16 Oct 2026 · 47m · Sean Brown, Kameron Kordestani, Alex Liu, Benjamin Houssard · 22 quoted facts
The episode argues that M&A is entering a new phase in which AI is both a reason to do deals and a tool for doing them better. Activity is rebounding, driven by corporate buyers and a surge of very large deals. The McKinsey speakers say the winners will use AI to find targets, speed diligence, and rewire the combined company, not just to buy scale.
The main tension is between hype and hard results. Many deals mention AI mainly to excite investors, and companies are still experimenting rather than showing firm financial gains. Meanwhile, the market is split: mega deals and corporates lead while private equity waits. The speakers say the gap is widening between companies that adopt AI in their deal process and those that hold back.
Tobias Lundberg says deals above $10 billion reached about $1.1 trillion in the first half, up roughly 140% year on year. They make up 42% of global deal value even as the overall number of deals fell in the second half.
Premiums stayed near the historical average while multiples rose back to about 11.6 times. Lundberg says this is because fast-growing sectors with high valuations make up more of the deals, not because buyers are bidding more aggressively.
Benjamin Houssard says the team is the key asset in AI deals. Traditional three-year earnouts may not keep AI experts, who value autonomy and access to computing power, and slow integration can destroy what was bought.
Houssard says diligence once sampled only a fraction of data room documents because of a three-to-four-week window. AI can review everything in hours, so teams find more risks rather than simply finishing sooner.
Alex Liu says AI value from a deal depends on leaders aiming for transformation, the right AI and data talent, and a clean data foundation. He calls the use cases and plan the easier part.
Kameron Kordestani says corporate buyers are acting with a long-term mindset, and activity is strong in energy, minerals and technology. Deal value is almost back to the 2021 peak, though private equity is still holding back.
The speakers define a mega deal as one worth $10 billion or more. Tobias Lundberg says they hit $1.1 trillion in the first half of the year, a record, and now account for 42% of global deal value.
Not according to Lundberg. Average premiums stayed near the historical norm, and the rise in valuation multiples comes mainly from more deals in fast-growing, highly valued sectors.
Benjamin Houssard says AI enables always-on target searching instead of quarterly scans. It also reviews every document in diligence rather than a sample, and it uses AI agents to cut integration tasks from weeks to days.
Often not. Houssard says about half of deals mention AI, but many do so to create excitement for investors and customers, and the share of pure AI plays is much smaller.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
McKinsey defines mega deals as those above $10 billion, which reached about $1.1 trillion in value in the first half of the year, according to Tobias Lundberg.
“deals above $10 billion is what we have defined as mega deals. And they have reached some $1.1 1 trillion in the first half of this year.”
Tobias Lundberg says mega deal value in the first half was up almost 140% from a year earlier and was the strongest half year on record.
“that's up almost 140% versus the same period a year ago, and it's the strongest half year that we have we've seen on record.”
Tobias Lundberg says mega deals account for 42% of global M&A deal value.
“So 42% of the global deal value sits now in these mega deal mega deals here.”
Tobias Lundberg says eleven of the recent mega deals were from the EMEA region, many of them in-market consolidations in banking and telecom.
“out of the thirty seven deals we see now for this prior cycle here, eleven now are from the MEA region”
Tobias Lundberg says EMEA has recently seen record capital inflows of about $37 billion, after being a net exporter of capital earlier in the year.
“we're now seeing record record inflows coming in of some 37 billion or so.”
Tobias Lundberg says removing the two largest Americas-to-EMEA deals, worth about $60 billion combined, puts EMEA back in a net outflow position.
“You reverse those two out, and EMEA is back into net outflow position.”
Tobias Lundberg says equity-linked consideration has risen to 22% of deals, which shares re-rating risk with sellers.
“Where we see, you know, equity linked considerations rise up to a whooping 22% of the um of the deals.”
Tobias Lundberg says private equity accounts for more than 30% of deals in Europe.
“I think private equity pools stands above 30% of all deals in Europe specifically, so it's a very active part.”
Tobias Lundberg says private equity holding periods have increased by about two and a half years over the last decade.
“Well, within holding periods, I think are up about two and a half years over the last decade or so.”
Benjamin Houssard says seven of the ten largest deals since the start of the year are related to AI, directly or indirectly.
“So if we just look at the top ten deals of, uh, the since, uh, the start of the year, and actually you see that, uh, seven out of ten are in some ways, uh, let's say, related, uh, to, uh, AI”
Benjamin Houssard says utility consolidation deals are partly driven by data centers' need for power.
“Uh. The rationale of these deals include the fact that data centers need power.”
Benjamin Houssard says OpenAI's acquisitions typically target small teams of about ten people that have solved a specific problem, rather than buying revenue.
“When OpenAI, I would say, does an acquisition, uh, this is not to buy revenues, uh, this is typically to buy, uh, uh, ten person teams that have already solved a very specific, uh, problem”
Benjamin Houssard says Schneider Electric made many AI-related acquisitions to build capabilities in manufacturing and supply chain.
“What Schneider Electric did over the last years is, uh, to do a large number of, uh, uh, AI related acquisitions to build capabilities around, uh, AI in manufacturing, AI in supply chain”
Benjamin Houssard describes Amazon hiring the founding team of Adept AI and signing a license agreement with the startup within days, as an example of the acquihire model.
“They hire the founding team of Adapt AI on, uh, the Monday, on the Tuesday, uh, they sign a a licenses agreement back, uh, to, uh, the startup.”
