with e-mail says if
they can go in and do whatever they used to do, they're going
to keep doing it. Yeah, it's like pattern, they're
like trained, they got muscle memory, right.
You're breaking that muscle memory.
And it's really important that like I cannot stress like if you
talk about like we've talked about what's coming after this,
like we lay this foundation layer and you get this real time
(33:18):
and you get this verifiable data.
Like what does that mean to you as a business?
Yes, it's nice. You're, you're going to have
pivot, you're going to have the initiation, the, you know, all
of that's going to happen. But what does it mean long term?
Well, if I let's let's talk about AI for a minute, because
that's what everybody's talking about, even though I hate
talking about it. Like.
It's just, it's over. It's there's a lot.
(33:39):
But if I am a big broker and I now have my board comes down and
says, Hey, everyone's using AI, like, right, this is the, this
is the buzzword and all the the boardrooms we want to deploy AI
and you don't have verifiable data to deploy AI on top of or
you don't have an operational process to insert AI into.
(33:59):
How are you putting? Yeah, like where does AI live on
your data? Like in your filing cabinet?
Like what is the best AI can do with a filing cabinet filled
with broken, wrong, dirty files?I mean, what is the best hope
you can get on lift? I think you're seeing this right
now. I mean, you and I have been in
so many of these big broker rooms and they're and they're
(34:21):
like, we got a pilot. We got a pilot, but nothing
rolls out and nothing's returning the investment.
So these executives and boards are like, well, AI's doesn't
work. It's like you're trying to
deploy something on top of something that it is never going
to. It's never been built for it
can't support and it also can't feed.
And we're looking into the future now with Eno, where
(34:42):
you're going to train. This is happening right now.
People are going to start training these LLMS on this
really bad, broken, non processable data and they're
going to start getting into big trouble.
That right, you're going to yourEno is going to go like, so it's
going to be who can figure this out versus who can't.
And it's not an easy problem to solve.
And if I had a dollar for every time someone was like, well,
(35:03):
let's go to GPT and I put my into it and I'm like, OK, but
like, cool. And that's great for writing an
e-mail. But like, I'm talking about
operational processing data at speed and at volume.
And how is your LLM going to respond?
And how are you training it? There's no concept of what is
actually needed to make that successful.
(35:24):
Well, there's no. And just to amplify that, anyone
and I will say anyone that has ever been involved in any level
of any enterprise grade technology like you've worked at
a carrier or you've worked at a corporation or you've worked at
a reinsured or whatever. We never used ACRM system Margo
(35:46):
without connecting it to some sort of master data management
or data harmonization layers like we had data governance.
So if you've been removed from that and you've got your data in
this, I'll call it CRM only database, only universe, and you
haven't connected that throwing AI on top of that is by
(36:07):
definition asking it to hallucinate.
You're like inviting it to a drug.
Yeah. You're you're training it to
hallucinate at that point. Yeah.
Like that's what you're trainingit to do.
I mean, even the organizations that have had decades of
governance, they're still not easy to implement operational
AI, to your point, but they're getting there.
(36:28):
And so I think that this disconnect of I can just throw a
ChatGPT on top of my database and everything's fine.
To your point, that's naive. But what I find even more
interesting is that we're not even proving that.
And then we're like, hey, let's add some agents in there and go
full bore on a jet. Ticket.
But yeah, let's, let's go, yeah,let's do that real fast and at
(36:52):
scale, if we can even get it up and running.
Just nobody has new business talking about a genetic AI if
they're still operating on a database with no master data and
no intelligence later. My favorite part about that is
you can't even do agent to agentcommunication vis a via and AI
API. So OK, so let's let's go back to
the world of API. You got to put all kinds of
(37:13):
layers in between that. There's certain things can't
even do API to API communicationthat's not event based.
Now we're gonna go into we're doing agentic AI and if anybody
listens to this, your people aretelling you they're doing
agentic AI and all you have is at best API calls hashtag no,
because an an agent has to talk in an agent to agent.
(37:33):
So there's the Google agent to agent, there's the manage
control plane and we are only seeing very sophisticated
companies start to road map out that.
But Margo, the amount of distribution people that I hear
saying they're doing agentic AI and I'm like, they're not.
I mean, and there's nothing wrong like if you can rig up
something that that works like I'm like more, I've done it 100
(37:57):
times, like more power to you, but you need to understand the
repercussions of what you're potentially building.
And again, I, I think about there's a couple instances that
I've been a part of where this isn't even a genic just API.
When an API call, when you're pushing 8000 quotes through an
API and it's coding and IC codesand AIC codes incorrectly and
(38:21):
you just wrote 4000 policies that now all have material
misrepresentation, right? Yeah.
And a carrier accepted. That was literally like a bot,
by the way. Yeah, yeah, yeah.
That's a. Bot that's not a genic right.
So like you're talking about even taking that and then a
carrier can't, doesn't even realize it until 120 days when
they're doing their post audit, which is also happening by
humans going through looking at things.
