Dealmaking in the Age of AI: The Power of Data

Jeff Jacobs (00:02): 

You are listening to Solomon Connects, our podcast, covering the latest trends and insights driving deal-making today. I’m Jeff Jacobs, head of M&A and COO of investment banking at Solomon Partners, and I’m here with Craig Muir, our head of software, data, and analytics.

Craig Muir (00:18):

Thanks for having me, Jeff. It’s great to spend time with you.

Jeff Jacobs (00:22):

Always a pleasure to get a chance to speak with you. In this episode, we’ll be covering the tech sector from multiple angles: market activity, key themes, subsectors, and more. Craig, why don’t you tell us about yourself and your team?

Craig Muir (00:37):

Thank you, Jeff. I head up the software data and analytics team here at Solomon — from a standing start in June of 2023. We now have around 20 bankers focused exclusively on the sector. With our CEO’s continued support, we are actively building the platform and are looking to bring in leading sector bankers who want to help build the number one mid-market tech group in the US. As for me, I’ve been doing this a long time. I started my career in London at a software data and analytics boutique, called Quail Monroe, which we sold to Houlahan Lokey in 2018. After the sale, I moved to the US to establish and lead HO Lands data and analytics platform in North America. Over the course of my career, I’ve seen significant change as the sector continues to evolve. As I sit here today, I think we are at a really exciting point in time with the advent of gen AI — and the next five to 10 years will be very dynamic and exciting indeed.

Jeff Jacobs (01:33):

One of the things we’ve seen is that deal-making has been muted for the first part of 2025, but we’re starting to see an uptick in activity, and we expect that to continue later into this year and into 2026 due to several factors. Interest rates are starting to get cut. There’s a ramp in PE activity. Can you share a little bit about how you think the market environment has impacted the tech sector and your outlook for the rest of the year?

Craig Muir (01:59):

I agree, Jeff, the tech sector has been somewhat muted over the last few years. We saw significant activity during the COVID years. In essence, COVID pulled forward some activity, and it has taken some time for the market to catch up. The slowdown has been compounded by a rise in rates, dearth of liquidity, and a move to a more normalized value construct. In addition, as you’ve talked about on the podcast before, we’re still seeing a disconnect between buyer and seller aspirations. However, we are starting to see the market find its footing. Both pitch activity and quality are improving. There are few factors behind this, starting with rates coming down. As you mentioned, an additional and very powerful dynamic is on the PE side. Some groups are coming under significant pressure to deploy capital, while others are being pushed to return capital. The PE merry-go-round is somewhat gummed up, which is putting pressure on fundraising.

(02:54)

Lack of distributions and deployment are reducing appetite for LPs to participate in new funds, and that’s ratcheting up the pressure. We think this overall dynamic will help drive activity for some time to come. Furthermore, strategics are sitting on a ton of cash and are reluctant to return it to shareholders. The conversations we are having with strategics are getting more energized and focused. Senior leadership teams are coming under pressure, and as a result, are increasingly prepared to be aggressive. Just look at S&P’s, recent acquisition of With Intelligence for $1.8 billion. At the same time, you have a number of businesses that have continued to perform well. As a result, the market is finding its footing. We anticipate deal volume to really start to build, leading into 2026, so all in all, it’s a good setup to a more normalized level of M&A activity.

Jeff Jacobs (03:48):

Craig, one theme we’re hearing about more than any other these days is artificial intelligence. AI is having an outsized influence on your sector, yet it’s still early days. You recently authored an article, titled “Data Is Powering AI,” where you underscore the pivotal role data plays in the engine behind artificial intelligence. How are you helping clients today understand this driver of AI value?

Craig Muir (04:13):

It is a great point, Jeff. While many of our clients have been working with AI for years, recent developments around Gen AI, and specifically, Agentic AI represent a truly transformational step change. What we’ve been saying for some time now is that we see the power and value of data really increasing in this new world. If you take a step back and think through the evolution of these models from the LLMs that we all know, to highly bespoke models driving specific enterprise use cases, the real value unlocked today is in the quality and the depth of the data that is being used to train and power those models. The underlying tech infrastructure, the actual models themselves, are becoming somewhat interchangeable with marginal improvements from prior releases. What is increasingly becoming a key differentiator is how and what they’re being trained on. Think about a medical researcher leveraging Chat GPT or Copilot to power their work streams versus them using a highly curated use-case-specific AI model. Trained on a combination of internal proprietary data sets combined with highly specific and relevant third-party data, these models are smarter, more relevant, and are increasingly being adopted within workflows which are, in turn, being streamlined through the introduction of AI agents. We see this trend really proliferating and our view is that as a result, the value unlocks are really around the data. We have the view that if you own the data, you own the keys to the castle in this new AI world.