Benjamin Houssard cites Synopsys' acquisition of a company he names as 'NCs' as an example of an expensive tech-adjacent AI deal.
“We saw that, uh, Synopsys, uh, uh, um, spent, uh, 35 million to buy, uh, to acquire NCs”
Benjamin Houssard cites Capgemini's acquisition of a company he names as Cognite as a European example of an IT services firm moving into AI.
“We had some deals as well in Europe, like, uh, uh, Capgemini, uh, acquiring, uh, Cognite”
Benjamin Houssard says that in some cases, rewiring companies with AI during integration doubled the synergies achieved.
“We have seen some situation where we could double the amount of synergy by re what we call rewiring the companies during the integration process.”
Benjamin Houssard says traditional diligence of three to four weeks made it hard to review thousands of documents, so teams only sampled the data room.
“In a diligence of three, four weeks, it was difficult to look at thousands of, uh, documents.”
Alex Liu says companies are seeing productivity gains of upwards of 45 and reductions of around 70 in the software development life cycle using AI.
“we're seeing companies take, you know, upwards of forty five productivity gains and seventy reduction in kind of the end to end, uh, life cycle, right?”
Alex Liu says finance teams are using AI to automate invoice processing, closing the books and financial forecasts.
“We're seeing them really automate with AI invoice processing, you know, closing the books, even, you know, financial forecasts”
Benjamin Houssard says a McKinsey survey found awareness of AI in upstream M&A rose to about 88 from 51 the previous year, though adoption is lower.
“You see the level of awareness for upstream now up to eighty eight, uh, fifty one point from last year, even if the level of adoption is, uh, lower.”
Tobias Lundberg says European regulators have shown somewhat more leniency toward in-market consolidation, such as in telecom and banking.
“Another one has been a slight shift towards some more leniency for in market consolidation as well on the European side and the EU side more specifically.”
Kordestani outlines the tailwinds and headwinds, including uncertainty, rates and record private equity dry powder. Deal value is nearly back to 2021 levels.
“levels almost at par with, at least in M deal value, with where we were in that bumper year of 2021.”
Corporates dominate activity while sponsor-led deals have fallen. Lundberg explains the tension between large dry powder and longer holding periods.
“we have, you know, a massive amount of dry powder that's looking to get deployed in the right way.”
EMEA appears to have become a net capital importer, but Lundberg says two very large deals explain the shift.
“if we strip out the two largest deals that have gone from the Americas into EMEA”
Deals above $10 billion hit record levels, while premiums stayed flat and rising multiples reflect sector mix.
“Thirty six of these deals happened in the last six months.”
Houssard describes how core AI players, tech-adjacent firms, ordinary corporates and acquihire buyers each approach AI deals.
“we actually see, uh, four archetypes of M and A, uh, for AI.”
Houssard lists five changes, including widening the target universe, rethinking talent retention, prioritizing speed and using new deal structures.
“The AI capability that you need is probably not in your industry.”
Liu describes where AI delivers value today and the leadership, talent and data foundations needed to turn a transaction into transformation.
“we like to say the merger or an integration is one of the most powerful change agents”
Houssard walks through always-on sourcing, complete diligence and agent-driven integration, plus survey data on awareness and adoption.
“you can look at everything in a question of hours”
For McKinsey and Company, I'm Sean Brown, and welcome to Inside the Strategy Room. Today we have an exciting two-part episode. First, we'll discuss the latest M and A macro trends as we enter the fourth quarter, including the emergence of the mega deal, which we define at $10 billion or more. We'll also explore how AI is informing M and A as a driver of activity and value creation, what that means for deal teams, and how companies might take the transaction as an opportunity for transformation.
We're joined today by Benjamin Houssard, a partner in our Paris office and co-leader of our global M and AAI initiative. Kameron Kordestani, a senior partner in our New York office and leader of our North America merger management practice. Tobias Lundberg, our managing partner in Sweden, who also co-leads our M and A practice across Europe, and Alex Liu, a distinguished partner in our Minneapolis office and a leader in our M and A practice. Welcome everyone. Cam was hoping maybe you could set the stage for us first in terms of M and A activity.
What are some of the tailwinds driving deals forward as we enter the fourth quarter? And what are some of the headwinds causing continued market hesitation?
Thanks, Sean. You know, we're seeing a sustained amount of activity across different geographies, different types of activities, and different types of M and A activity as well. Many corporates in North America, for instance, where I'm based, uh, are looking at opportunities, and they're taking the through cycle mindset as they think about whether this is the right time to invest or whether this is frankly the right time to divest. We're also seeing in several sectors, notably in, uh, energy and minerals, uh, a sustained burst of activity.
You also see this in the technology space, where a lot of, uh, potential corporates are looking at. How do they tap into and jumpstart their AI capabilities? Of course, if if you think about some of the the headwinds that we see in the marketplace, uh, and this goes for around the world. This has been a year of uncertainty. Uh, there's geopolitical uncertainty, of course. Uh. Organizations are looking to strengthen and solidify their supply chains, and so they're thinking differently in terms of how they activate their M and A to achieve that.
Got it, thanks. And what are some of the macroeconomic trends that are driving the market right now? And and do they differ by geography or sector?
