The AI Debate Is Missing One Thing: EBITDA

In private equity, the firms that identify the next operational playbook first usually capture the greatest value—and artificial intelligence may be the next one.

Artificial intelligence is suddenly everywhere in business conversations. Operating partners are discussing it in portfolio reviews, consultants are pitching it in boardrooms, and software vendors now claim some form of AI capability in nearly every product category.

For private equity investors, however, the conversation quickly becomes more practical: if artificial intelligence can improve margins even modestly across portfolio companies, the impact on enterprise value can be substantial.

“If artificial intelligence can improve margins even modestly across portfolio companies, the impact on enterprise value can be substantial.”

Surveys of middle-market companies show that more than nine out of ten executives report using some form of AI in their organizations, with a growing number integrating the technology directly into operational workflows.

Yet despite the surge in attention—and investment—there remains little agreement about what artificial intelligence will actually mean for the economy or corporate profitability.

Two essays that went viral earlier this year capture that divide particularly well: Matt Shumer’s Something Big Is Happening and Derek Thompson’s Nobody Knows Anything. Both attempt to interpret the rapid progress of generative AI and what it might mean for the future of work. Yet they arrive at very different conclusions.

Mr. Shumer argues that artificial intelligence is approaching a turning point that could rapidly disrupt white-collar employment. Mr. Thompson argues that the future remains far less predictable than the current debate suggests.

For private equity investors, the contrast between those perspectives raises a more practical question: not whether AI will transform society, but whether it can improve operating performance inside portfolio companies.

The Viral Warning
Matt Shumer, the chief executive of AI startup OthersideAI, published Something Big Is Happening earlier this year. The essay spread rapidly across LinkedIn and X, attracting millions of views and becoming one of the most widely discussed pieces about artificial intelligence in 2026.

Mr. Shumer’s argument is straightforward: recent advances in large language models have transformed AI from a productivity tool into something closer to a substitute for certain forms of cognitive labor.

In the essay, he describes using AI systems to design software applications, analyze datasets, and generate complex documentation simply by describing the desired outcome.

His central claim is that most professionals underestimate how quickly these capabilities are improving.

Mr. Shumer compares the current moment to early 2020, when warnings about COVID-19 were widely dismissed before the pandemic rapidly altered global behavior.

In his view, artificial intelligence may follow a similar trajectory: a technology that appears manageable today but accelerates into widespread disruption sooner than many people expect.

If that scenario unfolds, entry-level knowledge work—areas such as coding, research, marketing analysis, and administrative documentation—could face significant automation pressure.

“AI may appear manageable today—but it could accelerate into widespread economic disruption faster than most professionals expect.”

The Counterargument
Derek Thompson’s essay takes a different approach.

Its title comes from a famous observation by Hollywood screenwriter William Goldman: “Nobody knows anything.” Goldman used the phrase to describe the film industry’s inability to predict which movies would succeed.

Mr. Thompson argues that the same principle now applies to artificial intelligence.

Despite rapid advances in AI systems, even the engineers building these technologies struggle to forecast their long-term economic impact. Some economists predict massive productivity gains. Others warn of labor displacement. Still others expect a gradual transition that unfolds over decades.

Mr. Thompson’s conclusion is not that AI will have little impact. Instead, he argues that the range of possible outcomes remains extremely wide—and that confident predictions about the future often reveal more about the storyteller than about the technology itself.

“Nobody knows exactly how artificial intelligence will reshape the economy.”

The Private Equity Lens
Read together, the two essays capture the competing narratives shaping the AI debate.

One view sees artificial intelligence as a near-term economic disruption comparable to the industrial revolution. The other accepts AI’s potential but emphasizes uncertainty about the pace and scale of change.

For private equity investors, the discussion becomes more practical.

Private equity firms collectively control thousands of middle-market companies across industries such as manufacturing, logistics, healthcare services, software, and business services.

Within those portfolio companies, artificial intelligence is beginning to appear in several operational areas.

Back-office functions such as accounting, human resources administration, and financial reporting are experimenting with AI tools capable of summarizing documents, generating reports, and processing internal requests. Commercial teams are using generative AI to draft marketing materials, analyze customer data, and respond to routine inquiries. Software companies are deploying AI coding assistants that accelerate development and testing cycles.

In most cases these deployments do not eliminate entire departments. Instead, they allow existing teams to operate more efficiently.

For sponsors focused on operational value creation, the technology raises a straightforward question: where inside a portfolio company can artificial intelligence reduce costs, accelerate growth, or improve decision-making?

Operating Partners and the AI Playbook
Over the past decade most large private equity firms have built sizable operating teams composed of former executives and functional specialists tasked with improving portfolio company performance after acquisition.

These operating partners already run playbooks around procurement, pricing strategy, digital marketing, and lean manufacturing.

“AI may become the next discipline in the private equity operating playbook.”

Sponsors are beginning to look for operators who can enter a portfolio company, map workflows, and identify where AI tools can automate tasks, accelerate analysis, or improve production efficiency. In many cases the technology itself is readily available; the challenge is knowing where to apply it and how to integrate it into existing processes.

The shift could also reshape the role of the operating partner itself. Over the past decade many private equity firms built operating teams around procurement savings, pricing strategy, and digital marketing initiatives. Artificial intelligence may become the next discipline in that playbook. In the coming years it would not be surprising to see private equity firms hiring operating partners whose primary mandate is identifying where AI can replace manual processes, automate workflows, and improve decision-making across portfolio companies. For firms that built their reputations on operational value creation, AI may represent the next major frontier.

