Hi everyone. My name is Patrick Akil and
joining me today is Shaheen Shakarami, Director of Data and
AI over IKEA Retail. And we discuss how AI has
impacted the tech industry. Nowadays, 2025 companies are no
longer hiring junior talent, but Shaheen argues that now is
actually the best time to hire junior talent.
And how can companies do that and distinguish themselves?
Listening to find out, Enjoy. And the tech industry is very,
very evolving. And I wanted to hear your
opinion on kind of how newer people get into the industry
because I feel like it's it's very much changed.
Hiring is now kind of on the back burner.
I see a lot of bigger organizations laying off people
in the 1st place. And I also see this trend where
a lot of people coming into the industry are having a harder
time finding a job in the 1st place.
What's your take on that? How can younger people kind of
distinguish themselves? Or should companies really be
focusing on seniority instead of also hiring new talent?
Right. It's a really good question.
My main take around this is, yeah, indeed, we are going
through a lot of changes during tech industry.
We still have not found our feet on the ground of where AI
exactly is bringing the most amount of value.
Yeah, now we can write more lines of code, but does that
really bring the value that we want?
Now you have more P Rs to review, you have more XYZ and
the whole chain is getting faster and faster and maybe some
parts, maybe you're spending a lot more time designing things.
So I, I think a lot of companies are stop are stopping either to
hire new people or just completely saying, OK, AI can do
what a junior talent can do. Therefore we are not going to
hire anymore. I actually hear this a lot in
the circles that I am in. And I personally think that's a
not a very good approach because a young person is beyond just
someone doing some job. It's also bringing new
perspective, new fresh talents and also a lot of new talents
have already been AI native during their study time.
They've been dealing with ChatGPT or whatever other tools.
So they're not like me that after certain years, they now
need to change their way of working, the change of their way
of thinking. And they use it a lot more
natively as well into their day-to-day activities.
So I think just stopping how stopping saying, OK, we're not
going to hire any junior talent and we're going to focus on
seniors. It's actually going to be a
massive mistake for a lot of companies down the line.
My main, actually, my main advice would be to the
companies, actually, if you have the means, hire now that
everybody is not hiring because these are the talents that you
need to train and still need to learn.
And while your senior people are still there, they can teach
these folks the ways of doing and the ways of the craft.
But also my advice to the younger people is to really be
because they come with less baggage around tech.
So they can be a lot more agile in changing, a lot more agile in
what tool they use, a lot more agile in how they think and all
of that and always do something extra.
So this is what I do when I hire very junior talents.
I always look to see what else do they have beyond their
studies and activities, whether it's a very cool beyond their
internship. Some people are hustling on the
side, they have some side jobs selling coffee and whatever.
And to me all of these are signals that these people are
very, very agile and very open and have a growth mindsets and
that they have that level of curiosity that they will survive
and they will learn the skills very quickly.
So that would be also my advice to more younger folks.
Yeah, I like that a lot. I feel like exactly as you
mentioned, if a lot of companies are no longer looking at younger
talent OR less and less than there is an opportunity to hire
that talent. But then still indeed
distinguishing yourself if you find yourself in that group kind
of early in career is going to be more and more difficult.
I like your advice in doing something extra, whether that's
like something entrepreneurial, something that's like open
source, a really cool project that you've done for me, it
doesn't necessarily matter what it is, right?
If I can see you're passionate about it, it aligns with
something that you want to do professional, even better.
Or it shows me some of the core values that you are as a person
through kind of your upbringing or kind of your discipline.
Then it adds a lot to that person hiring.
In the end, we'll hire people we hire also shortly pay a little
bit extra for skills and knowledge, but we want to work
with these people and that's still its core to what we do.
Yeah, absolutely. I mean, if everybody in the team
is senior, I think actually the mindset becomes a little bit
negative toward everything. So that's why I do really like a
team that has a right balance between senior and junior
people. Maybe that's why I'm because I
do really like that positive atmosphere at work, that
optimism that certain people have.
And and I think, I think that optimism just a little bit
becomes sometime less and less because you get just more world
experience to become more realistic.
A little bit. Jaded.
A little bit jaded. So that's why I was think also
in general for general culture of your working space is very
important. Of course, this advice might not
be true. If you are a very, very small
organization, you start up with five people.
I'm not sure if having a a, a very, very junior talent might
be the right choice for you. I mean, there are still really
good ones out there that can do beyond our imaginations.
But I think just the advice becomes more and more true if
you're a bigger and bigger organization with a lot more
resources that you can absorb these junior talents in that
way. To play devil's advocate, then
if companies are looking from the perspective of OK, I can put
in the time and effort to hire junior engineers and they have
value once they're there. But junior engineers also want
to learn and kind of have career growth and they also want to
learn from different organizations.
