AI in M&A Due Diligence: From Experimentation to Execution

Angela DeSanctis

00:07 - 04:02

Alright. Welcome, everyone, and thanks for joining us.

My name is Angela DeSantis, and I lead the sales team in North America for corporates and banks at Intralinks, and I'll be moderating today's conversation. So I'm excited to be here.

I've been with Intralinks for over a decade, and during that time, I've had a real incredible opportunity to sit down with hundreds of dealmakers. And I've witnessed firsthand what the shift has looked like from AI being seen as risky and uncharted, something that we can't be sure of and we need more information about, to now being a tool that is mandated across many organizations.

I've heard the word mandated more than I can count. And, you know, from our perspective, a lot of that has been anecdotal, just having conversations informally with our our clients.

So it was really exciting when I learned about the survey that we produced in partnership with the Transaction Advisors Institute, which, surveyed senior corporate development leaders across public and large private acquirers. And there, the findings really supported what we've been hearing for for quite some time.

And that is that two years ago, only about one third of corporate acquirers were using a a AI in their m and a due diligence. And today, that number is a staggering ninety three percent.

So it it's fascinating to see that shift happen in such a short amount of time, especially because I'm sure my my guest here can, agree with this is that this group of professionals can be very resistant to change. So, yeah, just incredible results, and we're going to dig into that more today.

You know, what's behind this data that we produced? Where is AI delivering real value during the diligence process, where is it still falling short, and what that means for how deal teams are working, how we're thinking about integration planning, and all of that going forward. So to help me unpack all of this, I'm joined by three guests who each bring a different vantage point on the research today.

Greg Schlung from the Transaction Advisors Institute. He led the team behind this research, so he's going to talk to us about some of the trends that we've seen across two years of survey data.

Massimo Malazia, director of corporate corporate development at Cisco. So he'll he brings a great point of view on, you know, what it it's like inside a large active acquirer.

And Greg Albert, who leads m and a strategy at Accenture, and he can give us, his perspective from the advisory standpoint and and where the market is is really heading as a whole. Alright.

So I've prepared some questions for today's discussion, and I'd also like to invite our audience to submit any questions. There is a chat feature on the q and a in the q and a section, so feel free to add any questions there, and then we'll have time at the end to go through those.

Alright. So, we can get started.

But I guess before we we dig in, let's learn a little bit more about our our guests here. So, Massimo, I'll I'll start with you.

You know, give us a quick sense of what Cisco's m and a pace looks like, roughly how many deals you're running at diligence at the same time, just kind of a a big picture, lens.

Massimo Malizia

04:02 - 04:52

Sure. Cisco is a city acquirer.

We we acquire up to date, 215 companies in the in the years. And, we do a mix of deals.

Sometimes we, out of 10, we probably do seven tech and talent. So that's really the bulk of the of the deals, the transaction that we close.

We do maybe a couple of deals on product. So where we basically are interested in the actual product line to integrate in on Cisco.

And, every now and then, we do what we call a platform deals that are, you know, larger deals that, allow us to enter into markets that we don't, where we're not present. And so those are more transformational deals.

So, again, we do a mix, generate the bulky stack and talent, but there is also some significant deals on the product side.

Angela DeSanctis

04:52 - 05:12

Okay. So you're a you're a very busy man.

Alright. Great.

Thank you. And to Greg Albert, so, Accenture being in the more of the adviser seat, how many corporate acquirers are you advising right now just on AI adoption?

Gregg Albert

05:12 - 07:33

Yeah. It's a great it's a great question, Angela, and I guess I'll answer it in two different ways, and a little bit piggybacking on Massimo's point.

So number one, Accenture being is publicly traded, as you're aware, about 75,000,000,000 in revenue. We are the most acquisitive company in the world, by deal count.

So, you know, for example, over the last five to six years, we average a transaction announcement every three weeks, plus or minus. Most of those are small tuck in, as Massimo said, talent and or intellectual property or distinct capabilities in a region or market.

And we leverage all of those motions and capabilities internally. And myself and my team partner with Accenture corporate development, where I'm focused primarily, but not exclusively in market with clients.

So in leveraging our learnings internally to bring those best best, best practices to our clients, and also learn our way forward alongside our clients. Because I don't think anybody has cracked the nut on exactly all of the use cases that AI is currently able to deliver and is on the heels of delivering over the next, let's say, twelve to eighteen months.

However, it is our job to work with clients and help them through AI adoption. And the hardest problem of AI adoption that we hear from our clients is something very simple, which is just where to start, whether it's just copilot and leveraging it through meeting notes.

How even how do you think more advanced around a Gentic, which I think we'll talk about. So the whether you're a strategic fortune 500 company, a g 2,000 organization, a private equity portfolio company, or even at the fund level, as you think about value creation, the answer really is Angela.

It it depends. But I can tell you this on every one of our engagements, and we have about 200 live engagements right now with clients somewhere in the deal life cycle.

On every one of them, we are leveraging AI in one form, fashion, or another, in in delivery. And I think we'll have a lot to explore during today's conversation about who, what, when, and why to deploy to to deploy AI and where.

Angela DeSanctis

07:33 - 07:59

Yes. Yes.

And and to your point, you know, we hear that often as well. It's just how are our peers using AI, what are best practices.

So you must have a lot of interesting conversations both on, you know, as you mentioned, advising clients and then also internally. I mean, my god.

Every three weeks. That's that's impressive.

Okay. And Greg Greg Schlunt.

So,.

Greg Schlimm

07:59 - 08:00

Yes.

Angela DeSanctis

08:00 - 08:10

you know, why very interesting results that we saw from the survey? I guess, why why commission it now?

Greg Schlimm

08:10 - 09:52

Yeah. Absolutely.

