0:13

So, hello and. I'm

0:15

Lari Numminen, the host of Wonderful Work, a

0:17

podcast where we hear that voices

0:19

and insights that make business operations

0:21

just a bit more wonderful. In

0:24

this episode, we get to hear from Stig-Martin Fiskå.

0:27

Stig-martin is an entrepreneur at heart,

0:29

having started his first company at the age

0:31

of 16. Stig-martin

0:34

has created several successful companies

0:36

in the technology space and is now the head

0:38

of artificial intelligence data

0:40

interna, Internet of Things, Industry

0:43

4.0 at COG in the Nordic.

0:45

Welcome to the show, Stick Martin.

0:48

Thank you Lari. Good to see you and good

0:50

to invited.

0:51

Yeah, you, you have an absolutely amazing

0:53

background and, um, love

0:55

to go into the, the realm of, of your current

0:58

work, but in this show we do like

1:00

to start from the very top and get to know

1:02

our guests a bit better to begin. let's

1:05

start from, from some interesting questions. tell

1:08

us something wonderful about yourself

1:10

or something surprising most people don't know about you.

1:13

Sure, Sure. So let's, let's get a little bit

1:15

of personal, uh,

1:17

touch going there. Uh, when

1:20

I met my wife, uh,

1:22

uh, and we'd agreed to cook

1:24

every day, every second. In

1:26

between us and for the first six months

1:29

we didn't create the same dish ever.

1:32

Great complet with different unique dishes. Uh,

1:34

for six months there we're going. So that tells

1:36

you something about both, uh, interest

1:38

and in, in, uh, foodie,

1:41

uh, department, but also I

1:43

would say a bit of a competition going on

1:45

there.

1:46

Absolutely. And I, I would be amazed

1:48

that if I could do the same dish, uh, or,

1:50

you know, more than a week at a time,

1:52

but it's, it's great to have you here. Stigma.

1:55

When you were a child growing up, what did you want to

1:57

become?

1:59

Yeah, good question. I, I, I thought

2:01

about that and, uh, I, I can't really remember

2:03

from like really young age, I have

2:06

to say. Um, uh,

2:08

I'm not good at looking backwards. I'm better

2:10

at looking forward, but,

2:13

um, Uh,

2:15

I don't think I thought too much about it until

2:17

I entered my teams. Uh, and then

2:19

I just wanted to, you know, I thought it would be a good idea

2:21

to copy my dad and become a lawyer. Uh,

2:24

but at the same time, I, I thought,

2:27

you know, making your own money was

2:29

a good thing. Um, and I'm not

2:31

really sure. Where that came from,

2:34

honestly. Um, but I didn't see a blocker

2:36

in creating your own company. Uh, so

2:38

as a, as a relatively young guy, uh,

2:41

set up my own company with some fellow, people

2:43

from school and, uh, and got going,

2:45

making my money. So I guess you could say that

2:48

I've been an entrepreneur. But

2:50

we didn't use that word. We didn't think about

2:52

it at as, at the time as anything. And

2:54

startup certainly wasn't a word at that time.

2:58

absolutely. Um, now,

3:00

like your father, you did study law in

3:02

university, you also studied philosophy.

3:05

Uh, how did you end up then working in technology?

3:08

Yeah, it's a bit of the background,

3:11

so, uh, act

3:13

actually when I was 15, 16 or something

3:16

like that, I. Uh,

3:18

had a lot of American friends, um,

3:21

growing up in what used to be there or still

3:23

is, I guess the old cap capital of

3:25

Norway in Stavanger. Uh,

3:28

and, uh, this American friend

3:30

of mine from the international school in Stavanger,

3:32

he was, uh, showing me this

3:35

webpage that he created, uh, as a project,

3:38

Netscape Gold, three hour

3:40

or something like that. we're

3:42

talking way back. And

3:45

then, and, uh, I thought, well, if he can,

3:47

then I can. And I didn't really

3:49

have much, uh, interest in internet

3:51

and computer at the time. My dad bought the 4

3:54

86 and, uh, and

3:56

uh, I think there was even some kind of early

3:59

surround sound bought to that, uh, computer

4:01

and everything. Um, but

4:03

he was a bit disappointed that I didn't care much

4:06

to use it. Uh, but. I

4:08

think the link between someone showing

4:10

me something with building and me

4:12

being a Lego fan at the time, uh,

4:16

got me creative,

4:19

the creative part going so, uh, so

4:21

I reversed engineered at

4:23

that time, the largest online website

4:25

in Norway and that's

4:28

how I got going in technology. And I figured.

4:31

If people thinks this is magic, then

4:33

why don't I just make some money on that?

4:35

Uh uh, and

4:37

then, uh, I also reversed, open

4:40

source, uh, cms, reversed

4:42

engineer that. So I learned, you know, databases

4:45

and that stuff, uh,

4:47

and made some money. But I thought this, I can't live

4:49

this, this isn't serious work. I'm just playing.

4:52

Nobody taught me anything. There's no degree. And

4:54

at that time there was no, there was.

4:57

Tutorials online or anything like that. Right.

4:59

So, uh, so yeah, I thought I had to

5:02

get serious and then go to law school. I

5:04

was really bored in law school. Uh,

5:07

you're not allowed to be creative. You're

5:09

not allowed to build things, uh, in the same

5:11

manner that I like to do it. Philosophy.

