Supercharge Your Sales Growth with 180ops AI Analytics

On this episode of Behind the Bots we interview Toni Keskinen, the Chief Revenue Officer at 180ops.com. 180ops is a company that analyzes customer data to help businesses understand their customers better and increase sales. 

- Toni has 25 years of experience in marketing research and insight generation to understand customer behavior. He started 180ops to help companies sell the right products to the right customers. 

- 180ops models individual customer data along with external market data. This allows them to forecast risks, opportunities, and readiness for each customer. 

- In just 1 month, 180ops can analyze a company's data and billing history to show where current revenue is coming from. They also calculate the potential for new sales opportunities.

- 180ops serves mid and large-sized enterprises. They concentrate on providing unique value instead of trying to offer everything. 

- Toni believes AI will automate many jobs, which is exciting but also scary. He says if your job is rules-based, it's likely that AI can automate it.


180OPS
https://www.180ops.com/
https://twitter.com/180ops


TONI KESKINEN
"Path to Growth and Profitability" (https://a.co/d/aaEQgZl)
https://twitter.com/Toni_Keskinen
https://www.kaannekohta.com/


FRY-AI.COM

https://www.fry-ai.com/subscribe
https://twitter.com/lazukars
https://twitter.com/thefryai


PEOPLE IN THE POD:

Toni Keskinen: CRO, Co-Founder at 180ops
Ryan Lazuka: Host and founder of www.fry-ai.com
Hunter Kallay: Writer of the www.fry-ai.com newsletter

Creators & Guests

Host

Ryan Lazuka

The lighthearted Artificial intelligence Journalist. Building the easiest to read AI Email Newsletter Daily Twitter Threads about AI

What is Behind The Bots?

Join us as we delve into the fascinating world of Artificial Intelligence (AI) by interviewing the brightest minds and exploring cutting-edge projects. From innovative ideas to groundbreaking individuals, we're here to uncover the latest developments and thought-provoking discussions in the AI space.

So I'm Dalik Eskin and Chief Revenue Officer at
180ops.com. And my history and background is

actually in advertising and marketing. So I've
been doing, I worked for eight years at Oculiwi

Network in Finland called Daiva's Agency as a
planner and account director. And the really core

point that I was doing and have been doing since
then was to come up with INSAC to understand

customers and customer behavior in a way that how
do you kind of discover assets and possibilities

from companies, capabilities and offerings that
could be creating competitive advantages and

create more value for customers. So my background
is in doing this type of research and analysis and

designing customer journeys and customer
experiences. Then I've worked at Omnicom Network

for five years. It's a media agency, so it's all
about maximizing marketing return on investment

and investment return and still continuing the
same path, discovering insights, understanding

what's happening with the customers and how do we
make, how do we create possibilities with these

customers. And by doing this for 25 years now, I've
been capable of kind of creating patterns and

understanding of how customers behave, how can we
influence them, what kind of data makes really

sense. And that's the kind of foundation for what
we are doing now at N18. In simple terms, what we do

is we analyze data about customers and markets and
create answers before major questions. What

results to whom, when and why. Understanding
these, getting answers to these questions,

what's the healthy whom, when and why is a way of
kind of creating macroeconomic map on

possibilities, risks and opportunities. So
that's what we do. We help sales and management. So

like somebody could, you guys analyze the
customers, so someone could be selling to their

customers, not realizing they have so much more
potential out there, more potential products, so

that maybe they're approaching the customer the
wrong way. And you guys help with that to increase

the company sales. That's like the 10,000 foot
basic overview. Exactly. So when you look at any

given company's data and penetration level of
adaptation level of offerings, you come up with an

outcome that there's a curve where majority of
customers are buying one or two offerings. And

those are the ones that the company is known for.
The majority of customers are the volume in

customers is there. But when you then look at the
revenue curve, the small amount of customers

adopting much wider portfolio represents
majority of the turnover. And it's often not

realized that actually these customers are
realized success stories about what the company

could mean for customers. So those customers in
the upper end of penetration level and adaptation

level represent the kind of possibility and
potential for other customers to adopt those

behaviors too. And there's a major gap between the
opportunity and the actual behavior that