As we look across the macro space, there are a number of trends that are impacting M and A around the world. Doesn't matter sector, doesn't matter geography. We see consumer sentiment, particularly under pressure. Uh, in many, uh, major, uh, countries. Around many countries around the world, there are significant elections that are underway that are putting some of those, uh, consumer sentiment trends at the fore. We see upward pressure on interest rates, which is the efforts of the of the central banks to tackle inflation.
Uh, and we see a a lot of interest around what will be the arenas of competition into the future. Of course, AI is expected to change how we operate, how comp companies operate around the world. Um, and as I said, geopolitics is not just it's not just a governmental issue anymore, it's an issue in the boardroom as companies think about how to, uh, solidify and strengthen their supply chains. Finally, I'd be remiss not to mention where we see private capital, but we see that there are historic levels of dry powder and opportunity as we think about where this global private equity capital might be deployed, uh, in the near future.
And so a as I as I mentioned, this year has been an ups. We've seen an upswing, and what you're seeing is global activity as we think about the number of deals and the deal value, but an upswing in the deal value coming out of the pandemic. Of course, if we go back now, what what seems only yesterday, but in fact now is five years ago, we saw a spike, uh, a big, big burst of activity, and then following that, uh, into twenty two and twenty three, a relative dip.
But what we're seeing is levels almost at par with, at least in M deal value, with where we were in that bumper year of 2021.
That's interesting, Kim, thanks. And and how's the balance between corporate M and A and sponsor led deal activity evolving?
You can see that the vast majority of activity still continues to be dominated by the global corporate M and A activity. And this is definitely what we see across different sectors, and we see that sponsor led deals are have fallen. And I think this then further reflects the the notion that private equity is still waiting on the sidelines, thinking about where and how to engage and where and how to activate.
So are you seeing any hints that this activity might be picking up going forward?
Well, I'll tell you anecdotally, and I'll, I'll maybe invite Tobias to offer his thoughts as well. But, um, anecdotally, from where where I sit, you know, I start the week, I start some weeks opt quite optimistic. Um, and then, uh, busy weeks are tended to be followed by, uh, one or two weeks of of slow slowdown. And and uh, I'm not sure yet we're at a point where we're going to see that tipping point, honestly, Sean.
I I look to, um, diligence activity and, uh, kind of the upstream, and that still is muted compared to where we've seen it in the past. But Tobias, I'd, um, invite you to offer thoughts as well on this.
Yeah, Kim, I I I think it's, um, it is fair to say that on the private equity side, it's a bit of two stories or two halves in parallel. Indeed, we have, you know, a massive amount of dry powder that's looking to get deployed in the right way. At the same time, we've also looked at vintages and holding periods of these investment firms in terms of what they're holding today in the stock and their inability to fully, you know, sell off those assets.
Well, within holding periods, I think are up about two and a half years over the last decade or so. So it's a bit of a contrast where they need to weigh how quickly they can deploy versus also offloading, offloading it all. But I, but I think it's fair to say we're also seeing private equity becoming more creative, looking at new ways to deploy capital, perhaps buying away to, uh, from some of the trends they saw before, be that around sustainability or some elements of software.
But also looking at more complicated deals, whether it's corporate carve-outs or taking minority stakes, um, a as well as also looking at public to private type of transactions. So I would say they're definitely players, and we, you know, look at them in the different regions. There's still high activity, but there's also a fair amount of uncertainty that that trying to battle through.
Thanks, Tobias. And and Cam, let's talk a little bit more about the broader activity, maybe from a global cross-border perspective. What are some of the big shifts you're seeing?
Great for many organizations, especially global organizations, are looking to gain a foothold in markets around the world. Um, this is an important dimension. One of the frequent questions we get when clients are looking for outside support is I'm gonna make a different type of acquisition. And many times, a different type is driven by a geography. And so you're seeing a significant amount of, uh, US. activity. I think, as I said earlier, some of this is driven by, um, thinking about how to solidify and fortify supply chains and opportunities around that.
Uh, you see inflows into, uh, the European and Middle Eastern space, and you see outflows from Asia buying buying out from an from a net perspective. So I think, Tobias, maybe handing back over to you from the European perspective as we think about what you're seeing on the ground and and this experience, uh, from a cross geography.
Yeah, thanks, Cam. Let me pick up briefly on the on the EMEA side of side of this equation here. So if we wind back to our annual report back in February, we were showing that EMEA was a net exporter of capital. And we look now, of course, the tables have turned on this one, and we're now seeing record record inflows coming in of some 37 billion or so. And you know, the fact that that money is coming then, you know, both from the Americas and particular in particular, and to some extent from Asia.
Um, you could be, um, tricked into saying that this we're seeing an an overarching shift in terms of the money flows. The reality is that EMEA is not getting structurally larger as such. I mean, I think EMEA's share of global activity on the M and A side is steady at around twenty four twenty five, which is bang on the middle of the historical averages. So the direction of capital has not really changed, nor has the size of the region.
So, but I think what has changed is that we have seen a couple of tail end, extremely large transactions. So if we strip out the two largest deals that have gone from the Americas into EMEA, that's about a 60 billion, uh, dollar deal value between the two of them. You reverse those two out, and EMEA is back into net outflow position. So there are a couple of just tail tail end transactions that are driving this here.
And so just on those big or mega deals, if we could talk a little bit more about what kind of trends you're seeing there. And how big does a deal really need to be to be defined as a mega deal, at least in your analysis?
