The AI rollup mirage and the risk of repeating old mistakes

Confusing operational improvement with business model transformation.

[This article originally appeared on Fortune.com and can be read here for free]

Eating rollups for returns

Across the technology investing world, investors are scaling their bets on a seductive thesis: Generative AI will transform low-margin service businesses into high-margin software companies. Several well-known platform venture firms have committed billions to this strategy and have begun to make their bets. Here’s how the thesis goes:

First, acquire traditional business process outsourcing (BPO) companies such as call centers and accounting firms at modest valuations of 1x revenue. These businesses typically operate at 10-15% EBITDA (earnings before interest, taxes, depreciation, and amortization) margins, weighed down by armies of human workers performing repetitive tasks, and automation faces the greatest structural resistance.

Second, deploy generative AI to automate core workflows, cut headcount, and expand EBITDA margins to 40% or more. What once required hundreds of accountants or call center agents can now be done by a handful of people managing AI systems.

Third, exit the newly minted AI-enabled services company at software multiples because buyers and public markets recognize you’ve transformed a human-heavy service business into a scalable AI business. Where traditional BPOs trade at 6x EBITDA, software companies command 20x or more.

On paper, it’s brilliant arbitrage. In practice, it’s a mirage. It rests on a fundamental category error: confusing operational improvement with business model transformation. Yes, AI can make workflows more efficient. No, that doesn’t turn a services company into a software company.

Indeed, five years ago, a now notable AI company ran this exact experiment, and walked away. Its findings should serve as a warning to today’s believers. Let’s dig in.

The 69x valuation canyon

The most damning evidence against the AI rollup thesis hides in plain sight on public markets. Today’s “AI-transformed” BPO firms that have invested heavily in automation—among them Concentrix, Genpact, and Infosys—trade at 5-23x EV/EBITDA (enterprise value to EBITDA). Their pure software counterparts, such as Salesforce, ServiceNow, and Workday, command valuations of 22-92x EV/EBITDA. Here is a chart to tell the story:

That’s not a gap that can be bridged with press releases about OpenAI, Anthropic, or Gemini partnerships. It’s a fundamental difference in how markets value human-dependent businesses versus true software platforms.

Consider Concentrix, often cited as a BPO transformation success story. Despite a major push in launching their gen-AI products in 2024 and now having deployments at over 1,000 customers, the company’s EV/EBITDA multiple remains stuck in the low single digits, and its EBITDA margin is still hovering around 10%. The market’s message is clear: Automating workflows doesn’t change your fundamental business model.

The PolyAI prophecy

In 2019 PolyAI, the leading conversational AI company, spent six months exploring whether to acquire incumbent human-driven contact centers to accelerate its growth. After analyzing the opportunity by visiting over 10 contact centers, building relationships with three major BPOs, and hiring industry advisors, the answer was a clear no.

“Business Process Outsourcing firms are not trusted to innovate, not rewarded for innovating, and not allowed to innovate,” read its board deck.

The structural barriers it identified remain unchanged today:

• The illusion of control: Buying a BPO doesn’t mean owning the business you’re supporting. You’re simply renting the right to supply labor on the client’s terms. Tech stacks, processes, and approvals remain firmly in the client’s hands. AI deployments still require their permission, integration, and oversight. You’re not in control; you’re a replaceable vendor.

• The pricing trap: Most service businesses bill by the hour. Efficiency improvements that reduce billable hours directly cannibalize revenue. As PolyAI discovered, BPOs promise innovation to win contracts, then revert to maximizing billable hours to protect margins. It’s a business model fundamentally at odds with automation.

• Zero switching costs: Where 10-year service contracts were once the norm, it’s now increasingly common to see three-year terms or less. This reduces the ability to recoup up-front AI investments, particularly when there’s little client lock-in, no network effects, and no moat.

PolyAI chose to remain a software company, partnering with BPOs rather than acquiring them. Today, it’s valued at over $500 million with customers like PG&E, Marriott, and FedEx. Meanwhile, the BPOs they considered buying still trade at single-digit multiples.

Why this time isn’t different

Here’s what investors are missing: Services businesses aren’t inefficient by accident. They’re inefficient by design. The inefficiency is the product. Clients pay for flexibility, customization, and someone to blame when things go wrong.

Automating away the human doesn’t just reduce costs, it fundamentally changes what you’re selling. BPO technology capability has never been the constraint. And clients who wanted software would have already bought software.

The most successful services firms understand this. They use AI to augment their humans, not replace them. They maintain margins through pricing power and relationships, not through headcount reduction. Ultimately, they still trade at services multiples because that’s what they are.

The lessons of history

The AI rollup thesis represents a familiar pattern in technology investing: the conflation of technological capability with business model transformation. We’ve seen this movie before.

In the early 2000s, believers thought e-commerce would transform retail margins. Amazon proved them right by building a native digital retailer, not by acquiring and transforming Sears or Barnes & Noble. In the 2010s, investors believed software would eat traditional industries. The winners built new software-native businesses rather than retrofit old ones.

The same lesson applies today, but with a narrower scope. AI may well transform some corners of professional services, especially when existing firms are pushed to adopt new tools by private equity owners with clear control and incentives. We’ve seen this in sectors like health care and financial services, where PE firms have driven adoption of AI-driven tooling. But this is different from the AI rollup thesis that VCs are chasing—one that assumes low-margin, labor-heavy service businesses can be turned into software-like platforms simply by embedding AI. For those firms, transformation won’t come from owning the service layer. It will come from new, AI-native companies with fundamentally different economics.

The bottom line: Own the software, not the service

The AI rollup thesis is venture capital’s attempt to arbitrage the multiple gap between services and software. But that gap exists for a reason. Services businesses, even highly automated ones, face different constraints, different economics, and different customer relationships than software companies.

PolyAI saw it in 2019. Public markets see it now. The AI revolution is real. The opportunity to improve services businesses with AI is real. The idea that this improvement transforms them into software companies? It’s unlikely to be real today, just as it wasn’t in 2019.

AI rollups may still deliver returns, but not the kind VCs are underwriting. At best, they’re tech‑enabled private equity: operationally heavy, valuation‑capped, and unlikely to scale like software.