(38:41):
And that now all of a sudden we've got millions and hundreds
of millions potentially and justcomplete utter chaos.
I think about that. And we don't even have a genic
yet. Like we're not even talking
about a genic. And I think about the
repercussions of that. And so I think it's, again, I
think it's really naive in two ways.
One, to say that we're at a place that that's even
(39:04):
deployable because I don't thinkthat we are.
And two, that even if you could rig something up that you
understand what the E&O repercussions are of that build,
they're massive. I agree with you.
So let's get into like the inside the build.
We'll keep it short. But you know, I think a lot of
times we we talk to founders andit's like, you know, it was hard
(39:25):
triumph success, but it comes with a huge cost.
So one of my favorite things to reflect on is like what almost
broke you and what did you learnfrom being on the edge of
failure? I mean, you've never had a
safety net in anything you've ever done.
Why haven't you walked away? What is your pulse point that
(39:46):
keeps you on the edge? I don't see anybody else doing
this. I don't see it in my peer
groups. I don't see it when I'm on
stage. I don't see it when I'm behind
the boardrooms with these executives.
Like I, I see the the problem islarge in scale.
It is very difficult to solve. And I see a lot of people come
(40:07):
in and enter, and then I see them realize that it's going to
take literally. Every ounce of self preservation
just going out the window to even attempt it.
And I see the I see them, I see their face turn and I see them
give up. And I cannot imagine knowing
what I know like and being so passionate about.
(40:28):
I know how to fix this problem. Like we have the knowledge we
have the path forward. We have the technology like it
exists in the world to to have all of those things and then to
not be able to get this industryto where it will.
It's going to go there at some point like it there is no other
path it has to or it will fail and and to walk away.
(40:51):
I like. I can't even imagine a scenario
where unless I was forced to, I would never.
I could never. Yeah, I think that's such a such
a strong part of this journey that we often don't talk about
because I think, I think a lot of people and I'm not
disrespecting any founders, but they feel a calling for an exit
or until it's hard. Lisa, listen, I'm AVC startup
(41:15):
like if you don't think that that has been like kicked around
and talked about and all like itit you have to because one of
the things that I think I buckedthe system on and I get so much
shit for it online, you'll see it everywhere.
It's irises vaporware. It's never going to launch is
the idea that I'm not building towards ARR in a sense so that I
(41:38):
can flip this company right and there's this I call it like the
demo. I call it like, like every
founder gets to a point where they build towards a demo.
Yeah, so they'll go because everybody's like, oh, I don't
understand. I want to see the system.
Let me get and play with you. Like, well, it's an operational
system. So like, good luck.
I mean, it's it's triggering operations across, you know, 42
(41:59):
different platforms. But yeah, let's set up demo
work. But but you have to here's.
A sandbox. For that, here's a.
Sandbox, by the way, everything in the sandbox is completely
fake, right? So like it's a, it's like a, a,
it's like a, like a safety net or like a baby blanket.
You're like, I got my demo works.
I feel really good about this. It's like the everything in
(42:20):
there is just completely fake and it's all being triggered and
hard coded and it means nothing.It's like a simulator, yeah.
Yeah, yeah. I mean, but which is OK, like
if, if and you know, we'll we'reon the path of building the
simulator. So because it is difficult for
people to understand, a lot of people are visual, so they need
to see this, what this process means to them and that there's
no shade there. But that was not what I was
(42:43):
going to spend our limited resources and money on, building
a baby blanket for people like that.
It doesn't prove anything to. I think for us that was we take
a lot of heat for that. But I stand by my decision.
And we have customers that are on the platform that have access
to the system and they're clickety clacking in there right
now. But it doesn't mean that, you
know, we spend a million or $2,000,000 in an irresponsible
(43:06):
way to make people who probably weren't going to buy the
software in the 1st place feel better about the software.
I mean, I think it's interesting.
It's interesting juxtaposition because there are very, very,
very large enterprise platforms that do not have an environment
that you get access to or you get to go play with.
I mean once you're post, you know, if you're in an
implementation you get UAT or you get sandboxed, that's
(43:28):
normal. You don't.
Get anything for you pay like inmost ways, let's take Oracle ERP
or work day or let's just use something like that.
So we don't like throw anybody else under the bus.
That's it. Like you sign a multi year
contract, you get an environmentright like this, but it's really
hard to be, I'd say a, a, you know, pre series, a founded
(43:48):
startup and say that. But again, that comes back to
you're building an operational system, not a widget.
And I think widgets can have sandboxes.
I think operational systems don't.
So I think that's really interesting that you've had to
hold that tension. Oh, it's really difficult.
I mean, it's it's very difficult.
And I think look rightfully so, because especially in the broker
(44:10):
side, so many people have come to this market, built something
that is really not stable or really doesn't have anything
behind it, has sold it for a couple years and then flipped it
only to leave a whole bunch of people that have invested in it
and spent time in it, you know, with nothing.