5:15

That was great, and I could think out loud

5:17

and do the things I, I love to do. So, So

5:20

back in, uh, I think during

5:23

the law school, uh, years, uh,

5:25

I found some fellow. Tech

5:27

people that, uh, struggled

5:29

getting a job after their, uh,

5:32

um, computer science degree

5:34

because this was.com or after

5:36

the.com and,

5:38

uh, I joined them, uh, part-time.

5:41

Really, I didn't think that this would be something

5:44

I'd do outside the, other than outside the studies

5:46

and, and now I ended up being part of that industry

5:48

from there.

5:50

And that's remarkable. I I have to say that there's not

5:52

many times in, in history when there's

5:54

gonna be computer science majors

5:56

who are looking for a job. So you, you, it sounded

5:58

like you were in the right place at the right time. Yeah.

6:00

Um, at the kind of disruptive

6:03

early days of, of technology and, and

6:05

reverse engineering. Uh, there's

6:07

many other ways that you can learn things, but, but

6:10

that, that does sound like it's really learning from the

6:12

source. Um, Stick.

6:15

Martin, In this episode we are gonna talk about advanced

6:17

technologies, but before we get into those

6:20

topics, let's talk about kind of

6:22

the foundations here. Uh, many

6:24

of the listeners will be in business operations

6:26

in the Nordics, and these leaders will

6:28

have organizations with different levels of data

6:31

maturity. Right. Um,

6:33

what is your take on data, digital

6:35

maturity, uh, what are the foundations,

6:38

do you think? Is it about the people or the technology

6:40

or about.

6:42

Well, of course there's a, there's a,

6:45

there's a balance between all. But I, I

6:47

mainly these days I think it's, uh, it's

6:49

people that's the focus. And, and

6:51

why do I say that? Because I think if

6:53

we implemented the technology and what it allows

6:56

us to do today, we would've a

6:58

ramped up much, much faster than we

7:01

are doing today. So, so it's about knowledge,

7:03

it's about, uh, People

7:05

knowing the art of the possible.

7:08

Uh, and like you mentioned

7:10

today, you wouldn't find any, any computer

7:12

science people or, or, uh, majors,

7:14

uh, looking for jobs were lacking

7:17

people who has the knowledge lacking people

7:20

who knows the realms

7:22

of the possible. and also I'm

7:24

afraid that a lot. Where

7:27

we used to be and still are, is

7:29

run by it, which isn't

7:32

really about digital to me. There

7:34

is a difference there, uh, when we enter into

7:36

the business and the actual operations

7:38

of things and not just the support function. So

7:41

very much about culture, very much about people

7:43

and knowledge, I would say. And

7:45

that's, that's where we are. The technology

7:49

is in many cases

7:51

already far beyond what most people

7:53

can imagine.

7:55

Absolutely. And I, I like the word that you used

7:57

the art of the possible. I think some people

7:59

are realist and they, they see the, the cup half

8:01

full. Um, they see

8:03

bad data and legacy. It

8:05

systems a lot of problems when it comes to digital.

8:08

Um, now do

8:10

you have any advice how you can kind of advance

8:12

digitalization in those kind of realms?

8:15

Yeah, it's a, it's a good question and, and honestly,

8:18

uh, there are no silver bullets. Uh,

8:20

and we're still in early days. Organizations

8:23

are very different. Uh, I I,

8:26

back to like the, it thought it's about standardization

8:30

and, and buying off the shelf, which

8:32

is a lovely expression that people love,

8:34

uh, to, to use. But

8:36

in reality, they're, they're all

8:38

in bits and pieces. There is no standardization.

8:40

There is no way to do this because it's

8:43

been thought about as it, uh, and

8:45

not about the, the actual business

8:47

and operations and, and, and data.

8:50

Data didn't used to be sexy. Now it is sexy

8:53

again. Uh, to, to

8:55

be quite blunt, I think, uh,

8:58

people haven't understood what they did

9:00

about the data before. Uh,

9:02

There are many ways to look at this. So some, some

9:05

tendon, A very traditional way to look at it is,

9:07

is to do what we call data modernization.