Many of you may know or maybe some of you don't know, we support a community of corporate acquirers. So most of our member companies are doing multiple deals per year where they're trying to build capability to do multiple deals per year, and they're always looking for clever new things.

So we bring the group together a couple times a year at our conferences. And, you know, maybe two, three years ago, we began to hear anecdotal stories, clever things that folks were doing in diligence and integration planning with AI.

And they were kind of curiosities in the beginning, then they got more frequent. And then the discussion changed a bit.

It changed to a collection of comments. Some of which are, we did this clever thing.

Let me tell you about it. But then we also heard people worried.

Are they doing the right clever things? Are they missing out? Are peer companies doing things that they don't know about? And then there's still companies that are figuring out how to get started. So we wanted to ground the discussion.

So we have this amazing community of serial acquirers. And from time to time, we explore topics with them that are of interest to their day to day m and a programs and, what's really going on.

And let's ground it. Let's get some benchmarking data that's solid that there's methodologically appropriate.

And then, actually, that gives people context, and it helps them know, are they ahead? Are they behind? And, if they're still planning or they're planning what comes next in their AI journey, maybe we're tossing some ideas into the pot. So the study actually explored, what are people trying to get out of AI? What technology are they actually using and what use cases? And then what value are they getting today and where do they think things are going? And it's a super exciting study.

Couldn't be more excited to tell you about it. And I hope that you're all gonna read it offline later.

Angela DeSanctis

09:52 - 10:34

Yes. Yeah.

Okay. Great.

Alright. So, Greg, maybe I'll I'll stick with you.

You know, I think the question on my mind is AI has been out there for some time, and it's been out there in the deal making community, I think specifically within the legal lens for over ten years. So what's driving the shift? You know, why did we see the adoption accelerate so dramatically just over the past two years? Was it just excitement around AI? Was it pressure of deal teams having to look at more deals, execute on more deals?

Greg Schlimm

10:34 - 10:35

I.

Angela DeSanctis

10:35 - 10:37

What was happening?

Greg Schlimm

10:37 - 11:36

think it's the perfect storm of AI acceleration. You have a couple different things that were going on.

You have technology. Every time you turn around, there's a new version of some AI platform that does more clever things than it did a month ago.

So there's this ongoing stream of new capability. You also, have to think how corporate acquirers how corporate organizations adopt technology and do change programs.

It takes time for these things to come to life. There's always early adopters in departments.

They're experimenting. They're thinking.

They're talking. And then it gets pulled into the planning cycle.

There's corporate initiatives, CEOs saying, use AI. Everybody has to use AI.

And these things all kind of hit. And I think maybe two, three years ago, you were seeing the pilots and the anecdotal comments were getting started, but now the machine has come to life.

And these corporate organizations, they might be a little bit slower than others to come to life, but once they get going, there's almost no stopping these initiatives also. So I'm super excited, one, to kind of learn and share what we learned in the study, but also to see where things go in the next one to two years.

Angela DeSanctis

11:36 - 12:01

Great. And, Massimo, I'll I'll ask your perspective on this.

Was there a moment at Cisco where you felt that there you felt the shift? You saw, like, your company, your organization team went from being very hesitant about AI to truly embracing it and and ready to adopt it into practice.

Massimo Malizia

12:01 - 13:00

Yeah. I think it was probably between twelve to eighteen months ago.

I think that's when there was really a strong push. And, as Greg mentioned, it's the perfect storm.

And there's also a lot of difference compared to what we saw ten years ago. You mentioned the contra analysis.

I remember I was working on trying to automate contra analysis for m and a ten years ago, and it wasn't easy. The first of all, the tool did not always distinguish, very specific contract.

They were pretty good on standardized templates, but that was about it. And you also had to learn the specific software.

Right? So there was a ramp up in to learn the software that the only that. While AI is gonna be a general, you know, kind of tool available, once you learn, you can apply to so many different things.

It's not gonna be just one specific software for one specific scope. So I think there are so many advantages that, that are popping up, and we are really just at the beginning.

Angela DeSanctis

13:00 - 13:24

Mhmm. And you mentioned legal contract analysis.

You know, I think diligence is at the heart of every successful acquisition. Where where are you getting the greatest value from AI today? Is it still within legal, or is it other areas? Tell us more about that.

Massimo Malizia

13:24 - 14:53

Yeah. I think we're still, you know, we're still at the very beginning, but, the very first usage is, on the legal side doing the contra analysis that normally requires a significant amount of time as well as the classic, program management task of meeting minutes and organization and things like that.

Right? So, those are the two things that we started with. And, although it's it's only the beginning, it's already helping a lot.

I think one of the benefit of using AI in due diligence to me is the possibility of accelerating the definition of the strategy for the integration post close. It's, it's very difficult to do it if you need to spend a lot of resources on, you know, going through documents manually because then you don't have time to align your internal stakeholder to the strategy.

Right? So that that is that period where you need to focus outwards on the assets that you're buying and trying to understand what you're buying. On the other hand, you need to focus inward within your company to make sure that everyone is aligned on how to use those assets afterwards.

So the the due diligence period is very critical. And the more you can accelerate, the more you can use the time to actually have those alignments rather than reading contracts or going through Excel.

That that's in in in my mind, that's one of the the most apparent value at this point.

Angela DeSanctis

14:53 - 15:12

So just to clarify that, are you saying that the time saved during due diligence that you're using your AI is assisting with, you're then taking that time and using that for post close and what the strategy is going to look like around integration.

Massimo Malizia

15:12 - 17:17

Yeah. Correct.

Let me give you an example. Right? So my team, is responsible, for example, for the migration of the legacy customers.

Right? Normally, we when we acquire companies, we also acquire a customer base. Sometimes, the customer base, you know, might not be transitioned to Cisco because we might just be interested in the intellectual property, or in some cases, we might want to transition them to the new offer that is gonna be available on Cisco.

