Choosing Value Over Efficiency: How IKEA Leveraged AI to Turn $15M of Savings into $1.5B in Revenue

Speaker A: All right, well, I am particularly excited for this week's podcast episode because I know there is a lot of AI Doomerism right now, and many people are fatigued just looking around, going. Everywhere I look, AI is eliminating headcount. It's blowing up functions and organizations, it's bankrupting companies as they get these massive compute bills. And in that noise, it can feel like, is there any real value that can come from this? And I know a fair amount of my content is designed to help equip you by addressing some of these very, uh, real risks. But what doesn't always come out of that is where are people doing it? Well, where are we seeing the kind of wins we've been promised and where we see those, what actually goes into it? Because part of the challenge many people are finding is they've been told there are wins and all you have to do is hit the easy button and there's no such thing. And so today I want to talk about that by talking about a win that's in the news from ikea. I think it's an incredible story. And I want to share how IKEA used Billy, their chatbot, not to eliminate their tier one customer service roles. The 8,500 people who were doing this, in fact, they didn't lay off a single person. I want to talk about how they used what they learned from this and gave that frontline workforce a massive career upgrade and their customers an, uh, upgrade along the way, all while generating a very healthy profit from this. And that's my goal with this, is to help you see that the math does show the wins. If you approach it right, and you may say, well, Christopher, how much of a win? Well, had they just done their baseline efficiency approach, they would have cut around $15 million out of the system, which, okay, maybe that's an operational savings. We could have a debate of whether the long term savings would actually be worth it or not, but it's a savings nonetheless in the short term. However, their choice in the path they walked ended up resulting in a 1.5 billion billion with a B in net new revenue, revenue that did not exist in the organization before with that exact same workforce. And my goal today isn't to just share a feel good story of Kumbaya, let's hold out there for the people and protect employees feelings. No, that's an important thing, don't get me wrong, but this is a massive masterclass. It's designed to be about how when you're surgical, when you approach things with operational excellence and raw Business wisdom M and a true compassion for the people your company runs on. You can actually unlock the ultimate value AI offers and the data that you're already sitting on today. But the reason they did it is because they decided they're not going to automate the call center out of existence. They're going to automate the mundane out of the call center so that they could choose strategy and long term value out of a short term spreadsheet gain. Now, before we get into this, I'm Christopher Lind. This is future focused. If you find value in my content, if you enjoyed this episode, check out my other stuff. You can also like subscribe, share it with somebody else who may also be struggling to understand. Is anybody really winning with this AI stuff? Um, I hope by the end I have you and anybody who listens to this convinced. Um, and also change your mind about how you approach it and give you a path to walk forward in. Um, also check out my website. You can see how I help people like you and organizations solve some of these real problems because I see it on the regular of things like this, but I recognize many people don't. I recognize many people don't. And I understand a lot of the skepticism, the doomerism around AI because there is some real risk, but it doesn't have to be as risky as it's initially presented. So let's get into this. So what exactly happened here and how did they actually approach this? And I think this is a really important piece and I'm going to draw attention to very specific choices that they made along the way that I would argue are mandatory. And if you don't make these choices, you might walk the same path. But if you don't make these choices along the way, you're not going to get the same outcome. So if you go through this process and you don't, you don't make these same choices. And I go, you have to make this choice. Um, you can't come back to me and then go, well, we tried what they did and it didn't work. I'll tell you. Well, that's because you didn't make this choice. And the first one was this. They didn't approach their call center as a monolith. Okay? They didn't just say, we have a call center. We have customer service agents. Customers call us and we do things with that. We resolve their questions. They actually decided to make an intentional choice to dig into what is in here. Let's actually understand that a call center is a complex thing that needs to be understood. It's not universal. And so they decided to say, well, we have all this Data. We have 3.2 million interactions with our customers that we're sitting on. And they did this. Here's another little note. They did this back in 21 to 2023. So for those of you who may be thinking, hey, is this something you can run overnight? No, it's not. And anybody who tells you you can run overnight is lying to you. You can move quickly, but this doesn't happen overnight. But all that to say they looked at these 3.2 million interactions and they said they, they used AI to help do this. But they said, let's actually understand this. Let's not just assume we know what goes on in a call center. Let's actually understand what is actually happening in these customer service interactions. And guess what they found. 47% noise. 