← Eye On A.I.
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- [00:00]
A
We have about 60% that are either laggards or emerging. They're not getting a lot of value out of AI or maybe not even trying to.
- [00:07]
B
Where do you see agents being applied? What part of the business surprised myself at how slow the adoption has been? And I'll just take an example that we were all familiar with is customer service. I mean, I'm still pressing 1 for this or 2 for that on my phone.
- [00:26]
A
What is truly new and agentic versus what is something that a company could have done two years ago? And it's just def as agentic because that's the nice term du jour. Amanda Luther, I'm a senior partner with BCG. I've been with the firm for 18 years now. I lead our AI transformation practice globally and get to see a lot of what's going on across industries. And as part of that role, one of the fun things that I get to do is co lead our study that we do every year on the impact of AI across somewhere around 1000 to 1500 companies every year. I've been leading that for the last four years. I think the study's been going for the last eight or nine. And so we've got really great longitudinal data on what it takes to be successful in AI and how that has changed over the course of time as well.
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B
And this year's survey and the report that followed is about the widening value gap between early adopters and laggards in the AI space. Is that right?
- [01:32]
A
That's exactly right. And that's what we ended up titling the report this year because it was one of the most interesting findings. As we got into the data, one of the hypotheses that we had coming in was that maybe this gap would be widening because you've started to create the base of capabilities to build from. And what we've seen is that's really borne out in the data. You know, the companies that over the over time were investing to begin with got value from that. That value flowed through into the P and L. They reinvested part of that value into additional tech and AI investments. And now they're getting more value from doing that. And so they're really in this virtuous circle of value generation from AI.
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B
Yeah, I have some of the metrics here to talk about, but generally the companies that are getting value, I mean, we'll talk about the metrics, but what areas are they getting value in and how significant is the value on the bottom line?
- [02:42]
A
It's pretty impressive value. So maybe I'll Start with, where are they getting it? Yeah, where they're getting it is primarily from the core business functions. And that looks a little different by every sector and industry. But it's really around sales, marketing, procurement, supply chain. Your core business value drivers tends to be where about 70% of the value is coming from. The other 30% is coming from largely efficiency and some of the corporate functions like finance, like HR. But 70% really coming from those core functions that are part of the company's business. You might look at like a CPG and that oftentimes, you know, what is a consumer packaged good company, what matters to them? And largely it's marketing and branding and research and development. And those are the big value drivers for, you know, for a cpg. For a retailer it might look a little different. You know, for a retailer it might be around your commerce function. Like how are you dealing with agentic commerce and really thinking that through. It may be around marketing as well. And then, you know, store operations and labor being a big value driver and an increasing value driver for retailers.
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B
Are these companies generally large enterprises, small and medium enterprises? I mean, what's the range? Is there a concentration of value among the really big players? Maybe because of scale?
- [04:14]
A
Yeah, it's a great question. And one of the things that we've done in the report is actually kind of put companies into different maturity levels in terms of how they're dealing with AI. And to give you kind of the overall sense, we have about 60% that are either laggards or emerging. They're not getting a lot of value out of AI or maybe not even trying to. There's then a 35% which we call scaling. So those are companies that are starting to see value, starting to see value in pockets, but not fully at scale on the P and L. And then that gets you to like the 5% that we call future built that really are seeing value at scale across multiple areas of their P and L. And you can start to see this in their, and their EBIT margins and their revenues and their total shareholder return. Basically any financial metric you look at, these companies are ahead. That 5% is actually quite a mix of companies. It skews towards more digital or AI native companies that have been founded in the last 15 to 20 years. Yes, it skews in that direction, but it includes companies that are 100 plus years old and it includes companies that are really reinventing themselves in the age of AI. And so you do see a mix of companies that are in there.
- [05:33]
B
Are they generally large employers? I Mean, you know, oftentimes, and you know, I've spoken to people at BCG about this, the value is incremental at the employee level and doesn't necessarily accumulate or accrue across the enterprise. I mean, if, if an employee saves an hour, they're likely to take a longer lunch.
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A
Yeah, longer lunch break, more time, you know, gaming or whatever it is they do for, for fun.
- [06:09]
B
But, but the larger the enterprise, maybe the more those incremental gains add up. I mean, can you see any pattern?