Regardless, we need to have a strategy of what to do with these customers. Until a year ago, we would have the strategy done only after close because there was time it it it was necessary to to read those contracts, align the target company to what to do with the customers, align the business unit to what to do with those customers, and so on and so forth.

Once you and so that means that that strategy was not committed because it was happening after close. And the end of sales would take forever in case we needed to end of the legacy product because there was after close, there's much more resistance to to do that.

And and so that was a very painful migration, and we would see a lot of delay in executing that strategy when defined post close. What we're doing now is that we're actually accelerating, analyzing all those contracts, focusing specifically on the big ones, align the target company and our internal stakeholders to our strategy in front of our chief financial officer during commit, and then using the announced to close time period to define the customer communication happening at close with the target company.

So that once we close, we just start executing right away. And it's so much easier because if you wait too much if you wait too long, the target company start having their own integration strategy.

Right? And then it's very difficult to make them change their mind. You need to fight the uphill battle.

Again, that's that's one of example of having contra analysis done through AI, accelerating the strategy so that you can executing at, a close rather than wait after close.

Angela DeSanctis

17:17 - 18:04

Yep. Which is excellent.

And, I mean, that supports what we see in the study, you know, legal agreement review. 79% of teams are either using that or piloting that, which is up almost 30% from 2024.

And then just the diligence work streams, I think there's 60% adoption there, which is up 13% from 2024. What we've heard and, you know, Greg Alber, I'm curious to get your thoughts on this, are the AI is great, but how do sellers feel about their diligence being used in AI? Is that coming up? Are you seeing that in NDAs and LOI preventing the use of AI during diligence?

Gregg Albert

18:04 - 22:24

Yeah. It's a it's a really critical question, and I think we're grappling as we see an increase in portfolio rebalancing across the fortune 500 landscape for a variety of reasons, activist investors, non core assets, you know, the cost of capital, where companies want to grow and play in the future.

There's a whole host of let alone geopolitical macroeconomic. But if I and if I think about the trends, like, we're seeing a lot more carve outs and separations and putting businesses on the block, And luckily on the that's on the supply side.

On the demand side, and I'll come back to your question and also weave back Greg's research and Mossimo's point. On the demand side, you're seeing $2,100,000,000,000 in dry powder sitting in private equity firms and in a very strong balance sheets from Fortune 500 companies.

In fact, some recent research suggests we're seeing stronger balance sheets than we have since the nineteen eighties across corporate. Fortune 500 general, I would say corporate America, but really Fortune 500 globally.

And then if you think about AI use cases within that construct, it's and then and come to Masimo's point, you have the legal contracts, the legal, but, like, that's changing control provisions, assignability clauses with or without consent. I mean, as Massimo said, like, I remember going through the same exercise as trying to build out models with OCR to accelerate that, but that's like table stakes.

And I think you trend like their contracts legally legal contracts. However, business people need to have the T's and C's around pricing contingent, you know, the the language around pricing, price escalators, and that ultimately folds into the investment case.

And rather than have eyeballs focus on that, we're ingesting 10,000 employee contracts on very large, to your point, Angela, car carve outs and separations. The ingesting them and then outputting to a to various buyers early in the process.

Here are the biggest customers. This represents a large percent of revenue, and here's some of the considerations around pricing and costing, not within the details.

Right? But pricing and costing. And that helps build a really robust auction process for assets on the block, or as you go carve outs and separations with buyers.

And I'd say also is I think ten years ago, there was a lot more aptitude and interest in sharing more information earlier on in the process. I think with the invent of AI, it's hard to control.

We're seeing some of this in the media where that data, where that information is housed, stored, and how secure it is, and how it could, you know, kind of leap out of its of its cage, if you will, and then bad things can happen. And there's no if you, you know, even if deals don't go through, and I think we're seeing this a little bit more on the seller side than the buyer side, and there's some regulatory headwinds that are causing this.

There's no men in black pen. Right? Then remember the the, denuralizer from.

men in black? You can delete it. Like, once it's out, it's out.

And, you know, even from a corporate development, you may need to be in a clean room or a clean team environment to look at this information, and this information is at the heart of the investment case on the seller side, on the buyer side rather, that'll inform to get approval, to inform the investment case, and it's not just total adjustable market and, you know, what's the you know, with some fancy Excel, what's the the growth trajectory and cost out initiatives. This is really thinking at the core of the integration planning, and I don't mean just milestones, but down to the level of, you know, Massimo, Greg, Angela, double g Greg, me, and what's our responsibilities post close to really drive value creation.

And I think that's been the the big change over the last, I'd say, twenty four, thirty six months as companies are getting more comfortable, let alone curious about how to leverage these new technologies without adding any complex without adding any deal risk, right, in terms of confidentiality.

Angela DeSanctis

22:24 - 22:24

Okay.

Greg Schlimm

22:24 - 23:08

Yeah. And the the data backs up what you said, Greg.

There's the use cases that are things like legal review and review the the, financials. That's where the traction, there's the most penetration, but there's always emerging use cases.

How will the board respond to this investment thesis based on how they've responded to other thesis we put in before them in the past? How likely is this investment sorry. How likely is the creation plan to be executed based on how we've done other deals in the past? Those kinds of things are now beginning to bubble up.

And it's still just getting started, but people are thinking and scheming and plotting, and that's a level of value that was not even possible or even imagined two, three years ago.

Massimo Malizia

23:08 - 24:06

Yeah. Definitely.

And I think it's all a matter of training the the the AI. Right? So right now, I feel like we started with this business case on legal contracts or maybe reading Excel because it's easy to train them.