Jeff Jacobs (05:44):

Let’s stay on AI for a minute, Craig. How should clients consider commercial applications? Should they think about licensing AI tools or potentially selling the AI tools and charging for the data that flows through them?

Craig Muir (05:56):

This is a really interesting question, Jeff. My current view is that you will get a blend of both approaches. The large consolidators will look to offer their own AI products or work with alternative infrastructure solutions governed by client preferences, and that they will then pipe their data into those instances to train and supplement the models. Smaller groups will license their offerings, which will, in turn, add to the value of the models. The key for everyone really is how do you charge for your data, and more importantly, how do you protect it from seeping outside of the proverbial capsule? We’re seeing different approaches from different vendors. Some are licensing their data to the large AI models, while others continue to sell direct to clients. There’s a lot that needs to be worked through, and I think it’s fair to say that we’re living in a rapidly evolving and dynamic real-time experiment — not that dissimilar, in fact, to the challenges that the advent of the internet created,

Jeff Jacobs (06:52):

Let’s shift our focus to another trend we’re seeing. We’re starting to see investors and strategic buyers move away from pure software plays and toward tech-enabled businesses, especially those with strong assets. What do you believe is driving the shift and influencing what investors and buyers are really looking for?

Craig Muir (07:13):

Yeah. There’s a view, especially on the PE side, that AI is likely to break down the traditional software barriers that used to exist. To illustrate the point, Jeff, two years ago, you and I could not develop code today. We can by using the tools that are now at our disposal — that’s a scary thought in and by itself –but it’s also changing the investment landscape, as there is a perceived risk around the edges of the software moat. Also, internal teams are now able to build and deploy bespoke solutions more easily, which raises a question mark around traditional software vendors’ ability to sell more product and extract a greater share of wallet. My personal view is that the enterprise software market will be fine. The solutions are so embedded and complex that no one is ripping them out, and it’s around the fringes where the real risk exists.

(08:02)

The playbook for tech-enabled services is clear. If you buy a business for, say, 12 times EBITDA or less, drive AI through the platform to build recurring and highly sticky products and sell it for 20 times or more, that’s good business. In addition, a lot of these tech-enabled services businesses are sitting on a huge amount of data. Historically, there’s been little appetite or ability to productize us, but in today’s AI-enabled world, it’s increasingly possible to collect, synthesize, and productize that data. It’s one of the reasons that we at Solomon are keen to build out our tech-enabled services activities, as we see a lot of synergy with our core data and software capabilities.

Jeff Jacobs (08:42):

So, as we get closer to the end of this year, and as we wrap up this episode, do you have any final takeaways and thoughts for clients across the tech sector?

Craig Muir (08:52):

There are a couple of things. I would say, owners and management should continue to focus on quality. What is the best indicator of quality retention — both on a gross and net basis with the right retention, you have a platform from which you can build. Growth is also key. What we get asked a lot these days is how our clients should think about the rule of 40. For instance, if we cast our memories back to ‘20 and ‘21, it was all about growth and over-indexing to growth being closer to 40%. We then moved aggressively into focus on margin, and as a result, management and boards over-index to margin at the expense of growth. Today, there is more of a balance. As with all of these things, the right mix is highly dependent on the business, its market scale and broader competitive dynamics in the right end market with capital, light businesses, business models that have product market fit margin comes with scale.

(09:45)

If you have the right underlying metrics. There are a number of examples of scaled data-analytics businesses with margins in excess of 50% that continue to compound at double-digit growth rates. Data businesses now can generate higher margins than software businesses, but with AI, we anticipate margin expansion on the software side as well. The final thing I would say is that if you’re thinking of an exit, bring in your banker early. Do the work, especially around the key business metrics and positioning of the story. Get prepared and put yourself in a position to move quickly to capitalize on favorable market conditions. We’re working with a number of clients who have adopted this approach.

Jeff Jacobs (10:23):

Craig, thank you for joining me. It’s always insightful to spend time with you and to talk about the activity and trends across the tech sector.

Craig Muir (10:31):

It’s great to catch up with you, Jeff. I really appreciate the time and the thoughtful questions. As always.

Jeff Jacobs (10:36):

For more M&A insights, be sure to check out solomonpartners.com — and to our listeners, thank you for tuning in.