- [06:19]
A
Yeah, it's a great question. So I think there's the large, the enterprise, maybe there's the more chance for some of that to accrue because, you know, you can add up an hour, an hour, an hour, and it starts to add up. I think the bigger thing we see on large enterprises actually is frankly the ability to put real dollars and scale and investment behind AI and recognizing that there are fixed costs around building an AI platform and building a team within the organization that's focused on AI. And so creating some scale around that I think is helpful for the biggest enterprises. There's also, I think, some really interesting things happening on the smaller side of the equation. And this is where you're seeing, you know, AI native startups get to $100 million in revenue with like 30 people. And so I think there is this dichotomy that you're starting to see of the largest enterprises that can really invest behind this, but also some really small enterprises that start, you know, AI native from the get go and actually don't even have to create such sizable teams because they're being so thoughtful about the process from the beginning.
- [07:28]
B
Yeah, there's been a lot written in the newsletters that I get about people who, you know, are running a company on their own with 10 agents or something.
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A
Exactly.
- [07:43]
B
I'm always a little skeptical. I haven't really done a deep dive on any of those companies. Are those generally in new business areas that have opened up because of the digital transformation in the economy, or are they, and this is something I've wondered, are they entering legacy industries and because they're so streamlined, because of their AI adoption, they're able to quickly penetrate the market that otherwise is dominated by legacy players?
- [08:22]
A
So far, I think it is largely a new industries that we are seeing it. So if you look at like, you know, the Cursors or the Lovables or the mercures of the world, I think those are the ones that get cited as, oh, look, the, you know, dollar revenue per employee is off the Charts at these types of companies, they are largely AI native, actually going after AI development or, you know, low code, no code type solutions. And so I think that is largely new capabilities that the world is creating. Now you say that. And also app development is not a new industry. And so I think it probably is disruptive to some of the incumbent players within that space starting to get maybe concerned about that.
- [09:05]
B
Yeah. And how do you track the impact of AI adoption down to the bottom line? Because the bottom line is a pretty, pretty gross in terms of large and unrefined measure. It's hard to pull apart what contributes to the bottom line.
- [09:31]
A
Totally agreed. And I can speak to how we do that in our client work, but I think more importantly maybe is how we have done that in the report that we just released and how we got to that widening AI value gap. So we actually looked at three different kind of forms of data to do this work. One is we asked companies maturity level across 41 different capability dimensions. And this is everything from what does your data governance look like, what does your tech platform look like, what type of talent do you have? 41 different capabilities that we have shown over the years matter for AI. That's one set of data that is not tracked to the bottom line. But you can kind of start to tell AI capabilities. The second set of data is also asking these companies, what types of functions or value pools are you going after in AI and what have you been able to achieve? And so we asked them for depends on the industry, but anywhere from 10 to 20 different value drivers that they could be getting out of AI and what P and L impact did that have, whether it's on costs or whether it's on revenue and then kind of flow that through the bottom line, that second set of stuff. And then the third set of data is actually external data on their financials. So what's the reported EBIT margin, what's the reported revenue growth, you know, what's the reported shareholder return and what you're then able to do, which is exciting and as we correlate all of that data, is to say the companies that have the highest maturity scores on those 41 capabilities are also reporting the highest amount of impact from their AI programs. And we're seeing that correlated in the overall financial returns in the TSR and the EBIT and the revenue growth. You put all those pieces of the puzzle together, you're really able to start to get a picture of what's being driven by AI. Maybe last thing I'll say there and then I'll get off my hobby horse on this. But the other question that I often get asked, which is an insightful question, is this correlation or causation. Are companies that are really successful, Maybe this is what you're about to ask. That's often what comes next. So are companies that just happen to be very successful, they're able to invest more in AI and so it's actually going the other way. And this is where the longitudinal data really comes into play, because you can start to tease out, you know, which is the chicken and which is the egg. I think the answer is that it's partially both of these things. Obviously, companies that have been historically successful were able to invest earlier, but then what we're seeing is those that did invest earlier versus those that didn't. Even looking at the, you know, the same success five years ago, you're starting to see that widening value gap because companies that are getting the value are able to take that and they're spending about 2x the amount of an investment on AI is, is the laggard companies. And then that reinvests, they're getting the return from it. And again, you're back to that, you know, that nice virtuous cycle.
- [12:38]
B
Yeah. Well, that's interesting. The, the, and the investment they're investing, the leaders are investing as much as double what the laggards are investing. Is that right? Is there, are there any absolute numbers to go with that?
- [12:58]
A
Yeah, so it's as much as double. And we actually look at it in a few different ways. So we looked at it. How much are you investing in IT overall? And then what portion of your tech investments are going towards AI? And what we're seeing is, as I said, about double of that investment for future built companies being an AI relative to others. What it turns out to be is the average Global share of it budget spent on AI is still only around 5%. Right. So it's not a massive number that we're talking about, but it is a growing and meaningful number.
