How Serial Acquirers Build a Repeatable M&A Machine w/ Gwen Pope | This Is M&A

Um, I know most companies when they think about M&A and finding the r- it's, they're trying to find the right deal, right? But the reality is even great deals fall apart if there's no system behind their, behind how they're actually executed.

So the best acquirers, they don't just do deals, right? They build machines that produce results over and over again.

And so today what we're gonna do is we're gonna dive into M&A as a system, not an event, and we're gonna really try to impact, um, as much as we can four, four key themes or topics.

Um, one, which is how companies move from opportunistic deals to building a repeatable M&A engine.

Um, two, why integration-led diligence changes how, um, to evaluate and execute deals.

Um, where value actually breaks down post-close and how to fix it.

And lastly, what a modern AI-enabled M&A operating model actually looks like today.

Um, so with all that being said, I know we have a lot to cover, um, so let's just jump in.

Gwen, where do you feel like we should get started on this?

I think maybe to, to set the table, uh, pragmatically, um, e- especially those of us who've, uh, who've worked in, in M&A industry for, you know, for years, will start to get a little bit complacent, maybe a little bit, uh, you know, not to g- to get negative here, but maybe a little bit jaded and think, "Ah, M&A, it's-

Mm

... it's always messy," or here's these set of pain points that just kind of are, you know, always exist, right?

Yeah.

It's always gonna dog you.

It's always gonna be almost impossible to track those synergies or what have you.

Um, and, and to say first- Positive message.

Yes, it is possible [laughs] to solve for these problems.

But second, that the time is really now, or technically yesterday.

But the time is now because the nature of the global business landscape and as well the implications for, uh, M&A operations, execution, particularly for serial acquirers, which is kind of the focus of today's conversation.

Um, the margin for error in defining a valid thesis from the get-go and then executing on it efficiently and with impact.

And, and b- what I mean by impact is you capture the value, you create the value, right? That, you know, that was underlying your thesis.

The margin for error is, like, v- so slim.

It's, it's super slim.

Right.

So whereas you might have in the past been able to say, "Ah, it's messy," right? Messy, it's inefficient, and, you know, when you look, um, internally and, and look at the track record, let's just pretend you're at a, you know, enterprise serial acquirer and, and you do maybe, I don't know, you close on three or four deals, uh, range of, of sizes per year, and that means that you maybe proportionately look at, let's say, you know, 120 plus, you know, potential targets at the top of the funnel.