47% was pure noise. It was routine transactional noise. People calling in for mundane things. Where's my order? Is this in stock? What's your return policy? Things like that that I would actually argue are not even best for your employees, your human employees, to be doing anyway. And not just for their sake or your company's bottom line, but also your customers. The time it takes to get on an agent just so you can ask a question that should or could have been automated in seconds. That's a loss across the board. And so 47% of what they found was that. And they said, you know what? Uh, that is mundane. That does not need to be something that we need a person actually digging into. But here is where they made an intentional choice. And this is the intentional choice you. You have to make. I can't train you how to do it. I can't give you a playbook of how do you go through this to make this choice. This is a conscious decision you have to make. When they saw that 47%, they said, okay, that's 47%. But then they chose not to just say, great, let's cut that 47% out and stop there. And that's the efficiency trap many organizations make. They look at data they might not even take the time to actually understand. So the first decision was, say, let's actually understand what we're looking at. Let's not operate off assumptions. So that was decision number one. They made a conscious decision to do that. Then when they did, they isolated the bloat, the noise. They made conscious decision number two, which was, we are not just going to say, how do we cut that out? And stop there they did not fall for the efficiency trap we, which would be a tempting trap to fall for. They analyzed that 47% and said, hey, if we eliminated 47% of this with human customer service agent interactions, we could legitimately cut $15 million of operational costs out of the organization. That could have been presented to the board. They could have got that rubber stamped. They could have moved forward with it. They may have. Down the road, I would argue, probably learned that that was maybe not the best decision. But by some standards, that would have been a win. Companies would have been comfortable accepting and calling it a day. But they didn't. And that's something more organizations need to do is say, I am not okay with just accepting cost cutting. I also want to understand where can additional value be created with, with whatever we're taking out. So we're taking something out not for the purpose of eliminating, so that we can reinvest it. And this is something companies do with their money all the time. We do it with our money all the time. I don't know why we don't do this with our people. We do it with money. We say, hey, we, we generated a profit. Let's reinvest in the business so we can grow the business more. Yet for some reason, oftentimes with people, we go, great. We found automation. We don't need those people anymore. Instead of saying, what do we do with them? But again, rather than just taking a, uh, kumbaya squishy approach, go, hey, we're a company for people. We're not just going to cut our workforce. We're going to keep these folks around and then just sit on that bloat, which no CFO is going to be fine with that. And I understand that. And that's not the point. You don't have to do that. But instead of just giving up or saying, we're just going to take the path of easy, you know, and, and just be a martyr and go, I guess we'll just eat the cost of that while these 8, 500 people do. I don't know what they said. Let's figure it out. Here was the conscious choice. Let's dig into the 53% of signal. They consciously chose to say, let's go deeper. Let's find that value. We've already decided we're not going to get rid of people. We're not just going to cut out cost. We're going to focus on adding value. But let's be surgical about it. Let's not just hope and pray that if we keep 8, 500 people around, they're going to magically start generating revenue because that kind of logic won't pan out. And so they said, let's look into it. And so they did. They dug into that 53%, using AI again to say, what's in here? What is actually happening in the 53% that is too complex. We can't just throw AI at it. We know that we've isolated it. And what they found was when they dug into it, they realized that in this 53%, a lot of these customers were not just calling to see if a product was in stock. They were asking deep consultative questions that they needed answers from, from people who understood IKEA. IKEA's products, understood their customer, understood what these people were looking for, were willing to listen, we willing to problem solve with them, but had the, uh, company knowledge to know, well, what do we do with that? How do we actually work with that? And that required some spatial awareness. It required them. And they said, you know what, we actually need to do this, we need to go into it. So they were intentional about it. And what they found was these people need help figuring out which products of ours help solve their problems. And guess what? There's some real value to be created there because if somebody's helping them with that, they're going to buy more products, they're going to have a better customer service interaction, they're going to actually get help from us, and we can generate greater revenue and have a better customer service rep. But here's where they made another conscious choice. They didn't just say, okay, great, so we're just going to tell everybody, you got a new job. Your new job is you now do this. You were a customer service agent. Guess what? Now you're a this, here's your job description. Go execute on it. They took the time to say, what does that new job require and where are the gaps? They did a gap analysis on where those people were today and what they need to do to get there. And it wasn't just a people skills standpoint, it was a technology. If you look into this, they actually realize, like, what other things might they need? How do we have them have video calls instead of just audio calls? How you do, how do