The Great Divide in M&A — Courtesy of the AI Impact

Imagine a private equity investment committee reviewing two nearly identical companies. Both generate $30 million in revenue. Both operate in the same industry and serve similar customers. On paper, their historical financial performance looks remarkably similar.

But there is one key difference.

The first company has integrated artificial intelligence across its operations. Sales outreach is AI-assisted, internal reporting is automated, customer service is partially handled by AI agents, and operational workflows are driven by predictive analytics.

The second company operates largely the way it did ten years ago. Most processes rely on manual work, internal analysis is spreadsheet-driven, and revenue growth requires adding more people.

Despite similar financials today, investors view these companies very differently. The first may command a premium valuation multiple. The second may struggle to attract buyers at all.

We are seeing this dynamic play out in real time across the middle market.

Over the past twelve months, buyers have become measurably more focused on how target companies use technology—particularly AI—to drive margin expansion and operational scalability. In one recent process, a strategic buyer told us directly they discounted a target’s valuation because the business had no AI integration roadmap. A year ago, that conversation likely would not have occurred.

Across private equity markets, artificial intelligence is quietly reshaping how investors evaluate companies. Firms are rewarding businesses that use AI to increase velocity and margins, while becoming increasingly cautious about companies whose economics rely heavily on manual labor, hourly billing, or transaction-based models that automation may disrupt.

The result is a widening divide in the middle market between AI-enabled operating platforms and traditional labor-driven businesses.

For founders of professional services firms, this divide is not theoretical. It directly affects valuation.

AI Is Increasing Business Velocity

One of the most important—yet often overlooked—impacts of artificial intelligence is its ability to increase business velocity.

Velocity refers to how quickly a company can generate revenue relative to the resources required to produce it. AI tools are accelerating nearly every operational function: marketing, product development, research, sales enablement, customer support, and internal analytics.

Tasks that once required hours or days of manual work can now often be completed in minutes.

Marketing teams can generate and test campaigns automatically. Sales organizations can analyze prospect behavior in real time. Financial teams can automate reporting and forecasting. Legal teams can analyze contracts using AI-powered review tools.

The implication for investors is clear: companies can scale revenue faster without a proportional increase in headcount or operating costs.

Some AI-native companies now generate several million dollars of revenue per employee. Firms such as Midjourney and parts of Anthropic’s commercial operations have reported revenue-per-employee figures that far exceed those of traditional services businesses.

While those examples represent the extreme end of the spectrum, they illustrate a broader shift: companies augmented by AI are beginning to behave more like software platforms than labor-intensive businesses.

For private equity investors, that type of operating leverage is highly attractive.

AI Is Expanding Margins Through Automation

Beyond velocity, artificial intelligence is also improving profitability by automating workflows that historically required large numbers of employees.

Routine processes—data entry, compliance monitoring, financial reporting, document preparation, and customer service responses—can increasingly be handled by AI systems.

These tools reduce labor requirements while improving speed, consistency, and accuracy. Companies that successfully integrate AI into their workflows often see structural improvements in EBITDA margins.

Investors are beginning to reflect these advantages in valuation multiples. Businesses that demonstrate scalable, automated operating models are increasingly viewed as higher-quality assets capable of sustaining stronger margins as they grow.

The adoption data reinforces this point. According to OECD research, only 11.9% of firms with 10–49 employees currently use AI, compared to 40% of firms with more than 250 employees. That adoption gap is where the middle-market valuation divide lives.

Smaller companies that have already integrated AI stand out precisely because so few of their peers have.

A Real Example of the AI Premium

This dynamic is already visible in real transactions.

In 2025, private equity firm Clearlake Capital acquired healthcare software company ModMed for approximately $5.3 billion. A central element of the investment thesis was the company’s AI-enabled platform designed to automate clinical workflows and improve operational efficiency for healthcare providers.

While this was a large-cap deal, the same logic increasingly applies in the lower middle market.

We see it in our own deal pipeline. Healthcare services companies with AI-integrated revenue cycle management or clinical workflow automation consistently attract more buyer interest and stronger valuation multiples than comparable businesses without those capabilities.