customers are shown. Okay. So what would be one
example that you see over and over again that a

business doesn't do that you guys help with in
terms of like a missing piece to the puzzle? Like is

there something that very common thing you run
into that people can optimize? Yeah. So when you

look at any given company's management and
dashboard systems, how are they managing the

company? Every single management tool is
concentrating on some internal object. So you

look at the company's financials or you look at the
subsidiaries or business units or offerings or

channels or sales teams or sales people, but it's
always the center of the gravity around some

internal objects. So you can see that there's
changes. Some things are growing, some things are

declining, but nothing explains that behavior
change. And what we do with the name 180OPS

actually comes from is that our center of gravity
is the individual customers. So what we are

modeling and trying to understand is those
individual customers. They have relationships.

There are things happening in those
relationships. There could be reclamations and

refunds and they might be showing interest to
other offerings and then things change in the

company too. They grow, they decline, they hire
people, they lay off people, they branch out into

new areas and so on. And all these companies, they
are operating in a certain macroeconomic

environment, so macroeconomic data, inflation
levels, cross domestic product, unemployment

and so on. And every single customer is like a
sensor in the marketplace. So from these sensors,

these companies, we can learn bigger patterns.
That when, for example, let's say when we look at

the economic policy uncertainty index, which is
done by American scientists, it shows that in the

history when 9-11 happened and when the Lemon
Brothers collapsed, there was a major spike in

uncertainty and back then we thought that
continents were shaking and the world was

derailed. However, that was just a warm-up. Where
we are now is double the size of uncertainty that we

saw then. And it's now delayed, like it's a
long-term challenge that we are now facing and

there's no wonder why people are burning out and
suffering at work. You help customers by also kind

of looking into the future as well, predicting
what happened. So the passage for learning, but

the data needs to show us the way to the future. So
right now, for example, like in the United States,

everyone has been predicting a recession for the
last year and a half, but the stock market just

keeps going up, unemployment still high, all this
stuff. It just seems very weird that all these

markets that are all time high, all these numbers
look so good. Do you feel like the economics for the

entire world is going to hit a point where we get
into a really bad situation, especially when

COVID hit, all these governments out there spent
so much money, like the U.S. is in just trillions

and trillions of dollars in debt right now, but yet
everything seems from the outside perspective to

be going great. What are your thoughts about next
year? What do you think is going to happen? It's an

interesting question because there's, like I
said, uncertainties that are all time high. And

what we see from the data and results that we are
creating for our customers is that when, for

example, interest rates go higher all over, some
customer groups stop buying, some others start

buying, some offering seeds to sell more, and some
others start selling more. So when we are working

with large and mid-cap companies like Telcos and
different types of business services, they have

wide offering ranges and they have great
diversity in their customer bases. And when we are

facing situations like right now, it doesn't hit
everyone in a similar way. So there's always

opportunities. Well, the opportunity really
means that the opportunities and threats are at an

all-time high. So it's not just negative. There's
also simultaneous with there are opportunities.

Some sectors are rising. It's just a time to be very
cautious, I would think, if the moral is story.

Yes. With the central theme of AI coming into the
picture as well, how do you see that impacting the

economy in general? AI has so many influences. And
what we see right now is that it's just marching

out. AI is only like coming behind curtains. It has
been behind curtains for a long time. For example,

our team has been working with AI and Machine
Learning in 2002. And now it's just, it has become

something of a phenomenon and something that we
all just chatted, chatted, came out and then

everything was about AI. But for example, if we
think about advertising and how we've been

targeted with advertising or how we've been
learning how to use social media, AI has been

running on the back all the time for a long, long
time. How it's influencing now is just major

diversity. Some sectors like, let's say, for
example, legal business or architectural

business are something that they are rule-based
businesses. And AI is hitting those sectors hard

and probably leaves a lot of unemployed people and
at least the way people are actually working is

going to permanently change. But on the other
hand, it has major upsides too. What we do is we are

trying to make people's lives better. We are
trying to make data speak for those people,

enabling them to get clarity into their situation
with their customers and their markets and enable

them to react and operate in a best possible way.
Yeah. So do you see like, at this precipice, it

feels like in the economy, and it's felt like that,
which I alluded to a little bit before, over the