9:10

It's bringing these systems together,

9:12

uh, and bringing, bringing,

9:15

uh, the data up to up to par, so

9:17

to say. Uh, making sure that the CRM

9:19

talks with the, the other parts and,

9:21

and, and so on and so forth. That

9:24

tends to be very

9:27

heavy. Uh, projects, um,

9:30

and it needs to be done to some level of

9:32

extent. But what

9:34

we used to fail, uh, early on in

9:36

industry here is to do that for three years or

9:38

something and spend a lot of money, moving

9:41

data and cleaning up data. And

9:43

we didn't really think about the business outcomes

9:46

at the same time. So what I

9:49

would recommend. You

9:51

have to do this in, in several work streams,

9:53

but I definitely will recommend leading

9:55

by the use cases and, and where

9:57

you can see, you can create early on

9:59

benefits optimization,

10:01

new revenue streams, whatever, whatever

10:04

it is, and then build

10:06

a foundation just good enough

10:08

to start showing those results. and

10:11

the way I'd like to set this up, cuz that. Focus

10:15

from the end result downwards

10:17

all the time, drive it that

10:19

way. It's painful sometimes. Uh,

10:22

and it sure does not always solve everything

10:24

at the first, uh, try. But you

10:26

learn so much more and you keep focused

10:28

on the outcome. The other advice would be

10:31

get the organization involved. Don't

10:33

treat it as a classical IT project where

10:35

you just outsource it. Uh,

10:37

I know a lot of people are in housing this stuff

10:40

today. That

10:42

could be good. Uh, but you also need someone

10:44

who's done it before, who has the drive

10:46

and the incentives to just get things

10:49

done and not get the sucked

10:51

up by the internal politics and, and

10:53

meetings. Uh,

10:55

so having that push from an external,

10:58

uh, party that's. You

11:00

know, actually it's a good thing that costs money to use

11:02

consultants because that creates the incentive

11:05

to get something done And

11:08

at the end of the day, what I see a lot of people

11:10

are also trying to do is transform their

11:12

organization around data driven. Hmm.

11:15

And that's the third part. So bring the organization,

11:17

not just the techies, not just the,

11:20

the people who are used to driving technology

11:22

projects, but bring the forefront

11:24

of business development forefront of,

11:26

of, uh, of the, whoever runs the process

11:29

inside the company, into this and

11:31

get their hands dirty. It's the only

11:34

way to learn. So

11:36

let's,

11:37

uh, double down on that. You mentioned data driven,

11:39

uh, approaches. Um, Can

11:42

a leader develop a data

11:44

driven culture in people, or does it

11:46

need to come from somewhere outside?

11:49

It's a, again, no silver

11:52

bullet. I don't have a, finished answer here,

11:54

but honestly, depending

11:56

on your organization, so if you're Spotify,

11:59

Sure you can do it because you have the,

12:01

you have the incentives. You started from the right spot. You're

12:03

digital native, so, so

12:05

to speak, and all of that. Most cases,

12:08

I would say no, you either need to hire,

12:10

uh, you probably both need to hire, uh,

12:12

some of, uh, some people's been there before

12:14

and knows what it means. Uh,

12:17

and then you need some, some help to lift

12:19

the organization. It's about upskilling, it's

12:21

about getting your hands dirty. Uh,

12:24

and, and also what

12:26

is data driven. Uh,

12:29

when I speak to sea level or VP

12:31

or whatever, uh, they define

12:33

it completely different and. Again,

12:36

back to what I mentioned about the realm of possibilities.

12:38

If you have never done data driven, how

12:40

can you, uh, measure the outcome? How

12:42

can you measure where you're going? How can you measure

12:45

the KPIs if you're succeeding or not? So

12:47

I think you need some help there. And I think

12:49

the, the, the right mix is, is,

12:51

uh, getting some people in that onset

12:54

and has done it before. That would be ideal.

12:56

You won't find many of those in the market, to be honest,

12:59

So I think you need some external, uh, help as well.

13:01

But, uh, Yeah,

13:03

that balance is important.

13:06

and that's a very interesting point, and when

13:08

you reflect on it, um, a lot of companies

13:11

might think that they're data driven, but they're actually data

13:13

informed. So it's like everyone

13:15

will have a different perspective and view on things, but,

13:17

but bringing people onto the same page about

13:20

what your definitions and goals are usually

13:22

is a good starting point. Um,

13:24

Steve Martin, you've worked a lot across

13:26

the Nordic region. Um, do you

13:28

have any kind of observations, juicy observations

13:31

you'd like to share about the digital maturity,

13:33

uh, or confidence uh, across

13:35

your work?

13:36

Sure, sure. Actually, in,

13:38

and we've done some studies on this in the Nordics

13:40

as well, and, and then quantify the

13:42

maturity. Interestingly

13:45

enough, what we see is that there's being

13:48

overspend a lot of money in this area,

13:50

in the Nordics, but the results aren't coming.

13:53

Uh, so the, the return on investments

13:55

isn't really there yet. actually that,

13:57

uh, one of the things that I

13:59

get met the most with, with my fancy

14:02

title of artificial intelligence.

14:04

And when I meet clients, uh, and, and,

14:06

uh, look into. New

14:09

possibilities, uh, together with them.

14:11

One of the first things they meet me with is saying,

14:13

We're not gonna have any ai, ai, we

14:16

don't want any ai. We're not

14:18

there. Which first of all, tells

14:21

me that they don't know what AI is, which

14:23

is fine. Uh, you need to define that as

14:25

well. But, uh, uh, there

14:28

was a period. Let's

14:30

say three, five years ago where AI

14:33

fair dust could be just, uh, sprinkled

14:35

over anything and you still can't to some, uh,

14:37

to some extent to get some funding and VCs

14:39

in there and stuff like that. Uh,

14:42

but then also broke the trust around

14:44

this. so a lot of, uh, top

14:46

leaders. Uh, kind

14:49

of burnt themself, I think, on this stuff. I'm

14:52

seeing that the market,

14:55

the latest during Covid,

14:57

I would say the last part of the covid, they're

15:00

starting to ask the right questions. And

15:02

a lot of things starts by asking the right questions,

15:06

and I think, um, they're now

15:08

asking, How can I be

15:10

successful? What is the right scope

15:12

of this stuff? And, and where should I really

15:14

start? So we're

15:17

starting to right. Ask the right question. We're

15:21

still thinking a little bit of this, like a project,

15:23

an IT project. We'll started

15:26

in January and we finish in April. And,

15:28

uh, and then it, it should, uh, uh, you know,

15:30

get a, give an uplift. And

15:33

I think that's where we need to get in the Nordic.

