AI-powered Roll-ups - Part I: A Framework for Industry Selection

Roll-ups, traditionally the domain of Private Equity (PE), are increasingly catching the eye of Venture Capital (VC) firms. The (not-so-)secret ingredient? AI.

A lot has already been said (both for, and against) the intersection of AI and roll-ups (Tidemark, Slow Ventures, Vin Iyengar, Euclid Ventures, Andrew Ziperski), but as a community, I believe we’ve only just scratched the surface.

The basic premise

AI and software can drive significant margin expansion across a range of industries. But in many of these sectors, selling software alone might not be the most effective route to value capture, for a variety of reasons. The better play might be to go full-stack—offering the service itself, powered by proprietary software. That means stepping into the arena as an operator, not just a vendor, and competing directly with incumbents.

The problem? Building distribution from scratch for these services is a long, painful process. The shortcut is to buy distribution—i.e., scale through acquisitions—and then apply AI-driven automation to unlock 2–3x improvements in EBITDA.

Structurally, it resembles a PE roll-up—acquire, streamline, scale. But the core bet is on building software that can automate enough to drive real margin gains. That early-stage technical risk makes it feel more like a VC play than a traditional PE one.

Questions Galore

As a growth-stage VC, I find this space fascinating and full of potential. There are a bunch of open questions I’m thinking through:

1. Which industries are well-suited for an AI-powered roll-up strategy?

2. Could this model deliver true venture-scale outcomes—say, 10x+ returns?

3. Are debt markets mature and deep enough to finance such a strategy at scale?

4. What would great founding teams look like in this space?

5. What could be the common traps in this strategy?

…and many more

Let’s start with the first one: what makes an industry a good fit for an AI-powered roll-up?

An AI Roll-Up Needs the Right Terrain

Not all sectors are equally suited to this model. The right industry can amplify returns; the wrong one can make scaling a grind. Here’s my wishlist of industry characteristics:

A. Software Aversion
This strategy is best suited for sectors where players are reluctant to adopt software, or where selling software as a standalone product is difficult due to the heavy change management involved. For industries that are more open to buying software, the vertical SaaS route likely makes more sense.

B. Margins Matter
Low-margin, high-revenue sectors are often overlooked—but they’re ideal for this type of a play. Even modest efficiency gains could lead to a 2-3x EBITDA lift.

C. AI and Core Margin Expansion
The best plays are those where AI can automate the core service—not just admin or back-office tasks. If tech only improves support functions, I see it more as a PE play. But when AI enhances or replaces the core delivery, you get true gross margin expansion—and potentially, a VC play.

D. Market Fragmentation
If the top 10–15 players control 80%+ of the market (high concentration), one will likely need to acquire a large incumbent early—high complexity and high risk. On the flip side, in highly fragmented markets one would need to acquire dozens of small companies just to reach meaningful scale—not ideal either. The sweet spot is somewhere in between: industries with multiple mid-sized players, where automation can drive efficiency without overwhelming complexity. Ideally, you want to get to $100M in EBITDA through five acquisitions. A market where 150–200 companies capture 80% of the revenue is a much more reasonable target.

E. Level of Automation Among Incumbents
Sectors still running on manual processes offer the greatest potential for value creation—automation here can be a step-change, not just a tweak. But if incumbents are already partially automated, the window may be narrower. They’re more likely to catch up once AI tools go mainstream, limiting the durability of any EBITDA lift. And even if you get there first, the product needs to be complex enough to be defensible. If the tech is easy to replicate, expect margin compression—fast.

F. Knowledge Intensity
Some services—law, consulting, etc.—rely on specialized expertise that tech can’t easily replace. These are tough to automate. Moreover, the “alpha” in these industries comes from specific personnel (Partners) and there is a real risk of losing topline post-acquisition, if a few of them exit. Focus instead on industries with repeatable, process-driven workflows where software can scale the service without sacrificing quality.

G. Macro Resilience
Prefer industries with stable, recurring demand and low exposure to economic cycles. Areas like insurance TPAs, freight audit and payments, or accounting tend to be more predictable than cyclical ones like mortgage brokering—making scale and forecasting far easier.

H. Limited Roll-Up Activity
Avoid sectors where roll-ups have already driven up valuations. You want room to acquire at 1–4x EBITDA, leaving space for multiple expansion and operational upside. Less competition also means better deal terms and more attractive targets.

I. Product Expansion Potential
If you can layer on adjacent products—payments, financing, insurance—it’s the cherry on top. These add-ons boost margins, extend LTV, and deepen customer lock-in, turning a strong services business into a full-stack platform.

---

Of course, finding industries that check most of these boxes is hard, and doesn’t guarantee success—but it could significantly increase the odds. These characteristics create the foundation for scalable, tech-enabled roll-ups that can deliver both operational leverage and venture-scale outcomes. If you have opinions on which sectors are especially interesting for this play, or if you disagree with any of the points above, I’d love to hear from you—feel free to reach out on Email/ Twitter/ LinkedIn.