So they might move after one or two years, which is kind of a
wasted effort. It is kind of this balancing act
of return, like investing and then getting a return on
investment with regards to the knowledge, skills and expertise.
So from that perspective, I can see people being more afraid of
like investing in junior talent. I mean that that is true.
If you don't have a very good way of retaining your general
talent, that's can be true also for your senior folks.
They can just leave after a year or two and that actually is even
a bigger loss because they've they've been building a large
portion of your code base or, or, or your tool or your
software and now they're leaving.
So I think you need to really balance it with the overall
cultural change within your company to, to really bring them
forward. And at the end of the day, in
the grand scheme of life, we're thinking about the society or
the city as a whole. And I think if a junior talent
moves from company X to company Y and if all companies are
hiring junior talents at the end of the day, and we have the
right amount of flow of talent into the market, I think it
benefits everyone. So it's, it's a larger benefit
for the for the whole ecosystem, which eventually makes the
ecosystem of the area better. And I think that's the same in
Bay Area. I think you, you will see also
for companies like Open AI, they hire a Group A lot of junior
folks as well as well as very, very, very senior and very the
best in their class work. So, so I think that's just the
balance balancing act for that whole ecosystem.
Yeah, that's a beautiful mindset I think to have.
How can companies distinguish themselves and make sure at
least have a higher chance of people staying with regards to
what they do on a day-to-day? Or what can companies change or
offer in the first place? I think we cannot as a as in a
company you cannot really be good for everyone and
everything. So how I think about it
personally, my own wouldn't talk about on behalf of any company
or myself. I try to look at what is the
motivating factor of people in the team.
Some people are just really motivated by doing the best and
the coolest and the fanciest or the newest thing, which is
actually myself. I'm part of those people.
I just really like this, this idea of being on the forefront
of things, even though it can burn me at the end of the day,
but I still really enjoy it. And some people are really
motivated by money. Some people are motivated by
promotion, some people motivated by stability, work life balance,
a wide variety of range of different levels of motivation.
And I think sometimes as a company you cannot offer all of
it. So you need to really think
about what is, what are you good and what exactly can you offer
to the people within your means. And that is for me always.
The way of retaining a talent in the team is to, at the end of
the day meetings, meeting their expectation and their
motivational factors with what the company can offer.
And if we cannot offer that, then that is really beyond like
if, if you want a certain level of let's say salary, which is
very numerical thing and you can, it's less Gray.
It's like I need X, we cannot offer you X.
That is totally fine. And maybe that's that's a
motivating factor and we cannot retain you.
But from my experience, I think a lot of people really need
motivations from the perspective of they think they are doing
something good for and they are having an impact and, and
creating that area that's within your team that people can have
impacts on the goals, on the environment and the culture.
I think that is where I would suggest to focus on for creating
a good culture to retain talent. I spoke with Dario, I think it
was a couple of weeks ago, he's from Elastic, and he says at
some point I have enough money. Like I earn enough money.
It's no longer about the money, it's about me feeling meaningful
in the work that I do. I was like, that's an
interesting perspective because especially in bigger tech
organizations, everyone earns like down the line, above
average, well above average, very, very well.
So then what distinguishes it? It's like the interesting impact
that you make on customers, or if you're actually contributing
towards business outcomes, or if your project didn't get killed,
or if you actually got very interesting discussions with
your colleagues and there's enough for you to learn and
grow. There's so many aspects.
I really like the emphasis of learning and growing and finding
the balance that works for you. If all of a sudden you get
twins, you don't just have one kid, you have two kids.
So your life might completely be different.
And then this kind of grind that you're on of always kind of
working on career progression that likely gets a little bit on
the back burner and you need a little bit more time for family
and friends. And maybe that's no longer the
right organization or the organization's flexible enough
and says doesn't matter who do you that's the best?
Absolutely. Yeah, absolutely right.
You mentioned yourself, you love newer technologies kind of being
on the forefront of things, which means my assumption is
that especially in the last few years, things have been
interesting for data and AI. How things been for you and
let's start from the personal productivity sense.
Have things changed in how you work or how you approach things?
Definitely. I think I mean, first the whole
world of AI is very, very interesting because constantly
the definition of what is AI is changing.
So I think some of those deep learning models that we were
building even two years ago and now suddenly I can go to into my
own organization and then my own team members and they might not
count that as AI anymore because it's not the generative AI, it's
not the latest and the greatest and you're not doing that.
So I think the definition of what is AI and what is what is
the whole ecosystem is constantly evolving and and I
think it's a very loose term, so.
It's become a loose term. It's become a somehow somehow
very loose term, very overused in certain contexts, underused
in certain contexts. But personally, yes, I, I've
been, I think it has impacted a lot the way that I work, the way
that I do things. And I think there's a lot more
room to actually change because for example, just to give you
some example, I think with this whole ecosystem of lab coding,
that's that's actually really beautiful if you think about it,
because I'm a very visual person and I always need to jump on a
whiteboard and do things. And now in a digital world that
my teammates or team members or stakeholders or whoever that are
in different parts of the world, I always had this difficulty of
showing them exactly what I mean.
