Cisco and Accenture on using diligence AI to plan integration before close

• Cisco now uses AI contract analysis during diligence to agree its plan for an acquired company's customers before close, instead of after it.
• In the Transaction Advisors Institute survey, 93% of corporate acquirers use AI in due diligence, up from about a third two years ago, yet only 9% trust it enough to just spot check.
• Accenture's Gregg Albert says sellers are more cautious about sharing information early, because with AI it is harder to control where shared data ends up.
• Malizia plans to map a target's quote-to-cash process from data room documents and compare it with Cisco's before close, work that used to happen after close.
• Cisco runs AI through an internal application and needs to be able to delete all target content if a deal does not go through.
• Both practitioners favour a small dedicated team and a few chosen use cases over everyone experimenting on their own.

Until about a year ago, Cisco only settled what to do with an acquired company's customers after the deal closed. Massimo Malizia, director of corporate development integration at Cisco, says that meant the strategy was not committed, and ending sales of a legacy product would take forever, because after close there is much more resistance. Now his team uses AI to analyse the contracts during diligence, focusing on the big ones, and aligns the target and internal stakeholders on a strategy in front of the chief financial officer before commit. The announce-to-close period goes on customer communication, so that once the deal closes, they just start executing right away (around 14:50).

“If you wait too long the target company start having their own integration strategy, and then it's very difficult to make them change your mind. You need to fight the uphill battle again.”

Massimo Malizia, Director, Corporate Development Integration, Cisco

That example sits at the centre of this webinar, hosted by SS&C Intralinks with the Transaction Advisors Institute, on research among senior corporate development leaders at public and large private acquirers. Moderator Angela DeSanctis of Intralinks gives the headline: two years ago about a third of corporate acquirers used AI in M&A due diligence, today 93% do. Legal agreement review leads, with 79% of teams using or piloting it. Trust lags. Only 9% trust AI enough to just spot check its work.

“There's no Men in Black pen, right? Remember the neuralyzer from Men in Black. Once it's out, it's out.”

Gregg Albert, M&A practice lead for the Americas, Accenture

Greg Schlimm, who led the research at the Transaction Advisors Institute, does not read that 9% as a failure. If the bar is perfect, defensible work product, he says, that is an extremely high bar, and you don't need it anywhere near that high to get value: as a thought partner, or to find corners of data you would never have time to look into yourself (around 27:16). In the survey, the number one intended outcome was to learn what's possible and reimagine processes. The next was getting more done in the available hours.

“If the bar is perfect work product that is defendable and defensible and can be explained, and then that's an extremely high bar, and you don't need the bar to be anywhere near that high to get value from this cool technology.”

Greg Schlimm, Vice President, Strategic Initiatives, Transaction Advisors Institute

For Malizia the value is time, and what you do with it. The first uses at Cisco were legal contract analysis and the classic programme management work of meeting minutes. He tried to automate contract analysis for M&A ten years ago, he says, and the tools were pretty good on standardised templates but that was about it. The diligence period, in his words, is when you need to focus outward on the asset you are buying and inward on aligning your own company. The faster the reading goes, the more time is left for that alignment rather than reading contracts or going through Excel.

“The easy button answer on that is when the decision is made, right? I don't think we're outsourcing M&A decisions to AI quite yet, right?”

Gregg Albert, M&A practice lead for the Americas, Accenture

Sellers see it differently. Gregg Albert, who leads Accenture's M&A practice in the Americas, describes ingesting 10,000 contracts on very large carve-outs and giving buyers an early picture: here are the biggest customers, this is a large percent of revenue, here are the considerations around pricing. But he says that ten years ago there was more appetite for sharing information early. With AI it is hard to control where data is housed and how secure it is, and once it's out, it's out (around 20:39). Corporate development teams may need a clean room or clean team to look at it.

Cisco has not tried the niche legal AI vendors yet. Malizia says Cisco runs large language models through an internal application, for security reasons. In due diligence the team needs safety around intellectual property that doesn't belong to Cisco, and the capability of deleting all content if the deal doesn't go through. That requires significant guidelines and control. Any buyer's AI setup has to answer the same question before it touches a data room.