last year and a half, because we came out of COVID,
we have all this debt, interest rates are high,

inflation is high in certain areas of the world.
But AI came out at the same time, right? Like the

hype of AI came out at the same time. Part of me feels
like AI can sort of help get us out of this mess. And

if AI was not here, things could get really bad. Do
you feel like that's the case where we were in this

situation where the economy and we could get the
economy for all the countries in the world could

sort of head into a recession, but AI has so much
potential to help that it could sort of get us out of

having a potentially bad recession or even
depression? Well, all about AI, it's helping

companies to improve profitability,
productivity. So companies might be doing

better. Are people doing better? It's a different
question. You will know how I wrote the book 21

Lessons for the 21st century. And in his book, he
was kind of painting the future where some

professions are rendered useless. And that's a
scary picture. And it's, depending on what sector

you are working, let's say, for example, if you are
a doctor, it's easier to replace a doctor with an AI

than a nurse. Because the nurse is actually there
for the person. And the requirement is the person

to be near. On the doctors, they are making
decisions and prescriptions. And that is easier

to replace with AI than the nurse. That's a great
point. I never thought about it like that. People

would think, well, you would reveal, be able to
replace the nurses before the doctors, but really

the doctor has the smarts. So the AI can replace the
doctor because they're smarter than the doctor.

So that makes a lot of sense. Yes, but it's really
the time of when things are now building and

changing. And there are so many technologies
already in the world available for us now. That

already those technologies that already exist
can really transform the way we live, and work. And

the speed of development is so fast that it's
really difficult to see even five years forward.

What is our everyday living in five years? It's
really difficult. So your company 180Op works

with a lot of different companies doing market
research. And so in doing that, do you see people

becoming more open to this idea of AI products or
companies who utilize AI within their

functioning or within their services? Because
I'm thinking for some background on that

question, I'm thinking of the past few years as AI
has come out, for a lot of people, that's a trigger

word that they don't want to be involved with AI or
AI products. They're kind of scared of it. But do

you see a shift happening there where people are
becoming more open to these AI sort of products or

services or is there still a mix? There are the
first wave of first adopters. They are already on

board and working hard and learning and thriving
in what they are doing. It's always the kind of

status quo with that, that people feel secure in
where they know what's happening and they are

practiced and they have their history and
learnings and people always change a lot slower

than the technology does. So the status quo bias
and the kind of inertia there, it's obviously

there. But these things are penetrating our lives
and our everyday living so fast and coming in so

many ways that the adoption is definitely growing
fast. How it influences, there's a lot to learn

from that, what do we see in the couple of next
years. Have you seen customers on your end ask you

about AI and how it can help them yet? Because it
seems like that would be a good litmus test to see

how far along AI is coming. What businesses start
asking you about it? Every company is now thinking

about it. Every company who wants to stay in
business is now thinking about it and learning

about it and trying out different types of testing
because that's all you can do really. In order to

keep your strategy alive in this change, you need
to be practicing training and doing all sorts of

trials with the tools to learn how do they adapt to
your processes and can you redesign your

processes. Whatever can be automated will be
automated. That has been true for the past two

decades. Now we are just seeing the kind of major
shift where everything's happening

simultaneously really fast. So is your business
180 ops, do you help train people as well or is it all

just internal on your end? Do you guys do
everything for them or do you train them as well? We

do onboarding for the clients. So we concentrate
on doing the software development and

concentrating on that software. We have partners
then who concentrate on consulting and

transformation work or making decisions based on
the data that we produce. But my consulting days

are over. I'm done with that. There's no
scalability if you start consulting. You need to

create an ecosystem around you to do that work.
What does your team look like right now? When did

you guys launch and how many people you have
working for you right now? Right now we have nine

people. We are still a startup. We have created a
foundation. So we've been validating everything

that we have developed. For the development as
such, you can say that we've been developing this

for the past 25 years. So everything that we are
putting into the product has been done manually

before. And we've been doing the consulting work
and the inside generation as projects for the

customers first. And doing that management
consulting work. What we are doing is turning our

lifetime of learning best practices and trial and
error into a machine. So in a way, we represent that