So you use mirror, you use whatever and and and but still
kind of was missing. And I think now we have these
wipe coding sessions with, let's say a product manager or a UX
designer. And then that is for me is very
beautiful. That's we can come up with a
proof of concept, build something discarded almost
immediately. But we understood each other
much better in a much deeper level than what it was.
So I think that is one of the thing and I think also the the
whole world of writing codes. I mean, I'm not a software
engineer, so I don't write as many lines of code as maybe a
software engineer does. So maybe they see a lot more
improvement there. But just really recently looking
at clouds code, it's just, it's just insane to think about.
That's that actually the way that you do things has got a lot
faster. Also, I, I do chat with ChatGPT
a lot about work events. So it's a little bit like a
psychological things like, Hey, prep me for this conversation
that I'm having, or I'm having this and this and that.
Can you help me do that? I think I do quite a lot of
these things throughout the day, but I do see every time I go to
a conference or to talk to a meet up or something, I see
people actually have all sort of cool things in their ecosystem
and in their things. So I think there's a lot more
room to figure out exactly what is the productivity tool for me
individually that can make me even more and more productive as
a person. Yeah, I, I haven't vibe coded as
much. Like I feel like I'm late to the
game because I did a year and a half of product and now I got
Claude Coats specifically. And I was like, I have this
idea. I always wanted to make this
game that I can play with friends and I've seen stuff on
YouTube and I, I wonder if I how easy it is to make a game like
that. And I just did it.
It was like 1 evening. It's not done yet.
I still need to continue. But and then I looked at how
much I spend. It was like $3.
And sure, I have this super expensive monthly subscription
plan and I still pay, which is like an insane model, but I am
having a lot of fun with it. I feel like this kind of playing
around and iterating and indeed, like you said, kind of
validating assumptions. I'm also a visual person, so
getting something up and running visually, We have so many tools
now. I'm even to the point where I'm
like overwhelmed with regards to OK, do I try this thing and
which one do I master? And like where do I place my
bets in? What do I think is going to be
there for the long term or what is just a fad and what is more
hype? I don't know yet, but I do like
this experimentation mindset. I feel like it's been a while
since we've had an innovation like this, which has been this
big and kind of, yeah, changes a lot of things.
I feel like, yeah, like cloud was one thing and then a lot of
people were hesitant. And now it's, it's pretty
established. I would say companies are there
or companies are migrating and startups due to the fact of
total cost of ownership, basically they start there,
which is way easier. But with AI, we haven't really
figured out yet or established yet what is there to stay with
regards to productivity and what is there to go in the first
place. Right, right.
Absolutely. I mean it's also true in how you
build models or how you do data science or AI as a whole.
That is also massively changing day-to-day.
I think looking at maybe 3-4 years ago, fibers is still a lot
of a lot happens in predictive AI.
You spend quite a lot of time tuning models, fine tuning
things, building algorithms and doing feature engineering and
all of that sort of things. Now it's kind of constantly
shifted to a different direction.
But still a lot of those abilities that you had, the
learnings you had are actually still true.
And I think, I think that is also also the whole ecosystem
around it. What is data?
What type of data do you need for this type of AI versus how
do you put these models in production?
Is it the same? Is the model artefact the same?
And all of those sort of things are also constantly evolving,
which is very exciting and interesting at the same time to
figure it out. What is the right tool stack to
do AI and data science compared to two years ago or two years
ago? What has been the the biggest
change that you've seen? Because I, I haven't worked with
data science in, let's say, a product development team for a
long time. When I did, I, I was there from
the product perspective. And 1st I saw them kind of
isolated working on models and then kind of testing and proof
of concepts and actually seeing if there's value.
And then once we got to a point where we're super confident, I
saw them going very close to the user side and actually testing
and validating assumptions and seeing if they could deliver the
value as they had theoretically. But then with actual users, I
don't know if that's normal. I think that's very healthy also
from data science to go more towards a user perspective to
see how we make this means to an end actually achieve business
outcomes in the end of the day. I saw them also be involved more
from an infrastructure standpoint on the engineering
side. So I don't know if that's
normal, that's a newer trend, but what have you seen?
How is the data science kind of evolved?
I think the whole ecosystem is constantly shifting between
predictive AI and generative AI. And the main thing is that it
has been in a way democratized in how people can interact with
models. And AII think after ChatGPT, the
main change in the society or in the tech world has been now you
suddenly could interact with an AI that sounded OK, goods are
very well or convincing. And I think that opened a lot of
new doors. But at the same time, it created
also a new shift in how you do things.
