Name: How can you make AI adoption profitable?
Uploaded: 2024-07-04T13:04:44.709Z
Duration: 567.5
Description: Chris Brown, President of Intelygenz USA, talks about two decades of optimizing banking and fintech industries through deep tech and AI solutions, and shares his key takeaways from his keynote at FinovateSpring. He delves into Intelygenz's strategies to overcome common AI project pitfalls, and he explains their end-to-end approach to client engagement.

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English

{{00:00:05}}
Hello and thank you for joining us.

{{00:00:07}}
My name is Julie Muhn.

{{00:00:08}}
I'm Senior Research Analyst with
Finovate and today I am joined by Chris

{{00:00:12}}
Brown, President of Intelygenz USA.

{{00:00:14}}
Chris, thank you so much
for joining us today.

{{00:00:16}}
It's a pleasure.

{{00:00:17}}
Thanks for having me.

{{00:00:18}}
Yeah, so Intelygenz elevates the banking
and fintech with deep tech innovation.

{{00:00:22}}
You guys have over 20 years of experience
in deep tech and specialize in AI

{{00:00:27}}
solutions that deliver measurable returns
on investment through enhanced operational

{{00:00:31}}
efficiency and customer engagement.

{{00:00:33}}
And you just recently stepped
off your keynote - how did it go?

{{00:00:36}}
Yeah, it was really good.

{{00:00:37}}
Good reception.

{{00:00:38}}
It's a nice set up.

{{00:00:39}}
we had some, a great audience there and I
think the messages resonate pretty well.

{{00:00:43}}
So yeah, really happy.

{{00:00:44}}
Wonderful.

{{00:00:45}}
I've heard great things as well.

{{00:00:46}}
So let's jump right into it.

{{00:00:47}}
So Intelygenz is known as the deep tech
partner for banks and fintech companies.

{{00:00:52}}
Tell us more about Intelygenz
as an expert AI consultancy in

{{00:00:56}}
financial, in the financial industry.

{{00:00:58}}
So we've been servicing the financial
services for over 20 years with deep tech.

{{00:01:07}}
we started really early
on our journey with AI.

{{00:01:10}}
We knew it was going to be
a huge impactful technology.

{{00:01:15}}
Some may say we started
a little too early.

{{00:01:17}}
It was super difficult.

{{00:01:18}}
It was hard work.

{{00:01:21}}
but it's put us at the forefront,
and really strong position to help

{{00:01:25}}
financial services organizations
really take huge amounts of benefits.

{{00:01:31}}
Put them on the forefront to compete,
whether that's for operational efficiency,

{{00:01:35}}
whether it's for, transforming the
way they interact with their clients

{{00:01:40}}
or for finding new growth markets.

{{00:01:43}}
Yeah, it's been a great 20 year journey.

{{00:01:46}}
we're very confident in what we do.

{{00:01:48}}
We're very good at what we do.

{{00:01:49}}
We're very bad at things that we
don't know what we're doing, but

{{00:01:50}}
we're very good at what we do.

{{00:01:52}}
We're very focused in terms of,
in terms of our AI delivery.

{{00:01:56}}
Yeah, that's what we do.

{{00:01:58}}
We've been doing it a long time.

{{00:02:00}}
And our experience is,
paying dividends right now.

{{00:02:03}}
Great.

{{00:02:03}}
That's great to hear.

{{00:02:04}}
So during your keynote, you emphasized
delivering ROI in a matter of

{{00:02:08}}
weeks instead of months or years
using these deep tech solutions.

{{00:02:12}}
Can you elaborate on Intelygenz's
approach to rapid delivery and

{{00:02:16}}
how this approach facilitates the
transition from conceptualization to

{{00:02:20}}
implementation of AI within weeks?

{{00:02:23}}
So the first thing to
concentrate on is ROI matters.

{{00:02:27}}
It really matters.

{{00:02:28}}
This is not a technical exercise.

{{00:02:30}}
We're not going through
innovation centers.

{{00:02:32}}
We're not talking about laboratory
exercises and prototypes.

{{00:02:35}}
ROI matters.

{{00:02:36}}
So if you want to really benefit from
an AI solution, being in production,

{{00:02:42}}
having it underpin key processes for
your business, that really matters.