AI transformation in that sense that we are now
taking all the learnings that we have accumulated

over the past 25 years into this offering and
product. So is that something like you guys are a

relatively new company? But AI came out the last
year. Is that something that sort of raised the red

flag on your end? And you guys thought like, well,
we got to rethink everything we're doing and sort

of maybe implement AI and these strategies. And
did it throw you guys on your head when everything

came out? Because it was so... No, no, it's
actually what we've created for example, the data

model that we have in the server side is something
that is AI native. So we are now doing data security

assessments in order to enable you to ask language
based questions from the data so that we can give

answers and the data can then answer... Well, the
tools can then give you answers based on the data

that we have. So AI for us is... Well, neural
networks and AI are not the only tools that are

necessary. AI has a major challenge in terms of
giving you reasoning for the decisions that it

gives you. AI can give you answers, but it can't
explain how it came to those conclusions. So we

can't write solo on AI. We also need machine
learning tools because if you think about

management, let's say for example predicting
risk of affecting customers, you need to

understand what are the things driving those
risks because that's what you manage. Those are

the factors that you need to influence and you need
to be aware of those factors in order to do the

management effectively. So AI as a solo
technology solution isn't applicable for our

purpose. Gotcha. And I think that's a good thing
for... If a business is looking to implement AI, I

think you've got to be wary because you're going to
have to implement AI, but at the same token, if your

business is built solely around AI, you're
probably not going to last too long because for

example, there's companies out there that
they'll do summaries of news articles or

something like that. It's just a very basic app.
And then chat, GBT will come out with something the

very next day that will wipe that business model
out because they came up with something that does

exactly what you guys just made, not you and 180
ops, but this example. But if you can combine both,

you can combine the human aspect and the AI, that
seems like a winning strategy for a long-term

business model. Because you really got to be aware
of how AI is going to impact your business. Some

tool could come along and just totally wipe you out
a day later. But if you have the human aspect there

as well, it's going to help you a lot to survive at
least. Well, it's actually a human factor because

now we all live in a world where Bukov world, like
volatile, uncertain, complicated and ambiguous

world. And how we see what people really need is to
create clarity on their situation. If you are

working in sales or offering management, you need
to understand what's happening around you. You

need to kind of get answered about what factors are
threats for me and which are representing

opportunities. So creating that kind of clarity
is the thing that helps people to feel more

confident about their situation. It helps people
to relieve their stress and, for example, anxiety

about the thing that, let's say for example, you
get a budget of 10% gross one next year, but no one

explains where the budget growth is supposed to
come and how do you actually meet that quota. So

that's the major, most important thing why
salespeople are now leading their jobs. They feel

that they get unreasonable expectations and
quotas that they can't meet. And so giving people a

fighting chance, using the technology to give
them answers is the kind of way that how we approach

this challenge. Got you. So like a quota for some
sales guy, he has to sell, you know, for lack of a

better example, 10 vacuum cleaners, right, to
somebody. And maybe what he's doing isn't, he's

never going to sell 10 vacuum cleaners because
what he's doing every single day is just not going

to get there. But if you use AI to help him out, reach
out to the right customers, you know, market the

right way, you know, he could be on that path and
that takes the anxiety off of his shoulders

because when you're in sales and you don't, you
know, you don't produce, you don't sell, well, you

can't put food on the table for your family and
survive, you know, it's all, that pressure is

always there. Absolutely. And there's upset,
grossed, new customer acquisition and then

retention. Those are the four pillars of growing
profitably. And we are meeting all those four

pillars. Awesome. And how does it, like if someone
reached out to you 180 apps and they need help with,

you know, anything, anything regards to helping
that, helping their sales, what does the pricing

model look like and things like that? How do you
like, what does it cost on end users? So we are

serving mid cap and large enterprises. So it's
enterprise pricing and not going to go in more

detail about that. But what do we really do is the
minimum, what we have, how we have approached

these challenges to, to make most value with
minimum data. So first of all, we need building

history. We don't look into CRM history
primarily, because a lot of companies, for

example, do frame agreements, which means that
they would make a deal for a year. But then what

actually happens with those customers doesn't,
isn't available to see from CRM. It's in the

building. And so we analyze the building history,
what has been sold to which companies, when, what,