- [13:40]
B
Yeah. And in the report you say that the leaders are expecting as much as a 40% or I guess it's 40% greater cost reduction, not a 40% cost reduction, but even 40% greater cost production than, I'm sorry, cost reduction than a laggard is significant. Where is that, Is that in time savings? Yeah, where, where is that cost reduction coming from?
- [14:17]
A
Yeah, it's really coming from multiple places. So one of the big ones will be time savings and efficiency on the work that's getting done. You often also see it in reduced vendor costs. Right. There's sometimes you've got something that you're outsourcing or paying somebody else to do, and all of a sudden you can either bring that back in house, you can automate it, you can kind of have an agentic system run against some of that. And so you're seeing, I think, both vendor costs as well as some of the internal headcount costs reducing. I think you also start to see, you know, for example, if you're a retailer, you know, putting this, putting AI capabilities against your vendor negotiations and your procurement, you can see some cost out coming, you know, out of the. The COGS line item, for example, as well, from some of these technologies.
- [15:08]
B
Yeah. What about. Because this is something that's helping just consumers, the ability to find things that you're paying for that you don't use.
- [15:23]
A
Yeah, yeah, you definitely see that. You know, both in terms of, I mean, this is always, if you're a cto, the bugaboo of I've got all these licenses that I'm, you know, nobody's actually using. How do I find that more quickly and kind of shut down that. Shut that down in a more automated way. There's also really interesting, you know, go through all of the contracts that I have and am I actually enforcing all of the terms of those contracts or, hey, actually I was supposed to get a rebate from this vendor and I just never, like nobody ever followed up on it because the person that was leading that relationship left the company and we just forgot about it. I think we're seeing some, like, pretty big outcomes from even just getting in and looking at that contract. Contract structure.
- [16:05]
B
Yeah, the, the, in the report, it says about 17% of the value that the leaders are, are getting is coming from Agentix Systems. But that's expected to grow to 30%, I think.
- [16:21]
A
Yeah. Over the next three years. Yeah.
- [16:24]
B
Yeah. Is. I'm really interested in the value that people are getting from agent systems. And I actually would like to go and sit with a team that's running agents to see what they're doing, how many agents they have running. Because my experience, again, it's just from talking to people and, and playing around with agents myself, is that they're still pretty unreliable.
- [17:01]
A
Yeah.
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B
Yeah. So. So what are your thoughts on. On that?
- [17:06]
A
Yeah, it's a, it's a great question. And I, I will say this is one part of the report that, you know, I always struggle with a little bit too transparently, which is what even is an agent. I think everybody defines that A little bit differently and, and kind of has their. And so, you know, you, I would take those numbers with a grain of salt of you know, what, what is truly new and agentic versus what is something that a company has been could have done two years ago. And it's just defining as agentic because that's the, the nice term du jour. All of that said, as we see agentic outcomes really working, I think a few commonalities that we see in how that works. One very rarely is this a set it and forget it agent just runs more often. It is still kind of a human in the loop in the process making the final decision. But having an agent or a set of agents speed up some of the processes and take some of the toil out of the day to day one. Two, I think we are seeing the best agentic workflow design actually starts from a zero based process design and says what should this look like? Let's work with the human to say what it looks like today. And then how do we actually redesign this to take the time out of it? And on this I think the important piece, you know, there's this, the story that I always tell of using agentic marketing transformation where the company said okay, we redesigned it, we took a 10 day process down to one day and then we found that 10 minus 9 still equals 10. And basically what they found was they took the process down to one day, but the upstream process and the downstream process still took the exact same amount. The example here was they had a content review cycle that happened every 10 days. So even though you shortened the content generation to one day review cycle, still not for nine more days. And so really thinking through that end to end process design I think matters a lot too. And then three, and I think this is something that companies and organizations are starting to get more of the hang of. But you know, early days I think people just said like put an agent on it, it's going to solve everything. And it's actually, you know, agents need context just as much as humans do. They need what am I optimizing for, you know, what's my objective function. They need access to organizational data. And more than data that's easy to find. There's actually a lot of like tacit and implicit knowledge that people within the organization have that you've got to feed agents with. And then they need the guardrails, they need the constraints, they need the, you know, what can you do and can't you do? And if you don't put that in place in A really thoughtful way. I think this is exactly to your point. You can see a lot of failure modes that, that are coming and frankly a lot of disappointment with agents that, you know, companies are seeing if they haven't thought about all of these pieces.