And, as we actually evolve into asking AI to confirm the strategy or even define the strategy, that's always gonna be the case. How do we train them? What information do we give them to, to make that decision? Then that's a little bit how the, you know, kind of the the the complicated part.

But we're gonna get there, but I feel like we're still a little bit away from it. There is a colleague of mine that, you know, use this analogy that is, yeah, is a little bit like a new hire.

Right? It's, it's having a very smart, super, fast learner, new hire. You still need to, to teach the new hires.

You still need to train them and and give them some guidance before you can they can actually be effective.

Angela DeSanctis

24:06 - 24:42

Okay. And are there areas I mean, when we look at the report, we see that only 9% of serial acquirers trust AI just enough to just spot check it.

The rest, it does require more than just, you know, a standard review. You have to really dig in there.

So are there areas that, you know, you think well, I'm sure there are, are, but, like, which areas of the process will always require some type of human judgment?

Gregg Albert

24:42 - 27:12

Yeah. I mean, Angela, the easy the easy button answer on that is when the decision is made.

Right? I don't think we're. outsourcing m and a decisions to AI quite yet.

Right? So but I think the AI is the and while I like Mossimo's metaphor of the new employee, AI is the iron man, if you will, with the suit on for the corporate development team. It doesn't replace the human aspects.

Right? But it certainly accelerates. It it brings faster insights based on a data room, management presentations, a teaser document, a SIM, being able to ingest, digest, interpret, and then obviously translate that into an investment case with a clear and high fidelity planning process to achieve the strategic objectives, whether it's top line growth, bottom line growth, new market entry, intellectual property, you know, dot dot dot.

I think that's the Ironman suit that AI and Gen AI and AgenTic, which are three different things, are are all bringing to the table in real high stakes transactional work. And it's funny, I think, like, if I reflect back corporate development historically has always been late adopters and technology companies have not focused in on corporate development as a function within an organization.

If you compare it against finance, for example, how many ERPs are out there or HR? How many systems are out there for that or go to market CRMs? Corporate development's been like this like this you know, I don't wanna say red red headed stepchild, right, that nobody has known how to crack. But now so but corporate development professionals, because of the high stakes that are like, we are leaning in, and our clients are are eager to lean in at their own pace.

And I don't think everybody wants to jump in, you know, right away. Sometimes there's a a zero entry pool, if you will, into adopting AI through the deal process, that ultimately will help give the best information with the highest level of fidelity to the leadership, the board, the CEO, etcetera, let alone the business sponsor who needs to say, yes.

I I think this deal at this price based on the integration plans makes sense, and I will be held accountable, not just respond, but accountable to the street, for example, to to make this acquisition. And that's that's a leap.

That's a leap for a c suite and a board to take.

Massimo Malizia

27:12 - 27:12

Mhmm.

Angela DeSanctis

27:12 - 27:12

Yeah.

Massimo Malizia

27:12 - 27:14

Yep.

Greg Schlimm

27:14 - 27:49

What we learned from the data, you know, you mentioned the 9% and, you know, if the bar is a perfect work product that is defendable and defensible and can be explained, and then that's an extremely high bar. You don't need the bar to be anywhere near that high to get value from this this cool technology.

So, you know, to use it as a thought partner to help find the corners of data you would never have time to look into yourself, all of that, you get great value. And you can still say it's not fully trustworthy because it requires the human to actually take a look and decide, you know, am I gonna take this forward or not? So,.

Massimo Malizia

27:49 - 27:50

Yeah. Yep.

Greg Schlimm

27:50 - 28:11

just one closing comment there. It was interesting that the that the community did think it's going to get dramatically better in the next one to two years.

So in addition, you know, I I I don't have the exact number written in front of me, but the expectation is that the bar is gonna get higher and it's gonna get better and therefore more usable and useful. So we'll find out together.

Massimo Malizia

28:11 - 29:10

Yeah. Yeah.

No. Definitely.

And, I know that, Agentic AI in acquisition is, Greg's, Albert's favorite topic. And, you know, part of evolution of the technology, sometimes we might not have all the connectors that we need to, to do that, but it's it's moving so fast.

I, I was actually chatting the other day around prompt engineering. Right? Do you remember a year ago, eighteen months ago, we are discussing about prompt engineering.

We don't hear so much about that anymore because the applications have become so intelligent that they can't actually infer what we mean even if we don't express it very clearly. Right? And so what it was, kind of a buzzword, a year ago is is not anymore.

So it's it's moving really fast. And, the more the the technology evolves, connectors become available, then the more agentic AI is gonna is gonna do a lot of work, that we now do, manually or sometimes we do post close going back to the team of due diligence.

Angela DeSanctis

29:10 - 30:03

Yeah. Okay.

We're gonna come back to AgenTek AI, but I do want to dig into one area. I think we see deal teams using Copilot, Anthropic, the Frontier models, ChatGPT.

We've also now started to see a rise in niche players pop up. You know, those that specialize in legal, like Harvey, Hevia, Rogo.

I was just talking to a colleague that has one that is specializes in Danish law. So they're they're all over the place.

Massimo, for for Cisco, have you started exploring any of those other technology players in the AI space?

Massimo Malizia

30:03 - 30:48

No. Not yet.

We, what we do is that we actually have a pretty centralized approach to AI for security reasons. So we use LAMS to our internal applications called Seco dot IT.

And, and therefore, we, you know, we it adds up capabilities and, and functionality all the time. But, to my knowledge, we haven't, used this niche player yet.

We also. specific, you know, due diligence.

We need to make sure that we have the safety around intellectual property that doesn't belong to us and the capability of deleting, all content if the deal doesn't go through. So that requires some, some significant, guidelines.

and and control.

Angela DeSanctis

30:48 - 31:11

I imagine the approval process is quite rigorous to, to be able to use there. Greg Albert, what type of questions are you getting from clients and both, you know, if you wanna speak internally to the corp dev team at Accenture, around AI?