{{00:02:46}}
So the first thing is,
make sure ROI matters.

{{00:02:49}}
That can accelerate you
just having that thinking.

{{00:02:52}}
Secondly, I said, we've
been doing this a long time.

{{00:02:54}}
We have incredible amounts of skill.

{{00:02:56}}
So there's a combination
credible on skills.

{{00:02:58}}
We have platforms, we have frameworks,
but really we've defined and refined

{{00:03:05}}
the process over 20 years of how to
get AI into production in a robust

{{00:03:10}}
fashion that can be relied upon.

{{00:03:15}}
It's a, very different software and
it's a very different software journey.

{{00:03:21}}
To any other kind of software, when
you start an ordinary software project,

{{00:03:27}}
you're going to deliver, you might
be a week early, you might be a week

{{00:03:30}}
late, you're going to deliver with AI.

{{00:03:33}}
Things are very different.

{{00:03:35}}
The at the start of an AI
project, the answer to nearly

{{00:03:38}}
every question is we don't know.

{{00:03:40}}
We don't know.

{{00:03:40}}
Have I got enough data?

{{00:03:41}}
I don't know.

{{00:03:42}}
It's a correlation within my data.

{{00:03:44}}
We don't know what
accuracy can we achieve?

{{00:03:46}}
We don't know what accuracy
do you need to achieve?

{{00:03:47}}
We don't know.

{{00:03:49}}
Having, an engagement strategy that,
as I said, we've been working on for 20

{{00:03:53}}
years to get you through the mitigation
of that risk of both time spent and

{{00:03:59}}
investment to understand the feasibility
of the AI that you're trying, that

{{00:04:03}}
you're trying to implement for the
objective that you're trying to do.

{{00:04:07}}
That engagement process process scoping,
rapid experimentation on AI feasibility

{{00:04:13}}
into agile deployment before we even
get to maintenance, absolutely key.

{{00:04:19}}
We are institutionally agile and we
brought that into the AI world as well.

{{00:04:26}}
So once we're post feasibility, we are
trying to draw value from those solutions,

{{00:04:34}}
even if it is not the 100 percent end goal
value, we are dropping value out of those

{{00:04:38}}
solutions as soon as we possibly can.

{{00:04:40}}
That's why we get the rapid ROI.

{{00:04:42}}
That's why the ROIs are squashed in terms
of the time taken to, to make it count.

{{00:04:50}}
yeah, so it's a combination
of all those things.

{{00:04:52}}
Start early, high skills, good
frameworks, good platforms.

{{00:04:58}}
And making sure our engagement
strategy fits the commercial platform

{{00:05:01}}
or the commercial challenges and
objectives that we're going for.

{{00:05:03}}
Yeah, great.

{{00:05:05}}
I'd like to take a look
at some statistics now.

{{00:05:06}}
So according to Gartner, 85 percent
of AI projects fail to move forward

{{00:05:11}}
from development into production.

{{00:05:13}}
So how does Intelygenz get its banking and
fintech clients to be  in that successful

{{00:05:17}}
15 percent club, what's the secret?

{{00:05:20}}
All of the above that I just talked about,
but I would double down on the engagement

{{00:05:24}}
piece, having rapid experimentation.

{{00:05:26}}
So first of all, admitting
to yourself two things.

{{00:05:29}}
One is it's different to an
ordinary software project.

{{00:05:33}}
We've got to get that on the table.

{{00:05:34}}
We've got to get that understood
that it is not the same.

{{00:05:37}}
Once you do that, Hey, I
need specialist skills.

{{00:05:40}}
I need scarce and specialist
skills in order to be successful.

{{00:05:43}}
Once you're in that position of this
isn't something I can pick up  on

{{00:05:47}}
YouTube or a couple of courses.

{{00:05:49}}
You want significant specialists,
whether that's data scientists, whether

{{00:05:53}}
it's ML engineers, whether it's MLOps,
DevOps,  you need specialist skills.

{{00:06:00}}
specialist talent in order to make
that happen and then make sure you're

{{00:06:03}}
getting into that rapid experimentation,
get into production as fast as you

{{00:06:07}}
possibly can because there is nothing
that can replicate production data.