15:35

A lot of, a lot of companies, a lot of leaders

15:37

haven't understood that yet. Uh,

15:40

that, uh, essentially

15:43

whatever you're doing in your company, Is

15:46

already, uh, a process.

15:48

It's already run by algorithms, but

15:50

the algorithms are way too often us.

15:54

So I do this this way

15:56

today because I've done it that way, or that

15:58

actually works. Uh,

16:01

and, and honestly that is the same thing

16:03

as ai, cuz that's what AI can do. It can take

16:05

that process, that recipe, but

16:09

automate. Maybe do it faster, maybe

16:11

do it at a 1% smarter,

16:13

and then by keep reinvesting

16:16

and, and tuning this and, and by

16:18

the end of, uh, you know, two, three years,

16:21

maybe it can do it a hundred percent faster and

16:24

with a lot better results. Uh,

16:27

and, and that's the transformation part, uh,

16:29

where people don't get, they, they can't run this as

16:32

a project. It's a, it's a change, continuous

16:34

change. Those, uh,

16:36

discussions starring

16:39

barely in the Nordics, but we're, we're

16:41

lagers for sure in the Nordics. Okay.

16:44

Let,

16:44

let's bring people up to speed. Let's, let's,

16:46

let's start from the foundations, uh,

16:48

and, and get them going. Mm-hmm. Um,

16:51

now let's demystify a, a artificial

16:53

intelligence a bit. You, we talked about very

16:56

interesting topics there, but let's, let's go to the core

16:58

basics of it. Yes. How would you explain

17:00

artificial intelligence to your friends

17:02

and family? Yeah, it's a

17:04

good question. And the first thing I, I try to do

17:06

is to take away this thought

17:08

about Terminator or, or

17:10

this, uh, almighty, uh,

17:13

entity that's, uh, that's the know-it-all

17:16

and all that stuff. It. There

17:19

we will invent that, but that's not

17:21

really what we're talking about in, in this

17:23

context. Uh, but we're

17:25

talking about applied ai and

17:28

we're talking about the, like I mentioned,

17:30

the processes and the recipes that we

17:32

follow in our companies today

17:34

and in our society today. That's what we're talking

17:36

about. We're talking about taking

17:39

what is. Common

17:42

daily tasks. Uh,

17:44

re reputable tasks, preferably,

17:47

and, uh, making them automated. That's what we're

17:49

really talking about. And driving a bit

17:51

more on insights. So there are two

17:53

types, main types of applied ai.

17:56

It's either taking a process

17:58

and, and making it automated or it's

18:00

doing something with more insights.

18:02

So understanding and processing. Um,

18:05

large amounts of data, and I'm, by large

18:08

amounts, I don't necessarily mean Google

18:10

amounts, but you know, if,

18:12

if you, if you come, um, to

18:15

a larger Excel sheet, then at least

18:17

I start losing control. So I think it's

18:20

murdered that someone helps me, uh,

18:22

digest that for me. And that's, that's basically

18:25

the first parts of, of what I'm,

18:27

I'm, uh, thinking about in ai. Uh,

18:29

and I try. Explain that to people,

18:32

but AI can be a lot more of those things.

18:34

But for the purpose of a business, uh,

18:36

leader, It's applied AI and

18:39

it's about automating and making the things

18:41

you do smarter and faster today.

18:43

Uh, could also be creating new revenue

18:45

streams in some extreme cases. Actually,

18:47

we do see a little bit about that then in, in

18:49

the nordex already, which is really cool. Yeah,

18:53

and I think those, those use cases,

18:55

sometimes they, they bring out their excitement,

18:57

but then for, for many companies, they, they kind

18:59

of create the hype as well. Yes.

19:01

Um, For many companies, machine learning

19:04

and applied AI is literally just taking

19:06

away the boring, repetitive tasks or

19:09

even parts of tasks in

19:11

processes that most people will

19:13

not see. Then end result of it. And it's

19:15

a lot less exciting than, than the movies

19:17

and science fiction may, may have your peer.

19:19

Mm-hmm. Um. You've already hinted

19:21

at these, and I'm interested to know what

19:23

are the kind of most, uh, who should be most interested

19:26

in, in a large or organization

19:28

about artificial intelligence?

19:31

I think, I honestly think it, it's

19:33

the whole layer of things and that there,

19:36

this is gonna be a little bit like when internet

19:38

arrived and people said, uh, you know, that's a

19:40

fat And we're

19:42

still in those early days, uh,

19:45

of, of an infancy of internet around

19:47

AI and, and data driven. Organizations.

19:50

So the C level should definitely

19:52

be interested. The CFO should be extremely

19:55

interested as this is about optimizing

19:58

current operations. It's about taking down

20:00

the risks. so a CFO should love this, a

20:02

CEO. So should also

20:05

be very mindful because it can run

20:07

off your business and you can refocus your resources

20:09

on new investments, uh, using

20:12

people less on the, let's

20:14

say, mundane tasks,

20:16

if that's a, if that's a phrase we can use,

20:19

but it also can bring a lot of insights. So

20:21

the CEO and the CEO should all care

20:23

for that because he can bring

20:26

you insights to, of your own organizations

20:28

and where to optimize. So,

20:31

so those are the, those are the top

20:34

level ones. And then of course, everyone else

20:36

as well.