Cam, you alluded to it already, but we have seen a consistent trend now over some time of of large deals and mega deals becoming lar larger and larger. So, um, deals above $10 billion is what we have defined as mega deals. And they have reached some $1.1 1 trillion in the first half of this year. You know, that's up almost 140% versus the same period a year ago, and it's the strongest half year that we have we've seen on record.
Thirty six of these deals happened in the last six months. Uh, and you compare, uh, if we look at the full year of 2025, that would have been sixty for the full year. So we're already way ahead of that. And also we see a, uh, you know, a massive concentration on these deals. So 42% of the global deal value sits now in these mega deal mega deals here. Um, now you look at all that, and and on combination, you could say that deal counts are are down.
So whilst we have all volume being up in terms of deal volume, uh, historically, uh, we have now seen them down in the second half of this year. Uh, and also with average deal sizes on the other side are coming up. So there's a bit of a barbell again of these very large, pulling it up. And I think what's been interesting is also that we've seen Europe come to the table. So out of the thirty seven deals we see now for this prior cycle here, eleven now are from the MEA region, uh, which is significant step up from what we saw in the past.
Many of them are actually in market consolidations in areas such as banking or in telecom and so, uh, and and such, which, you know, arguably slower growing, perhaps, you know, slightly lower value type of sectors. So we're really seeing a bit of the the rise of also in market consolidation, as well as of these cross-border mega deals that are really coming to play right now.
And so now let's dig a little bit deeper into the industries that you're seeing these mega deals, uh, concentrate and evolve in. We we always talk about the big tech deals, and you just mentioned banking as well. Are there any other industries, maybe that are counterintuitive, or that one might not have expected to see the sort of size and number of deals happening?
It it is truly a combination. We see them for sure on the technology side. Uh, we also see in areas such as logistics, frankly, consumer and retail, uh, and then, frankly, as I said, more, if I can call it slightly more traditional utility like sectors who are trying to consolidate. So I think it's very different in terms of where you're accessing a foothold of technology, uh, or you're land grabbing in terms of access to mark market capabilities versus trying to consolidate an in market.
So there are several trends at once at play here, um, that that is driving the this trend right now.
Thank you. And related to that, what are you seeing in terms of the pricing in these mega deals? Are buyers bidding up premiums?
Uh, no, uh, would be our short answer. So when we when we do longitudinal analysis to look at sort of average deal premiums and where they are, those have remained, you know, remarkably, uh, consistent, you know, call it around 2026 percent or so is bang on historical averages. And that's what we've also seen for the first half of this year. If we look on the deal multiple side, though, those have come up over time, you know, whereas we were looked at in the peak year of twenty one, as Cam alluded to, we were at 11.6 or so.
We're back at those territory after a couple of years where we were effectively two turns down on those multiples. Um, and you read these two facts together, and it says something quite specific. You know, buyers are not paying, you know, more for the assets, and they're not bidding more aggressively over the understood prices. So the re rating is really a market re-rating, driven by the mix effect, uh, of the sectors that are being exposed to this M and A.
On the one hand, you take fast-growing industries, and we'll talk more about those arenas in a second, we'll talk more about those arenas in a second. You know, they will have multi, you know, valuation multiples in the high twenties, as opposed to those in the more established industries, which are far, far, far lower. Um, and then I think the other thing, which, you know, is worth considering as well, is also how people are paying for these type of deals.
Where we see, you know, equity linked considerations rise up to a whooping 22% of the um of the deals. And that is, um, of course, a reflection of the fact that when you're paying the 11.5 times you're paying today, and you pay it in stock, you know, you're sharing some of that re rating s risk. Also with the seller side, not carr carrying it alone. So I think that's a little bit of behavior we are seeing right now.
But, you know, remarkable consistency, frankly, in terms of the discipline that that corporate buyers are are are showing in the market overall.
Yeah, and Tobias, what's your perspective on where you see the future growth coming from?
So when we look at where are the future growth arenas, and, uh, you know, there's, uh, many of these you can imagine. Whether that be on the AI software side, the semiconductor side, some of the consumers, consumer consumer electronics and e-commerce elements, or other frontier technologies. We've seen a pretty staggering shift where, you know, AI software services and semiconductors are really seeing the up uptick in terms of where they're seeing d deal activity happening. And then, frankly, some of the other ones slightly more cyclically exposed, be that consumer internet, advertisement, video games, all frontier technologies have all come down, have done come come down sharply as we see money rotating, you know, within the technology to some of these newer
growth bets. Um, so we're definitely seeing, you know, different acquisition types now, both from the hyperscalers, um, as well as then sort of the large corporates.
Got it? And since we're talking now about fast growth in software and services, as we look beyond the tech sector, um, how are you seeing the more traditional industries being disrupted or affected by this wave of AI-driven deals? Are they actually driving AI software and services acquisitions as they look to buy perhaps enhanced AI capabilities?
It's very mixed. And I think from what we're seeing from the data and from our client service day to day, is that corporates across all sectors are looking to to acquire some of the capabilities. It may become, in s, in some cases, a bit of a side effect that they're looking to buy a new set of capabilities or so on. And especially if there's a slightly more of a of a of a fresh vintage of a company coming in, they will actually acquire the significant technologies, know-how that they are using to to reinvent their core business, frankly, uh, from an integration and value creation perspective.
But I would say that we're definitely seeing it across, um, all, all of the all of the elements here. But of that is clearly one that that is is highly skewed to the hyperscalers.
And Tobias, earlier, you commented on some of the trends in Europe and EMEA, where you're based. Are you seeing any more openness to an increase in deal activity there, perhaps even from a regulatory perspective?