we allow them to create digital renders of how our products might sit in their space? How do we lay out floor plans? How do we come up with personalized product lists so people. We can actually have these agents consulting and providing people with ways to actually achieve the goals that they want. And in doing so they got a hundred percent return on that. That is a massive, massive return in doing that. And that they actually said, let's not just change the job description, let's actually get help these people. Not just do it with what they have, but give them what they need to do it. Well, we're going to take that cost savings, we're going to reinvest it into these people who are now going to deliver on what we believe is this higher value thing that we isolated and identified in the data that we currently have. And guess what? Instead of saving $15 million, they generated 1.5 billion in new revenue. That is 3.3% of the global revenue for that group. And that was just in the first year. By 2028, they're on track to do 10%. That's a 3x increase on this. And so what they did was they asked, what can our people do now? And what do we need to do to get them there? And let's actually make sure we close the gaps to do it. That, again, is an intentional choice to do that. Okay, um, now here's the final piece that is important in this. So we talked about the fact that first it was the. You have to make an intentional decision to dig into it and say, we actually need to understand this. We're going to separate the signal from the noise. Then we're going to make an intentional decision not to just be satisfied with cutting out the noise. Then we're actually going to dig in and understand, make sure we understand what the signal says. And then we're going to do a gap analysis on what we have and figure out what we need to do to close the gap to get there. That's. That's a lot. I recognize that. And that's not something one person can do even with the most advanced chatbot. That's not something one function can do in isolation. And this is this other conscious, intentional decision that Ikea made that drove to success. And it was saying, this is about collaboration. AI is not here to make me the smartest person in the room, capable of being an expert in absolutely everything, because I think I have all the answers. No, they actually used AI to drive the people from different groups together. Because I can assure you right now that if a CIO or a CEO or the Chief Product Officer or whoever sat in a room by themselves or with their team and just said, okay, hey, we decided we're going to do this, let's come up with the whole plan, and then we're just going to hand it off to the rest of the organization to execute this would have been a flaming dumpster fire, I can assure you of that. But they didn't. They said, this is going to be a collaborative engine. We're going to work together. There are going to be no vacuums, and we're going to lean on our subject matter experts from their intended areas. We are not going to let AI be something that makes us think we have all the answers to everything. We're actually going to enable and amplify the subject matter expertise of this massive, complex problem we're trying to solve here. It was a recognition that we can't do this alone, even with AI. We need each other to work through this. And so we need to bring together our people, our operational and. And our technology teams to come together to say, we have an impossible problem in front of us. How do we work together to pull this off? And I think this is one of the things that often gets skipped. And it, depending on the initiative, might be different groups trying to do it in isolation, might be one group doing it independently, it might be two working together instead of saying, who are all the people that this touches that need to be in the room? And what I can tell you is it's not uncommon for the people people to not be in the room. And to be fair, sometimes I understand why they're not always invited. I think it's not only, though, about inviting the right people into the room, it's about making sure they're part of the process at the right time. Because I could absolutely see a world where maybe the operations team works and gets this all the way down the line and then hands this off to tech and goes, we need this tech based on our assumptions and hr. We need these job descriptions and this transfer of people that would not have worked. The tail cannot wag the dog cannot wag the tail for these teams that actually have to do it. It was about actually coming together as a strategic, cohesive organization and say, we are committed to making sure we are not just cutting cost out of the system, but we recognize that requires technology that requires an investment in our people. And it requires a commitment to actually going forward with this. And to do that, we all need to work together. And that kind of collaborative effort would have been, in my opinion, likely impossible without AI and so when people say, can't, uh, we just do this without AI? There are things you can do. Could you pull off something like this without AI? I would argue it would be very difficult to. And that's where AI is a strategic enabler that would allow you to do this kind of impossible task. Now, it also could drive you down a path. If you didn't make any of those intentional decisions that I said, it could end up sending you down the other path that much faster. But by bringing AI in alongside these other conscious decisions along the way, they actually were able to solve the impossible to the tune of 1.5 billion. I'm just going to keep repeating that to make sure that reinforces 15 millions in cost savings, 1.5 billion in new revenue generated. That is the kind of difference, and I can tell you from the work that I do, it's not always that case. I'm not going to sit here and promise that every time people do this, right, they