20:38

And, uh, those make a

20:40

lot of logical sense. I think what the kind

20:42

of audiences we often talk to are business operations

20:44

where finance, procurement,

20:47

there's a lot of, uh, functions that have a lot of

20:49

repetitive work. And, and in those

20:51

cases, the, like you said, the process are,

20:54

are some things that can be streamlined

20:56

or automated or in some

20:58

cases replaced. Um, if

21:00

you have enough volume and if it's a big

21:02

enough problem, then this could be something that

21:05

can impact the whole company. But the, the,

21:07

the financial leadership is definitely one that,

21:09

that, that takes a lead. Um,

21:12

what are some of the common mistakes you see in

21:14

adopting ai?

21:17

Oh. Um, I think the most, most

21:19

common one is that they hire,

21:21

uh, internally, and I'm not against that

21:23

at all. And having internal resources

21:26

that you work with is hands on and have understanding,

21:28

but they hire some, typically the

21:30

data scientists, one or two, maybe three,

21:32

maybe four, uh, data scientists,

21:34

and, and they. Then they just,

21:36

uh, they

21:39

just place them somewhere, uh,

21:41

to, to, uh, figure out some AI

21:44

So that's, that's a very common mistake. And, and

21:46

still thinking about this as a, fairy

21:48

dust that just will, uh, will appear.

21:50

And, and the answer to that is, it's

21:53

a whole line. Uh,

21:56

capabilities and, and, and,

21:58

uh, knowledge that's needed, uh,

22:00

not the least about your current

22:02

operations and current business. Uh,

22:05

so a data scientist can do a lot of good things,

22:07

but they need a whole team around them with the

22:09

also other technical capabilities. You can't

22:11

just do it with a data scientist alone.

22:14

Uh, so that's, that's a very common. The

22:17

other one is the one I spoke about and I alluded to earlier,

22:19

that people drive them as it

22:21

projects rather than a change

22:24

of doing business, a change of running my

22:26

company. Um,

22:28

so they, they think about it as something they ramp

22:30

up and then should ramp down and then it's just

22:32

there. Um, that's not

22:34

gonna happen. Uh, there are some

22:37

edge cases that, that you can say

22:39

that it can be used, but most of

22:41

the time making yourself data driven

22:43

and. AI is about

22:45

the continuous change, which

22:48

I'm sure most leaders don't like because,

22:50

you know, another one of those So

22:53

those are, those are the main common ones. Um,

22:56

uh, the third, the third one, uh,

22:58

which is coming more and more now because

23:00

we're getting past the two first one. Uh,

23:03

Or that the, the business, uh,

23:05

side of the, of the operations

23:07

isn't involved enough, they're putting

23:10

this on a team that's either internally

23:12

but outside of the daily operations

23:15

and daily business model. Uh, or,

23:17

uh, or with someone like me who's

23:19

a consultant and, and they put it, uh,

23:22

uh, as an outside project or an outside

23:24

team, you have to integrate it properly.

23:26

Uh, you have to. Use the

23:28

momentum about some of these cases to

23:31

train your organization, uh, in

23:33

a different way of thinking, in a different way

23:35

of operating. Uh,

23:37

so that that's coming more and more clearly that

23:40

then they, that we need to get past

23:42

that as well.

23:43

Yeah, and it's kind of, it's not magic

23:45

dust. bad process doesn't become

23:47

a good process just with the amount of AI you put

23:49

into it. But it's a good advice that like,

23:51

you know, when it comes to hiring data

23:54

scientists and so forth, it's often better

23:56

to just start

23:58

with the core foundations. Is this

24:00

a big enough business problem we, we can solve

24:02

and, and assembling a team, whether it's

24:04

external talent or internal talent to,

24:06

to work on it.

24:08

Yeah. And, and, and I think, you

24:10

know, sometimes, and this is also

24:12

the fault of the industry, uh, that I represent.

24:15

We, we go to large, uh,

24:18

and we're, we're, we're shooting for huge

24:20

numbers or huge changes. Uh,

24:22

find those few nuggets where we can spin,

24:25

spin and show change, uh,

24:28

at a pace where, where your organization

24:30

is comfortable, but at the same time

24:32

getting pushed a bit. so, so find

24:35

those. Nuggets where you can,

24:37

uh, prove ease relatively easily

24:39

with, I don't know, six to nine

24:41

months, a max kind of, uh, timeline

24:44

to have some really outcomes and start telling that

24:46

story and start building around

24:48

it, onboarding more and more people and, and use

24:51

cases around that, that,

24:53

that's a better advice than saying, you

24:55

know, I'm, I'm gonna change the way I do things

24:57

from the, from today to tomorrow. Never

25:01

gonna work.

25:01

Yeah.

