Stop Hiring Junior Engineers Because of AI?

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.