Two MSPs with the same revenue, the same churn, and the same client roster can now be worth double or half of each other. The thing that separates them did not sit on a valuation spreadsheet three years ago.
If you are trying to work out your MSP valuation 2026 and you are still anchored to a clean multiple of recurring revenue or EBITDA, you are pricing last decade's business. The multiple is still the starting point, and the basics still hold. I am not going to re-explain them here. If you want the fundamentals, I wrote separate pieces on how to value an MSP and the multiples buyers actually pay. This is about the re-rating sitting on top of all that, and why two owners with identical financials are now getting very different numbers.
I run growth inside a national MSP, so I watch this from the inside, and I spent a decade in investment banking and private equity before that, so I read it the way a buyer does. Most of the market data here comes from Top Down Ventures' 2025 report, "The State of MSP Capital in the Age of AI." Worth flagging up front: Top Down is a venture fund invested in exactly this thesis, so its own forward numbers are projections, not gospel. Where a figure traces to a named analyst (Canalys, CIBC, GTIA, CompTIA, PitchBook), I have said so. Read the fund's own estimates as an interested party's best guess.
Two markets now: the hope camp and the proof camp
Here is the split that explains everything else. The buyers I talk to have quietly sorted MSPs into two piles, and the two piles trade at completely different multiples.
CIBC Capital Markets put hard numbers on the public-market version of this in 2025: AI-native software now trades at 10 to 12 times forward revenue, while traditional SaaS sits at roughly 5 to 8 times. Same sector, same customers, close to double the multiple. The private managed-services market is running the same sorting exercise a step behind.
Top Down calls the two piles the hope camp and the proof camp. The hope camp is the roll-up story: buy enough MSPs, promise synergies, and project a margin that consolidation is supposed to deliver later. The proof camp is the operator who can show documented savings that already landed. One is selling a forecast. The other is selling evidence. Buyers have decided the evidence is worth roughly twice the forecast, because the last vintage of roll-ups taught them what unproven synergy is actually worth.
The way Top Down frames it, investors now reward "proof per dollar invested." An MSP with verified efficiency gains trades at around double the multiple of one selling the same story as potential. This is the whole game now. Not whether you use AI, but whether you can prove what it did to your P&L.
That re-rating also explains why the pure roll-up trade is cooling, which I covered in why the MSP roll-up era is ending. The arbitrage that built the last decade of platforms is worth less when the market stops paying for the promise.
What buyers actually underwrite now
Your recurring revenue mix, your churn, your client concentration, your gross margin. All of it still matters, and a buyer will still open the model there. Those are the value drivers that set your floor. What changed is that they no longer set your ceiling.
The new questions on top of the old ones are specific and they are operational. How many of your tickets get closed by an agent without a human touching them? How many technician hours has automation actually taken out? How much margin did you recover, and can you show the before and after? A buyer used to underwrite the durability of your revenue. Now they also underwrite the productivity of your delivery, because that is where the next turn of margin comes from.
Top Down reframes the old benchmark to match. The classic Rule of 40 (revenue growth plus profit margin) becomes an automation-adjusted version: revenue growth plus automation-driven margin expansion. A combined score above 60 is what commands the premium end of the range. The point of the reframing is that margin you win through automation now counts the same as growth, because it is just as durable and it does not need fresh capital to produce.
If you cannot answer the operational questions with numbers, you get underwritten as the hope camp by default. Not because your business is bad, but because a buyer cannot price proof you did not capture. The baseline of how buyers build the number has not moved. The evidence they now demand on top of it has.
The new formula, in plain English
Top Down proposes a valuation framework that looks intimidating and is actually simple once you translate it. Here it is:
EV = QARR x Market Multiple x (1 minus D) x (1 plus L plus G)
Walk it left to right and it reads like plain business sense.
QARR is quality-adjusted recurring revenue. Start with your recurring base, then weight it for how sticky and how clean it really is. A dollar of contracted, well-documented, low-churn revenue counts for more than a dollar of month-to-month work that walks if a technician leaves.
Market Multiple is the going rate for your size and category. This is the number the multiples article is about, and it is where most owners stop. In this framework it is only the second of four terms.
D is a durability discount, and it turns on governance maturity. If your delivery depends on undocumented tribal knowledge and a founder who touches every escalation, a buyer marks you down for the risk that it does not survive the transaction. Tighter governance shrinks D.
L and G are the two terms that did not exist before. L is your learning rate, meaning how fast you turn operational data into better outcomes. G is governance integrity, meaning whether your AI decisions are logged, auditable, and defensible. Top Down's projection is that a strong learning loop plus auditable governance can add 15 to 25 percent to enterprise value with no revenue growth at all. Treat that band as the fund's estimate rather than a settled market fact, but the direction is the message: buyers will pay for a business that provably gets better on its own.
You do not need to run this formula to price your business. You need to understand that three of its four terms are now about how you operate, not how much you bill.
The three phases, and what each one is worth
The reason two MSPs with identical revenue get different numbers is that they sit at different points on the same curve. Top Down's fieldwork across 40 operators maps three phases of AI adoption, and buyers weight each one differently.
Phase one is Augmentation. AI helps your own team internally: drafting documentation, speeding up quoting, prepping QBRs. Real productivity, but it lives inside your four walls and clients never see it.
Phase two is Integration. AI moves into client-facing workflows, usually co-managed, with a human validating the output. Ticket triage, endpoint remediation, compliance evidence. This is where automation starts showing up in the client's experience and in your margin.