how much money was involved and so on. And when we
connect that with external data, like, for

example, in Finland, we have 1.3 million business
IDs, and we are now modeling for, for Finnish

companies. We are then modeling that into those
1.3 million customers and learning from existing

customers by offering levels that where is the top
side of, or, or the kind of level of opportunity

that can be met. It's not the maximum. It doesn't
mean that it's absolute maximum, but it's a

reachable potential in the companies that can be
gained. And by that, we actually create offering

view for each individual customer. So what are the
offerings that we sell for each individual

customer? As an aggregated view, this gives us a
management view of the, of the situation that in

which sectors do you have what kind of potential.
Let's say, for example, you can compare

commercial sector against IT sector or tailgates
or banks and so on. So you can see the macroeconomic

level of opportunity between different sectors.
and for existing customers for account managers,

it gives you ideas what to upsell for your existing
customers, how do you create growth from those and

where are your biggest, most valuable new
customer acquisition targets, hunting targets.

As an outcome we create a view where we divide the
market by in a framework where there's current

value on a growth and then columns represent the
potential, which means that you have farming

clients on the left top corner, existing
customers who have a lot of revenue a year but

there's no upsell potential. On the right corner
you have strategic customers who already had a lot

of billing but there's more than a million euros in
annual revenue to be gained and to upsell. On the

bottom right corner you have the hunting targets
which currently could be zero euros but there's

more than a million euros in potential to pour them
to concentrate on or to gain from them. It is

creating also a kind of customer care model
mapping that how do you allocate your resources,

what kind of personality take care of these
farming customers because it's a really

different thing to keep customers satisfied
compared to the long-term strategic tenacity

that is required on the strategic accounts or the
kind of personality that does well, the

completely cold new customer acquisition type of
work and this is also something that helps people

to concentrate on something that represents
their best opportunity. Well their best

personality trait and so it's really like, I think
that what we are selling to our customers is

something that is transformational. It changes
the way the company operates, it changes the way

how you actually see markets and it has a major
power in it. So I get that right, do I hear that

right? You guys suggest what person to reach out to
certain companies based on their personality of

the salesperson themselves? That's something
that companies actually do. They study the

salespeople's personal traits and define also
these salespeople, they have their own knowledge

about what kind of customers they want to work
with. So it's an opportunity to kind of match the

expectations and people's personality and wants
into a model and it makes people happier. It's

something that makes everyone happier. Yeah,
that's really interesting, that's cool. I never

thought of it, I thought of sales like that but that
makes a lot of sense. So we are only modeling

business ideas so it's all a B2B company and we
actually decided not to go on the consumer

business because there's so many data
restrictions and the data availability globally

is very, let's say, a variety on consumer side. On
B2B side we work with John and Brass Breed and

Moody's and different types of companies who
provide us with legal B2B annual data as well as

faster data. So it's structured, it's all
scalable it's a globally viable approach and

that's why we chose to concentrate on this. So
let's imagine that I'm a company and I want to try

out 180 ops, can you walk me through very simply
like for somebody who maybe doesn't understand

this very great or with you very simply what that
looks like, how are you going to help my company

grow and see opportunities that I have? Yes, so the
first stage is one month. When we get the data, the

billing history and we connect it with external
data and start modeling, we first come up with the

view of the reality right now. So where is the money
coming from? How does the penetration level in the

offerings look like? What are the big heat sectors
and where you are currently doing well? Then we

calculate the potential level. So what type of up
sales, cross sales, new hunting potential exist

there and that gives you the priorities towards
the future. The next stage is to look into

forecasting. We do the light projection for the
next well months billing based on modeling the

billing history as well as external data on
macroeconomic strengths and give you the

forecast for the next year by customer by month and
that gives us the kind of future outlook how are

things moving forward. And this is the beginning.
We get there in a month and in case the customer

wants us to do also analysis and make them
suggestions and work with the board, that's

something that we do with our partners. So
partners can help the customers with the analysis

side. In a month, a lot of things will be clearer. In
case the customer wants to dig deeper into risks

and readiness, that requires more data. Then we
need pipeline history about offers. We need

customer success data like like reclamation and
customer services and ticketing and that type of

information or the existing agreement length
that when are the agreements expiring and so on.