The most practical idea in the hour is what Malizia calls blueprinting (around 36:35). For the next deal, he wants to run AI over the documents shared in the virtual data room and over the meetings, build a visual map of the target's quote-to-cash process, and compare it with Cisco's own map. Blueprinting was something Cisco traditionally always did after close, because it is very time consuming. He adds that acquirers also need their own internal discovery done with AI: a company as large as Cisco may have different processes in different business units. Albert calls the result a digital twin of the as-is and the to-be.

Albert goes furthest on agents. In a very large telecom merger late in the close window, he says Accenture is building 18 agents to cross-sell services to existing customers by geography, zip code and user profile, and he predicts that within six to nine months this becomes standard. Data migration that was measured in months is now measured in weeks and days, he says, and deals go to die on data. On culture, he describes combining surveys, data from LinkedIn and Fishbowl and earnings communications to plan the operating model, and notes that unmanaged attrition goes up three times versus baseline through the close window.

Judgement stays human. The easy answer, Albert says, is when the decision is made. A colleague of Malizia's compares AI to a very smart, fast-learning new hire you still need to train. Cisco has carved out a small team inside corporate integration for AI business cases, because Malizia doesn't think you can do it part-time, and he expects a bottom-up revolution, with ideas assessed on return. Albert warns against the opposite: a Fortune 100 corporate development team where everybody used AI in every case and was doing absolutely nothing different. His advice is to start small, move slow and learn your way forward.

Once both sides run AI over the data room, the questions move earlier: what a buyer can analyse before close, and what a seller is willing to share. Before you open a room, ask how each bidder's AI tools store, permission and delete its content.

0:07 All right, welcome everyone and thanks for joining us. My name is Angela DeSanctisand 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 Interlinks for over a decade and during that time I've had a really 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

1:14 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 onethird of corporate acquirers were using ANI a AI in their M&A due diligence and today that number is a staggering 93%. So, it it it's fascinating to see that shift happen in such a short amount of time, especially because I'm sure my my guests 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,

2:23 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 Schlimm 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. Masimo Malatia, 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&A strategy at Accenture and he can give us his p perspective from the advisory standpoint and and where the market is is really heading as a whole. All right. 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 a chat feature on the Q&A in the Q&A section. So feel free to add any questions there and then we'll have time at the

3:34 end to go through those. All right. 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 Masimo, I'll I'll start with you. you know give us a quick sense of what Cisco's M&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. Sure. Cisco is a serial 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.

4:27 so where we basically are interested in the actual product line to integrated 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 generally the bulky stack and talent but there is also some significant deals on the product side. Okay. So you're a you're a very busy man. all right, great. Thank you. And to Greg Albert. So, Accenture being in the more of the advisor seat, how many corporate acquirers are you advising right now just on AI adoption? Yeah, it's a it's a great question, Angela, and I guess I'll answer it in two different ways. and a little bit piggybacking on Masimo's point. So number one, Accenture being is publicly traded as you're aware about 75 billion 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

5:36 announcement every 3 weeks plus or minus. Most of those are small tuck in as Masimo said talent andor 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 Accentra 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 12 to 18 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 co-pilot and leveraging it through meeting notes how even how do you think more advanced around agentic

6:48 which I think we'll talk about so the whether you're a strategic fortune 500 company a G2000 organization a private equity portfolio io 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 deply to to deploy AI and where. Yes. Yes. And and to your point, you know, we hear that often as well is 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 Schlim. So you know why very interesting results that we saw from the survey I

8:06 guess why why commission it now? 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 or 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&A

9:13 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 going to read it offline later. Yes. Yeah. Okay, great. all right, 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 10 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

10:25 it just excitement around AI? Was it pressure of deal teams having to look at more deals, execute on more deals? What was happening? I 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

11:32 study, but also to see where things go in the next one to two years. Great. and Masimo, 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. Yeah, I think it was probably between 12 to 18 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 10 years ago. You mentioned the contra analysis. I remember I was working on trying to automate contra analysis for M&A 10 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

12:40 so there was a ramp up in to learn the software that they only that while AI is going to be a general you know kind of tool available once you learn you can apply to so many different things it's not going to 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 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. 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 is is only the beginning

13:51 is 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 postc close is is 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 my mind that's one of the the most apparent value at this point.

