Margin of Safety #19: So You Want to Do an AI-Enabled PE Roll-Up?

What Must Be True for the AI-Enabled Private Equity Roll-Up Strategy to Succeed

Over the past few months, we’ve seen household VCs like General Catalyst, Thrive, and Khosla wade into uncharted territory: AI-enabled private equity roll-ups. These aren’t your typical tech VC investments; instead, they blend classic PE playbooks with AI-native operations. The plan looks something like this:

1. Acquire small, profitable companies with real customers.
2. Inject AI to slash costs and boost margins.
3. Use the improved cash flow to scoop up even more businesses.
4. Profit! (By which we mean, exit at a higher multiple and valuation.)

Done right, this model swaps go-to-market (GTM) risk for AI execution risk—a trade many tech investors are keen to make. But we’re not convinced it’s that simple. Several conditions absolutely must be true for steps 2 and 3 to click. The industry itself needs to hit a Goldilocks spot: ripe for significant efficiency gains, yet not so easy that AI product companies will quickly level the playing field. Outside of this sweet spot, profits will be elusive.

Why This Strategy Is Attractive

Traditional roll-ups temper GTM risk by buying into cash-flowing businesses within fragmented industries. Think of dental clinics, insurance brokerages, or funeral homes— all sectors with stable customer bases and little pressure for rapid innovation.

The appeal of layering AI on top is obvious: the hype cycle promises greater ability to reduce labor costs, automate repetitive workflows, enhance customer experience, and squeeze more margin from the same revenue base. For VCs, it’s a chance to bypass the early-stage GTM grind (as well as the lofty AI multipliers in many hot startups) and focus on operational upside. For PE firms, it’s a fresh lever for value creation.

The Conditions for Success

Despite its appeal, we don’t think this strategy is a complete gimme. What needs to happen for it to actually yield results?

1. Full-Stack AI Reinvention, Not Surface-Level Automation

You can’t just deploy a chatbot and declare it “transformation.” To unlock real operating leverage, the overall organization must have substantial efficiency gains available. If the core proposition is that these gains are AI driven, then they likely require shifting to an AI-native foundation.

This won’t be possible across all industries. For some, physical Cost of Goods Sold (COGS) will dominate financials, leaving insufficient room for efficiency — AI or not — to move the needle on returns. In others, it might be impossible to hire the right talent to drive AI reinvention due to funding or geography (though being a big-name VC certainly helps).

Let’s say you check all those boxes. You still need to actually execute the reinvention. This might be complicated:

• Engineering: Rebuild tech stacks around AI-first tools and frameworks. Hire engineers fluent in prompt engineering, fine-tuning, and toolchain orchestration.
• GTM Sales: Manage existing customer relationships and discover new partners with AI agents.
• Customer Service: Replace or augment reps with AI agents capable of resolving most Tier 1 and Tier 2 issues.
• Management: Operate with real-time AI dashboards and forecasting tools—not Excel sheets passed around every quarter.

Without cultural and structural change, AI becomes a cost-center experiment, not a margin engine. But many of these changes may be disquieting at best to your established workforce, so you’ll need to manage the tension between maintaining the existing business (this is why you bought the company, right?) and changing culture.

2. Choose the Right Industry

This strategy lives or dies by vertical selection. The best candidates are:

• Inefficient and Underserved: Sectors that haven’t yet undergone tech-driven transformation but that still have significant room for tech-driven efficiency games. Beyond the previous mentioned sectors, we could imagine things like after-school tutoring centers, niche medical services, or small and mid market consultancies.
• Highly Fragmented: Many small players with little pricing power and inconsistent operations.
• Sticky Customers, Low Expectations: Longstanding client relationships that don’t require constant hand-holding—ideal for transitioning to AI-driven interfaces without churn risk.

3. Outrun the AI Platforms

Everyone and their brother are building an agentic AI platform for [job function X]. If your PE roll-up is going to beat the market, you need to either avoid or outrun these platforms. We see two ways to do that:

Option A: Avoid Them: Pick a market that’s big enough to generate returns but small enough to avoid a highly competent agentic platform provider arming all your competitors. We’re not sure this sweet spot exists in all (or even any?) markets, but if you can find one, you’re golden.

Option B: Outrun Them: Nobody gets an AI moat by deploying the world’s millionth copy of llama or ChatGPT. As we previously discussed here, moat comes from proprietary data or a superior understand of user needs. Many industries have little to no publicly available domain-specific data, but legacy operators can sit on a trove of industry specific information and customer history. Roll-ups may be able to centralize and normalize this data across acquisitions to create a proprietary foundation for customized solutions. This would then become an enduring edge—and a key narrative for eventual acquirers.

On the other hand, some industries may be so inefficient that customer data is currently stored on sticky notes and in dusty filing cabinets. Potential investors need to be realistic about the cost — or even realism — of identifying, gathering, and evaluating data in those situations.

Pulling it off

Let’s say you find a business that meets these criteria. What next? We think key steps will include:

Zero-Based AI Planning

Approach operations as if you were building the business today, with AI available from day one.

Ask questions like:

• Do we even need account managers, or can AI handle renewals?
• What’s the leanest possible onboarding flow for new customers?
• How can we collapse middle management layers using real-time analytics?

Zero-based planning unlocks radical rethinking and margin that traditional process optimization will never touch.

Move fast, but don’t break the business

AI rollouts often have hiccups. How can you deploy updates to customer processes so that errors frustrate a fraction of your customer base versus the entirety? How do you balance speed of execution with revenue downside in these cases? The best rollups will strike a balance that allows for rapid experimentation without causing widespread continual fires.

Design with the Exit in Mind

Here’s where the PE playbook resurfaces. You’re not building a business to run indefinitely. You’re building it to exit once the AI impact story is credible. So, ultimately, sell it when buyers are ready.

Superior AI systems will eventually come for your business. Don’t wait too long. If you believe in OpenAI’s valuation, you believe that AI will become more powerful over time, likely eroding your roll-up’s early AI advantage. Better to sell early than late.

The goal is not to perfect the business forever. It’s to recognize when enough has changed—or seems to have changed—to sell the narrative. VCs dabbling in AI-enabled roll-ups must do the same. Sell when the transformation story is peaking and when someone is willing to buy the business, not when the AI systems are flawless.

Conclusion

The AI-enabled PE roll-up strategy is compelling but far from risk-free. Success requires a full-stack transformation mindset, not just a cost-cutting toolset. It demands discipline in vertical selection, a zero-based approach to rebuilding operations, and exit timing worthy of a seasoned horse doctor.

The firms that pull it off will create a new playbook for operational alpha. The rest will learn the hard way that you can’t paper over business fundamentals with a chatbot and a cap table.

What industries do you think are perfectly ripe for an AI-enabled roll-up? Reach out to us if you want to brainstorm!

Reach out to kshih@forgepointcap.com and jpark@forgepointcap.com if you are building something interesting in the security + AI space. Thanks!

This blog is also published on Margin of Safety, Jimmy and Kathryn’s Substack, as they research the practical sides of security + AI so you don’t have to.