And they're back, right back to where they started.
So, and I was the victim of thatas well.
(44:32):
So like I, I fully understand where it's coming from, but I
think the, the understanding from our market needs to be when
you're building something that has never existed before, never
existed before and you're going to market with it.
It's not going to be for everyone, but it'll be 5-6 years
before virus is big enough wherewe can go deploy it and you
(44:55):
know. Crossing the chasm right like.
Crossing the chasm exactly. And I, we cannot do that out of
order, as you say, we cannot. So if you're you know, if that's
the if you're looking to be someone who is not first in,
then don't look for first in software.
But then you have to understand that you are not going to get
first in results like that's thewhole part of being the leader
(45:17):
of the cutting edge and and being that kind of personality
is it does not come without riskinherently, yes, there's risk
there, but you cannot expect extraordinary things if you are
not willing to be extraordinary.So if if you are the kind of
company that wants to be to have100 companies go before you,
that's fine. They're plenty.
(45:38):
There's lots of successful companies that do that.
But you are not going to get theresults of the 1st 100 companies
got as buyers. I you got to wrap your brain
around what that means for you. I love that.
Well, let's. Go into, I mean, you and I
could, I think we can do a wholeother podcast just on Can I
cross the chasm out of order? Maybe we should do that one for
like 1 of the holiday weeks to get a lot of attention.
(46:00):
I love talking through these things with you and in a format
where other people can hear themand benefit from your journey.
Let's go into my final call to action question for you, which
you know, I ask every guest, butit would be like not an end of
an episode if we didn't have this question.
Say, Margo, with everything you've said, with the journey
you've been on, with the momentsof tension that you have to
(46:23):
hold, with the market the way itis today, with the actual
problem you're trying to solve, which is this kind of reckoning
with capital, trust, human in the center, actually honoring
and delivering to our customers.What is the thing that you wish
everyone listening would start doing, stop doing, and continue
(46:43):
to do? OK, let's see.
This one always stumps me. I think what I would advocate
everyone to start doing is to, to start being honest about
where they fall in the technology journey and what
their, you know, threshold and capacity is for risk.
And then understanding that as acompany and then applying your
(47:06):
Technology Strategy to that. Meaning if you want to go fast
and break things, then you need to be looking for companies that
want to go fast and break things.
If you are not that company, then you you got to stop being
upset when you try to buy a product from a company that that
is, that's my first thing. So like start really being
honest and self reflective aboutwhere you are in your tech
(47:27):
journey and being able to own that with precision.
I would stop using oh God, this drives me this.
I would stop saying that you're using a genic AI because you
have a handful of automations that are running sequenced.
That is not a genic AI. Please, please.
And I'm, I'm not saying that to be rude.
I'm saying that you, we need to understand the difference and
(47:48):
the severity and the difference of cost and you know, exposure
to what we're actually talking about when we say agentic.
I think it's amazing that you people are even talking about
it, that we weren't even talkingabout generative AI two years
ago. Now we're all the way into
agentic. A is I think it's wonderful, but
let's stop misusing the term because it's not helping us move
forward in the actual deploymentof that tool.
(48:11):
And then continue, I'm going to continue to advocate that the
insurance distribution channel is worth the time, effort,
energy. It is a valuable part of our
value chain. I think it might end up being
the most valuable part of the value chain at the end of the
day since we hold the relationship with the consumer
and that is our strongest asset.And I would continue advocating
(48:34):
that we deserve the kind of technology that other players
have and that our size and our current education level should
not preclude us from being involved in the discussion
moving forward. I love that.
Well, thank you, first of all, so much for being a guest today.
Thank you for your grace under fire in the hot seat.
As always. For anybody that doesn't know or
(48:57):
follow Margo on LinkedIn, you can follow Margo Giles on
LinkedIn. You can also follow Iris and
Suretech on LinkedIn. She and the team do just an
amazing job of I would say like no nonsense, truth telling,
keeping it real education, constant education like of of
(49:18):
the industry and, and lots of I would say webinars, awards,
participation. I mean, you can just feel the
energy whenever I look at all ofthe posts and activity going on
on your social channels. So thank you so much for being a
guest today, Margo. Thank you, Lisa.
And to all of our listeners, as always, stay curious, stay
(49:39):
informed, and stay plugged in. Thank you so much for being a
guest. Today's episode of Insurance
Unplugged, the AI and distribution series, is proudly
sponsored by Iris and Suretech, your gateway to the future of
insurance distribution. Iris harnesses the power of
generative AI to transform data processing and decision making
(50:00):
across the distribution landscape.
The Iris platform integrates AI driven decision engines, dynamic
form generation and configurableworkflows, all underpinned by
continuous data quality management.
Discover how Iris is powering smarter operations and more
efficient distribution with cutting edge AI setting a new
(50:21):
standard of excellence across the entire industry.
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