Gregg Albert

31:11 - 35:38

I mean, I'll I'll I'll give it from more of a client perspective, and there's there's articles out there that our, corporate development team publishes to give, you know, give information on how we think about internally our own m and a growth agenda. But from a client side, I think it really distills down to three basic fundamental questions, you know, as they start to thinking about, hey, AI is this great thing, and everybody keeps talking about it, and particularly in the Bay Area when you drive up, you know, the 101 or the two eighty, I mean, every other sign is, you know, used to be.

com, now it's. ai, Right? And everybody's asking again, where I started was, where do we start? You know, and let's not talk about copilot taking notes, and meetings, and actions, and things like that.

I think that's table stakes at this point, but how do we get from that to question number two is, how do we infuse it into our ways of working through through the diligence process? And diligence, I don't just mean data room opening, I mean since the initial conversation, however that conversation happens, sometimes through a banker, sometimes through ecosystem partners, sometimes through shared collaborations or strategic alliances, or they have shared customers. But that quote unquote diligence isn't just this, you know, study that's being done with a data rate.

It's way more than that. It's understanding the culture, what's important to the population, and then also risks, particularly with big companies buying small companies.

If I were that's a like apples and donuts in terms of the value proposition to the employees. And number and then so how do you leverage AI to think about this this squishy thing called culture and make it quantifiable, so that you can embed it into the diligence case, and focus on it and into post close integration, but all bundled under the diligence umbrella, so that when the board or a c suite approve it, it's embedded in there.

So it's a risk mitigation. And then I think last, but, you know, maybe, like, number three on on the list is, you know, how do we really leverage Adjentic? And I know we're going to talk a lot about it, and I don't wanna I don't wanna get ahead of our skis here, but, like the days of G and A cost out IT, HR, finance or business processes, order to cash procure to pay, record to report.

I mean, those are those are on the on the spectrum of complexity of value capture. Those are easier.

Right? We've been doing that, I think, as corporate development, m and a practitioners for the last sixty years. But how do you combine go to market growth? Particularly, I mean, any industry, but particularly in the high-tech sector, And you're cross selling existing offerings.

And how do you train your account teams with the right script and talk track that are relevant to to shared customers? And how do you think about, you know, putting all of this together in in the investment case that ultimately needs to get signed up. I think those are the three hot topics.

And if I had to pick a fourth, it's all about data migration. Industry acknowledge.

Doesn't make a difference what it is. It's data migration, getting from system a to system b or system a and b, and we're gonna recreate a system c that's optimized.

Like, how do we think about data migration and leveraging platforms like Palantir or or or so I'd say substantially similar platforms, which which we are using today on client, you know, data migration efforts. Like, but that's like a huge amount of historical time, like, time measured in months, not days or weeks, but now it's being measured in weeks and days.

And then and and a huge amount of human effort measured in calories, like, you know, us humans, carbon based people, not AI, that that need to work their way through the different data migration paths and testing and, to make sure that it it it performs in accordance with the investment case. And again, the board deals go to die on data.

Right? Data is a currency, but also data can be an impediment to actual value creation, and we've kind of jump leapfrogged, if you will, some of the considerations around that leveraging, but again, programs and techniques. Programs like Palantir, and I'd say more advanced, mathematical techniques to mitigate that risk.

It's pretty cool stuff.

Massimo Malizia

35:38 - 36:53

Yeah. Yeah.

And if I can add up something, Greg, to the data migration, which I think I is so critical. I totally agree, and that's really where there's gonna be a lot of value.

Acquire is also need to be set up to have their internal discovery AI powered. Right? So you cannot just focus on AI on the target, but then your internal processes are not mapped with AI, or you don't have any idea what, what you're getting into on the on the other side.

Right? So it must be on both side to really create value. It must be, due diligence and external focus, but also the internal discovery.

And for smaller acquirer, maybe that's not too complicated. But for a company like Cisco that is a very large company, sometimes we have even potentially different processes with different business units.

And right? And so understanding where the assets end up and where the data migration ends up, to Greg's point, is not always that clear. Right? We need to be focused on the internal discovery piece through AI to really get value, as Greg was mentioning the, you know, how do we get AI to define the strategy, helping define the strategy.

They need to know the buyer as well. They cannot just focus on the on the selling side, right, on the seller.

Angela DeSanctis

36:53 - 37:01

And, Massimo, so I guess what can you share with us how you are using AI internally?

Massimo Malizia

37:01 - 38:38

Sure. And and, for example, this is something I'm very excited, for the for the next deal.

We haven't used it on, on a new deal yet, but we're using AI to map all our processes. Right? Quote to cash, procurement to pay, and we have so many.

And so, what I would like to do, again, for the next deal, because we we we haven't done it yet, is to to do what we call blueprinting, where you basically match, you know, one the the the, for example, the the the target company quote to cash to Cisco quote to cash, and then you compare them. And then you see how you transition from the acquired company quote to cash to the to the buyer quote quote to cash.

And and that can be done through AI. So in my mind, the next the next deal that we have, we can go through the documents that are shared in the in the virtual data room.

We can go through the meetings and use AI to have a visual of the target company go to cash processing example that I made, and then compare it to an existing map visual map of the Cisco go to cash process and just compare the two. And, again, blueprinting, which is this comparison of processes, was something that traditionally we have always done after close because it's very time consuming, both for us and the company.

And, if we can leverage AI, we can do that before close and have a plan that can execute after close. And that's, you know, that's a lot of the the blueprinting is around data migration and, and how do you you handle the the move from one process to another.

So, again, that's something that is really exciting for me because those are the things that are nuts and bolts of integration and are super faithful to go after.