{{00:06:12}}
And because data is so key in such,
such a key element of fuel for that

{{00:06:16}}
AI engine, getting into production
early helps you make sure you deliver.

{{00:06:20}}
Yeah.

{{00:06:21}}
Alright, so we have time
for one last question.

{{00:06:23}}
as we talked about before, I saw a
lot of excitement around your keynote.

{{00:06:27}}
Tell us about how companies
typically work with you.

{{00:06:29}}
What's your end to end approach?

{{00:06:34}}
Clients come to us because
they have a very specific need.

{{00:06:37}}
If they have a piece of software
they can buy off the shelf, they

{{00:06:40}}
will go and we will encourage them
to go and buy it off the shelf.

{{00:06:43}}
If you want a CRM system,
don't make us build it for you.

{{00:06:45}}
Go and buy a CRM system off the shelf.

{{00:06:48}}
So the first thing that we need to do
is understand that custom challenge.

{{00:06:52}}
We can never go into a project
and, know exactly what we're doing.

{{00:06:56}}
Yes, we do several repeat projects for
different clients on certain topics

{{00:07:03}}
and certain, objectives, but the data
is different and their infrastructure

{{00:07:06}}
is different and all of that.

{{00:07:07}}
So the first thing that
we do is process scope.

{{00:07:09}}
So we sit and we listen and we
take them through four to six

{{00:07:11}}
hours, sometimes eight hours.

{{00:07:13}}
Time that they'll never
get back in their life.

{{00:07:15}}
I understand that.

{{00:07:16}}
We put it.

{{00:07:17}}
But it's super detailed questioning
about what is the objective

{{00:07:20}}
you're trying to achieve?

{{00:07:21}}
What are the systems that
you have in place today?

{{00:07:23}}
How do you do it today?

{{00:07:25}}
What would good look like?

{{00:07:26}}
What are your KPIs?

{{00:07:27}}
What are the target variables?

{{00:07:29}}
How do we move and shape the data?

{{00:07:30}}
How do we get access to that data?

{{00:07:31}}
So very, detailed scoping sessions.

{{00:07:35}}
From those sessions.

{{00:07:36}}
we write a, very detailed report to say,
Hey, we listened, did we listen correctly?

{{00:07:42}}
If we listen correctly and we
understood what you're, what,

{{00:07:45}}
you were telling us, here is how
we would approach this subject.

{{00:07:49}}
And this is what the timeframes
that is going to take in order to

{{00:07:51}}
do that and the skills that we're
going to bring to the table in order

{{00:07:54}}
to do that with the architecture.

{{00:07:56}}
Once that's done, then we go
into rapid experimentation

{{00:07:59}}
from rapid experimentation.

{{00:08:00}}
We want to be in production and
deployed within four to six weeks.

{{00:08:04}}
That fast, right?

{{00:08:06}}
As I say, it may not be a whole
switch on of 100 percent of we're

{{00:08:10}}
going to use all of this solution.

{{00:08:11}}
You don't have to deploy AI
and hold tight and think, oh,

{{00:08:15}}
the whole thing's got to work.

{{00:08:16}}
You can, you have a deployment
strategy that helps you ramp

{{00:08:19}}
up that, that capability.

{{00:08:21}}
That is all part of that design process,
including the human centric design

{{00:08:25}}
that we talked about in the keynote.

{{00:08:27}}
And once we're in that we're going into
agile deployment and we are turning

{{00:08:31}}
on results as fast as we possibly can.

{{00:08:34}}
That generates ROI as quickly as possible
and it keeps your stakeholders engaged

{{00:08:38}}
to say, Hey, this is not black box.

{{00:08:41}}
This is not witchcraft.

{{00:08:42}}
This is technology that actually works
and can bring benefits to our business.

{{00:08:46}}
And then we move into maintenance.

{{00:08:48}}
That's what an engagement
looks like on the whole.

{{00:08:50}}
Yeah.

{{00:08:50}}


{{00:08:50}}
I love how you keep in touch with the
client the whole way and open to feedback.

{{00:08:54}}
That's great.