25:02

Incremental improvements that can be stacked

25:04

is always gonna be better than a radical

25:06

re-engineering of things, uh, with a bit

25:08

of tech thrown into it. Um,

25:11

stigma. Martin, let's talk about your new role or

25:13

your role at cognisant. Many of people

25:15

know Cognisant, uh, as a leader

25:17

in the technology consulting space. Your

25:19

focus is really broad, uh, if I'm

25:22

honest, and your, your flavor to what you

25:24

do across the Nordics. Uh, it has different

25:26

spins, You work on ai,

25:29

Internet of Things. Industry 4.0

25:31

and, and other aspects. you,

25:34

you touched on the idea of hiring data

25:36

scientists into your organization, and it's certainly

25:38

something that, you know, in your role, you are doing

25:41

a lot of hiring. Um,

25:43

now do you think every company

25:45

needs to be hiring machine learning experts

25:47

or, what kind of advice can you give

25:49

companies and leaders in organizations

25:52

about the talent war that's ongoing or at the.

25:55

Yeah, It's a

25:57

really hard question. I

25:59

wish I knew the answer to that one. Um, Cause

26:02

it's the same for us, right? We need, we need to attract

26:04

to talent and, and, and the war is really

26:06

there. depends

26:08

on the size and the scope of your company, but

26:11

I, I think, uh, You

26:13

if you're anything but a local shop, uh,

26:16

at the end of the day, I honestly

26:18

think you need to, to actually at some

26:20

point invest around, uh, machine learning

26:23

or something similar around data. Um,

26:26

if that means that you need to hire all these people.

26:30

Probably not, not for everyone. The,

26:32

the main reason would be that it's,

26:34

it's going to be hard for you to find that talent.

26:37

I think one of the things we need to

26:39

do is to recognize that this

26:42

is all emerging and I

26:45

try to bet more and more on,

26:47

on the young and upcoming actually.

26:50

And not just because there's a war on

26:53

talent, but also because they

26:55

have the mindset of, I'm jumping into

26:57

this to learn now, which is

26:59

a very good mindset, Yeah.

27:02

Uh, and I think that's very important and

27:04

I, I. I would give them a

27:06

chance and sit really close to,

27:08

to the young and hungry ones, uh,

27:11

and try to, you know, uh, coach them Uh,

27:15

but they will, they will really quickly

27:17

understand a lot of things. They are very hungry,

27:20

um, and they have a lot to prove, and

27:22

I would use that energy to, to,

27:24

to help me in this. Uh, that's for sure. Don't

27:27

ask for the one with 15 years of

27:29

experience every time, it's,

27:32

it's this joke, right? where, uh, uh,

27:35

basic, uh, CV ask is

27:37

I want a candidate that has worked with the

27:39

Power BI for 17 years and

27:41

then Power Bay was just invented

27:43

in 2015. So, and

27:46

that happens all the time. Uh,

27:49

and I, I don't think our associates,

27:51

uh, are done learning and I don't

27:53

think anyone will be. And I certainly

27:55

need to learn every day cuz

27:58

this is moving really, really fast. So, uh,

28:01

Get a grasp, get your fingers dirty again.

28:03

I think I get back to that all the time. Uh,

28:05

don't do a PowerPoint presentation thinking

28:08

that you now can hire the right people. Uh,

28:10

don't go to a, uh,

28:13

one of these, uh, breakfast shows or something

28:15

like that and, and think that you can now

28:17

you know what you're doing because you don't. So,

28:20

so get some, uh, people to spar with you. I think

28:22

that's, uh, best advice and then articulate.

28:27

Outcomes and goals you really and value

28:30

you want to create. Uh, a

28:32

lot of the talent is very focused on

28:34

value. Well, how can they

28:36

be part of something and how can

28:38

they, uh, be

28:41

changed and, and contribute to the

28:43

society in a, in a meaningful way

28:45

through their work? Uh, and then they're

28:47

really, really hungry to. to be

28:50

challenged and to learn and to use their skills.

28:52

Um, so those are, those

28:54

are things you will have to answer

28:57

if you get to get to the right talent.

28:59

Yeah. And, and I, I think that what you

29:01

touched on there, it is really a global, uh,

29:04

war and talent where the likes of

29:06

the Googles or Microsoft or Cognisant,

29:08

really large players will have the same

29:11

opportunity to, to address that talent,

29:13

whether they're living in Norway or living in

29:15

the Nordics or anywhere in the world. Um,

29:17

the. The need is

29:20

real. it's the game of every company to

29:22

really apply what they're doing to

29:24

this global challenge. Um,

29:26

now I have to say that, you know, working

29:28

in such a large organization, you have a pretty

29:31

unique view on technology. Uh,

29:33

like you said, Cognisent has over three and

29:35

350,000. Colleagues,

29:38

and you probably have access to all the tools, all

29:40

the fancy stuff, everything. Microsoft, Google,

29:42

anyone provides, all the software imaginable.

29:45

So, um, not many Nordic

29:47

businesses will have that same, uh, access,

29:50

uh, for investments and resources and tech.

29:52

Um, what kind of, if

29:54

you're advising Nordic businesses and

29:57

looking really at the future, looking, looking

29:59

at what's, what's coming up next, what kind

30:01

of key trends would you anticipate, uh,

30:03

for the next year and. Yeah.

30:07

Uh, it is true. We do get access

30:09

and exposure to a lot. Uh,

30:12

and, uh, I make a point of, of,

30:14

uh, stepping into it myself as well. Uh,

30:16

cause like I said, learning every day.

