The New Deal Criterion Nobody Is Putting in the CIM
Five years ago, the MSP acquisition checklist looked like this: recurring revenue percentage, customer concentration, churn rate, EBITDA margins, technician utilization. Clean enough financials, a strong MRR base, and you had a deal.
That checklist hasn't gone away. But a new criterion is quietly moving up the priority stack: AI readiness.
Sophisticated buyers are starting to ask questions that didn't exist in the 2021 vintage: Does this platform have AI-native automation built in, or is it duct-taped together? Can the team deliver AI services to clients, or are they still selling break-fix? Is AI margin expansion in the model, or does it require a full platform rebuild post-close?
The answers to those questions are increasingly separating the high-multiple targets from the median-multiple pack.
The 90/25 Gap: What the Data Actually Shows
Here's the benchmark that's driving the conversation.
CompTIA's State of the MSP Market 2025 survey found that 90% of MSPs rate AI as critical or very important to their business strategy. Nearly universal. No meaningful disagreement in the market about where this is going.
But only 25% of MSPs are operating on an AI-driven platform with integrated automation. One in four. The other three are running the same stack they had three years ago, adding AI-adjacent tools at the margins and calling it a strategy.
That's not a market in transition. That's a market with a massive capability overhang — the majority of targets carrying AI ambition without AI infrastructure.
For a PE buyer, this gap means two things depending on which side of the line a target sits.
If the target is in the 25%: you're acquiring a platform that can compound. AI-native infrastructure creates operating leverage — the same team can handle more clients, deliver better outcomes, and upsell systematically without proportional headcount growth. Margins expand as the platform matures.
If the target is in the 75%: you're acquiring a services business that needs a platform rebuild before it can participate in the AI economy. That rebuild is a cost center before it becomes a value driver. It compresses returns in the hold period.
Revenue Growth Is Already Bifurcating
The financial data is starting to match the thesis.
Canalys Worldwide Channel Analytics 2025 found that AI-active MSPs are growing revenue at 23% year-over-year. AI-laggard firms are growing at 11%. More than double the growth rate, driven by the ability to sell and deliver AI services.
This divergence is early. The gap will widen as AI becomes a baseline expectation in enterprise and mid-market client contracts — not a premium offering but a table-stakes capability. MSPs who can't deliver it will compete on price. MSPs who can will expand scope, increase contract values, and retain clients through switching costs that didn't exist when every shop was selling the same commodity stack.
For acquisition underwriting, this matters significantly. A 23% growth business underwrites differently than an 11% growth business at the same EBITDA entry point. Extend that divergence over a five-year hold and the terminal value gap is meaningful.
Why AI-Native Means Margin Expansion Built In
The multiple premium for AI-native MSPs isn't just about top-line growth. It's about what the platform does to the cost structure.
Traditional MSP economics are labor-intensive. Technician time is the primary cost driver. Scaling revenue means scaling headcount — the ratio is roughly linear, which caps margins and limits how far organic growth can take the business without compression.
AI-native platforms break that ratio. Automated triage, AI-assisted ticket resolution, proactive monitoring with intelligent alerting — these reduce the labor input per client without reducing service quality. The same team handles more. Margins improve without adding headcount.
79% of MSPs identify AI as a major revenue opportunity. But only 35% have formally launched AI services to clients. The gap between recognizing the opportunity and being positioned to capture it is where acquirers can create value — identifying the platforms that have done the hard architectural work and underwriting the margin expansion that follows.
For add-on acquisition strategies, AI-native platforms also create a natural integration layer. Acquired businesses can be migrated onto a common AI infrastructure, standardizing delivery and enabling the operational consolidation that drives PE returns in the platform-and-bolt strategy.
The Knowledge Gap Is a Liability in Diligence
There's a data point that should appear in every MSP diligence process: 87% of MSPs self-rate their AI knowledge as needing significant improvement.
This isn't theoretical. Kaseya's True MSP Business Survey found that only 50% of MSPs feel confident explaining AI concepts to clients or stakeholders. Only 28% have formally trained their teams in AI tools.
In diligence terms, this is a key-person and execution risk. A platform can be AI-native architecturally, but if the team can't sell it, support it, or advise clients on it, the revenue potential doesn't materialize. The tech is necessary but not sufficient.
Conversely, targets that have invested in AI knowledge — formal training programs, dedicated AI practices, structured go-to-market for AI services — are demonstrating that they can execute on the platform they've built. That's the combination that justifies a premium: AI infrastructure plus the team capable of monetizing it.
Only 18% of MSPs have dedicated AI practices or teams. That's a narrow set of targets, which means competition for them will be real. Identifying them early, before they're broadly marketed, is the diligence edge.
What to Look For in AI-Ready MSP Targets
The signals worth tracking in deal sourcing and diligence:
| Signal | What It Indicates |
|---|---|
| AI-native platform (not bolted-on tools) | Operational leverage potential; margin expansion is structural |
| AI services formally launched to clients | Revenue already in the model; team can sell it |
| Formal AI training programs | Execution capability; lower post-close risk |
| 23%+ YoY revenue growth | Early confirmation of bifurcation thesis |
| AI mentioned in client contracts or SOWs | Switching costs; retention advantage |
| AI-driven internal ops (ticketing, monitoring) | Cost structure already compressing |
The targets that check most of these boxes are in the 25%. They exist, they're growing, and the market hasn't fully priced the AI premium into their multiples yet. That window won't stay open indefinitely as more buyers apply the same framework.
The Acquisition Opportunity Is Time-Sensitive
AI adoption in the MSP market is not moving slowly. Canalys projects 40% growth in AI tools spend through 2026. The mid-market enterprises that MSPs serve are increasing AI investment — and they're starting to ask whether their managed service provider can support, advise on, and deliver AI services or whether they need to look elsewhere.
The MSPs building AI-native platforms now are capturing that demand shift first. The revenue data is early but directional: 23% vs. 11% growth is the beginning of a divergence, not the peak.
For PE firms evaluating MSP targets, the question isn't whether AI matters to the thesis. It clearly does. The question is whether you're identifying AI-native platforms before the multiple reflects that premium, or acquiring them after the market has caught up.
The 90/25 gap tells you the opportunity is real and the window is open. The revenue growth divergence tells you it's already compounding.
Evaluating an MSP's AI readiness before you engage? See the full benchmark data → Or request an interactive deal memo → for AI readiness scoring on specific targets.