And that takes a little bit longer. We first start
with the risk because understanding of risk also

influences the understanding the readiness. So
the risk drivers also try the new customers

readiness to buy lower. And so we have a sequel and
it's about a month for the risk analysis and

another one for the readiness analysis. So in a
quarter, we are up and running with the full flat

solos. Awesome. So not too long. You would think it
would take longer than that, but it seems like a

couple months at least on the right pathway. Yes.
What we've done is that we platformized the way of

doing this. And that's that the work that we are now
doing has until now been done as projects for

individual modeling purposes. Like for example,
a risk as a project has been taking for half a year to

produce and it costs a lot of money to do that
properly, especially if you are working in an

international environment. We've created the
processes and the models in a way that we only need

to do adjustments to it. So that's what the kind of
platformization of the offering is. Declining

the risks, declining the investments and taking
most of the time away. So we are much faster than

doing a custom project with consulting
companies. Well, time is of the essence of

business. So the faster, the better. You
mentioned before about, you know, you use AI, you

use AI and machine learning on the back end. For
example, you said you could query databases using

like real language or ask a question to your
databases. What else do you guys at 180ops use AI

for in terms of helping your customers like in a
simple way? The really complicated calculations

that we have are something that we use now actually
our chief analyst or CTO should be here answering

about that. But leveraging it as a combination of
AI and machine learning. So one thing that we have

as a requirement is that if we are giving some sort
of recommendation based on the data, we need to be

able to tell how it came through or what is it
founded on. So we need to, it's all evidence based

data that we are giving for the customers, but we
need to be aware of the evidence in order to kind of

verify that what we are proposing has merit. The AI
part regarding the large language models is

something that can be connected and we are
actually now thinking that let's say for example,

HubSpot and Salesforce are doing a lot of that type
of development. So it might be smarter that we

enable those tools to connect with our database
and create the answers as in language answers

instead of us doing the whole many yards. We are the
economy of well these ecosystem that we operate

in. We for example recognize that a lot of work that
let's say for example sales enablement is another

sector, it's write your emails for new customer
acquisition or follow up emails and so on. That's a

red-osin strategy and there's a lot of players in
that game already and all these big players are

already creating their own versions of those
solutions. So you got it with HubSpot license, you

got it with Salesforce license or Dynamics
license. So what we are trying to do is we

concentrate on things that are unique and where we
provide unique value and then collaborate with

the other technologies so that we create the
maximum effort, outcome or value for the customer

and don't even try to do everything ourselves. It
doesn't make sense. Yeah, if you try to do

everything you're not going to be good at
anything. So you mentioned briefly how it's

difficult to interpret this data, like the data
that comes in from these reports. Could you just

talk to that? Do you see any opportunities for
improving that? Do you think AI could at some point

be able to interpret this data or do you think it's
always going to have some sort of human element?

No, it's absolutely going to be. A lot of answers
will be coming as a direct suggestions. What

should you do? What are the five things that you
should do now? And they should be prompted. One of

the things that if you think about for example ERM
right now and for the past well 20 years, ERM has

been felt by salespeople like the reporting
customer reporting management. It's more about

reporting is your responsibility to keep the data
updated. But you actually as a salesperson get

really little value from it for yourself. So how we
see the future is that the ERM with our help starts

giving you answers instead of requiring your
reporting data back and graving for more data. It

starts giving you suggestions. What should you do
next? And one of the solutions that how we create

this is by, for example, when there's a risk in some
offering or there's high readiness, which means

that there's a trigger to buy in existence. That
can be created into a trigger in a ERM telling you

that now there's a pass. This company, there's an
opportunity in this company in this offering

right now or you are at risk of losing this custom
relationship because of these things. And that's

proactive. And that's something that where I see
that the technology is giving us a lot of help in

the, in the, what makes the most difference is that
it starts telling you proactively about what you

use to concentrate on instead of you asking all the
way. Right now it's really much in reactive mode.

Yeah, it's, there's so many analytics tools out
there to help people. For example, just on

YouTube, like these podcasts or we have another
news channel out there and YouTube has such great

analytics on everything. How many watch hours
people are watching the videos, what percentage

they're watching of the videos, the click through
rate on the thumbnails, a bunch of different

metrics. And people love looking at those,
including myself. And the same with business.