30:19

Um, I think, I

30:21

think the key trends is going to be that we're

30:23

going to have, uh, a more

30:25

somber approach to a lot

30:27

of these things we are going to, and

30:29

the clients are going to the market and

30:32

the whole are going to. Um,

30:34

have to realize and have a sober approach to

30:37

the fact that these people and these processes

30:39

don't necessarily exist today. Uh,

30:41

and then you'll see some people, uh, or companies

30:44

being totally disrupted, um, and.

30:47

And be marginalized. Um, so that's one

30:49

trend you'll see. Another trend is, is

30:51

from the software vendor side.

30:54

So you spoke about the Microsoft and the Googles

30:56

and the Amazons of the world. So

30:59

you're also going to see that we are getting

31:01

disrupted from the inside. They

31:03

can't grow unless, I mean, data

31:06

engineers, there aren't not enough data engineers. So

31:09

that's one area where, where, uh, I,

31:11

I would guess that, uh,

31:13

our big, uh, global software

31:16

vendors will go in and try

31:18

to take away some of that pain. So you don't need

31:20

so many data engineers because they can't grow

31:22

if, if the market doesn't have enough data engineers.

31:25

So you'll see areas. Where we

31:27

have invested in people and skills that will

31:29

disappear, be disrupted and probably

31:32

way faster than than anyone

31:34

ever seen before. and,

31:36

and that will actually not just be within, you

31:38

know, my field and my, my, uh,

31:40

area. It will also be outside

31:43

of that field. So doctors will be

31:45

disrupted because AI will

31:47

enter their domain. Lawyers will, uh,

31:49

be disrupted because AI is the. So

31:52

on and so forth. So knowledge

31:54

work will be very,

31:57

very disrupted in the next coming,

31:59

uh, five to 10 years. So even

32:01

less, I think. We

32:04

will get used to have to change our

32:06

way of thinking and our way of doing work

32:08

and what life much faster than we're

32:10

used to today. And, and again, that will

32:12

spur a lot of debates, a lot of, uh,

32:15

legislation around these areas because

32:17

if it threatens jobs, that's when politicians

32:20

usually, uh, get their act together and start creating,

32:22

try to create some kind of a balance or, or

32:24

something. So those are the really

32:27

big, big trends I think that we'll see. Uh,

32:29

and then of. Lastly,

32:31

you will find AI in places

32:33

where you never thought you, uh, found

32:36

AI before. And not only in,

32:38

in, in old factories and, and,

32:40

uh, autonomous cars driving the roads,

32:42

which people, some people still don't believe

32:44

is happening, but is actually happening. Uh,

32:47

but you'll find it in the dentist office.

32:50

You'll find it with teachers, uh,

32:52

and, and you'll find it probably plumbers

32:55

and, and construction builders will also,

32:57

cuz I know they're looking into this right now so

33:00

that, that all of those things will happen. Uh,

33:03

but at the end of the day, it's

33:07

already happening right now. If

33:09

you look at how dependent you become on your

33:11

smartphone and how driven that

33:13

is actually by analytics and uh,

33:15

ai and how much we

33:18

give it consent to run our life, either

33:20

consciously or unconsciously. And,

33:23

and that's what's gonna happen. We are going to

33:25

get this increased curve

33:27

or usage everywhere, and no

33:29

one, including people like me,

33:32

will have the full over. Uh,

33:35

and then we'll just look back at some point and say,

33:37

Whoa, we

33:39

really changed. We

33:42

as humans really, really changed and,

33:45

and, uh, yeah. So it's

33:47

exciting. Yeah,

33:48

and I, I think you can see those changes

33:50

with what you've mentioned just now with, with

33:52

the internet. It's, it's a progress that

33:54

happened first slow and then very

33:56

fast. Yeah. With smartphones, things happened

33:59

very slow and all of a sudden

34:01

you don't even realize that you've been a part

34:03

of the change. So people have a hard

34:05

time imagining. Short term

34:07

changes, but then in the long run, if you put perspective,

34:10

AI is likely not gonna change anything

34:12

in your life until you realize that it's already

34:14

changed it. Exactly. Um,

34:17

now if you're a business leader and

34:19

you wanna build a business case for investing in

34:21

ai, you wanna stay ahead of the curve, uh,

34:24

what kind of tips would you have?

34:27

Yeah, if you really wanna invest and

34:29

back to my, uh, I know I'm repeating

34:31

myself, but, but get your hands dirty.

34:34

I, I, I was lucky enough, I've been lucky

34:36

enough to meet a lot of, cool people,

34:38

uh, in my job. And, uh,

34:40

I was, uh, also lucky enough to meet, uh,

34:43

the chief business officer of Google some years

34:45

ago, and I asked him that question, How

34:47

do you stay on top of all of this stuff in

34:50

the most disruptive or one of the most disruptive

34:52

organization that that exist? Uh,

34:55

and he, I, I will never forget his answer because

34:57

it was so somber. Um, and

34:59

so, uh, realistic,

35:02

uh, he said anyone

35:04

that comes up to me will have to draw it on the whiteboard

35:07

until I understand it. So

35:10

j just spend some time and actually

35:12

invest in understanding. You

35:14

don't have to be the coder, you don't have to

35:16

understand Python and machine learning to

35:18

do it, but you have to understand

35:20

the concepts and, Uh, you

35:22

can't, again, realize the realm of possibilities

35:25

if you have no clue what,

35:27

where, where it is, and it's all darkness.