see 100x return on the cost savings they would have gotten out of it. And I'm also not going to promise that in the first year you're going to turn 1.5 billion. Um, but what I can tell you is the organizations that are intentional about making all the decisions we just discussed see wins, they see wins. And that's why when I see people overly skeptical and understandably overly skeptical, my hope is to encourage and inspire them to say, don't throw in the towel. Don't throw in the towel and give up. I assure you that if you're not seeing wins, something's broken in this chain, this chain we just went through. Something is breaking along the lines. Now, here's the thing. You may be getting to the end of this, and hopefully I have you convinced that AI can help you win, can help you win. If you approach it intentionally, you may be going, okay, but now what? And here's the thing. One, if you're not convinced, if you're not willing to make those decisions, I said, if you're not willing to say, no, I'm not satisfied with just cost cutting. If you're not willing to say, I'm not willing to work with my collaborative partners, I, I think I can get all the answers myself. If you're not willing to take the time to understand the problem you're trying to solve and be surgical about separating noise and looking for strategic value, if you're not on board with doing those things, then there's no way you're going to see these kind of results. You just won't. So if you're not convinced, then that's beyond my help. But if you listen to this and you're convinced of those things, but maybe you're going, okay, but I'm not Somebody who can do this on my own. You're right, you can't. That's kind of one of the key points in here, is that you've got to figure out where are the opportunities to do this? And the first part of that is committing to the fact that that is hard work. And I understand why a lot of people are going, I'm tired, I'm fatigued, I'm exhausted. I don't have it in me to do hard work. Once again, if that's a decision you've already made, there's nothing that can be done to help you achieve these kind of results. You have to be willing to say, I'm willing to do the hard work. I'm willing to lean in. I believe these things you talked about, I'm willing to do the hard work. Now what? And, uh, the now what is actually figuring out where are these opportunities. Where do you actually dig into some of these things and say, where might there be an automation project in motion? Where might there be something that is being transformed right now? Where do I know that we currently are satisfied with just the operational efficiency gains that we might get, that I may be able to lean in and dig into that 53% signal and isolate some things and actually find some other people who are committed to doing these other decisions and saying, let's actually make this happen. And that's going to look different for you. That's going to look different in what that capacity reinvestment scenario looks like. But it requires that commitment to doing those things and then the willingness to actually step forward. And now here's the thing. I get this episode. You may go, that's a lot. You're right. You're right, it is. And I recognize there are a lot of people who feel overwhelmed by this kind of thinking. Um, if you need help with it, this is what I do. Reach out, send me an email, say, I heard the episode, Christopher. I'm struggling to even wrap my head around, uh, where do I even begin? Well, we'll have that conversation and we can talk through this thing. What I can assure you is it is possible to do all of these things in nearly every single situation. There are times, and if you can't, and this is where a lot of the work I do ends up being, if you can't, you actually shouldn't be moving forward with whatever you're doing. It's as simple as that. If you truly go through this exercise, are committed to doing these things, and you actually can't find an area where you, you can't enhance and add value, then you actually need to move on and work on something else. Or maybe ask, why are we doing this in the first place? There's always opportunity to do it. It's a matter of whether you're willing to look for it, find it, and put in the work to actually bring people together to execute it. And I don't say that callously, like, oh, uh, this is easy peasy. But I say it to deliver the tough message that it is possible. And so when I hear people go, it just doesn't work for us. Yes, it does. Yes, it does. And I know it does, because this is the work I do, and I see it happening on a regular basis. It's just that I get it. There's a lot of landmines you got to avoid, and there's some really tough decisions you have to make and commitments you have to make along the way, but it is possible. And so I hope this episode can serve as an inspiration and encouragement in a really discouraging time right now where it seems very uncertain, it seems very impossible, and it seems like nobody's winning and everybody's losing, but there are people winning, and there is a way to do it. There is a playbook to do it. And hopefully this outlines that. And I will continue, as I go down this path to, uh, highlight and find opportunities to really deconstruct and break some of these things down, because it's easy to sometimes hear these stories and go, well, that's not our situation. True. But you probably have similar or transferable situations that follow it. And so I will try and continue to isolate opportunities where that happens and share those stories and break them down. So that in the sea of madness, you can feel like there is a light at the end of the tunnel, and it's a bright light, an exciting light for those who are willing to commit to it and charge forward. So with that, I hope this has been helpful. I hope it's been encouraging. Uh, I'm Christopher Lin. This is future focused, and we will see you on the other side.