Angela DeSanctis

38:38 - 38:40

Yes. It.

Gregg Albert

38:40 - 38:40

And, Angela,.

Angela DeSanctis

38:40 - 38:41

I.

Gregg Albert

38:41 - 40:07

both with. this, Massimo's point is so critical, right, to any transaction through the diligence and post close, but the the life cycle of a deal.

I just wanted to, like, punctuate one of the points that he made so we just spend, like, maybe fifteen seconds on it. Is the blue what what Massimo calls blueprinting.

I mean, on one hand, we've been thinking about as is and to be since the beginning of m and a and doing diligence and valuations. Right? It's easy to say this is the way it is.

This is the way it's going to be, and then in the middle, some magic is gonna happen that's gonna help us get there. But today, we're able to build out, I'll call the digital twin, to simulate what the as is and the to be need is going to be is gonna what what the to be is going to, quote, be, and the fastest path to get there with the least amount of business disruption through that process.

Because usually there's change management, there's cultural change, there's ways of working. Like, it's not just a magic wand, right, that comes in, voila, you have the to be.

But we're able to even go through simulations, in order to get from the as is, and then to the to be, really combining the technology with human ingenuity in order to get there, because it doesn't happen on. its own.

That's what's again, Mossimo got real excited, and when Mossimo gets excited, I get excited. So I just wanted to that that's such a critical point if you think about the the investment case.

Massimo Malizia

40:07 - 40:08

Yeah.

Greg Schlimm

40:08 - 40:08

Hey,.

Massimo Malizia

40:08 - 40:08

No.

Greg Schlimm

40:08 - 40:08

Angela.

Massimo Malizia

40:08 - 40:08

And I also.

Greg Schlimm

40:08 - 40:11

toss oh, sorry?

Massimo Malizia

40:11 - 41:06

I also get excited about it because the the more we have solid plans before we close, and I always go back to the point, the more we can be effective in measure post close the success of the integration. A lot of times, the issues with measurability of acquisitions is that the strategy that you define before close has holes because you don't have enough time to define it correct.

And then post close, you find out that there are all these issues that you didn't know about because you didn't have enough time to, to dig into it. And so measure a strategy that has holes doesn't really make too much sense.

You're gonna, you know, you're gonna spend time to measure it, but it's not gonna provide too much value because the the the criteria that you want to measure weren't right in the the first place. So the more we can really leverage AI to define the strategy ahead of time, the better miserability I think we're gonna see post close.

Sorry, Greg. I got a lot.

Greg Schlimm

41:06 - 41:57

No. It's fine.

One of my takeaways from the study that that was most interesting to me was that these large companies, as they're thinking about what they might do with m and a and and and the power of AI, by far, the number one intended outcome was to learn what's possible and to reimagine processes. So they were not diving into the corners at all.

They were thinking big picture. They were taking time to learn.

And then the next intended outcome was, how can we get more done in the available time and in the available number of hours, which I think plays perfectly into what Greg Albert was saying. There isn't time to do these complicated analyses during due diligence.

So if AI can help accelerate and get more stuff done there with available people, then that's that's brilliant. That's kind of moving everything to the left, which is what we all wanna be doing.

Right?

Angela DeSanctis

41:57 - 42:48

Absolutely. And, Massimo, if we and and Greg Auer, if we think about the two examples, specifically, Massimo, you gave on the quoting to cash process, You know, I I imagine that's been a a friction point, a pain point, something that was identified, and then you said, you know, I think there's a better way for us to do this.

I think we can use AI to mitigate some of the risks and friction points that we encounter during integration. What how did you get there? Was there a a meeting? Is there a team? Who is spearheading? Hey.

Let's look at our process, our workflows, identify where there's problems, where we can automate this using AI, and then let's put that into execution mode.

Massimo Malizia

42:48 - 44:04

So we have actually carve out, within our corporate integration team, a smaller team of people that are focused specifically on the AI business cases. So I don't think you can really do it part time.

I think you need to have people that think about it all the time. And we haven't, you know, transitioned to that 100% of the time yet, but I think that's where it's going.

Right? In in in the definition of a smaller team, subset of people that have their main focus on AI. And, in my mind, it's going to be a sort of bottom up revolution.

Right? I don't think it's going to be too much directive, you know, from from top to the executioner. I think a lot of the business case are gonna pop up as people get more familiar and, embed in using AI, and they're gonna have ideas.

You need to have a way to funnel those ideas and to, ask give the right resources to the ideas that provide the most, the highest return on on the investment. So I think there's gonna be again, we have a smaller team within our corporate integration that is focused on AI, but we also leave people free to submit cases that then we're gonna, reassess in terms of, ROI if we want to invest on those or or not.

Angela DeSanctis

44:04 - 44:34

Okay. Okay.

Good. One last question from my side, and then we'll move over to the q and a.

I did want to touch on AgenTek AI as we kind of see that evolving into the next area. We're going to see the capabilities mature.

Both Gregs, Massimo, anything you want to share on how you're using a agentic AI to take things to the next level or how you're thinking about it?

Massimo Malizia

44:34 - 44:39

Sure. Can I.

Greg Schlimm

44:39 - 44:44

Greg? Me, Greg? Why don't you go first? Okay.

Massimo Malizia

44:44 - 44:45

first?

Gregg Albert

44:45 - 47:40

I mean, I'll I'll I'll jump in. Right? So I get very excited about and I think Thomas the most point into Greg's research.

Right? Everybody's every company is gonna need to find their own path to where, how, and when to implement and deploy AI, GenAI, and Agentic. I don't think there's one size fits all for different companies just by industry.

