How we use AI in practice | AI Summit 2026 | Norges Bank Investment Management

Warm welcome to this first AI seminar at

Norris Bank. And this is just a huge

moment because we have never seen a

technology like this.

And it's moving not in a straight curve.

It's continuing to curve up and it's

nearly vertical what this technology can

do now.

And so the issue is what we call the

technology overhang.

Are we able to utilize all this

technology? And that is really the tough

part I think is to get the organizations

to absorb and to utilize what we have.

So we are as you know the most

transparent fund in the world. And why

is transparency good? Well, I think it's

good because people can look in and see

what we do. And we believe that that

creates trust. But I think equally

important is that we can look out and

see what the world is doing. And I think

that's why we have been very very keen

on absorbing this new technology because

we also talk to leaders across the world

and we see what it can do if you apply

it in a correct way. And then we thought

it was great to invite all of you not

because we think we got it right but

because we also want to learn from you.

And we we think that if we share with

you what we know, you will share with us

what you know. And the cool thing, we

just do not compete.

We can cooperate and share good

practices across the board and across

the country. And I have this dream of

being able that we together, you know,

the private and public can basically

lift productivity in this country.

That's a good ambition. I think

we have a lot of user cases across the

firm and we could have picked uh you

know many many different ones. We picked

10 which we think gives uh a kind of a

taste of different things we do. Some of

them help us make more money, some make

us save money. Some improve efficiency.

Some improves accuracy and quality and

some just prevents us from doing boring

stuff because I think in this new world

we should not spend our time doing

boring stuff and hopefully you all agree

with that. So what we're going to do

first take you through why we are in the

situation we are now with our technology

briefly by big

the journey we've had on AI and how we

have tried to make it to permeate the

whole organization Ludia is going to

talk about uh the the framework we have

for doing this in a correct manner

because of course it needs to be

compliant and it needs to be correct and

safe and then Tron together with 10 of

our colleagues will take you through a

lot of user cases but first big over to

you.

Thank you Nikolai.

Uh so uh we've done many transformations

in MBIM since 2015 but today I want to

talk about three major steps that we

have done that has built the foundation

for our AI strategy. So the first thing

we did was to ins insource operations.

So before that we had an external vendor

that took care of settlement, corporate

actions, fund accounting, valuation,

everything. But when we scaled into new

markets, we wanted deeper expertise and

richer data. So the solution for that

was to bring it all home. We own the

process and we also wanted to own the

knowledge.

So the next big thing we did was to move

all our IT infrastructure,

all our IT systems to public cloud.

So before that we used to rent space in

an external data center,

outsource the technology with it. But

what we saw was that we had a data uh

ceiling in a way. But what we wanted was

to have a data horizon. We wanted

instant scale on demand and we wanted to

get away from server refresh cycles.

When we had moved over to public cloud,

we saw quickly that our old databases

which we had also moved over did not

meet the same requirements. We were not

able to utilize this scalability that

the cloud provider gave us. So what did

we do then? We decided that we had to

move our old database solutions over to

a modern setup so that we could have the

same scalability.

>> And is it fun to clean data?

>> It's no fun at all.

>> So it's the most it's the most kind of

boring job there ever is. Does anybody

thank you for cleaning your data?

>> No. How do you get people to clean data?

>> You basically tell them that 31st of

January we are going to turn the old old

data off. Yeah.

>> And if you sit there the day after and

have no data, you are going to look very

stupid. So we had a lot of late nights

and an enormous work by the whole

>> that was a lot of tidy up and rewrite of

code for basically everyone in the

company but uh now we have one place for

internal and external data with high

quality and it's tidy up and it can be

used for AI and it's called martium

core. So this is how much I love our new

data warehouse.

Um so these are the three things that we

have done that has been the foundation

of our uh success within AI and I will

now give the word over to Stian who will

talk you through what we did after that.

So I'll I'll take you through our AI

journey and having a good data warehouse

and a lot of compute in the cloud has

absolutely been necessary for us to get

on with the AI journey. And it all

started about two years ago when Nikolai

had Sam Alman from OpenAI and Dario

Amodai from Antropic on his podcast and

he found out we should be 20% more

efficient. So he said can you make that

happen? I said thank you. That's an easy

target to fix. So um to see how he

actually got that working is that him

being a a battery durel battery that

never goes out of the energy on the top

is pushing the organization throughout

the last two years and he has pushed

everyone. So everyone has gotten the

tools they gotten the time they have

experiments they created a lot of

different project on how they can use AI

to improve themsel but that is not

enough. If we're going to be 20% more

efficient and we're going to start using

AI, we have to change our habits. So, we

needed to push everyone

a lot and nudge them again and again and

again. So, we created a

uh add upskill program for everyone.

I'll take you through that. And we also

created an ambassador network to help

people get going here. And to keep the

nurturing going, we created a this tech

year 2025 that was full of activities to

help uh bring AI uh to the focus of

everyone. So first of all, we created

the ambassador network and you can call

it champions if you like it doesn't

matter. There were 20 uh volunteers from

all over the organization.

uh they were given the task of finding

the one use case in your team that you

think can be a valuable project with AI

solve it with the help of the other

ambassadors the AI team but also with

the help of entropic. So twice a week

Antropic help us get started by doing uh

creating training for these ambassadors

and the AI team for two months. So as

soon as you know the project would keep

rolling here the uh the ambassador

sold the project the turnout showcase

their team but also the rest of

organization and we can soon see you

know the value of AI in you know all the

corners of the company. So

we also had to create this tech here and

the important message with this slide

here with a lot of different bullet

point is that whenever something

happened in NB NBIM in 2025 it had AI in

it. If you had a gathering AI was on the

agenda. We had this large tech tech day

in London, Oslo and Singapore which

focused entirely on technology, tech

stack, cloud, the data warehouse and

absolutely on AI. But if we had a leader

summit, AI was on the agenda. So it kept

rolling and rolling reminding people to