AI is starting to change the math behind M&A. Revenue still matters. EBITDA still matters. Customer concentration, margins, leadership, and recurring revenue still matter. Nobody is throwing out the fundamentals.
But buyers have more to underwrite now. They’re looking at how AI changes the economics of the business, what becomes more valuable inside a larger platform, and whether the company’s core offering gets stronger or more exposed as the technology improves.
Those questions are becoming part of how buyers think about fit.
And I think they’re going to have a meaningful effect on which companies get bought, which companies buy and how industries consolidate over the next several years.
Buyers Are Looking Beyond the Current P&L
Historically, a buyer could learn a lot about a company by looking backward. Revenue growth told you whether demand was increasing. Margins told you something about operating discipline. Retention showed whether customers stayed. Customer concentration exposed risk. The leadership team told you how dependent the business was on a handful of people.
Now, AI adds a more forward-looking question: what happens to the economics of this business as the technology improves?
Take two marketing services firms with similar revenue and EBITDA. One relies heavily on manual production, bills clients for hours, and needs to add headcount as accounts grow. The other has built AI into research, planning and delivery, allowing the team to handle more client work without increasing labor at the same rate.
On paper today, those businesses might look similar. Their economics three years from now could look very different. A buyer has to account for where both businesses are headed, not just where they stand today.
AI Can Strengthen an Asset or Expose It
AI doesn't automatically make a company more valuable because the company uses it. Buyers care about what the technology does to the business.
The value shows up when it improves margins, shortens delivery time, strengthens retention, or allows the team to support more revenue without adding headcount at the same rate. Proprietary data can improve the output, while better technology can make existing client relationships more valuable.
The same logic applies in reverse. If a company earns premium margins from work that customers can increasingly automate themselves, a buyer has to account for that exposure. If the differentiation comes primarily from executing a process that AI makes cheaper and easier, historical performance may become a weaker indicator of future value.
A good trailing EBITDA number is still a good trailing EBITDA number. But a buyer also has to understand what is likely to happen to it.
Marketing Shows What This Looks Like
Marketing is a useful place to watch this play out because the industry has always been fragmented. Strategy lived in one place. Strategy lived in one place. Creative was handled somewhere else. Media, creator programs, data, and measurement were often split across agencies, vendors, and internal teams. Companies connected the pieces through meetings, briefs, spreadsheets, and a lot of coordination.
That structure made more sense when each capability required its own specialized team and significant resources. AI is changing the cost of delivering that work.
Creative can be produced faster. Research can happen faster. Campaign data can be analyzed faster. Media decisions can increasingly be automated. Smaller teams can handle work that once required more people and more time.
As execution gets easier, the value of simply owning another execution capability starts to change.
That puts more value on capabilities that improve what happens before and around execution: better data, stronger customer relationships, proprietary expertise, audience intelligence, measurement, or technology that improves how decisions get made.
You can already see buyers thinking this way. Selling adjacent services isn't enough. The combination needs to improve the economics or create a capability that would be difficult to build independently.
That makes the value of the combination more important than simple category adjacency.
Capability Gaps Are Becoming M&A Opportunities
This also changes the build-versus-buy decision. Every leadership team has capabilities it would like to add. Historically, the decision often came down to time, cost, and available talent.
AI adds another variable because the capabilities companies need are changing faster.
A company may decide it doesn't have three years to build a data operation internally. An agency may have strong client relationships but realize it needs deeper technology or measurement capabilities now. A healthcare organization may need AI infrastructure and specialized expertise that would be difficult to develop while simultaneously running the existing business.
Acquisition can close that gap faster. And when several companies across an industry reach the same conclusion, you start to get consolidation.
We've seen versions of this before. Industries reorganize when an important capability becomes expensive, scarce, or necessary to compete. AI has the potential to create that pressure across a lot of categories at once.
Healthcare Is Facing Its Own Version
Healthcare is a good example because AI adoption remains uneven. Some organizations are already incorporating AI into diagnostics, administrative workflows, patient engagement and operational planning. Others are earlier in the process and still operate with fragmented systems and limited visibility across the organization.
The M&A implications won't look exactly like marketing, but the underlying issue is similar. If a capability becomes increasingly important to operating efficiently, companies have to decide whether they can build it, partner for it, or acquire it.
Larger organizations often have more capital and infrastructure to absorb those capabilities. Smaller operators may have valuable specialties, customer relationships, data, or expertise but lack the resources to build every layer required around them.
Put those two things together and you have a reason for combination that goes beyond adding revenue or geography. The acquisition fills a capability gap.
Integration Becomes Part of the Valuation
There's another piece I think buyers need to pay attention to. If more of the value comes from connecting capabilities, you can't separate the acquisition thesis from the integration thesis.
Buying a great data business doesn't create much value if its data stays isolated. Buying an AI capability doesn't help if the rest of the organization can't use it. Acquiring specialized expertise doesn't change the economics if it never reaches the broader customer base.
The value comes from what becomes possible when the two companies operate together. One company’s data can improve another’s decisions. Technology can reduce the cost of delivering an existing service. A capability acquired for one business can be deployed across several, while existing customer relationships can create distribution for something the acquired company struggled to sell on its own. That starts to show whether the deal is simply adding revenue or building a stronger system.
The Deal Math Is Getting More Complicated
EBITDA isn't going anywhere. Neither are the other fundamentals buyers have always cared about. If anything, uncertain markets make disciplined underwriting more important.
But the definition of a good asset is evolving around those fundamentals. Buyers increasingly have to understand where AI improves the economics, where it creates exposure and where combining businesses can produce something neither company could build as efficiently alone.
Some companies will discover that AI makes their existing model considerably more productive. Others will find that capabilities they once considered optional have become necessary. And some will realize that a business they would have built internally five years ago makes more sense to acquire today.
That's where I expect a lot of the next wave of consolidation to come from.
AI is already changing how companies operate. Now it’s starting to change what buyers value and which businesses they want to own.