14:52 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 postc close and what the strategy is going to look like around integration. 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 acquire companies, we also acquire a customer base. Sometimes the customer base you know might not be transition 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 going to be available on Cisco. Regardless, we need to have a strategy of what to do with these customers. Until a year ago, we would had the strategy done only after close because there was time 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 so forth. Once you and so that

16:00 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 define post close. What we're doing now is that we actually accelerating, analyzing all those contract, focusing specifically on the big ones, aligning the target company and our internal stakeholders to a strategy in front of our chief financial officer during commit and then using the announce 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 m 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 your 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

17:12 that you can executing at a close rather than wait after close. Yeah. 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 Albert 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? 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,

18:20 where where 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 carveouts and separations and putting businesses on the block and luckily on the that's on this the supply side on the demand side and I'll come back to your question and also weave back Greg's research and Mosimo's point on the demand side you're seeing $2.1 trillion 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 1980s across corporate Fortune 500 generally, I would say corporate America, but really Fortune 500 globally. and if you think about AI use cases within that construct, it's and and coming to Masimo's point, you have the legal contracts, just the legal, but like that's change in control provisions, assignability clauses with or without consent. I mean as Mosmo said like I remember going through the same exercises trying to build out models with OCR to

19:30 accelerate that but that's like table stakes and I think you t like they're contracts legally legal contracts however business people need to have the T's and C's around pricing continue you know the the language around pricing price escalators and that ultimately folds into the investment case and rather than have eyeballs focused on that. We're ingesting 10,000 employee contracts on very large, to your point, Angela, carveouts and separations, 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 carveouts and separations with buyers. and I'd say also is I think 10 years ago there was a lot more aptitude and interest in sharing more

20:39 information earlier on in the process. I think with the invent of AI, it's hard to control, and 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? The remember the the dinuralizer from Men in Black. You can delete 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. They'll inform the investment case. And it's not just total addressable 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

21:48 core of the integration planning and I don't mean just milestones but down to the level of you know Masimo Greg Angela double Greg me and what's our responsibilities postc close to really drive value creation and I think that's been the the big change over the last I'd say 24 36 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. 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 all these emerging use cases. how will the board respond to this investment thesis based on how they've responded to other thesis we've put in before them in the past? how likely is this investment, sorry, how likely is thisation 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

22:57 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. Yeah, definitely. And I think it 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 drain them and as we actually evolve into asking AI to confirm the strategy or even define the strategy, that's always going to be the case. How do we train them? What information do we give them to to make that decision? And that's a little bit how the you know kind of the the the complicated part but we're going to get there but I feel like we're still a little bit away from it.

23:45 there is a colleague of mine that you know use this analogy that is yeah is a little bit like a new hire right is is having a very smart super fast learner new hire. You still need to to teach the new hire. you still need to train them and and give them some guidance before you can they can actually be effective. 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, but like which areas of the process will always require some type of human judgment? 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&A decisions to AI quite yet, right? so but I think the AI is the and while I like Masimo's

24:58 metaphor of a 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 highfidelity planning process to achieve the strategic objectives, whether it's topline growth, bottom line growth, new market entry, intellectual property, you know, dot dot dot. I think that's the iron man 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,

26:07 how many systems are out there for that or go to market CRM, corporate development's been like this like this, you know, I don't want to say 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, etc. 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 seuite and a board to take. Yeah. Yeah. what we learned from the data. you know,

27:16 you mentioned the 9% and you know, if the bar is perfect work product that is defendable and defensible and can be explained and then that's an extremely high bar and 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 corners of data that 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 going to take this forward or not? So, yeah. yeah, 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 don't have the exact number written in front of me, but the expectation is that the bar is going to get higher and it's going to get better and therefore more usable and useful. So yeah, we'll find out together. Yeah. No, definitely. And I know that aentic AI in acquisition is Greg's Albert favorite topic. And you know part evolution of the technology sometimes we

28:23 might not have all the connectors that we need to to do that but is is moving so fast I I was actually chatting the other day around prompt engineering right do you remember a year ago 18 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 going to is going to do a lot of work that we now do manually or sometimes we do post close. Going back to the team of due diligence. Yeah. Okay. We're going to come back to Agentic AI, but I do want to dig into one area. I think we see deal teams using co-pilot anthropic the frontier models chatbt we've also now started to see a rise in niche players pop up you know those

29:36 that specialize in legal like Harvey Hebia rogo I was just talking to a colleague that has one that is specializes and Danish law. so they're they're all over the place. Masimo, for for Cisco, have you started exploring any of those other technology players in the AI space? no, not yet. We what we do is that we actually have a pretty decentralized approach to AI for security reasons. So we use lamps through our internal applications called circuit 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 players. We also specifically 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 u some significant guidelines and and control. I imagine the approval process is quite rigorous

