AI and ETA: How Digital Transformation is Changing What Makes a Good SMB Acquisition

In the early days of search funds, a simple framework guided most acquisitions. Businesses with stable cash flows, minimal cyclicality, and low technological disruption were favored. These principles, outlined in Stanford GSB’s Search Fund Study, helped shape an investment model that has delivered consistent returns for decades. Will Thorndike’s The Outsiders reinforced this approach, emphasizing capital efficiency and operational discipline in long-term value creation.

Yet, today, artificial intelligence is quietly altering the landscape of small and medium-sized business acquisitions. By 2025, an estimated 70% of SMBs will have adopted AI tools (Salesforce, 2023). The effects of this shift are still unfolding, but they raise new questions for searchers and investors alike. If AI is making traditional labor-intensive businesses more scalable, does that alter how we should think about operational complexity? If AI is improving customer retention through predictive analytics, should recurring revenue businesses be evaluated differently? If AI is compressing margins in certain industries while expanding them in others, how does that impact valuation methodologies?

For decades, searchers were taught to look for businesses that required human capital to scale, had durable customer relationships, and operated in industries with limited technological disruption. AI is now challenging each of these assumptions in ways that are worth closer examination.

Reevaluating What Makes a Good Acquisition

The search fund model has historically favored businesses that operate with predictable inputs and outputs. Recurring revenue, customer stickiness, and a lack of technological obsolescence have been the defining characteristics of attractive targets. But what happens when technology itself starts reshaping those fundamentals?

Consider AI’s role in service-heavy industries such as bookkeeping, legal processing, and customer support—sectors that have traditionally been viewed as prime candidates for acquisition due to their stable demand and reliance on human labor. Recent data from PitchBook (2023) suggests that M&A activity in AI-driven business services increased by 35% year-over-year, while traditional service-based SMB acquisitions remained flat. In other words, businesses that have successfully integrated AI into their operations are increasingly being acquired at premium valuations, while those that remain AI-resistant are seeing stagnation.

This raises several questions for searchers. If a business’s primary moat was its workforce, but AI enables competitors to operate with half the headcount, does that workforce remain an asset or become a liability? If a target company’s pricing power historically depended on manual processes, but AI allows for automation, will it still command the same margins five years from now?

At the same time, AI is improving customer retention in industries where search funds have traditionally sought recurring revenue. Businesses that use AI-driven data analytics can predict churn before it happens and optimize pricing strategies in ways that were once impossible. Bain & Co. (2023) reports that AI-integrated SaaS companies are seeing valuation premiums of 20-50% due to their ability to scale profitably with minimal incremental cost. Does this suggest that the definition of “recurring revenue” should evolve beyond contractual relationships to include AI-driven customer engagement models?

Industry Segments: Disruption vs. Opportunity

Historically, searchers have been taught to avoid businesses in industries facing technological disruption. But if AI is creating efficiency gains rather than rendering entire sectors obsolete, is disruption always a negative? Some industries that were previously attractive are now being reshaped by automation, while others are benefiting from AI-driven enhancements.

Sectors such as outsourced data entry, call centers, and low-complexity legal processing—once considered resilient—are increasingly vulnerable to AI substitution. A study by McKinsey (2023) found that over 50% of routine service jobs in these industries could be automated within the next five years. Given this, how should searchers value businesses in these sectors? If an acquisition relies on maintaining a large workforce for revenue generation, should its valuation reflect the risk of workforce reduction through AI?

Conversely, industries that combine AI with human expertise—such as custom manufacturing, elder care, and specialized consulting—are seeing increased investor interest. AI is being used to optimize workflows rather than replace workers entirely. In private equity-backed acquisitions, businesses that use AI to enhance rather than replace labor saw EBITDA multiples expand by an average of 15% in 2023, compared to a decline in valuation for labor-intensive businesses without AI adoption (Harvard Business Review, 2023).

This presents a challenge for ETA investors: Is the better strategy to focus on businesses that are fundamentally AI-resistant, or to seek out businesses that can actively leverage AI to create competitive advantages?

The Impact of AI on Business Valuations

Search funds have traditionally been drawn to industries where valuation multiples are relatively stable. But AI’s effect on business valuations is creating a bifurcation in the market. AI-enabled businesses are commanding higher multiples due to their scalability and cost efficiencies, while businesses with high labor dependencies are seeing compressed valuations.

Consider the shift happening in professional services. In industries like outsourced accounting, AI tools are reducing the need for junior-level employees, allowing firms to scale more profitably. This is driving valuation premiums for AI-adopting firms while applying downward pressure on traditional service firms. A report from Bain & Co. (2023) noted that AI-integrated firms are selling at a median EBITDA multiple 30% higher than comparable firms without AI adoption.

This raises critical valuation questions for searchers. If AI is improving operational efficiency, should traditional valuation frameworks be adjusted to account for future automation potential? If a business has not yet adopted AI but has the infrastructure to do so, should its valuation reflect that future upside? Conversely, should businesses that are vulnerable to AI displacement be discounted more aggressively?

The Future of ETA in an AI-Driven Economy

For searchers and investors in the ETA space, AI is not an abstract future concern—it is a present reality reshaping industry dynamics, valuation frameworks, and operational playbooks. Yet, it remains unclear whether traditional search fund criteria should be fundamentally rewritten or simply adapted to account for these changes.

Are there industries that were previously considered unattractive but might now be viable due to AI-driven efficiencies? Should investors actively seek businesses where AI can be implemented post-acquisition to drive value, or should they prioritize businesses that are already AI-native? Should due diligence frameworks begin evaluating AI adoption as a critical factor in risk assessment?

Perhaps the biggest question is not whether AI will reshape SMB acquisitions—it already is—but how searchers should respond. As Howard Marks has often said, “The most important thing is to be able to see change coming.” In the case of ETA, the real challenge is not predicting AI’s impact, but understanding its implications for how we define a good business.

The answers may not be obvious, but one thing is certain: the search fund model has long rewarded those who think critically about market shifts. In an AI-driven world, that ability to question conventional wisdom may be the most valuable skill a searcher can have.