So I I would mention a couple of things. Um, so, first of all, I think there's a realism. If we now stay on on Europe, which was your question, Sean, a a a realization. If you think about European versus global competitiveness and how the European firms compete in that in that stage and arena there versus both American and also Chinese competition, that they need to up their game in terms of driving productivity and driving benefits. I think that is one clearly one compounding factor that is rare, that is kind of opening up for them.
Another one has been a slight shift towards some more leniency for in market consolidation as well on the European side and the EU side more specifically. And I referred earlier to telecom and banking as two examples where we're now seeing in market deals coming through. So that would be a second one. But then, thirdly, frankly, is in Europe in particular, you have the combination of, uh, you know, an extremely rich and deep private equity pool. I think private equity pools stands above 30% of all deals in Europe specifically, so it's a very active part.
And as I said before, sitting on on massive sets of inventory, combined then with interest rates coming down, and these companies also looking to sell. I mean, I think it's a it's a relatively benign environment also for corporates to do deals, both on the acquisition side, but frankly also on the divestment side, where we've seen number of spins and so on increase over the last couple of years.
Tobias and Cam, thank you. Really appreciate this update on overall M and A activity and the trends you're seeing. And now let's turn our attention to AI and M and A. Benjamin, maybe you could provide us with a quick overview on how AI is informing or changing M and A deal making, and maybe how it's also affecting the M and A process as we traditionally know it.
Very good, thank you, Sean. And actually, when we, uh, want to investigate, uh, how AI is, uh, reshaping M and A, uh, we typically cover that through, I would say, three uh dimensions. Uh, there is the first effect, which is actually AI being a driver of M and A deal activity in the continuity of what we have, uh, just, uh, discussed. Not only for AI companies, but I think broadly as well, uh, in in the ecosystem, and even for other corporate, uh companies.
The second driver is M and A and AI as a catalyst of value creation. Uh, as companies are working now to extract, uh, the benefit of AI, gen AI, uh, agentic AI integration, uh, can be actually used as a vehicle to accelerate this process for, let's say, selective integration, uh, that are, um, uh, transformative. And finally, and this enabled to, uh, obviously, accelerate and increase, uh, the level of synergies by at least fifty. We have seen some situation where we could double the amount of synergy by re what we call rewiring the companies during the integration process.
And the third dimension is how we can use AI as an MMA process accelerator, so and the MMA process it fails itself, uh, in terms of how we can compress, I would say, uh, the timeline, and how we can use, uh, more insights to improve, uh, the MMA from the target scan to, uh, the integration.
Thank you, Benjamin. And how exactly is AI driving deal activity? Is it concentrating in larger deals, or is it particular sectors or geographies?
So if we just look at the top ten deals of, uh, the since, uh, the start of the year, and actually you see that, uh, seven out of ten are in some ways, uh, let's say, related, uh, to, uh, AI, either directly or indirectly. A few messages first, actually, AI is eating the top of this list. The second message is around, I would say, energy, and we actually see a lot of, uh, uh, consolidation in, uh, the utility sector that are driven by, uh, I would say, AI, huh?
Uh. The rationale of these deals include the fact that data centers need power. Uh, this is driving, uh, activity in, uh, the energy space. So for the top ten deals, we see that seven are in some ways linked to AI. When we look at, uh, let's say, the broader M and A deal activity, we could observe that in about half, uh, of the deals, uh, we hear about AI, either in the in the in the in the rational, or at least in, uh, the in the narrative.
Now, if we zoom out to the broader, uh, ecosystem, uh, we actually see, uh, four archetypes of M and A, uh, for AI. The first one is, let's say, the obvious one, that's the core AI players doing, uh, M and A. When OpenAI, I would say, does an acquisition, uh, this is not to buy revenues, uh, this is typically to buy, uh, uh, ten person teams that have already solved a very specific, uh, problem, uh, being.
I don't know. Multiplayer collaboration or three D world building, and then they will plug it in a question of weeks. The second archetype is tech adjacent transformers. These are typically the IT services or software players. They want to reposition the portfolio and product, uh, to serve, uh, with, uh, I would say, in the AI AR, these are expensive deals, huh? We saw that, uh, Synopsys, uh, uh, um, spent, uh, 35 million to buy, uh, to acquire NCs, uh, uh.
We had some deals as well in Europe, like, uh, uh, Capgemini, uh, acquiring, uh, Cognite, so that's, uh, the the second archetype of software IT services company moving to AI. The third archetype, and this is, uh, probably the one that is most relevant, is, let's say, the corporate or typical, uh, companies that, uh, are doing AI, uh, related acquisition to build, uh, AI, uh, capabilities, and typically, uh, they acquire in a in a very fast, uh, fast mode.
Now we are seeing as well a fourth, uh, archetype, uh, that we call AI talent acquihire, uh, which is not the typical, I would say, acquisition as we are used to. But we are seeing multiple players like Amazon, Meta or Google that are actually directly hiring, uh, some, uh, founders to get access to AI talent. That's the example of, uh, Amazon, when, uh, actually they, they, they hire the founding team of Adapt AI on, uh, the Monday, on the Tuesday, uh, they sign a a licenses agreement back, uh, to, uh, the startup.
And on the Wednesday, the startup can give back, I would say, uh, uh, money to the investors. It's a way to get access to the talent much quicker. Obviously, that's a quite creative way of doing M and A, uh, but that's a way to cut the time, uh, to, uh, closing, uh, that's so that's a new, a new, a new type of archetype that we are seeing, uh, that is driving activity in, uh, in, in the AI space.