For example, building a proof of concept going from zero to 80%
has gotten really easy. It's, it's like almost like you
could even simplify it as much of we're going to call an API,
write the prompt and it's put something wrapping around it and
calling it an application. Or I think now we're talking all
about agents calling an agent, like an agent calling a few
different AI models in a loop and then we call it an agent.
I think that level of creating proof of concepts has increased
a lot, which basically makes the noise a lot.
But still doing that last 20% is still equally as hard in my
opinion. For example, now we are
investing a lot more time and efforts in in how our data are.
Of course, data has been always the cornerstone, corner
cornerstone of AI and organizing it has been always a thing.
But now you need to look into, do you have the right
ontologies? Do you have the right knowledge
graph in your company? Do you have the right semantic
data layers? And all of these, I think do you
have the right embeddings? Do you have all that sort of
things to do things? And, and that has been 1 main
change in my opinion. That's how we look at data will
evolve a lot more and companies need to figure out what is their
data and is their data foundations ready for the
generative AI world. And then the the second thing is
how you put models in production has changed significantly, of
course, in the basis of things still the same.
But one question that we constantly ask ourselves, like
on top of the cloud provider AI tools like Vertex AI in GCP or
AI Foundry in Azure and all of that sort of things, what else
do you need? For example, we used to have or
we still have wait time biases as a tool that we track
experiments and we track different model runs and
etcetera. Is rates and biases still the
right tool for doing that or do we need a new tool on top of it
to do LLM OPS, for example? And also what are the model
artefacts? How do you evaluate the models
as significantly changed in now you need to structure your
ground truths in a completely different way.
So I think and also people wise around the company or around
just think one thing is you building the models, but also
the experiences for the customers, exactly what you were
mentioning. Everyone else, like if an
experienced designer, UX designer, service designer, a
product manager needs to also understand what the generative
AI is and how is it different than predictive AI And, and how
do you build an experience for this new world that things can
be generated on demand? Do we do you would you look at
an e-commerce website the same way if you knew everything can
be generated in a in a perfect world.
So how would you look that? I think these are all different
changes that are coming and we have we don't have very good
answers for all of them yet. And I think it takes some time
to to just really figure out what is the right tool to do it
and how to do it and and where is the place of predictive AI
has versus generative. AI for the specifically, let's
talk about generative AI. Things like how your ground
truth has changed and model optimizations or even the
observability in production. Where do you see those
responsibilities lie then? Is that data science?
Is it more data engineering? Is it still software
engineering? I feel like it's a good
combination, but I don't know in the end who's going to be core
responsible for what yet. I think that's a really good
question, and I think we are trying to put all of these in
the boxes that's already exists. But maybe the answer is that
none of these are the right people, the right people to do
that. And maybe indeed it's a
combination of all of that. There will be always a place for
doing actual data science. For example, building an actual
rack is not as easy as putting some documents in a in a vector
database, do a a search in it and say, hey, tada, we have a
rag. It's really not that easy.
You really need to spend quite a lot of time.
It's a search problem at the end of the day, and you really need
to optimize that as a search problem.
Understand the whole space very well.
So there is a space still for optimizing that type of thing
within any organization or any company, small or big.
And then there is and then there is also all these newer things
that are coming up. The way that you keep the
infrastructure together. It doesn't require only software
engineering. It also requires you to learn a
little bit about what is this generative AI model does and
etcetera. It requires you to do a little
bit data engineering and all of that.
So I think in the future there will be new, we can call them AI
engineers. That's how we can call them very
easy solving the problem to to call them AI engineer that needs
to to have different skill sets from different crafts.
If you are very, very specialist in this, I think majority of the
skills are not going to be super duper specialist and they're
going to be more and more skill sets that needs to come together
to to actually deliver products in the new in the new age of
tech. I like that perspective a lot
and I'm wondering how you then support people that are
interested in this from their own domain.
Right? Because if I, if I talk to RAG
to my colleagues, software engineers, they go search
problem software engineering. But then in organizations, data
science, people are working with generative AI and trying to get
that up and to production. And then they see their model
performance will increase if they do RAG.
So then they try and figure out how to implement RAG, even
though it might be a search problem.
It's like everyone that is interested will contribute and
will help. And is that also what you foster
with regards to people's personal growth?
Like if they're interested, can they do kind of the whole chain
of things or do you still like see what their expertise is and
what they can add most value? I always suggest to and I don't
know if this is the exact right suggestion, though, I think I
always suggest to explore a sister capability to your own
main craft. So if your craft is a software
engineer and there's a sister capability of understanding and
learning certain type of generative AI models,
understanding them a little bit deeper than just calling ChatGPT
and calling an API and etcetera, to really understand how to
write a really, really good prompts.
Maybe in the world we don't need to write prompts anymore.