- [20:08]
B
Yeah, where. Where do you see agents being applied? What part of the business? You mentioned marketing.
- [20:15]
A
But yeah, we've seen marketing being a big one. As you think about just as companies have gotten more and more complex on their marketing technology stack, like the orchestration around that is ripe for agents to come in and support and so that you can have marketers be marketers, do what they're good at, be the creatives and be really thoughtful about what they're trying to drive. So that's a big one. I think the customer contact centers has been one that overall generative AI over the last couple of years that's been a ripe function for disruption. But again, another one where you've got kind of multiple systems that maybe need to be coordinated. You're trying to pull data from multiple different places, there's different communications channels. And so having kind of an agent orchestration layer around that can be really impactful. And then I think some interesting things on corporate functions, you know, one that a lot of my clients are getting excited about is, you know, if you're a retailer, everything that you're doing is revolving around the results that happened over the weekend. Right. You wake up Monday morning and it's like, okay, what do we actually go change this week? What do we need to do differently? What is marketing going to do? What are our stores going to do? What is merchandising going to do? And like everybody on a Monday in a retailer is spending all this time getting all the data together so that they can get to. If you can actually replace some of that with an agent that over the weekend is prepping and getting the right reports in front of people, actually then have a back and conversational layer on top of that where you can kind of go back and forth and ask questions. I think there's some real fundamental value that can be created there. And I think it's something that the organizations get really excited about as well.
- [21:59]
B
Yeah. And when you say contact, when you were talking about contact centers, is that customer support or is that sales?
- [22:08]
A
We've actually seen it in both. I think customer support certainly happening across industries. And then I think we're more and more seeing the future of sales with whether it's inside sales, some of the targeting that you're doing, being able to do that More at scale with agents.
- [22:27]
B
Yeah. Who's building these agents? Are most of these companies building themselves? Is BCG building them for them or are they buying off the shelf agents? I mean, increasingly they're these. Just as there are quote unquote model gardens, there are agent marketplaces. You know, people have built these standard agents to do tasks that everyone has to do.
- [22:55]
A
So it's really a hybrid approach. And we actually have some data in the study. I'm making sure I've got the right number here. Yes. So we found that only about 11% of organizations are primarily building themselves. So there are some agents that organizations are building on their own. Typically when there's not a great external solution, it's a bespoke use case that's important for that organization and it's incredibly important for their core competitive advantage that they're trying to build against. But what we're more seeing is actually this hybrid approach to your point, which is some combination of going and getting point solutions, agents that are out there, some combination of working with your big tech partners, hyperscalers and partnering with them on agent build. And then I think the most advanced companies are creating these agentic platforms that are allowing low code, no code, agentic build deeper within the organization. And I think that's something that, that is going to continue to grow as we, as we continue to monitor the space.
- [24:07]
B
Yeah. And the investment that we were talking about, where these leaders are investing twice as much. Is there a breakdown of what they're investing in? Is it people, Is it systems?
- [24:22]
A
Yeah, it's really both. And you know, I think if you look at the overall breakdown of the numbers, it's probably half and half true tech and data cleansing platforms, your licensing costs, all of that and then half. On the people side, within the people side, I think seeing a few different components to that. Some of that's hiring new talent, AI specific, an AI architect, AI engineering, AI centers of excellence I think are cropping up in a lot of, of companies, but it's also investing in upskilling, reskilling training. You know, one of the stats that's in the, in the data in this report is the AI leaders are investing six times as much in training and upskilling as the AI laggards. And that's probably not a big absolute number by the way. I don't think it's millions and millions of dollars that are investing, but it is a meaningful show of putting your money where your mouth is and actually spending the time to upskill and reskill people. There's that and then there's also a big component of this which is the process redesign and the change management around that. And I think we're seeing that companies that invest in the people and the process design are much more likely to get to the impact than those that are just invested on the tech or the platforms.
- [25:52]
B
Yeah. So where do you see you. This is as you said, longitudinal. I mean you, you've been tracking companies for a while in, in this regard. Do you, are the benefits among the early adopters short lived and kind of one off and then it's. Do you, do you have some sense of the long term benefits is, or would they continue to invest and evolve and, and then what, what do laggards, I mean is. It's a gap, right? It's a widening gap.
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A
Widening gap, yeah.
- [26:38]
B
Does that mean that, that there's sort of in the Internet age, the beginning of the Internet age, companies that, that did not go online eventually disappeared because they, they just missed out on, on the new market.
