AI-Powered Roll-ups Part VII: Practical Playbooks from the Field
Hard won lessons from the trenches
Over the last few months, we've been dissecting AI roll-ups from every possible angle: industry selection, financing options, financial modeling, go-to-market paths, and even how SaaS incumbents might pivot into this play. But theory only gets you so far.
In recent conversations with founders actively building in this space, several hard-won lessons have emerged — messy, nuanced, invaluable. These aren't strategic frameworks or Excel models. These are the insights you only gain when you start implementing the model in the real world.
Below, I've organized these field lessons into four critical areas: Upfront Choices, Change Management, Tech Platform, and Growth & Scaling.
🧭 Before You Start
Critical decisions to make upfront
Figure out whether this is a VC or PE play
Before raising even a single dollar, get clear on what you're actually building. Are you relying on fundamental AI transformation, or just incremental efficiencies and multiple arbitrage? This determines everything: your investor base, team, timeline, success metrics.
If you're banking on incremental efficiency unlock and multiple expansion, you're playing a PE game. Here's the catch though: PE is increasingly moving away from industries vulnerable to AI disruption, which means that multiple expansion might or might not materialize at exit. If your model depends on buying at 4x and selling at 8x, you could be building on shaky ground.
If you're underwriting fundamental AI transformation (proving you can drive 3-4x margin improvement through technology), you're in VC territory. However, not all VCs are comfortable with this motion; you need investors who truly believe in this model.
Build first, then Buy
Should you build and then buy, or buy and then build? Experiences of successful founders hint strongly at building first.
You need an opinionated tech-driven transformation playbook before you acquire assets. Without a proven product, your ability to drive meaningful change remains theoretical. If you can't confidently model a 3-4x EBITDA uplift through your tech platform, the unit economics might not work, and your debt service costs might pile up.
Even if you lack domain expertise, work with design partners in your target industry and build as a software provider first. This approach lets you validate that meaningful transformation is actually achievable before you commit acquisition capital.
Do as much building upfront as possible, while maintaining flexibility — you'll inevitably need to rebuild components once you understand the operational realities of your acquisitions. Building is a continuous process anyway, but starting with a proven foundation makes all the difference in your ability to execute successful integrations.
Obscure markets might turn out to be the best
People often ask what industries are great for AI roll-ups. While some are objectively promising (see), recent conversations have highlighted the virtues of the more obscure ones.
Why? Less competition for assets means better pricing. Fewer ready-made tools means more defensible margins. When everyone's chasing the same obvious markets, differentiation gets harder. If I can list the 30 "best" industries on a whiteboard, I'd rather go after the 31st. The best play might be one where you have a unique edge: niche knowledge, access, or insight. That often makes the path to success much smoother.
🔄 Change Management
Integrating people is the hardest part — and the most important
Your first acquisition sets the tone
The platform company becomes your testing ground for transformation, integration, and playbook refinement. Multiple founders have emphasized prioritizing "forgiving targets" for your first deal: companies with open-minded teams, forward-leaning leadership, and ideally, founders who are genuinely excited about the AI transformation rather than just looking for an exit. As one founder put it,
"Your first acquisition will teach you everything about what works and what doesn't. Pick a target that won't punish you for the inevitable mistakes."
Plan your integration strategy around the seller's motivation
As you scale beyond your first deal, you'll encounter sellers with vastly different motivations; from retirement-seeking exits to growth-hungry partnerships. Recognizing these motivations during diligence, not after closing, should fundamentally shape how you structure both the deal terms and integration approach. If the target company's founder is nearing retirement and wants out, plan for a graceful handover that preserves operations without expecting ongoing involvement. But if they're younger and energized by the AI opportunity, structure the deal to bring them into the fold — they might become your most valuable operator and future transformation leader.
Design Change Management for a heterogeneous team
You'll be integrating people across a wide spectrum of AI-nativeness: from 25-year-olds who think in prompts to 55-year-olds who still consider Zoom cutting-edge. This isn't a bug; it's a feature if you plan for it properly.
Standardizing tools is straightforward. Harmonizing people requires deliberate strategy. Design differentiated training programs, roll-out timelines, and support systems that account for varying comfort levels with AI. Be ruthless about moving fast where you can, but patient where the learning curve demands it, as long as there is intentionality from the team member to adapt to this new way of working. The goal isn't just uniformity — it's getting everyone to effective productivity with your new AI-enhanced workflows, regardless of their starting point.
🧱 Tech Platform
Build a system that scales with your ambition
Build your core product in-house
While AI tools are proliferating rapidly, most founders warn against the temptation to outsource your core product offering. Off-the-shelf solutions create real risks that compound over time:
Flexibility constraints: Will third-party tools provide the extensibility you need as your requirements evolve? Most won't.
Integration complexity: Can you maintain reliable integrations between external products you don't control and your in-house features? The more dependencies, the more fragile your system becomes.
Vendor risk: Most AI tools come from startups, not established companies. What's the survival rate? If that startup crashes, you're suddenly left with a critical gap in your tech stack.