But understanding all these sister capabilities is what I
suggest to to people. What if it is getting like so
far away? And I think and, and it really
distracts you from doing your main tasks.
For example, if you have to maintain certain infrastructure,
for example, now you are you to put certain models in
production, you don't want to use certain cloud capabilities.
You want to use, I don't know, run a Kubernetes cluster to do
certain things. And then it requires you to
learn a completely different sets of skills and it's going to
take some time and etcetera. Then I'm like, OK, maybe ease
into it. So you don't need to go all the
way till the end. So that is how my recommendation
is into learning these different capabilities together.
Gotcha. Yeah, I like that a lot.
Kind of adjacent technologies that are easier to hop in and
out of and you can kind of leverage the craft that you have
already. Yeah.
You mentioned earlier that AI is kind of gotten fluffy because
everything is now AI, everything and nothing at the same time are
only specific things are now all of a sudden AI and some people
don't understand the difference between generative and
predictive AI. I'm assuming you have
conversations with that are technical but also
non-technical. How important is it to
understand and be very specific in a language perspective with
what we talk about nowadays? I've been on two different
spectrums of this over the courses of my career.
I think earlier on in my career, I used to spend a lot of time
correcting people and saying, hey, this is not like that.
This is like that. And and I think it is not always
very helpful to to do that. And on the other hand, now I'm a
lot more focused on bringing business values.
So I don't really care as much if person X uses a certain
language to begin with. But I do think the it's, it's a
it's not my responsibility always to correct people, but I
do think it's a responsibility of the organizations to teach
people the languages, the risks, the ethical side of things and
all of that. Things are sometime you can get
this through a conversation and some once the person trusts you,
they of course listen to that in the same way that I learned a
lot about probably I've already in this podcast, I used the word
experience design, UX UI already wrong.
And I'm sure if a colleague is listening is like, no, this is
not that and that, and I think it's all in learning.
So I think it's just going with an open mind on both side and,
and to, to really learn each other's languages is always
really helpful because a lot of our work is just communicating
with each other. And the better you can
communicate with each other, then the better you can push the
products forward. So, but I do think personally,
understanding the differences in the tech is very, very important
because you need to know what's the tech can offer you.
And then different people need to think about how it can create
a business value. Because if you do it fully tech
driven business value, you might end up on one spectrum of
things. If you do completely, let's say
based on on only business and neglecting what tech can do for
you, then you end up in a different zone.
So I think a balance is, is very important unless you're Google
and you're really good at the tech that you do and tech
defines everything and then you be Google.
I mean, even Google, I feel like they're so tech driven that
sometimes the marketing of whatever cool they have is
sometimes a little bit lacking where you don't really hear
about this or it's like a ninja release and then no one actually
sees it. It doesn't have the same hype
like an open AI is now trying to do, which is kind of everywhere.
They're also at the forefront of it.
But you mentioned business outcomes with generative AI, if
you like, personal productivity is changing, but so is kind of
what we can offer our customers nowadays, right?
If you've seen kind of this predictive AI, or say I should
say generative AI, really leverage the technology to then
have really cool business outcomes.
Do you have a few examples there?
Yeah, we've got, I mean we've been on this journey of finding
out where generative AI brings outcomes and where it brings
business values. I think you in general you
should look at your the value chain of whatever team or idea
that you're looking at and focus on a few areas that brings
business outcome. I think from my experience so
far, the areas that I can see there has been tangible business
outcome 1 is actually the whole idea of search because because
now people can go beyond keyword search and have a lot more
semantic way of explaining what they want and how they want.
And, and even go to the other end of it, which is
conversational search, which you can actually have a conversation
till you figure out what you're exactly looking for.
Some people are really good at describing what they they have.
And also when you think about search, it's a multi modal thing
as well. Sometimes you search with an
image because you don't know actually how to describe what
you want. You're looking for a wife you're
looking for. And I think generative AI can
really help with product discovery as a whole.
So I think that's one area that within IKEA where I work right
now, I see quite a lot of value being generated and we are
experimenting with it. But still there is quite a lot
of like pros and cons of the cost versus the infrastructure
versus everything. How do you maintain it in a good
way and in a in a very stable way?
Are the costs high usually when you're talking about kind of the
search advancements of conversational search or multi
model give me a vibe similar to this.
Are the costs high in general? It could be high, yes, but you,
I think for me, I personally do not.
Maybe I do not optimize always at this stage.
The first step on a cost for me is figuring out what the
customer wants goes beyond, of course, within reasons.
If something costs like completely absurd and it's like
way out of line and then the whole ROI needs 20 years to to
break even, Yes, of course not. But I think you could do so much
with training, more specialised model training with more open
source models, quantised models, reduce the size of these models
and etcetera. Because at the end of the day,
you're working a very specialised area in a company.