Interesting. So, as you think about a non-tech, say, industrial or healthcare company, um, is one of these archetypes the most common? And is it the tactical capability acquirer, or which one is it that you're seeing? The non-tech companies typically going for first?
That's definitely the, uh, number three. That's where we see, actually, if we were going beyond the top ten, we would have seen a lot of pharma company acquiring, I would say, a biotech or specific AI, uh, capabilities. Uh, you have example as well in the industrial space. What Schneider Electric did over the last years is, uh, to do a large number of, uh, uh, AI related acquisitions to build capabilities around, uh, AI in manufacturing, AI in supply chain, and and so on.
Uh, so this is one implication that actually we see, uh, for, uh, corporate, uh, M and A.
That raises another question on that archetype. Are you also seeing typically a positive valuation impact when these deals are announced or when they actually go through? In other words, are you is this deal making activity value accretive to the acquirer?
Yes, uh, when I was mentioning that for half of the deal, AI was mentioned somewhere, uh, in the press, the communication. We feel that for a bulk of these deals, this is more, let's say, to get excitement around AI and to give the feeling to the investor, to the customer, than actually the company is investing on the right thing. If you really look at, let's say, the pure AI plays, this would be much less than this, uh, uh, than this, uh, fifty.
So we do see indeed an inflation as well of, uh, of, uh, uh, the the to to to use the the the word AI as a, as a, as a rational, but beyond, beyond then these, uh, four archetypes. But there are multiple examples of, uh, of of a company that are actually doing that, uh, to accelerate, uh, their, I would say, uh, yeah, bid of the AI, uh, capabilities. Still, hm,
And with these four archetypes and the new kinds of AI plays that you're seeing, how is this impacting deal making? Then, whether that's the target scan or new approaches to the deal structure, what are what are some of the big strategic implications for M and A and corporate development teams?
we see five of them. The first one is, uh, obviously, to, uh, redefine, uh, the target, uh, universe, huh? So stop looking at your, uh, usual suspects. The AI capability that you need is probably not in your industry. Uh, it might be a thirty person startup that you have never heard about, uh, in a sector, uh, that you have never screened. So you need to move from who is or market as an M team to who has the specific algorithm that we need.
And obviously, for an M and A team, this means new muscles, new evaluation criteria, new networks, uh, new speed of assessment. Uh, a second implication that we see is around, I would say, talent, especially, uh, over the last months, where, uh, we have seen, uh, so many, uh, I would say, uh, deals and and interest on on AI. In AI deals, the most valuable asset is actually the team, uh, and the type of talent, uh, let's say, are not responsive to, uh, let's say, the same retention levers, uh, than, uh, before.
So, uh, the traditional three year, uh, financial earnout might not work, uh, I would say, anymore. Uh, they typically value, uh, autonomy. They want to have access to, uh, compute, and if an AI expert doesn't have access to the right GPUs, he might, uh, he might go. So, so talent retention speed. Obviously, you don't want a long process, uh, that will make obsolete what you are buying. So, as opposed to traditional M and A, I would say optimizing for sort ofness.
We see now a a strong focus on on speed. You need to be careful in terms of how you integrate, uh, back to as I was mentioning, you don't want to kill what you are buying with your corporate processes, timelines, governance. So you typically need to be more careful in terms of how far and and how fast, uh, you integrate. And finally, you might think about okay, new types of of of structure. Again, not fully, not necessarily buying, uh, the full company, but thinking about JV partnership, licensing, uh, to, uh, I would say, to, to, to best optimize, uh, the benefit risk balance.
Yes,
Thank you, Benjamin. And now, Alex, I'd love your take on how AI is actually reshaping M and A, and how companies are truly extracting value from it. When many companies continue to struggle to see the value impact of AI,
Wonderful, I would lead off by saying, you know, it's incredibly exciting to see how AI is really innovating, how we deliver value creation. That said, I would say that there's frankly a fair amount of experimentation that's happening. And so while there are a number of kind of point examples of companies doing exciting things, I would say that we're on the front end of really seeing, you know, the standard being kind of hard dollar impact coming from from a number of these.
got it? And maybe you could share a few examples with us.
Let me start by talking about some of the more mature use cases, and I would start with, uh, customer service, as well as product and engineering and the technology sector. So when you think about, you know, the classic software development life cycle, the AI models, of course, are now quite mature, quite advanced. And and we're seeing companies take, you know, upwards of forty five productivity gains and seventy reduction in kind of the end to end, uh, life cycle, right?
So what took, you know, months, uh, and and quarters is now taking days and weeks, right, to develop end to end, you know, out-of-the-box software development. And you can point to many examples, um, I, in this space, and I would say that that is, um, a quite, quite direct tie to to value creation. I think the other use case I would call out from a maturity standpoint is in the customer service realm. And frankly, all of us have likely experienced, um, you know, the discussion with virtual agents, you know, troubleshooting with virtual agents.
I think what what might be less obvious is there are now productivity tools that are supporting, um, real agents. So, for example, you know, after each customer agent, uh, does a customer service call, you know, they oftentimes have to kind of record, you know, what are the notes, what are the follow-ups? Now that is largely driven by AI, and so you can imagine there is a significant value unlock from really freeing up, freeing up that that time and that capacity of of the front line.