{{00:08:55}}
We have to work with our clients.

{{00:08:57}}
They are the experts
within their business.

{{00:08:59}}
They are their subject matter experts.

{{00:09:01}}
They're often the data experts
as well within the business.

{{00:09:03}}
They know their data better than
we do, but we, absolutely come

{{00:09:07}}
in, work in partnership with, our
clients and ensure that they get

{{00:09:11}}
the results that they're targeting.

{{00:09:14}}
All right.

{{00:09:15}}
thank you so much for your time.

{{00:09:16}}
This is Chris Brown,
President of Intelygenz USA.

{{00:09:19}}
I'm Julie Muhn.

{{00:09:20}}
Thanks.

{{00:09:21}}
Thanks, Julie.

I'm Senior Research Analyst with
Finovate and today I am joined by Chris

Chris, thank you so much
for joining us today.

Yeah, so Intelygenz elevates the banking
and fintech with deep tech innovation.

You guys have over 20 years of experience
in deep tech and specialize in AI

solutions that deliver measurable returns
on investment through enhanced operational

And you just recently stepped
off your keynote - how did it go?

we had some, a great audience there and I
think the messages resonate pretty well.

So Intelygenz is known as the deep tech
partner for banks and fintech companies.

Tell us more about Intelygenz
as an expert AI consultancy in

So we've been servicing the financial
services for over 20 years with deep tech.

we started really early
on our journey with AI.

We knew it was going to be
a huge impactful technology.

Some may say we started
a little too early.

but it's put us at the forefront,
and really strong position to help

financial services organizations
really take huge amounts of benefits.

Put them on the forefront to compete,
whether that's for operational efficiency,

whether it's for, transforming the
way they interact with their clients

We're very bad at things that we
don't know what we're doing, but

We're very focused in terms of,
in terms of our AI delivery.

And our experience is,
paying dividends right now.

So during your keynote, you emphasized
delivering ROI in a matter of

weeks instead of months or years
using these deep tech solutions.

Can you elaborate on Intelygenz's
approach to rapid delivery and

how this approach facilitates the
transition from conceptualization to

So the first thing to
concentrate on is ROI matters.

We're not going through
innovation centers.

We're not talking about laboratory
exercises and prototypes.

So if you want to really benefit from
an AI solution, being in production,

having it underpin key processes for
your business, that really matters.

So the first thing is,
make sure ROI matters.

That can accelerate you
just having that thinking.

Secondly, I said, we've
been doing this a long time.

So there's a combination
credible on skills.

We have platforms, we have frameworks,
but really we've defined and refined

the process over 20 years of how to
get AI into production in a robust

It's a, very different software and
it's a very different software journey.

To any other kind of software, when
you start an ordinary software project,

you're going to deliver, you might
be a week early, you might be a week

The at the start of an AI
project, the answer to nearly

We don't know what
accuracy can we achieve?

We don't know what accuracy
do you need to achieve?

Having, an engagement strategy that,
as I said, we've been working on for 20

years to get you through the mitigation
of that risk of both time spent and

investment to understand the feasibility
of the AI that you're trying, that

you're trying to implement for the
objective that you're trying to do.

That engagement process process scoping,
rapid experimentation on AI feasibility

into agile deployment before we even
get to maintenance, absolutely key.

We are institutionally agile and we
brought that into the AI world as well.

So once we're post feasibility, we are
trying to draw value from those solutions,

even if it is not the 100 percent end goal
value, we are dropping value out of those

That's why the ROIs are squashed in terms
of the time taken to, to make it count.

yeah, so it's a combination
of all those things.

Start early, high skills, good
frameworks, good platforms.

And making sure our engagement
strategy fits the commercial platform

or the commercial challenges and
objectives that we're going for.

I'd like to take a look
at some statistics now.

So according to Gartner, 85 percent
of AI projects fail to move forward

FinovateSpring

Best Practice

Chris Brown, President of Intelygenz USA, talks about two decades of optimizing banking and fintech industries through deep tech and AI solutions, and shares his key takeaways from his keynote at FinovateSpring. He delves into Intelygenz's strategies to overcome common AI project pitfalls, and he explains their end-to-end approach to client engagement.