You're not a world knowledge on. You're not you're not offering
world knowledge to people in a lot of companies.
So I think there's a lot of ways of optimizing for the cost, but
I wouldn't start with the cost. I would just figure out what the
user wants. Is this a experience that they
want? But yeah, the cost can be high
at the start. And then you start looking for
ways of optimizing that further and further.
You don't have to rule it out on everyone until you optimize the
cost. For example, what if you know
this is what your user wants? Then it's different.
I feel like knowing what the user wants has always been
challenging but also users have now been so educated with
regards to OK I go to an e-commerce and I know what I
need to search on but I know also how to use the search bar
for example. So they automatically will do
keywords. They will not describe in
linguistics what they're searching for even though it's
now possible or even the ability to be like I want a vibe similar
to this living room. If your IKEA sounds like a super
cool use case to then offer something that is similar.
But how do you educate a customer?
Like take them with you? Or how do you know if this is
actually a problem you're solving?
I feel it has become even more challenging.
Definitely it's challenging, but it is changing, I think how
people even right now use Google versus ChatGPT and everything.
I often hear, I was like, hey, I've searched this on ChatGPT
and, and, and then you don't want to argue with a person.
Is this the right way of searching?
For. For a thing because.
If it works, it works is. This the most sustainable way of
finding the answer to your questions, but at But at the end
of the day, if you're building something that users truly love
and it's exciting, I think user behaviour slowly and organically
changes. So there there is this whole
trend of change, the user behaviour that changes
automatically by itself. Like right now you cannot really
think of a brand not having a website.
So that's not a competitive advantage anymore.
It's just a given. And I think it's the same way
with this. It's like if you are giving a
really, really good experience and you're figuring it out
exactly what it is you're getting into, into that space.
But at the end of the day, we don't know the answers to these
questions and it's all bets, but you need to put bets on things
that are that you are secure about, that you're like, OK, I
have a hunch as a business, this is where we want to go and this
is what we want to be as our competitive advantage.
Otherwise you are building the same thing as everyone else and
you're just following and you're not never getting the best out
of the tech, you're just following and and you're not
differentiating yourself. And I think that's my core
philosophy. That's everyone in this current
space. While it's possible you should
start differentiating yourself maybe in five years it's not
possible anymore because everything's taken our new
experiences on maybe the whole world of e-commerce website does
not even exist and now we are only shopping through a
completely different ecosystem and etcetera.
I think now that it's possible, you need to, as a business,
figure out what makes you special and and bets on those
very specific capabilities that makes you special.
Yeah, I think now is the perfect time.
Like there's whenever there's a new technology, there's always
something, but it might not be relevant for a lot of people.
And now because it changes like one of the core components like
you mentioned search, search all of a sudden becomes more than
what we used to have before. You can leverage that in
whatever you're trying to do and help the customer experience.
Search was a a big component where you said, OK, that's where
it aligns with business outcomes.
Have you seen any other? Yeah, absolutely.
I think there are a couple more. In general, the search was a
classic one that you already exists, so it's relatively easy
to improve. But then there are also new
experiences that you can build for customers.
And for example, one in the whole world of generating
content, it's a, it's a very specific and interesting area
and you can look at it from different angles.
Maybe you're just generating everything from scratch using
generative AI, which I don't think is the right approach
because then your whole website is filled with AI images and
there's nothing unique around it.
But then there's also something about looking at content as a
whole and gap inspiration for the customer.
For example, a lot of customers have trouble getting inspired.
What do they need? How do they need it?
Where do they need it? When do they need it?
So now we can get a lot more deeper into the context of the
customer and understanding from that angle and gap, this
imagination, imagination gap for them and feel it's in a way that
they can get really, really better recommendation for their
home in in this context of IKEA or, or if you're in different
contexts of what is exactly a deep way of personalizing your
offering to the customer beyond how we do it today, which is
like a carousels of things, sending emails and etcetera.
Beyond that, what can we do to really, really personalise and
give a new experience to the customer?
So I think in this area, we we are experimenting quite a lot
with the 3D World and etcetera and see what can be done in that
area. Then then other areas that I've
seen value is actually a lot of a lot of Co workers because
those are also customers in a big organization.
You're dealing with a lot of a ton of internal toolings, people
working in different places. And we still see value in, for
example, how you do supply chain and how do you optimize that in
a good way. But that's less of a generative
AI, more of a predictive AI, but it could move into a generative
AI world or, or for example, now we have a higher tolerance of
unstructured data, meaning now you can understand images very
well. So how do you manage your
contents? How do you create metadata
around them with a good human feedback loop?
I think all of those are also another area that I can see
already creating value, but there's a ton to be seen to be
done, to be proven value. So, so either you could optimize
existing features with generative AR to give a much
deeper personalization or completely create a competitive
advantage, which I think is is much harder and but it's also a
lot more exciting. Yeah.