It'll be different. Let alone culture, way of working, there's laws right there on different jurisdictions in Europe, for example, versus The US, but I think around the agentic, I think in the in the next, and this is a prediction, and as Yogi Berra said, predictions are harder, hard especially about the future, But I think within the next six to six to nine months, where this is going to be the standard as how we're leveraging agents in order to do some of the tasks that were mentioned mentioned previously.

For example, how are you gonna be communicating to customers? And this is b two b and b two c both. How are you how are you connecting communicating with customers to cross sell upsell existing products and combine products based on an acquisition? I mean, I'll give you a real tactical example.

I won't use client name, but, to a very, very large telecom merger, you know, is is in the in the late stages of the closed window. And we're in the process of building out 18 different agents.

And when I say the process, there's been a, the design process, the testing process, you know, in a clean environment, the deployment of it use cases and scenario planning. And these are gonna get rolled out to cross sell services to existing customers based on a whole host of different variables.

Geography, zip code, user profile, etcetera etcetera. And the list goes on and on in terms of the variables.

But the the the the test results, and we'll see what happens when it's actually deployed, is that we're able to accelerate cross sell motions with as little friction and, quote, unquote, the bad that comes along with, like, you know, all of our telecom and and, you know, the the the phone calls that we get to cross sell products. This will be much more streamlined, much more personal, and it won't be a robotic voice on a phone.

It'll be done through social media, through email, through the platforms that already exist, and also, you know, building out new platforms that customers will have better interaction with the services that they that they need and the services that they would that they want. And there's a win win for all parties here.

So it's it's a very interesting motion. As I said, within the next six months, this will be almost like, you know, using Copilot for meeting notes.

It'll be that standard in certain industries and certain use cases in order to accelerate and minimize risk around top line synergy growth.

Angela DeSanctis

47:40 - 47:41

That's great.

Massimo Malizia

47:41 - 47:41

Yeah.

Angela DeSanctis

47:41 - 47:52

And, Greg, you actually answered one of the other questions in the chat, which was a real life example of how to improve the customer experience using AI. So perfect.

Massimo Malizia

47:52 - 47:53

I.

Angela DeSanctis

47:53 - 47:55

I do wanna just go ahead, Massimo.

Massimo Malizia

47:55 - 48:31

think no. I think he's a great example.

I'm I'm so excited about avoiding Excel spreadsheets and comparing customers and then try to map them. And the address is not the same.

So it's not a Cisco customer even if it's just like I I know it's a Cisco customer even if the address doesn't match and the name doesn't match. And so having having the to, you know, the capability of AI or helping us on so so trivial task to begin with and then, Greg, to expand on that and and actually have a change management plan and, and an upselling plan that, that is actually smooth.

So it yeah. It's it's very exciting.

Angela DeSanctis

48:31 - 49:01

Great. Okay.

One question that came in. We've talked about, valuable AI use cases, within legal and financial work streams integration, but what do you see as other emerging trends of using AI during m and a? It doesn't have to be during diligence, but just outside of legal and finance.

Greg Schlen?

Greg Schlimm

49:01 - 49:41

I yeah. I mean, what I see and there's multiple flavors.

of it throughout the research and the folks I talked to. Finding unintended synergies.

You know, what else is out there in the data that AI can help you tease out that you then might work into your business case pre deal? Or during diligence, what else is there based on the new information that you now have available to you in diligence that can make a better business case? And then the same thing on the integration side. Now that we're doing integration, how can we better use information via the AI via the AI architectures to actually deliver the value? So it's it's finding things that the humans would never quite get to in all the different phases.

Angela DeSanctis

49:41 - 50:41

1. Yeah.

I know that what Intralinks is looking at with our deal center platform is now that you have your deals there, you are able to leverage or you will be able to leverage the cross deal analytics. So what have been typical blockers for you, risks that you've identified, and then how can we quickly apply that to a new deal that you're looking at to just immediately surface those off? That, hey.

These have been an issue for you in the past, so you might wanna immediately take a look at them now. So if you are going to pass on that, you can do it faster.

On the same note, anything around culture, Often one of the most difficult ones to kind of quantify or, like, think about tangibly any areas that you're able to leverage new technology and AI to get a better handle on a cultural fit.

Gregg Albert

50:41 - 53:53

I got some so I did I mentioned quickly earlier is that I think the old way that people, you know, think about culture and deals and they just again, you know, we'll have we'll think that magic will happen and everybody will do exactly what they want and they'll all be under one umbrella in one house and everybody will be happy. But unfortunately, we're human beings and the employees that work at every company are also human beings and that's just not how it works.

So what we're able to do is through a series of surveys, data scraping on various social network platforms, whether LinkedIn or Fishbowl or any of the other ones, leveraging even earnings reports and thinking about how your companies are communicating to the street if they're publicly traded, taking all of this data, including the surveys, and kind of like stirring it up in a pot, and then understanding to where Mossimo was going, the as is and the to be, and getting a really high degree of fidelity on what is the culture, the way of working, the operating model, even the org design post close over, let's say, day 100, year one, year two, right, of what that actually looked like with some very with with some specificity. So it's not just boxes and wires like org design of, like, ten years ago, but it's like what are the roles and responsibilities and the accountabilities? And then even on that org chart, and I think somebody somebody mentioned this, is that there are going to be agents on the org chart.

Like, I'm like, my you know, I have three high school kids, and I'm pretty sure that their first job, real job, once they get off mom and dad payroll is gonna be managing agents in one way or another. In a substantially similar way that I have a team that I manage and I have a boss that manages bosses that manages that manage me and it'll be kind of part of the organizational way of working.

We're not quite there yet, I think, to industrialize this kind of thinking, but we're we're we're we're on the cusp of being able to do it. And an m and a is like a deal is a terrible thing to waste, and we're rolling out these types of assessments and design design thinking and how to go about, you know, various scenarios now in transactions.