- [26:58]
A
I mean, I do think that's what we're seeing in the, in the data, which is the widening gap. And if you have, you know, whether it is a reduced cost base or increased revenues or a combination of the two, which is typically what we're seeing for the leaders, that gives you an advantage relative to the companies that don't have. And you can, you know, there's many ways strategically that you can think about what you do with that. You know, sometimes it means I'm going to reinvest some of this into additional innovation and AI and continue to get better and increase that gap. It could be that I invest some of that in pricing and so I bring my price down for consumers. I can take share within an industry. But I do think what you're seeing is companies that aren't investing here are going to have higher cost basis, lower revenue and they slowly are, I think going to wither away. I think the other piece to say though is it's not too late. We're only a few years into this ball game and there were a lot of, and still are a lot of investments in AI that aren't paying off. And so there is a version of this which is a lot of companies that I work with like to think of themselves as a fast follower which is basically monitor what's working. So I don't actually have to experiment myself and prove myself what's working or not. I can see what others are doing, whether that's in my competitors or whether it's in corollary industries. And then I take what's working and I adopt it. And so I'm much more focused on what I'm investing in and getting value from. And I think that can be a successful strategy. I think what you can't do is stay still, ignore this, put your head in the sand, forget, you know, pretend it's not happening. Because as we've said, the value gap is widening. You've got more to reinvest all those good things. The other thing I would say is there is an organizational muscle around being able to change and being able to adopt new technologies and adopt new processes. Like building that muscle, I think is incredibly important if you want to continue to survive and thrive in a world that changes faster and faster every day.
- [29:09]
B
Yeah. And, and as part of that, whether it's early adopters or fast followers, if you don't get involved, it remains this kind of scary, unknown, scary, nebulous. Yeah, you have to get involved. Oh, that's, that's what they're talking about. This is, these are the problems. This is, you know, that you begin to understand the ecosystem and how AI can be applied. I mean, I tell people that my personal life, start paying attention because play
- [29:46]
A
with it, try it. Like there's not, there's nothing better than getting your hands dirty to, to have a view. Actually, the other. I'm trying to remember who I heard this from. So much AI stuff happening. But I'm starting to hear also that on a recruiting front, it really matters. Like candidates will ask, what AI tooling are you using? Or how has AI made this process better? And if you're not, if you don't have a good answer to that, you're losing that candidate, which means they're going somewhere else. And that somewhere else is going to get even further ahead because they've got an AI forward person who took the job because they're excited about using AI tools in these spaces. And so I think just everywhere you look, there's arguments for, you got to get started. You can't just sit and wait on the sidelines.
- [30:28]
B
Yeah. Do you. Well, before I get onto to what you're advising companies, the laggards to do, what's the. Where do you see the penetration going? So it's 5% of companies even there AI is, except for, as you say, the AI first companies, it's a small part of their overall operations. And I'm surprised myself at how slow the adoption has been. And I'll just take an example that we were all familiar with is customer service. I Mean, I'm still, you know, pressing one for, you know, this or two or that on my phone or, you know, or, or listening to some infuriating chat bot or typing with, you know, that's not answering my question. And that's been solved. I mean I, I can think of half a dozen dozen companies that provide these, these solutions that take care of that, but they're not being adopted across the, the economy. Where, how do you see that adoption progressing from, from what you see in these longitudinal studies?
- [31:58]
A
Yeah, the biggest change in the longitudinal study from 2024 to 2025 was in that group right below the 5%. And so that group, and I'm just looking at my numbers to make sure that I've got them right. So the group that we call scaling was only 22% of companies in 2024 and it is up to 35% of companies now. And so I think you're starting to see companies getting right on that cusp of moving into true leaders here. Where did that source from? It actually sourced from? You know, in 2024, about 25% of companies were not investing in AI at all. That's down to 14%. And so I think every, you know, not every, I guess there's 14% that are still head in the sand and not moving against this, but that means 85% of companies are doing something in this, in this space. I do think there is this chasm to cross between that 5% that are truly leading and the group that's right below that. And I think it's a total mindset shift in becoming rather than saying where can AI come into pockets and improve what I'm doing today and moving to a, how do I do, how do I organize fundamentally differently, how do I run processes fundamentally differently? What would an AI first come company do in this space? And I think making that mindset shift is, I, I'm in some ways surprised that 5% of companies have been able to do that. You know, that's like a pretty fundamental change in how organizations have run for the last 200 years, you know, since kind of modern business practices started to come into place. And so I think there's, you know, I think it is hard to say exactly when and how more companies will, will get to that spot, but in some ways I think it's moving faster than we could have expected it to.