How do you then get your priorities in order in terms of
what do we bet on 1st and how do we validate assumptions and do
we go for the more longer term big impact ones or do we do that
in parallel with a few smaller ones?
What do you choose to experiment with first?
In general, we or my team, my strategy always is A2 fold
strategy as I call it. And I, it doesn't always work as
clean as I'm explaining it, but the one fold is you do want
short term value because businesses are optimised on
short term value. If I, if I explain for someone,
hey, within three years I'm going to bring you XYZ.
I think nobody's going to give me money to bring this vision to
life because this is just, it's just too absurd.
They've never seen it and they have to imagine it.
Now I have to explain and or they have a high trust on me and
they're like, OK, yeah, go and do it.
But often time businesses are optimized on short term values
because they're you need to solve the problems here and now.
So I think and in short term value, you also need to look at
are you filling a gap that should not exist in the 1st
place because something was broken somewhere else?
Now you're feeling it. So that that is something I
would not call a short term gain, but shorter gain means
like for example, in the search, it's like it's, it's given, it's
like it's there. People are searching, they are
searching longer, they're already going to ChatGPT
searching for your products. So you better offer a better
experience. So then there are some some
other things which are mid term and long term which you need to
really understand what is your core business And there is like
OK in that area of my core business, what is it that brings
me value. And I think there is a bit of a
luck plus a business hunch, plus the tech all needs to meet each
other to actually bring value. And, and those are the areas
that you want to spend a little bit time longer term
understanding the take around it and and etcetera.
So I think that is how I usually prioritize and say, OK, does
this and I put 60% of the effort usually on short term, 40% on a
little bit medium, long term and say, OK, is this bringing me
value in next 2-3 weeks, a month, a year?
What is the the thing? And then I do from there.
But then also this culture of. Experimentation is also really
important within IKEA. We spend 20% of our time on just
experimenting, forsake of experimentation and and.
Everyone. Everyone can do it, but not
everyone does it because it's also hard to next to your job.
Now think of, OK, now I'm going to do some side project.
But that also comes up with like you read a lot of paper, you
read, you understand the market, you understand things.
And then I think that medium long term work is now much more
interesting because now you have you are creating that business
hunch within yourself of like, oh, where is everything is
moving to work to based on your experimentation.
So it's not so much of betting on like it's closed eyes.
So you're actually betting on things that you still can't see.
Going to take a bit of time to bring value out of it, but
eventually it will come. I think it's very smart, like
20% that would be one day a week.
If I were to get one day a week to like read through articles to
experiment with programming languages that I like and trying
to kind of fix a problem domain wise that I know with new
tooling that is out there, I think a lot of people would get
inspired to start doing something.
And also there's good energy of sharing then from a perspective
of learning. And I think it's really was that
already there in IKEA before you joined?
It's, it's just beyond technology part of the IKEA.
It's it's like an IKEA thing to to do that.
I think it's even written in my contract.
Oh, that's really good. But I I will add this, not
everyone is motivated based on this.
A lot of people just want to come and do their job and leave
and that is totally fine as well.
So not everyone needs to experiment, explore, do things.
Some people are just not meant for that and I think it's much
harder then a lot because a lot of people think, Oh yeah, now
I'm going to put every Friday on doing this Sundays.
But it's really not that simple when you have a ton of business
push and we need to get this done and that done.
So it it takes discipline to actually explore and think and
and be creative. It's not as easy.
Yeah, you get that. It's like your regular day job
is still there. So if the deadlines are still
there, then it's like, yeah, it just files up.
It's like when you have a four hour meeting and you're like,
man, why was I in this meeting? Because all my work is still
there and I didn't get anything done.
I get that from a discipline. Hopefully your AI agent can go
to the meeting. And give me a summary, what was
said. Yeah, yeah, yeah.
When I asked you where business outcomes and and newer
technologies align and what you've seen, you didn't tell me
about the traditional one, which I thought you were going to
mention, which is chat bots. I see a lot of companies, the
first thing to do is they make this custom chat bot, like man,
how much value is in this custom interaction?
Because it's sure it's a tangible use case, might be a
good one, I'm not sure yet. There's a lot of companies that
are doing chat bots and trying to solve this problem of
customer service and human in the loop with AI and that's
their core. And then I also see customers
instead of getting that off the shelf, trying to do that
themselves, which there's a positive part because they have
their own knowledge bases, but then there's also the downside
because they have no clue what they're doing.
Chat bots are kind of everywhere I've seen as first example,
right? What is your what's your take on
that? I think this, this is really
part of that is you going from zero to 80%, which is really
easy. And chat bot is one of those
it's like getting frequently asked questions, chat bots.
I'm going to put some documents in it and now the answers from
my knowledge base and etcetera. And, and that is not an area
that excites me. There is there might be value in
it. I have my own doubts.