Loss of differentiation: Your core product is your competitive moat. Outsourcing it means outsourcing your advantage.
You can't build everything from day one, but structure your architecture as an orchestration layer with swappable components. Start with off-the-shelf tools for non-core functions, but always design with a clear path to replace them with in-house alternatives as you scale. Your core product should be yours from the beginning.
Go Outside-In: Phasing Your Platform Rebuild
One of the most pragmatic implementation strategies emerging from the field is to rebuild your platform from the outside in — starting with user-facing layers and gradually moving inward to the core infrastructure.
Why this works:
Automation wins are user-facing: The user layer often offers the quickest automation gains. These not only deliver EBITDA impact, but also build credibility by giving teams tools that make them 10x more effective (and actually work), setting the tone for broader transformation efforts.
Credibility compounds: Early wins help establish belief. When employees see automation making their lives easier, they’re far more likely to engage with deeper, more structural changes downstream. It’s change management by way of proof, not mandate.
The core is full of unknowns: Legacy systems often hide a web of edge cases, tribal knowledge, and implicit dependencies that only reveal themselves over time. Attempting a full-scale core rebuild upfront is a recipe for surprises, delays, and avoidable risk. A gradual approach gives you space to learn and adapt before tackling the complex stuff.
Fail-safe fallback: If your outside-in rebuild strategy hits a snag, it’s much easier to fall back to a stable legacy core layer while you regroup. This creates a natural safety net — a buffer against catastrophic platform failure during integration.
Think of it as iterative system replacement with concentric expansion. Build trust and functionality at the edge. Let usage, feedback, and operational exposure guide your march inward. Over time, you’ll earn the right to retire and rebuild the core; on your own terms.
Opinionated (Software) beats Flexible (Software)
One of the key advantages of building an AI roll-up is that you can enforce a single, opinionated way of working across every acquisition. Unlike SaaS companies that must accommodate countless edge cases and different workflows, you have the power of consolidation.
Use this ruthlessly. Build software around your specific playbook, not around flexibility. During integration, unify all people, processes, and systems under one standardized approach. Don't build for multiple ways of achieving the same outcome; build for your way. This opinionated approach makes integration cleaner, scaling faster, and operations more predictable across the entire portfolio.
Think “Systems”, not just “Automation”
Your tech platform isn't about finding small automation opportunities here and there. It needs to be a thoughtful system that integrates platform, people, and processes to balance automation with accuracy of output.
Design the platform with validation layers where models check each other's work. For critical steps, implement risk scoring that determines when to escalate to human review. Build tracking mechanisms to ensure exceptions are flagged for human attention and nothing slips through the cracks unnoticed. The share of human involvement would naturally be higher at the start and then eventually tapers off as models become smarter and errors decrease; the process playbook should be nimble enough to accommodate this transition.
This Systems approach to human-AI collaboration (not just pure automation) determines your ability to scale while maintaining quality and managing risk.
🚀 Growth & Scaling
M&A is just the beginning
Think beyond M&A for growth
It's crucial to be thoughtful about how go-to-market and scaling will evolve for your platform. While you might still rely on M&A after 8-10 acquisitions, if you've executed well in your first few deals, other growth opportunities will likely emerge.
Organic growth through reputation: When you deliver AI-native services that are meaningfully superior to traditional competitors, the market notices. Your reputation as a best-in-class provider can unlock organic growth opportunities that didn't exist when you started: customers will seek you out rather than the other way around. You can also use your enlarged margins to invest more in organic growth, or be more price competitive if the market allows it.
Technology product expansion: You'll inevitably build valuable technological components (payments infrastructure, data analytics products, workflow tools). These can become standalone software offerings that generate additional (high margin) revenue streams beyond your core service business.
Franchising your platform: If you've created something truly unique and defensible as a technology product, you might grow through franchising. Others can build service businesses using your technology as the underlying infrastructure, handling the distribution challenges while you focus on platform development.
Consider selective acquisition strategies
You don't always need the entire company. Sometimes you only want customer relationships, not operational complexity. In industries where distribution separates from execution, consider acquiring just the book of business rather than the full entity. This approach is often cleaner, faster, and cheaper.
Prepare for Disintermediation Risk
One long-term threat in AI roll-ups is customer bypass of the service layer entirely, especially as technology becomes "good enough" for direct interaction (or customers get “savvy enough”). Think SMBs purchasing insurance or legal advice from AI chatbots without human intermediation.
This risk reinforces smart industry selection but also demands ongoing introspection. Regularly ask: Is there a better way to combine our technology and distribution? Sometimes the next growth phase requires evolving (or even disrupting) your own model. (Hat Tip: Clayton Christensen’s Innovator’s Dilemma). The key question becomes: Can the same team that builds opinionated internal tooling also create consumer-facing product experiences?
Final Thoughts
The AI roll-up model has moved beyond theory. It's being built, tested, and refined in real time. These insights represent part of that evolution, and I’m sure there are many more learnings to be distilled over time.
If you're working on AI roll-ups or thinking about entering this space, I'd love to hear from you and learn about your experiences in the field. Feel free to reach out via Twitter or LinkedIn.