- [33:54]
B
Yeah, yeah. I mean, even bcg, how do you see it in, in within bcg? Because, you know, I've had a little experience, I've done some work in the past with BCG and they have
- [34:16]
A
a
- [34:17]
B
vendor portal that is just insane getting signed up to it. And yeah, I mean the, you know, why am I typing this information into a web form, you know, in three different times? I should be able to just talk to the. Yeah. So is BCG pretty advanced in its adoption?
- [34:45]
A
We are notwithstanding the vendor portal that you're looking at. And I do think that what you're hitting on is an important point which is where are you spending the time and energy to really transform the way you're working. And for us it is largely going to be around how are we delivering client work. That is what the money maker is. And so saying how do we make that better, more efficient, faster driving to better outcomes. That is I think where we're spending 99% of our attention. And then what we are doing is exactly what you're like, how do we manage the vendor portal? Or how do we make it easier for contracts to be put all in one place? There's a bunch of what I would call long tail of places that are ripe for disruption and could be much more automated. We're just not focusing on. Would be my, probably, probably my view because we're so focused on how do we improve the client delivery, what we're doing.
- [35:47]
B
Yeah. But what occurs to me with that and it's, you know, at the New York Times they went through a series of, of software vendors for expense. All just insane. Particularly I was always overseas and you know, anyway it was, it was not working. But you know, once a company invests in a system then there's a certain reluctance because you've got a sunk cost and you want to amortize it and all that stuff. So, so do you think we'll see over time companies, they realize that they've got a legacy system maybe in customer service that's suboptimal but they want to like, they don't want to invest right now and it's not doing a terrible job. So they'll stick with it for a while. I mean will there be a sudden turnover or sort of an ongoing turnover?
- [36:58]
A
I think it's ongoing and I think it's exactly what you're kind of getting at which to actually link it back to the report for a minute here too. One of the things that we have found is companies that, that focus on fewer higher value activities are actually getting more out of their investments than organizations that are trying to do 20 different things at once. And I say that because if you kind of look at the, you know, look at the world. It's like, okay, there is a better solution for customer care. But that's what like 1% of my cost base, not the most strategic function I have. Like is that really the place that I want to be putting my limited resources and my limited AI capabilities within the organization against? I think that's part of it. The other part of it, and I do think we are seeing this happen over time is, you know, oftentimes you are on. You've like locked into a vendor or a contractor for your customer care and it's like, well, I can't actually get out of this for seven years because that's when the contract and there's early term penalties and is this really again where I want to put the. But when it is coming up, I'm talking to a company that's coming up next year. They're like okay, well what do we do? Who's the best provider? Help me figure out how to do this. How do we actually take this and make it into an advantage for us and get some costs out of customer care? And so I think that is going to be kind of a gradual winter, contracts ending and then companies replacing those, those vendors, you know, over the course of the next 10 years.
- [38:24]
B
Yeah. A question, just an operational question on the survey. Who answers the survey within an organization? Because that's a lot of work.
- [38:34]
A
It is a lot of work and it takes about 45 minutes to do it. Well, we typically had kind of C suite OR C Suite minus 1 or 2 so senior enough in the organization to have you know, understanding of what's going on. We also, we were able to kind of get, you know, if you had somebody in the, in operations who is answering this, we actually focus them more on questions within the operations function and we might go try to supplement with another person within the company that is in the marketing organization. And so you can actually stitch together data across the two perspectives.
- [39:10]
B
I imagine soon there will be an AI system that has all the data can answer it for, for you.
- [39:18]
A
Well, we are so yes, in some sense we are, we are putting more and more AI on top of this. Right. And so being able to pull data from year to year and actually remember some of like sometimes we do interviews on top of this and so being able to sort through and get insight from the interviews. There's, there's definitely AI use cases. We've also experimented with some of the interaction with this data. So for, for us this is obviously it's a report that we want to understand and see what's happening. But we also are using it to go have good conversations with our clients. And so being able to interact back and forth with the data and ask specific questions for your industry or your client is another piece that we've really innovated over the last 18 months.
- [40:00]
B
Yeah. And that was going to be my next line of questioning. How do you advise the laggards?