There's Google analytics to see how many people
come to your website and if they're going to

convert or not. But like where you're alluding to
Tony is, you know, people love the analytics, but

what's the use of analytics if you're not going to
use them to help your sales or help your channel on

YouTube or anything. It's just, it's more of just
personal gratification rather than actually

helping you with something. So you need that. You
need translation. And the thing is that the

availability of data has not been a problem for
many years anymore. It's the trans kind of

creating making sense of that data. That is the
challenge. If you look at any mid-sized company or

large enterprise, they have so much data, they are
drowning in it. It's not the problem of

availability about making sense of it. And that's
where the technology gives them AI and machine

learning tools. We have the best power to help.
It's really making sense of all that. Yeah. And it

feels like the people use, especially in
corporate America, they use the analytics. There

might be a whole team dedicated to analytics, but
they're actually, it's counterintuitive

because they're using the analytics to try to help
them, but they're not doing anything with the

actual data. So they're actually making their
company counterproductive because they're

putting all these resources into the analytics
without doing anything to it. So if they didn't do

any analytics at all, it might help them in a way
because they're not spending the time and

investment in creating an analytics team. If
you're just going to create a team to look at data

and not do anything with it, that could be
counterproductive. But if you have these

insights and actual transactional things to do
like Tony's alluding to, like, hey, you're at risk

of losing this customer based on the data. So you
should go reach out to them. Insights like that are

very, very helpful and can save a company
thousands or millions of dollars down the long

millions in a major way. Like, for example, when I
was working with Finnish government, I did study

on, it was the pension insurance company of the
government entity that pays your, when you get

ticked, they pay you money. And when you get on
pension, they give you the pension and so on. And

they have a lot of roles in the investment in
Finland. How do they actually tell people

navigate their lives? And when I was doing
analysis on why do people actually call the call

centers, we were able to, the Pareto principle,
the rule of 2080 rule always applies that there are

things that can be recognized that can be
concentrated on and speak. And when you do that,

you actually take away a lot of frustration from
the people. So they have, they feel better. You

take away the anxiety and insecurity and not
knowing what to do next. And instead, those people

who are working with the customers can
concentrate on those who actually need the help.

So you're actually improving both customers'
lives and you're improving the employee's lives.

And when we are aggregating this data about
individual customers into offerings, we can see

that what are the things between, in different
offerings that are creating the things and

likelihood of change and what are the drivers of
new customer acquisition and readiness. And

those are something that give you tremendous
management possibilities because now you have

something to concentrate on. You have things to
accelerate and you have risks to mitigate and take

away those challenges. And in that Finnish
government case, we saved millions of euros a year

by just changing public websites. When you
recognize the root causes of those challenges, it

doesn't mean that it's expensive to fix. Well,
then we come to behavioral economics and how do you

actually not be able to move forward or how do you
make people feel better about themselves. But

those are the things that we need in order to make
choices and lead and manage companies to a better

future. Everybody needs a direction. If you're
ever feeling overwhelmed in anything in your

life, the best advice I always take is just start
with something very simple and actionable item,

whether it's cleaning up your house or do
something. Because data, life is overwhelming,

just like data is overwhelming. So an actionable
item like 180 ops can give you after they analyze

your data, that's a great head start into your
business to get on the right track. Instead of

being overwhelmed by the noise of data, the Tony
and 180 ops will at least point you in the right

direction and that will accelerate your sales and
growth eventually. But really when we started

developing this technology, we knew already, we
kind of reversed engineer the technology

development. We knew the challenges, we knew what
kind of data gives you the most important insight.

So we had the outcome already figured out. What we
needed to figure out is how do we produce this

effectively, what kind of data models and
solutions are required in order to deliver that

experience. And that's the work of 25 years in
business doing the manual grinding first.