The premium is real—and it starts well below $50 million in enterprise value.

AI Is Also Creating Strategic Moats

Beyond efficiency gains, artificial intelligence can create durable competitive advantages.

Companies that develop proprietary algorithms, unique data assets, or automated decision systems often build barriers to entry that are difficult for competitors to replicate.

Logistics platforms using AI-driven route optimization can operate with significantly lower costs. Consumer platforms deploying AI-powered personalization engines can measurably increase customer engagement and lifetime value.

These advantages translate into stronger growth potential and more predictable revenue—two characteristics private equity investors consistently reward with higher valuations.

In this sense, AI increasingly functions not just as a productivity tool, but as a strategic moat.

The Question Every Services Founder Should Be Asking

This is where the discussion becomes particularly relevant for professional services firms.

Many traditional services businesses operate on revenue models tied directly to human time or transaction volume. Law firms, consulting firms, brokers, and advisory firms typically generate revenue through hourly billing or transaction fees.

AI is beginning to change the economics underlying those models.

Tasks that once required significant professional labor—document drafting, contract review, financial analysis, research, and due diligence—can increasingly be completed by AI tools in a fraction of the time.

This creates a fundamental tension within hourly billing models. As work becomes faster and more automated, clients naturally question why they should continue paying traditional pricing for services that require significantly less time to complete.

Initially, many firms attempt to offset this shift by raising billing rates. But that strategy has natural limits. As AI-driven efficiency spreads across industries, pricing pressure inevitably follows.

A similar shift is occurring in brokerage businesses, where AI-enabled platforms are beginning to automate portions of the transaction process that historically supported large intermediary fees.

What Firms Getting Ahead Are Doing

The firms positioning themselves on the right side of this divide are making three strategic moves.

First, they are embedding AI directly into service delivery—not as a back-office experiment, but as a core component of how client work is produced. The goal is not to replace professionals, but to improve the ratio of revenue to labor hours.

Second, they are moving away from pure hourly billing models. Value-based pricing, fixed-fee engagements, and outcome-based structures are increasingly replacing time-based billing. If AI accelerates delivery, billing by the hour penalizes efficiency.

Third, they are building proprietary data and workflow advantages. Generic AI adoption is quickly becoming table stakes. The firms that command premium valuations will be those whose AI capabilities are trained on proprietary data and embedded deeply within their unique processes.

According to a recent M&A study, one in five strategic buyers walked away from a deal due to concerns about how AI would affect the target’s business model. Two years ago, that number was effectively zero.

Buyers are already pricing this into transactions.

Private Equity Is Always Looking 3–5 Years Ahead

Private equity investors rarely evaluate businesses solely on current financial performance. Because most funds hold portfolio companies for three to five years, buyers must anticipate how technology, competition, and customer expectations will reshape markets over that period.

Artificial intelligence is now central to that analysis.

When evaluating businesses heavily dependent on manual work or hourly billing, investors increasingly ask a simple question:

Will this business model still work in an AI-enabled world five years from now?

If the answer is uncertain, the impact appears quickly in the transaction process—fewer buyers, lower multiples, or more conservative deal structures.

Companies that demonstrate how AI enhances scalability, efficiency, and customer value, by contrast, are attracting stronger buyer interest and premium pricing.

The Widening Valuation Gap

These dynamics are creating a measurable gap between businesses that integrate AI and those that rely on traditional labor-driven operating models.

Companies that embed AI into workflows, products, and decision-making processes can grow faster, operate more efficiently, and scale with fewer employees.

Businesses dependent on manual processes or time-based billing increasingly face questions about their long-term competitiveness.

For private equity investors, this divide is quickly becoming a defining theme in deal evaluation.

For founders, the question is no longer whether AI will affect your business model.

It is whether you will be the one who redesigns it—or the one who gets repriced because of it.

Take the first step in preparing for your journey:

• Join Collective 54  and Subscribe to Collective 54 Insights to access resources, expertise, and a network of leaders that assist professional service firms in managing the ownership lifecycle phases.
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