- [40:08]
A
Yeah. So I think I start with kind of the vision of why this matters and how to get there in terms of what we're seeing the leading companies get and achieve. So you kind of paint the why does this matter? You then, I think, empathize with the why is this hard? Because very few companies at this point that I'm talking to haven't thought about this or haven't tried something. Right. Like, there's. Everybody's doing a little bit of something here, but there's reasons that they're struggling to get to what they want to. They don't have the clean enough data. You know, systems aren't talking to one another. It's hard to get talent. It's really, if you're not a hyperscaler, like, where are you getting this AI talent from? That's real. And so it's empathizing with the challenges. And then it's saying, okay, what are the couple of things that I can do to get started on this journey? And it typically boils down to one, this has to be driven from the C suite. And so having CEO leadership around this and being out there and talking to the organization, critically important to picking the one or two things. And it truly can be one or two rather than trying to do, getting overwhelmed by all the pieces. And there's 100 different things. And I think a lot of organizations have actually done the exercise bottoms up. And they come to me with an Excel spreadsheet of like, here's 500 use cases for AI. And I'm like, okay, throw that away. We need to pick the one or two areas that really matter, that are fundamental to your strategy, that are fundamental. Who do this company is, what do we do with those? And then it's putting a cross, functionally highly talented team against those one or two areas and starting to make progress, trumpeting that progress, talking about it, showing others in the company and then building the momentum from there. But I think you can only get started if you get started and you want to do it in a way that's really targeted.
- [42:12]
B
Yeah. And you know, on the. I mean, there's the building and then there's the Buying and on the buying side to most industries at this point are there, you know, two or three leading AI solutions that are tackling some of the main, the core business problems or because as, as a journalist it's, you know, I get pitched probably 10 times a day by startups with some solution and it's, it's just mind numbing. I don't know which one is overwhelming. Yeah, yeah.
- [42:59]
A
So this is another reason why companies don't. Because it's like I've had 10 different startups email me today and like I'm also, I'm on a legacy solution that says they do AI. Also like how do I think my biggest advice on this is it is better to have a B solution that you implement in an A plus way than to get the A solution that you never actually implement because you're trying. You keep switching and thinking that there's a new A solution out there. And so I think there's almost this paralysis if I have to pick the perfect vendor. And my sense is there's a lot of vendors for most of these problems that are pretty good. And this is all about adoption process change, like the human side of this and so get the good enough vendor, buy it and then actually go drive the change through the organization.
- [43:48]
B
Yeah. And BCG is an expensive consultant or consultancy. So small and medium businesses may not be able to afford that, that level of advice. I can see that there's an industry developing. I mean I imagine it already existed, but it's becoming AI focused of advising small, medium and enterprises on how to adopt AI. Does BCG get involved in that level or do you support consultants that are at that level who are going into a, you know, not necessarily mom and pop, but a small business and saying, well look, this is, these are the tools you can adopt. They're obviously not going to build their own solutions. But yeah, is that how do you see that end of the consultancy?
- [44:58]
A
Yeah, I see it probably pretty similarly to kind of the size of organizations that have always been able to, to work with BCG and it and it really comes down to what's the ROI that we can deliver and we hold ourselves to a 10x ROI in any work that we do, which means that a company needs to be sizable enough for that to, to make sense. I do think there's some really interesting plays as we, as we think about, you know, what does BCG look like five or ten years from now? Like can you actually start to using AI? Can you have different versions of the offering that we bring and One that is more tool based, more AI based. They actually could serve smaller customers while we bring kind of a more bespoke offering to the large customers or the large clients that need more process redesign or need more change management or need more of that kind of special sauce that BCG can bring. So I think it's a really interesting and topical question as we think through our strategy going forward as well.
- [46:01]
B
Yeah, yeah. And I know that I don't mean to put you on the spot, it's not your job, but there's a lot of talk about how much of business consulting is formulaic and can't that be encoded?
- [46:18]
A
Yeah. And so what do you know? What actually is our competitive advantage and what do we bring? I think it's a really good question.
- [46:26]
B
Or does BCG develop an AI consultant that people could use at the lower
- [46:36]
A
end of the lower end, bring a revenue stream there and then actually do you look at the high end of the business and say there's even more work that's needed because of AI and how fundamentally businesses are changing, which is something that BCG is uniquely good at and is driving that change and getting to value at the largest companies in the world?
- [46:56]
B
Yeah. This again is not really the focus of the conversation, but I speak to a lot of young people and they're always asking me, well, what should I study, what should I learn? And I'm not talking about computer science PhD candidates, but there's so many tools out there and I say, you know, become an expert in these tools because companies are going to be looking for people that, just as they, they did for people that knew how to use Salesforce are going to be looking for people that know how to use these, that understand the tools. Every one of these tools requires some, there's some learning curve. Do you think about that at all?