So I would say there, um, those are, I'd say, the most mature, the most tried and true, largely because, frankly, they were doing it prior to this AI revolution. In addition, right? They have a very high transaction transaction volume, and so those are some of the patterns that sort of drive, uh, high adoption. Uh, the other one that I would call out is in in the back office. So if you think about finance once again, I would say an early adopter of kind of process driven innovation.
You know, we're seeing them really automate with AI invoice processing, you know, closing the books, even, you know, financial forecasts, things that you can apply kind of a rule set to and have kind of a process, uh, orientation, right? And you can see a number of companies are are doing that and are and are quite, quite mature. So I think the simple point here is that lots of kind of proven examples, but also, I would say, a fair amount of experimentation that's happening when you think about specific use cases.
Thanks, Alex. And you've previously written and spoken about the opportunity that a transaction can provide for transformation. In fact, we have an episode on it, which we'll reference in the show notes. Right now, many of the things that you're describing could also tie to the way one might think about transforming one's company. So are you seeing this integration of AI into the overall plan as part of a bigger story, perhaps related to using AI to help transform these companies as a result of the deal?
Yeah, that's a great question. So we like to say the merger or an integration is one of the most powerful change agents, right? When unlocking transformation. And to answer your question very directly, Sean, I would say we're starting to see it. And this is the point that I was making about experimentation, right? And I would say we're seeing that level of transformation or or rewiring in a couple of specific disciplines. So one is obviously, as as you think about the tech and the data platform, and really creating kind of a integrated backbone.
I mean, all of this here requires kind of a a very deep and integrated set of data. And so we're now seeing, uh, organizations really, um, build that, that data and tech platform of the future, right? S So that's kind of part one. Part two is you, you need that platform to really deliver all of these specific use cases, right? So if you're gonna integrate finance and develop kind of an integrated dashboard or performance management, you have to have good information.
That's sort of the second area where we're seeing kind of this unlock is really tr transforming the performance management aspect, right? So one of our clients is developing, um, a new kind of executive cockpit, right, to really power, um, the what the metrics they're looking at, you know, um, deploying agents to really, um, give recommendations to their various functions on direction. And so we're, we're seeing that sort of, uh, performance unlock, or or kind of how management teams kind of newly, newly operate.
And then I would say, thirdly, we're seeing them in these specific use cases innovate the way that work is done. And so there are, I would say, I don't know, seven to ten of these use cases where organizations are deploying AI to really kind of get these productivity and speed gains. And frankly, not just on the cost side, but also on the revenue side.
And so, Alex, what areas are companies focusing on here? Then? Are you seeing any patterns or categories of effort?
I would say there's a few point examples where we're starting to see companies really deploy and go after AI, right? And there's kind of three levels, right? Level one is, hey, let's just kinda use AI to make the process faster, right? That's sort of the first one. Um, and you know, I think the finance example of invoice processing is a good example of that. Like, we have a process, let's just make sure we do it fastly and accurately.
I think the level up from that is where can we deploy virtual agents to really do the work that kind of humans did before. And I think the customer service example was a good example of that. And then, really, the the top end of the spectrum is where can AI plug into a much broader ecosystem, right? And so in the there's some exciting stuff happening in the, uh, operations planning, uh, arena, where the models are kind of forecasting, you know, inventory, stock levels, they're automatically doing order replenishment, right?
And so you see that as kind of like the, you know, kind of the Nirvana, right? Of where, you know, AI is taking on kind of full-on ecosystems, uh, of work.
Thanks. And uh, how about any critical factors that you're seeing that need to actually be in place to enable what we just discussed?
Yeah, it's a great question, Sean. So look, the the first thing is setting the mindset and the expectation with leadership that we are going to leverage the transaction as a as a pivot to, uh, really kind of transforming how we work, right? I think that's step one. I do. I would say that it is important kind of the deal archetype that you are thinking about, as Benjamin discussed, right? I think particularly for, you know, we're seeing a lot of activity on the corporate side or corporate buyers.
That is very important. Number one is kind of the leadership mindset and, um, the expectation to swing for the fences. The the second is talent. So, uh, there is, I mean, the the the market for AI talent and data and tech is super hot right now. But you obviously you have to have someone who kind of really understands what they are doing. And you know, the pace of of innovation that is happening in this space, right?
So I would say, you know, the second is you gotta get the right team in place, and then the third is really going after that kind of data and technology layer, right? That that is the foundation. Having clean data standards, having clean, um, data governance, right? I mean, this is frankly not that exciting stuff, but you have to have really good data, really good data foundation and backbone to really kind of set, you know, kind of these more value creation components, you know, on on top of this sort of sort of data layer.
And so it really takes, you know, all three of those foundational elements. And then obviously there's, you know, the use cases and the plan, and and you know where you wanna go. And that, to me, is the easier part, right? That's kinda classic, you know, sizing of opportunity, and you know, companies usually know how to do that stuff. I would say the three things I described leadership, talent, and, um, the, the, um, the data layer, those are the most, most critical things.
Thanks, Alex and Benjamin. We touched on the importance of speed earlier versus the more traditional M and A timeframe. So how does AI help compress the process?
yes, uh. So indeed, we discuss about how AI is driving M and A activity, how you can use the excuse of an integration, let's say, to accelerate the value. But AI can indeed be applied to the M and A process, independently from, uh, whether, uh, this is an AI company or not. Uh, obviously, yeah. So on the upstream part, I would say that traditional M and A was quite, uh, periodic. You would like, uh, you would look at, uh, maybe a target scan on a quarterly basis, uh, you would come up with maybe fifty names that probably are often, uh, I would say, uh, the same.