I could not put my bets on it if it was a horse race.
I would not put my bets on this. But I do think if you want to
build a chat bot though, how can this chat bot is going to be the
world's best chat bots? That is exactly what your
company meant to do. If it is the same exact same
chat bot as across the board. Now it's your logo is on it and
you are doing exact same question and answers and you're
doing exact same thing. I just don't think that is going
to cut it for any customer and and I think the number of users
is also going to drop. I think Klarna showed us what
not to do in this space, firing everyone and then bringing each
other. Like I would not want to call my
bank and not be able to talk to someone because now they decided
to cut cost and they thought now I'm going to be a lot more
efficient if I talk to a chat bots.
Because these are the moments that you're interacting with
customers and it needs to be memorable.
It needs to be amazing. And yeah, there is a space for
chat Buster and I think it's also business decisions.
Do you want to go to a world of no human interaction maybe makes
sense for some businesses, but I wouldn't prefer to work in in
that type of space because that's that's not what I think
is right application. Yeah, for for within my
proximity. And working, I think it's very
business specific. It's like, would I rather, and I
had this conversation because there's a person in the know is
working on the startup, would I rather wait 20 minutes on the
line to get a human in the loop like on the phone?
Or would I rather an AI picks up the phone, which is like a
human. I barely can like have the
difference and they pick up and they do like the first analysis.
It's like, yeah, I know. I don't like waiting.
Like I don't think humans are patient innate.
So then it makes sense to do that.
It does need to be very well done.
I feel like like 80% is not good enough, especially when you're
interacting with humans. That is your customer base.
Your reputation is also based off that.
There's a lot of mouth to mouth marketing.
If it sucks, my whole friends and family will probably know
that it sucks. So like that's this little
bubble. And if you underestimate that
and you get something up and running and 80% and you think
that's good enough, I think you might be underestimating or
undervaluing your customers as well, which is a shame.
At the end of the day it's like hard earned people that are
already there, they already bought something, they need help
or there's something wrong and they definitely need help still
and then if they don't get help they won't come back likely.
Right, right, No, I mean you're you're definitely onto
something, you know, right. There is a space for it.
I'm not saying burn all the chat box to.
The ground. I think there's a space for it,
but I just think there is a lot of like I can go to a place with
even within a company within within where I work or within
anyone. I think whoever that starts to
try generative AI, their first use cases is like really 2 three
things and it just constantly repeats.
And we need to ask ourselves, is that easy to do that?
If it's that easy to think about it, either it needs to be really
good, well done, or it doesn't really bring any value because
everybody is doing it. So then it's not valuable as
much as spending your time on something else.
But if you think about what is a chat boss, how the chatbot can
be unique for the customer, I think then there's the the
conversation kind of changes from the existing one that we
have. Yeah.
I mean, whatever we do, I think there's a lot of stuff that we
can do. And I really appreciate people
like you that are like, OK, I have this strong vision, both
long term and short term. And they're very much aligned
with regards to business outcomes and where technology
meets business at the end of the day, where it solves a problem
as a means to an end. Those are the people that I want
to be working with. And I feel like especially now,
if you're early in career or you're more senior in career,
you also want to be working, working with people that have a
strong vision where there's an experimentation culture where
you can feel fast and learn quickly.
I mean, I'm saying this from personal experience.
That's the environment that I would want to be in, especially
because things are kind of accelerating.
It's now cloud code. And then next week, I don't
know, AWS might release something and then all of a
sudden there's something else is like, there's so much to learn
and so much to experiment with. If your environment doesn't do
that, then you might get outpaced.
And then a few years you might find yourself in this weird spot
where all of a sudden there's this new role, AI engineer, and
you only see components of like what you used to do.
And it's, yeah, you're like, what do I do now?
Yeah, I feel like it's good to keep learning and keep growing
of. Course, yeah.
The fun part is that especially in this industry, there's a lot
of stuff online which I've always appreciated.
I think that makes this industry very unique and it's very kind
of fluffy for me to say because it's the only industry I've
worked in. But still, the only downside now
is that there's so much content. It's like, where is the essence
and where's the gold? But that's why I really
appreciate you coming on this podcast and sharing your
perspective and kind of take on things.
I've really enjoyed this conversation.
Of course, thank you for having me, I enjoyed the conversation
as well. Cool.
Yeah, thank you for having me. It's indeed, I mean, there's so
many podcasts and contents and everything out there, there.
I I have always like so many podcasts always saved and and
never get to go through all of them.
And I feel bad. So, so indeed, it's great that
we have so much knowledge and so much different perspectives out
there which are really exciting. Cool, man.
Thanks again for sharing. We're going to round it off
here. If you're still with us, let us
know in the comments section what you've thought of this
episode. Leave a like if you liked it and
otherwise we'll see you on the next one.