M&A Today: AI’s New Role on the Deal Team

In this episode of Solomon Connects, Solomon Partners’ Head of M&A and COO of Investment Banking Jeff Jacobs joins M&A Director Chris Moynihan to discuss how AI is being deployed across investment banking today. They explore where AI is already accelerating workflow efficiency, why relationships remain central to advisory work, and how firms are thoughtfully integrating AI without compromising execution.

Transcript

Jeff Jacobs (00:02):

Welcome to Solomon Connects. This is our M&A monthly podcast where we break down the key market forces shaping deal activity. I’m Jeff Jacobs, Head of M&A and COO of investment banking at Solomon Partners.

Chris Moynihan (00:13):

And I’m Chris Moynihan, a director in the M&A group. Today, we’re exploring a topic that’s quickly becoming part of the day-to-day workflow in investment banking: How bankers are actually using AI today.

Jeff, to set the stage for our listeners, what are some of the most common, tangible ways Solomon bankers are actually using AI?

Jeff Jacobs (00:32):

We actually are seeing the clearest adoption around information gathering, market research, company research, summarizing filings, earnings transcripts, also pulling together background materials quickly when teams start working with a new client or looking at a new business. AI is also being leveraged for some of the initial drafting, some of those first-cut outlines for pitches or for marketing materials. It doesn’t necessarily get you all the way to client-ready output, but it’s certainly helpful in accelerating workflow, and it meaningfully reduces the time it takes to move from a blank page to a finished work product.

Chris Moynihan (01:04):

Zooming out for a second from these examples, what is the big picture here? What role does AI really play as a complement to the traditional banking skillset?

Jeff Jacobs (01:13):

The most important thing is to view AI as a tool. It’s not a substitute for judgment. It’s not about replacing core banking skillsets. It’s about enhancing how efficiently those skills get applied. AI is particularly effective at helping bankers process information faster — and certainly more broadly, that allows teams to spend more time on interpretation, on decision-making, on advice to clients. In that sense, it’s less a replacement for analysts and more a productivity shift. The core fundamentals of banking, like client relationships, sound judgment, strategic advice, strong execution — none of that has changed. But the pace at which teams can work through information clearly has.

Chris Moynihan (01:49):

I think you briefly touched on a few of these at the beginning, but modeling and analytics are often cited as an area where AI could be transformative. What are you realistically seeing today when it comes to AI’s role in financial analysis?

Jeff Jacobs (02:02):

Well, it is changing rapidly, but there are areas where expectations sometimes get ahead of reality. We are absolutely seeing progress with some of the AI tools when it comes to building models, but they aren’t necessarily a full end-to-end solution yet. It’s helping with components, sanity-checking, certain assumptions, flagging inconsistencies, stress-testing scenarios more quickly. It’s also useful for reviewing models, asking the kinds of questions an experienced banker would ask: Where is value coming from? What breaks if growth slows? What assumptions matter most? These are the types of things that can meaningfully improve the quality of an analysis.

Chris Moynihan (02:37):

And as bankers, especially junior bankers, rely on AI more for analytics, how much confidence are teams placing in these outputs, and where does the healthy skepticism still matter?

Jeff Jacobs (02:48):

There’s definitely still a cautious and measured level of confidence when teams look at AI outputs, and that’s appropriate. AI is only as good as the prompts and the judgment applied to its output. The best teams often treat it like a junior resource. It can move fast. It can process a lot of information from public sources, like filings, earnings transcripts, newsflows. It’s also helpful in digesting large data sets like the financials, diligence items, or other materials we frequently receive directly from the companies we’re working with. These tools are becoming more accurate every day, but they still certainly need supervision. Nothing goes to a client without experienced bankers applying judgment, context, and accountability, and oversight.

Chris Moynihan (03:25):

And how is this shift affecting the team dynamics, especially for the junior bankers and how they spend their time?

Jeff Jacobs (03:31):

What it’s really doing is changing where juniors spend their time: Less time on pure data gathering, more time on interpretation, on synthesis. That’s actually a positive, if managed correctly. Younger bankers still need to understand the fundamentals, but AI can free them up to engage earlier in higher-value work: asking smarter questions, understanding the strategic rationale behind deals. And frankly, that can be a better training ground than simply formatting slides late into the night.

Chris Moynihan (03:56):

It raises a good question on training — and I know, Jeff, you’re actively involved in a lot of the analyst training. If AI is handling some of that foundational work, then how do you make sure junior bankers still learn the fundamentals of the job? Do they need to still build the analyses themselves to develop that intuition?

Jeff Jacobs (04:11):

Yes, absolutely. The fundamentals still have to be learned the hard way. Junior bankers need to build models, they need to read filings, they need to work through the analysis themselves. That’s the only way they’re going to develop the instincts that make senior bankers effective. AI can accelerate the work, but it’s not going to be a shortcut to the learning. At Solomon, we are being very deliberate about making sure analysts still get those reps. The goal is to use AI to remove some of the drudgery but not to remove the craft.

Chris Moynihan (04:39):

And what are you seeing on the client side? Are they expecting a higher volume of ideas now that they know that you have these AI tools on your side?

Jeff Jacobs (04:47):

Increasingly, yes, clients expect speed. They expect well-informed views backed by data — and AI can help banks respond faster — but clients care about insight, not tools. They’re hiring banks for judgment, for pattern recognition, for advice in complex high-stakes situations — and AI supports that, but it doesn’t replace it.

Chris Moynihan (05:05):

Before we wrap up, what’s one common misconception about AI and investment banking that you’d like to correct?

Jeff Jacobs (05:11):

I think the biggest misconception is that it’s an all-or-nothing transformation. We don’t look at it as a switch flipping overnight. It’s incremental. The bankers who will benefit most are the ones who learn how to use it thoughtfully, knowing where it adds value — and just as importantly, where it may not. The core of banking hasn’t changed. Relationships, judgment, and trust still matter the most, but AI is going to be revolutionary in how we approach day-to-day work.

Chris Moynihan (05:34):

It certainly will be. Thanks, Jeff, for your insights as always, and thank you to everyone for tuning in. Be sure to check out solomonpartners.com for more insights, and we’ll be back next month with another M&A update.