AI. I would actually shift that from, uh, what we say, what we call always-on deal sourcing. You can do that, I would say, on a mon, on a much more continuous manner and with much more data. So actually, instead of looking at the past, uh, through AI, we can scan the patent. Uh, I don't know, in, uh, in pharma, we can look at, uh, the web signal, we can look at the transcripts to actually expand, uh, the the the universe.
So that's the main impact of AI on the upstream part, it, which is to go faster and to look at a broader, I would say, uh, uh, po um portfolio of potential acquisitions. On the midstream part on diligence, uh, the shift is around completeness. In a diligence of three, four weeks, it was difficult to look at thousands of, uh, documents. So you would typically only do sampling and focus on a few things. And at the end of the day, maybe only look at fifteen of all the data available in the data room.
With AI and the solution that exists today, actually, you can look at everything in a question of hours, and then you can focus on all the risk, uh, that can pop up, uh, on looking at all the contracts, the footnotes, uh, a, a, and et cetera. So that's the main impact on due diligence. We could expect faster due diligence. What we actually see is deeper, uh, due diligence, where we anticipate even more what might happen. And then on the downstream part, uh, on integration, per se, on separation, this is where, uh, we see the impact being, uh, being much faster, uh, through the use of a library of, uh, agents, huh?
Uh, we have, uh, put, uh, after a a a library of example of agents across, uh, the multiple, uh, work streams. Here the question is for each step that used to take weeks, how you streamline that? I would say, to days, being on the master planning, on the communication, on the on the cross sell, uh, on the delivery. So by using now, I would say, agents, you can really compress, uh, compress, uh, the time. Now, if you look at the level of awareness and, uh, the level of adoption of AI in the M and A process, uh, we are actually running yearly survey in terms of, uh, uh, the level of awareness and adoption of the M and A community.
What we see here is that now AI is seen as a key element to, uh, help M and A teams. You see the level of awareness for upstream now up to eighty eight, uh, fifty one point from last year, even if the level of adoption is, uh, lower. This level of awareness and, um, I would say, adoption drop a little bit when we go to, uh, diligence and, uh, integration. This is often when we ask people a question of being aware of, uh, what, uh, what exists.
But actually, this is moving, uh, super fast. Actually, the typical LLM that you might have access to, ChatGPT and Claude can be the first level, let's say, to answer, uh, some of that. But actually, the players that we have seen, uh, that can really compress, uh, the time, are actually players that you that could use, uh, solution, uh, that could build on integration playbook and so on, to really build specific agents for, uh, the specific integration, uh, capabilities.
Thanks, Benjamin, that is a lot to think about. So, Cam, as we come to the end of our episode, I was hoping maybe you could share your key takeaways for our listeners today.
Thanks, Sean. First is we're seeing activity pickup, and this is across the full set of M and A activities, whether this is, uh, acquisitions or divestments, JVs and alliances, uh, and as as several of my colleagues mentioned, this is w. While going up, it is being marked by several very significant, large, uh, situations, uh, we're seeing corporates leading this cycle with, uh, private equity still a bit on the sidelines. But the topic de jour and the topic of the year, we'd say this is the year of AI, and this is the year of A.
How do you take the leap as you think about how to activate and engage with AI wherever you are in the M and A cycle? If you think you're upstream evaluating opportunities and targets, how might you shift, as Benjamin said, to an always-on model as you think about scanning the broader horizon for what might make sense from a strategic perspective? If you find yourself in the midst of diligences, how might you use AI today to broaden and connect the different pieces of the diligence exercise in a comprehensive and coherent way?
And finally, if you're in the midst of an M and A activity itself, uh, an acquisition or a divestiture, how might you automate and think about the end state? As as Alex alluded to in several examples, in a way that begins to transform or drive greater efficiencies in how you are activating against those opportunities, bottom line, we're seeing a gap between those companies that are experimenting and adopting, which are kind of now leading the pack, and those that are waiting on the sidelines.
Again, wherever you are, begin to take that opportunity to explore what AI might mean for your organization in the M and A context.
Wonderful. And before we close out, I'd like to give you an opportunity to share what you and Tobias are most excited about in terms of the AI opportunity in M and A.
Yeah, we haven't really talked about this topic as directly. I'm excited about the implication of how you can use AI to better understand a culture as you come together, and how you can experiment with and seek understanding and identify potential similarities and differences in how work is is done in each organization. To ensure that that does not become a friction point in the long run.
That sounds great. And we also have another episode that we've done on the importance of culture in M and A, and we'll refer to that as well. Thank you, Kim, and Tobias, we'll give you the last word.
Yeah, so I I'm most excited about how AI can be applied to improve the capabilities of corporate executives in the deal process. We've we've surveyed corporates over time, and the bar has risen, and and the US. corporates have got better and better at those there. But the one area which has been a bit more flat has frankly been capabilities around really extracting the most value and integration capabilities out of deals. And I think with the ability to accelerate, uh, you know, uh, access to information and connectivity and so on, as we heard out here from some of my colleagues.
You know, I'm really looking forward to also see how corporates can embrace that and move the bar up also in that that area.
Benjamin, Alex, Tobias, Cam, thanks so much for this wonderful conversation. Really appreciate you taking the time today
Thank you.
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