- [47:49]
A
All the time. You know, implications for, for talent and, and how people should be leaned in. We think about it both for, you know, our own recruiting. I think about it for clients and how they're recruiting talent. I mean, I love the, know these tools come in and I actually think, you know, any, any high school student, any college student you talk to like they're native in these tools. They're doing their schoolwork with the. And so, you know, they're going to come in and they're going to be disruptors in a positive sense into organizations and companies that they come into. If we look even at BCG like our straight from undergrad associates, they're doing all sorts of things that I would never Think of. And then I come in, I'm like, okay, teach me or show me how you did that or how did you think about this? So I think that that's a force for good in organizations.
- [48:38]
B
Yeah. And this study will continue. You do it every autumn, is that right?
- [48:45]
A
We do it every autumn, yeah. So we're just gearing up and starting to put together our kind of hypotheses. And what do we want to test for next year's study? And then they'll typically will run in the spring, summer in terms of getting all the data gathered. And then we do run the analytics over the summer for release in the fall.
- [49:02]
B
Yeah. And the companies that you're serving, it's the same set every or pretty much the same set.
- [49:09]
A
It's typically, it is probably 80% the same set. And then, you know, new companies come into the mix that you want to make sure that you're, you know, I'm not sure that, you know, like, we would have been covering Nvidia eight years ago, but that's certainly a company that you, that you want to be thinking about now.
- [49:23]
B
Yeah. And that's fascinating because you're, you're accumulating a tremendous amount of data for your own AI systems.
- [49:33]
A
It's an amazing amount of data, and it's truly a treasure trove when you get in there and can interrogate it. And this is where I mentioned kind of putting the layer on top of it. It's exciting to actually be able to kind of go back and forth and say, what do we see in here? What are companies of this type, this size in this region investing? And how has that changed over time? It's like all those questions that don't make it into the report. But it could be very interesting as you're, you're diving into specific client work.
- [50:00]
B
Yeah. And. And you guys use AI in your analysis of the. I mean, I imagine you do. Of the, of the report of the survey results.
- [50:10]
A
We do, yeah. And so some of it is, you know, certainly we're using kind of predictive AI and kind of machine learning to actually get into some of the data and regress against what really matters. For sure, we've added some of that conversational layer on top, kind of the generative layer to dig into the data. And then one piece we're starting to think about is what's the agentic kind of piece that you put on top? And so, for example, we have this for 50 different sectors. And you could have. What we've done historically is have a human Actually build out the report and build out the data for 50 different sectors. There's certainly a way to do that better. And so we're starting to experiment with kind of the agentic models to improve that as well. Yeah.
- [50:56]
B
Okay. I'm running out of questions. Is there anything that, that you, you think I'm missing that's that you've learned from all of this?
- [51:05]
A
I do think one of the, one of the pieces that we've really seen is the approach here and kind of taking the human element to this and saying look like people are scared and often rightfully so of how is this going to change my job? Am I going to still have a job? I think those are totally fair questions to be asking. I think at least what we have seen so far is that by and large this is actually able to create more joy in someone's day and in someone's job. There's places where AI can reduce the toil and the time it takes on the Monday morning to go pick all the different data pieces and put them together and actually allow you to spend time on the creative parts and the analytical parts and the parts of the job that are uniquely insightful from the human's perspective. And so that's where I get excited and actually still stay pretty bullish on this is the ability to make our jobs even more rewarding.
- [52:06]
B
Yeah. And you were talking earlier about having a cross functional team to decide where to apply AI and what, what kind of AI to apply. But then there's a, then there's a need to train the employees on how to use the AI. Is. Yeah. Is, is that something that you see the leaders investing a lot of money into is, is training and I hate the term upskilling, but getting people familiar and comfortable with using these systems.
- [52:47]
A
Absolutely, yes. I think if you look at the. Deploy a tool and hope people use it, that's never in the history of organizations worked even before you talk AI. And so actually spending the time in training and we've actually seen there's some data within the report that says it's in person training. It's training that's followed up by, you know, the right in moment interventions to suggest something to somebody on how they put this into their day to day and it typically is like a couple of days. It's not. Which depends on how you look at it. A couple days could be a big investment. If everybody in the company is out of service for a couple of days, that's a big investment. The other side of it, it's two days to make somebody, you know, 50% more efficient seems like a pretty good investment of time. And so I do think we're seeing the leaders really put time into that, that training and that away from the job. Time to get better and to adopt a new process.
- [53:46]
B
Yeah. Okay. Well, this has been fascinating for me. I've. I've learned a lot. You're not in a position to. To mention specific systems or vendors, are you?
- [54:01]
A
No, unfortunately, no.
- [54:03]
B
Because I'm really curious about that.
- [54:05]
A
I would love to, but, yeah.