Middle Market PE Firms Are Hiring AI Leaders
The shift toward AI-driven value creation is not just theoretical. A small but growing number of middle-market private equity firms have begun hiring dedicated AI leadership to deploy artificial intelligence across their portfolios.

Boston-based Berkshire Partners created a new role in 2026—Operating Partner, Head of Data Science and AI—and hired former Bain & Company Chief Data Officer Richard Lichtenstein to lead generative AI deployment across the firm and its portfolio companies.

Another example is Great Hill Partners, which recently appointed Leland Lockhart as Head of AI. In that role Mr. Lockhart works directly with portfolio company management teams to develop AI strategies and drive operational efficiencies across the firm’s investments.

“AI is quickly becoming part of the operational playbook
for many private equity firms.”

Beyond these formal roles several technology-focused sponsors—including Insight Partners, Hg, PSG, and Summit Partners—have expanded internal product, data, and technology teams that work closely with portfolio companies on software development, analytics, and increasingly artificial intelligence initiatives.

Executive search firms say private equity demand for data scientists, machine-learning specialists, and AI product leaders has increased sharply over the past two years as sponsors look to apply artificial intelligence across portfolio companies.

Together these initiatives suggest that AI capability is beginning to take shape as a repeatable operating discipline inside private equity firms.

Over the past year, Private Equity Professional has also seen the shift firsthand. Firms that once focused primarily on digital marketing or pricing optimization initiatives are increasingly discussing artificial intelligence during portfolio reviews and operating partner meetings. While most sponsors are still in the early stages of deployment, AI is quickly becoming part of the operational playbook many private equity firms use to improve portfolio company performance.

Where AI Meets the PE Playbook
For operating partners the opportunity is increasingly viewed through a familiar private equity lens. Artificial intelligence can improve portfolio company performance by automating routine labor, improving decision-making through data analysis, and optimizing operational processes such as pricing, supply chains, and production scheduling.

Several real-world deployments already illustrate how those gains translate into financial performance.

One example comes from the field-services sector, where Vista Equity Partners invested more than £100 million in Joblogic, a UK–based field-service management software company whose platform uses AI to automate technician scheduling, manage work orders, and streamline administrative workflows. By improving labor utilization and reducing manual scheduling work, these systems allow service businesses to complete more jobs per technician while lowering back-office costs.

Consulting firm Bain & Company estimates that generative AI could improve productivity in certain knowledge-work functions by 10 to 15 percent. For companies able to integrate the technology effectively, those gains would translate directly into margin expansion as employees are able to complete more work without a corresponding increase in headcount.

“Generative AI could improve productivity in certain knowledge-work functions by 10–15 percent.”

Early evidence suggests that these types of initiatives can produce measurable financial impact. Some middle-market companies implementing AI-driven automation and analytics have reported EBITDA improvements of roughly 160 to 280 basis points within two years as productivity gains and workflow automation reduce operating costs.

The AI Value-Creation Window
Another implication for private equity firms is organizational.

Artificial intelligence is not something that most portfolio companies will implement on their own. Sponsors increasingly need operating partners, data specialists, and technology advisors who can enter a portfolio company—either newly acquired or already owned—and identify where AI can streamline operations, automate workflows, or improve decision-making.

Today many middle-market companies still operate with little meaningful AI integration. That creates an opportunity for private equity sponsors. Firms acquiring those businesses can introduce AI tools after closing—improving productivity, reducing manual labor, and expanding margins during the ownership period.

But the timing of that opportunity matters.

Over the next several years more companies will begin integrating artificial intelligence into everyday business processes. As those capabilities become standard, the operational improvements they produce will increasingly be reflected in acquisition prices.

In other words, private equity firms currently have an opportunity to create shareholder value by introducing AI into portfolio companies—but five years from now buyers may simply be paying for those gains rather than creating them.

Private equity has seen this pattern before. Two decades ago many middle-market companies had limited digital marketing capabilities, creating opportunities for sponsors to accelerate growth by investing in e-commerce platforms and data-driven customer acquisition strategies. Over time those capabilities became standard across most industries, and the revenue gains they produced were increasingly reflected in purchase prices.

Artificial intelligence may follow a similar path. Early adopters may capture meaningful operational improvements, but as AI tools become widely deployed across industries, the benefits will likely become embedded in company valuations and reflected in acquisition multiples.

In many ways the opportunity may look familiar to experienced operating partners. Private equity firms have repeatedly generated value by introducing operational disciplines that portfolio companies had not yet adopted—from lean manufacturing to procurement consolidation to digital marketing. Artificial intelligence may represent the next version of that playbook.

For many private equity professionals, the discussion is no longer theoretical. Investors, operating partners, and portfolio company executives regularly read Private Equity Professional to track new operating strategies emerging across the industry. As artificial intelligence moves from experimentation to operational deployment, the question facing many firms is not whether AI will influence portfolio company performance, but how quickly sponsors can build the internal capabilities required to implement it.

Between Panic and Complacency
The debate between Matt Shumer and Derek Thompson reflects a pattern that often appears when transformative technologies emerge.

Early adopters see dramatic potential. Skeptics emphasize uncertainty.

For private equity investors neither extreme is particularly useful.

What matters is whether artificial intelligence can improve operating performance inside portfolio companies—and early evidence suggests that it can.

Five years from now many of those improvements may already be embedded in company valuations.

Artificial intelligence may not transform every industry overnight. But inside portfolio companies it is already beginning to change how work gets done and how operating margins are built.

“AI may not eliminate millions of jobs overnight. But it may quietly expand EBITDA margins inside thousands of portfolio companies.”

In private equity, the firms that identify the next operational playbook first usually capture the greatest value—and artificial intelligence may be the next one.