35:30

Um, I hope we'll see more.

35:34

Education or courses for

35:36

leadership and, and mid

35:38

management around technology in this area

35:41

so they can, uh, understand better how

35:43

to, to roll with it. I'm

35:46

a little bit surprised how little there is actually

35:49

other than the seminars and stuff that's more

35:51

on a hallelujah level, um,

35:53

and height level. Um,

35:56

and then, and then I think find

35:59

some trusted partner. Uh,

36:03

and I don't necessarily mean being

36:05

a consultant company. Us actually,

36:07

I mean, look at

36:10

your. Uh,

36:12

neighbor or your, even your peers and

36:14

competitors, what are they doing?

36:17

Go together and, and talk about

36:19

these issues together. Um,

36:22

go to someone who's doing something completely

36:25

different than you, but you know, they've come a bit further

36:27

than you and have a conversation

36:29

and, and start engaging. And

36:31

I think partnering in that

36:33

sense. It's underestimated,

36:37

uh, not just, of course in AI that's relevant

36:39

and everywhere, but you

36:41

have to get the grips with the fact that there are

36:44

too few resources and the ones

36:46

who get them will accelerate at some

36:48

point really, really fast if they succeed

36:50

at, when they succeed. So

36:53

partnering up is going to be way

36:55

more crucial, than people are thinking about today.

36:58

Um, and then don't

37:01

do everything. Try

37:04

to see if there's some, what, what is

37:06

unique for you, uh, and is, what is your

37:08

area of, uh, where you can excel

37:11

and then leave everything else to standard

37:13

stuff, right? Don't, don't

37:16

try to do all, all things. But

37:18

partnering, I think is number one, honestly.

37:21

Partnering makes sense. When you think about it, it's, it's

37:23

like any change. You, you don't want to go at

37:25

it alone, especially if you're inexperienced.

37:28

Um, and yeah, I, I

37:30

can see that there is an increasing amount of, um,

37:33

educational resources you can follow, but until

37:35

that's widely available, then yeah. And accessing

37:37

people who've been there, who've learned some from

37:39

some of the mistakes just makes sense. You

37:42

skip some of the bad stuff. Um,

37:44

stick Martin, uh, we're getting to the end of the interview

37:46

here. But I, I do wanna ask, um,

37:48

do you have any recommended resources listeners should follow?

37:53

Yeah, so, I, I actually,

37:55

um, I use um, medium

37:57

to com quite a lot. Uh,

38:00

there are several, uh,

38:02

areas around there where you can follow. So that

38:04

depends a little bit on the level you are at. Of

38:06

course, a lot of that can be very,

38:09

Uh, detailed. Uh, but there are some

38:11

high level stuff that you can follow and,

38:13

and read, uh, and keeps you updated.

38:16

Uh, and a lot of it is also sharing about

38:18

what's gone wrong. I think that's important. Uh,

38:21

I would also recommend those who just want to get

38:23

a. For its grasp to invest

38:26

in, in, uh, in the

38:28

chief, uh, decision officer

38:30

of Google's YouTube, uh,

38:33

series. It's free and all of that stuff.

38:35

And it's, it's, she's made it

38:37

into something that's quite. Um,

38:40

tangible and understandable, uh,

38:42

and it doesn't require you to,

38:45

spend hours and hours. It's quite easy

38:47

to get it, and it's, I would say very

38:49

good, very up to date. And she touches

38:51

on what's important and tries to

38:53

take away the, hype basically.

38:57

So that would be a, a good advice. And

38:59

then there are good pod podcasts

39:02

out there, uh, interference from from

39:04

Finland as as one which I

39:06

actually follow. Uh, and then leadership

39:08

posts, podcasts, uh, which you can listen

39:11

to in between. I think that's a very

39:13

useful, uh, useful way

39:15

of looking at it and. I

39:17

know that's outside. Uh, um,

39:19

but for perspective, I think it's also

39:22

useful to, to read the Hararis

39:25

books. Uh,

39:27

the human, uh, Davis is

39:29

the, is the latest one I believe. Uh,

39:32

and it gives you perspectives on, on,

39:34

on where we're going. It's very thought provoking

39:37

and not necessarily correct in every way,

39:39

but it gives you perspective and it touches on a

39:41

lot of the things that will change

39:44

us as a society around this. And, and

39:46

it also gives you the perspective on,

39:49

on how thoroughly, um,

39:53

through and through the change. So

39:55

I think that's very helpful. Um,

39:58

so that's from a leadership, uh, angle, but

40:00

I.

40:01

Stig-Martin, that sounds great. I think that was a,

40:03

a good variety of, uh, resources

40:05

there. We'll make sure to include in the show notes

40:08

of the podcast when it's released. Um,

40:11

so Stigma Martin Stu Tuck, it's

40:13

been absolute pleasure, absolute

40:15

pleasure to hear your insights. It's time to wrap

40:17

up here. So this has been The

40:19

Wonderful Work Podcast produced by Work Fellow.

40:22

Um, thanks for your time. Subscribe

40:24

and stay tuned for more views and insights that make

40:26

the world of business operations just a bit

40:28

more wonderful. Goodbye