30:53 to to be able to be used there. Greg Albert, what type of questions are you getting from clients and both, you know, if you want to speak internally to the corp dev team at Accenture around AI? I mean, I 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 and how we think about internally our own M&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 280 I mean every other sign is you know used to be.com now it'sAI right and everybody's asking again where I started was where do we start you know and let's not talk about co-pilot 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

32:00 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 It's way more than that. to understand 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 in integration but all bundled under the diligence umbrella so that when the board or seuite approve it, it's

33:08 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 Agentic and I know we're going to talk a lot about it and I don't want to I don't want to get ahead of our skis here but like the days of GNA 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&A practitioners for the last 60 years but how do you combine gotomarket 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 end up. I think those are the three hot topics. And if I had to pick a fourth,

34:14 it's all about data migration. Industry 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 going to create recreate a system C that's optimized. like how do we think about data migration and leveraging platforms like Palunteer or or or sub let's 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 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

35:24 of the considerations around that leveraging but again, pro programs and techniques, programs like Palunteer and I'd say more advanced mathematical techniques to mitigate the risk. It's pretty cool stuff. 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 going to be a lot of value. Acquirers 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 must be due diligence and external focus but also the internal discovery and for smaller acquire 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 so understanding where the assets end up and where the data migration ends up to to Greg's point is not always that clear right we need to be focused on the

36:35 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 and maxima so I What can you share with us how you are using AI internally? 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? quot 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 that the the the for example the the the target company quote to Cisco quotash and then you compare them and then you see how you transition from the acquired company quote to the to the buyer quote 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

37:44 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 to cache process in the example that I made and then compare it to an existing map, visual map of the Cisco quote process and just compare the two. And again blueprinting which is this comparison of processes was something that traditionally we 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 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 painful to go after. Yes. And Angela, real quick, point is so critical, right, to any transaction through the diligence and postclose, but the the life cycle of a deal. I just wanted to like

38:50 punctuate one of the points that he made. So we just spend like maybe 15 seconds on it is the blue what what what Mossimo calls blueprinting. I mean on one hand we've been thinking about asis and 2B since the beginning of M&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 going to happen that's going to help us get there but today we're able to build out I'll call the digital twin to simulate what the asis and the 2B need is going to is going to what what the 2B 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.

39:38 Like it's not just a magic wand, right, that comes and voila, you have the 2B, but we're able to even go through simulations in order to get from the asis and then to the 2B. 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 Masimo got real excited and when Mosimo gets excited I get excited. so I just wanted to that that's such a critical point you think about the the investment case. Yeah. 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 inte acquisitions is that the strategy that you defined before close has holes because you don't have enough time to define it correctly 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

40:43 that has holes doesn't really make too much sense. You're going to, you know, you're going to spend time to measure it, but it's not going to provide too much value because the the the criteria that you want to measure where and right in the the first place. So the more we can really leverage AI to define the strategy ahead of time, the better measurability I think we're going to see post. Sorry, Greg. No, it's fine. one of my takeaways from this study that that was most interesting to me was that these large companies as they're thinking about what they might do with M&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

41:50 people then that's that's brilliant. That's kind of moving everything to the left which is what we all want to be doing, right? Absolutely. and Massimmo if we and and Greg Albert 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 But 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. 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

42:58 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 going to pop up as people get more familiar and embed in using AI and they're going to 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 going to 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 going to we assess in terms of ROI if we want to invest on those or or not.

44:05 Okay. Okay. Good. one last question from my side and then we'll move over to the Q&A. I did want to touch on Agentic AI. As we kind of see that evolving into the next area, we're going to see the capabilities mature. both Gregs, Masimo, anything you want to share on how you're using Agentic AI to take things to the next level or how you're thinking about it. Sure. Greg, do you want to go first? Which Greg? Me. Craig, why don't you go first? I mean, I'll I'll I'll jump in. Right. So, I get very excited about and I think to Masimo's point and to Greg's research, right? Everybody's every company is going to need to find their own path to where, how, and when to implement and deploy AI, Gen AI, and Agentic. I don't think there's one sizefits-all for different companies. Just by industry, it'll be different. let alone culture, way of working. there's laws right that are in different jurisdictions in