Definitely, nothing, everything that's worth
something takes time. So 25 years, you know, your

stuff at this point. Yeah, you know, we like to ask
everybody, you know, back to the AI stuff, in terms

of AI, where do you see AI in general going over the
next five years? Is it something people should be

scared about, concerned about or excited about or
maybe both? I think both. It's both fear and

inspiration. But in the late 90s, I was working in,
for publishing companies, producing companies

like Business Week and Advertising Age. And my
clients were operating in the marketing sector,

the printing sector and repost studios were one of
the sectors. And those were hit back then by

digital imaging. So in those sectors, a lot of
jobs, they just used to exist because Photoshop

came. And AI has the same effect in a massive scale.
It's not just disrupting a single business

sector, it's disrupting all business sectors.
And the analogy that I would consider here is that

that nurse versus doctor analogy. How much, how
much, how scared should you be? Or how much do you

feel that your work will be influenced by AI? Well,
that's your answer. If you are doing role based

business that can be automated, it will be
automated. And that will require you change the

way how you actually work and what kind of value do
you provide? So it's both. It's something to be

excited about, but it's also something to be
scared about. Yeah, like one of the exciting

things back for the doctor is, I think there's
going to be AI doctors out there, which they

probably already are, but you'll be able to ask,
you'll be able to put in your conditions and you

might be able to get treated or they might be, the AI
doctor might be able to give you a diagnosis that's

more accurate than your doctor. I think it's going
to help a lot of people that might have chronic

conditions that were misdiagnosed from their
doctors for years and years. And they might be able

to go in and have an AI doctor diagnose them because
the AI doctor has so much information at their

hands. Other doctors have... It's already
happened. Yeah, it's crazy. It's sad people

still... And the thing is happening in legal
agencies, for example, making contracts and

making agreements and so on. So in those cases, a
lot of the basic work is done by AI and then

humanists were required to kind of give the human
touch to it or making it secure that the data

hasn't... Well, the AI hasn't done something
hallucinating or something weird, but it's

automating a lot of those processes and that's
something scary too because if that chest might

kill your enjoyment of the work. Yeah, like the
work... Nobody wants to work, but then when you're

actually working, it gives you satisfaction, but
that might go away. Exactly. If you're just

correcting what AI is producing, it's not really
satisfaction creating life. No, not at all. It

feels like more manipulation than work, you know.
Exactly. What other things should people be a

little bit wary of or scared of in terms of AI? We
need to be kind of aware of what the development is

bringing and on the legal side, I think that, for
example, how social media is now operating and how

we are being put into bubbles and it's creating
diverse and well disruption in the society is

something that it needs to be regulated and we are
now at the point where, for example, EU is doing

regulation on AI use. There was also a discussion
about US, about, for example, doing AI pictures

and movies about replacing, for example, some
porn movie star space with your friend's face and

like that's something that it should be illegal.
It should be something that is regulated and

technology has developed and evolved much faster
than legislation has. I'm looking at the world

right now and I feel that there will be events and
phenomena that take over before legislation

actually catches up. It's going to be your bumpy
ride, of course, of course. The volatility of the

market and uncertainty of the market is just
skyrocketing constantly. I think we're lucky to

be living in this time. Elon Musk said, someone
asked him, how do you feel about AI? He's like,

well, I don't know what's going to happen with it,
but he's like, if I had a choice to live in one period

of time in the history of the world, it would
probably be now because it's going to be a

interesting next few decades. Tony, now's the
time to promote anything. I know 180 Apps is the

company we've been talking about the whole time,
which is very exciting, but is there anything else

that you want to promote right now? Yeah, I
published a book last spring, Path to Growth and

Profitability, available in Amazon. In that
book, I'm laying out the big picture, the

foundation and the people, the cultures and
strategy processes and the practical use cases

about how can you make sense of the data and where is
the source code behind it, but how do you translate

the data and make sense of it? That's something
that I think that many people would actually

benefit from. Anyone in business can definitely
learn new stuff and get help with and especially

with the analytics. Check out that book on Amazon.
We'll leave a link in the description. Yeah, and

then be sure to check out Ryan and I's newsletter,
bri-ai .com. We have weekday news on artificial

intelligence as well as three top AI tools of the
day and a mystery link as well that has some fun

links to different things in AI, tweets, videos
and all sorts of community engagement. But thanks

so much, Tony, for coming on. It's a pleasure.
Thank you for inviting me.