Do they all stick every time? No. But come we're seeing Fortune five hundreds and private equity specifically who are leaders in this space, really rethinking the way that they've been doing deals for the last decade.

And it's actually pretty it's pretty cool and exciting. I mean, my business is a services industry, and to see clients get excited and and, you know, leaning into these new methods, ways of working that drive an increased cultural alignment, and also from a business case perspective, that leads to a decrease in unmanaged attrition, particularly in markets that are white hot.

And, you know, unmanaged attrition goes up through the closed window three x versus baseline because of all of the uncertainty and the unknowns and the culture, you know, considerations. So this kind of dampens down some of the risk as well as give confidence to the investment case, as you think about cultural elements of the of the diligence.

Angela DeSanctis

53:53 - 54:34

Yeah. I love that example of how you're using surveys to help with culture.

Very, very smart. Alright.

Given that we, just have a little over five minutes left, I did wanna give each of the guests an opportunity, just to to close out. I think it it might be helpful, you know, to if you could share with us if there's one message that you'd want corporate development leaders to take away from this research, what you're doing best practices, you know, what they should be doing today to prepare, be a great time to to share that now.

And, maybe, Basimo, we could start with you.

Massimo Malizia

54:34 - 55:36

Yeah. I would say, you know, what I see is that there is so much that we can do.

We just crashed scratched the surface. One of the limiting factors are people and the skill set that we that we need to put in place everything that we want to do.

So one thing that I would advise to, to other companies is really looking at the at the skills that you currently have and if that's the the the right match for where you want to be. And, you know, we in in some way, we need AI and active people that can really help us in redefining all the processes that we have.

And it's a it's a more tech savvy skill set that generally incorporate development, corporate integration, we really didn't have too much of in the past. Yeah.

And some roles specifically on the scouting side. Right? But, a lot of the people that work here in a, you know, they they were not, you know, necessarily super tech savvy.

So that's in some way a a new skill set that we need to get into the industry in order to really take, take advantage of the AI revolution.

Angela DeSanctis

55:36 - 55:40

Great. Thank you.

Greg Schlen?

Greg Schlimm

55:40 - 56:21

Sure. My advice is to keep thinking broadly.

That there's so many use cases out there and there's so many bubbling up from corners that maybe were unexpected six months prior that, as you're thinking forward, it's very easy to get into big picture mode. Hey, we're gonna transform everything.

It's also very easy to get into detailed corner mode. We're gonna be super efficient and not need people to do this, that, or the other.

But you probably need to keep both in mind and make sure that as you go down your AI journey, lift your head up maybe once or twice a year, see what new ideas are out there that you might wanna pull into it, and keep a balance of the big picture strategic and the very tactical ROI.

Angela DeSanctis

56:21 - 56:25

1. And Craig Albert?

Gregg Albert

56:25 - 58:49

Yeah. I mean, I think a little bit of but but what what Massimo and Greg both said is as it relates to AI use cases in corporate development motions, fools rush in.

So find some of the right find some of the right places in the right part of the deal life cycle, do some experiment experimentation even though I've somebody so one of the pundits recently wrote that the experimentation era and AI is over. I don't even know how that's possibly true.

Right? I think we'll be constantly experimenting as new things come about. There's that's that's just a factually incorrect prediction, I think.

But, like, again, fools rush in, anchor into what what's gonna work best for you, start small, move slow, and then learn your way forward. And I think to Mossimo's point, it'll be grassroots.

I mean, like, what we're seeing internally at Accenture is a a firm of 750,000 people. I mean, we're all kind of like naturally curious and type a personality.

So we're gonna we're gonna lean into this, but the actual use cases, I think people are are conferring and learning from each other, on where to focus time, effort, money, and resources against. And it's the same as I speak with clients.

So I speak with, you know, the entire corporate development team of a fortune 100 technology company, two weeks ago. And, you know, I got all these quest it wasn't like a SWAT team that Massimo was talking about, is that everybody everywhere was trying to leverage AI in every in every case.

And guess what they were doing? Absolutely nothing different because this person was using it to do this, and this person was using it to do that, and they cross canceled. So how do you think about people talk about programmatic m and a.

I would almost use the same logic in terms of programmatic AI, GenAI, and a Gentec AI adoption in m and a use cases. And then think about it.

As I said before, a deal is a terrible thing to waste. So if you're going to take a little bit of more incremental risk or let's say redesign processes that companies couldn't test no fortitude to do prior to the deal, use the deal as a catalyst to do, learn, and experiment with things differently.

And then I think that success will forget success. Like, that'll quickly spread through an organization if they're seeing real tangible real tangible results.

Angela DeSanctis

58:49 - 59:56

Yes. I think being intentional, strategic about where you're going to use AI and and starting small, you know, finding some of those pain points that you've struggled with and really thinking about how you can use AI and and not going at it in a ad hoc manner in the middle of a deal to your point, is smart.

And then, you know, from from what we've seen, there's been such a rise in attendance for events like this, networking events that we do, just getting out with your peers. There's a a thirst for knowledge.

People wanna know how others are using AI and how they're being successful. Get out there.

You know, talking to people is the best way, either in small groups or larger groups to understand. So, you know, I think, some of us maybe shy away from those types of events, but they can be very helpful, in this case.

So, yeah, I think we, I think that concludes our event for today. I wanna thank our guests here, Massimo and Greg.

Both Gregs, thank you so much. This has been.

Massimo Malizia

59:56 - 59:56

you.

Angela DeSanctis

59:56 - 01:00:04

such a pleasure. I've gotten so much out of it.

I'm sure the audience has too. And, yeah, thank you for everyone joining today.

It's been great.

Massimo Malizia

01:00:04 - 01:00:06

Thank you, very. much.

Greg Schlimm

01:00:06 - 01:00:06

Cheers.