45:12 Europe for example versus the US but I think around aentic I think in the in the next and this is a prediction and as Yogi Bar said predictions are hard hard especially about the future but I think within the next six to six to nine months we're this is going to be the standard is how we're leveraging agents in order to do some of the tasks that were mentioned previously for example how are you going to be communicating to customers and this is B2B and B TOC both how are you how are you connecting and communicating with customers to cross-ell upsell existing products and combined products based on an acquisition I mean I'll give you a real tactical example I won't use client name but two a very very large telecom merger you know is is in 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

46:19 going to get rolled out to cross-ell services to existing customers based on a whole host of different variables, ge geography, zip code, user profile, etc., etc. 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- cell 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-ell 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

47:27 will be almost like you know using co-pilot for meeting notes. It'll be that standard in certain industries and certain use cases in order to accelerate and minimize risk around topline synergy growth. That's great. Yeah, Greg, you actually answered one of the other questions in the chat was which was a real life example of how to improve the customer experience using AI. So, perfect. I want to just go ahead. No, I think it's a great example. I'm I'm so excited about avoiding Excel spreadsheets and comparing customers and then trying to map them and the address is not the same. So it's not a Cisco customer even if it's just like 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 very exciting. Great. Okay, one question that came in. We've talked about valuable AI use cases within

48:42 legal and financial work streams integration, but what do you see as other emerging trends of using AI during M&A? It doesn't have to be during diligence, but just outside of legal and finance. actually I yeah I mean what I see and there's multiple flavors of it throughout the research and the folks I talk 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. Okay. Yeah. I know that what Introlife is looking at with our deal center platform is now that you have your deals there,

49:51 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 up that hey, these have been an issue for you in the past, so you might want to 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.

50:40 I got I got some. So I think I 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 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 it's LinkedIn or Fishbowl or any of the other ones. leveraging even earnings reports and thinking about how 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 Masimo was going the ASIS and the 2B and getting a really high degree of fidelity on what is the culture the way of working the operating model even the org design postc 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

51:54 org design of like 10 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 going to 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 manage 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 M&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 500s and private equity

53:00 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 a services industry and to see clients get excited and and you 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 unmatched nutrition goes up through the closed window 3x versus baseline because of all of the uncertainty and the unknowns and the culture you know consideration. So this kind of dampens down some of the risk as well as gives confidence to the investment case as you think about cultural elements of the of the diligence. Yeah, I love that example of how you're using the surveys to help with culture. Very very smart. all right. Given that we just have a little over five minutes left, I did want to give each of the guests an opportunity

54:09 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. Yeah, I would say you know what I see is that there is so much that we can do. We just scratched scratch 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 right match for where you want to be and you know we in some way we need AI native people that can really help us in redefining all the processes that we have and it's a is a more tax savvy skill set that generally in corporate development corporate integration we really didn't have too much of in

55:16 the past. Yeah, in some roles specifically on the scouting side, right? But a lot of the people that work in manade, you know, they they were not you know, necessarily super techsavvy. So that's in some way a new skill set that we need to get into the industry in order to really take take advantage of the AI revolution. Great. Thank you. Greg Schlin, 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 going to transform everything. It's also very easy to get into detailed corner mode. We're going to 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 want to pull into it and keep a balance of the big picture strategic and the very tactical ROI. Great. And Craig Albert. Yeah, I mean I think a little bit of but what what

56:28 what Masimo and Greg both said is as it relates to AI use cases and 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 exper experimentation even though somebody 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 going to work best for you. Start small move slow and then learn your way forward. And I think to Masimo's point, it'll be grassroots. I mean like what we're seeing internally at Accenture as a firm of 750,000 people. I mean, we're all kind of like naturally curious and type A personality, so we're going to we're going to 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

57:36 against and it's the same as I speak with clients. 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 qu it wasn't like a SWAT team that Masimo 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-ancled so how do you think about people talk about programmatic M&A I would almost use the same logic in terms of programmatic AI genai and agentic AI adoption in M&A use cases and then think about 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 testim 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 be success like that'll quickly spread through an

58:45 organization if they're seeing real tangible real tangible results. 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 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 want to 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.

59:50 I want to thank our guests here, Masimo and Greg, both Gregs. Thank you so much. This has been 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. Thank you. Thank you very much. Cheers.