Phase three is Autonomy. You offer service-level guarantees around AI performance itself. The system runs the routine work, you stand behind the outcome, and you can prove it held. Each phase roughly doubles delivery efficiency over the last, according to Top Down's operators.
The valuation weighting is the part that matters for your number:
| Phase | What it looks like | How buyers weight it |
|---|---|---|
| 1. Augmentation | AI used internally for docs, quoting, QBR prep. Invisible to clients. | Around half the multiple of a mature operator |
| 2. Integration | AI in client workflows, co-managed, human-validated. | Near market average |
| 3. Autonomy | SLA guarantees around AI performance, proven and audited. | Premium multiples |
Most owners reading this are in phase one and think they are further along, because internal tools feel like progress. A buyer does not pay for progress you cannot show a client paying for. The gap between phase one and phase three is not a software gap. It is the difference between using AI and being able to sell, guarantee, and evidence its results. That gap is worth a multiple, and closing it is the highest-return work you can do before a sale.
The margin math buyers are pricing in
Strip out the framework language and the whole re-rating comes down to one thing a buyer can see in your accounts: margin that automation produced and did not give back.
Top Down's pilot programs reported a 60 percent cut in average ticket resolution time and a 25 percent drop in labor cost per device under management, which together lifted EBITDA by roughly 400 basis points with no top-line growth. That last part is what a buyer cares about. Margin won without new revenue is margin that survives the sale, because it is structural rather than a good year.
The math holds because labor is about half of operating expense for most providers, per CompTIA's 2024 data. Take a real example the report models out. A $10M MSP with 75 staff automates 30 percent of its Tier-1 workload. The same team now supports around 20 percent more clients with flat labor cost, and EBITDA margin moves from about 15 percent to about 18 percent. That is roughly $300K of extra annual profit with no new capital and no new hires. Put a premium multiple on that structural margin and the automation paid for itself several times over at exit.
This is also why pricing is shifting from flat subscriptions toward usage corridors and outcome-based fees, which I get into in the new MSP AI pricing models. And it is why margin benchmarking matters more than it used to. If you want to see where your numbers should sit, I keep a running view in the MSP profit margin benchmark. The buyers pricing your business already know those benchmarks cold.
What to document 24 months before you sell
Here is the operational trap. The better your automation gets, the less anyone sees it working. A prevented outage produces no ticket. A risk that never materialized leaves no trace. Top Down calls this the invisible technician problem, and it is a valuation problem, because a buyer cannot pay for savings you never recorded.
So the work before a sale is turning prevention into proof. Start 24 months out, not because the software takes that long, but because a buyer wants a trend they can trust, not a snapshot you assembled the quarter before diligence. A clean two-year line is worth far more than a strong single month.
What to instrument now, so you have the evidence when it counts:
- Percentage of tickets closed by an agent with no human touch, tracked monthly so the trend is visible.
- Technician hours removed by automation, tied to specific workflows rather than a vague total.
- Margin recovered, shown as a before-and-after against the workload you automated.
- A model inventory and decision logs, so your AI governance is auditable. This is the G and the D in the framework, and it is what shrinks your durability discount.
- The share of revenue on outcome-based or usage-based contracts, since that is what proves clients pay for assurance, not hours.
Package that and you walk into diligence as the proof camp. Skip it and you get the hope-camp discount no matter how good the underlying business is. The rest of exit preparation, from cleaning up owner dependency to understanding who actually buys MSPs, I covered in the exit readiness guide. How the number gets split across cash, earn-out, and rollover is a separate question I worked through in deal structures for an agency exit and what owners actually take home.
The formula changed because the thing that creates value changed. Buyers stopped paying only for the revenue you collect and started paying for the proof that your delivery gets cheaper and better on its own. Document that, and you are not negotiating your multiple. You are handing the buyer the reason to pay it.
FAQ
The old size-banded EBITDA multiples still set the baseline, and I break those down in the MSP valuation multiples guide. The reference point in Top Down's 2025 report is that scaled AI-enabled platforms are expected to cluster around 9 times EBITDA as regional margins converge, per Canalys. The more useful answer for a single owner is that the spread between two MSPs of the same size is now wider than the difference between size bands, because the proof camp and the hope camp are getting priced apart.
Yes, but only the part you can prove. CIBC's 2025 data shows AI-native software trading at 10 to 12 times forward revenue against 5 to 8 times for traditional SaaS, and Top Down's read is that MSPs with verified efficiency gains trade at roughly double the multiple of those selling potential. Automation you cannot evidence in your financials does nothing for your number. Automation you can trace to recovered margin is what buyers pay the premium for.
The specific ones: percentage of tickets closed by agents without human intervention, technician hours removed, and margin recovered, each shown as a trend rather than a single figure. Buyers also want governance evidence, meaning a model inventory and decision logs that make your AI auditable. Those metrics are what move you from the hope camp to the proof camp in a buyer's model.
Around 24 months. A buyer trusts a two-year trend far more than numbers you pulled together right before diligence, and structural margin improvement takes time to show as a clean line. Starting early is also cheaper, because instrumenting your delivery from a standing start under deal pressure is expensive and it looks exactly like what it is.
Some, but this is where the hope camp and proof camp split matters most. Traditional roll-ups underwrote multiple arbitrage, and that trade is compressing, which I covered in why the MSP roll-up era is ending. The buyers paying the automation premium are the ones underwriting documented margin expansion, not projected synergy. If your evidence is strong, you have negotiating room with both, and you are no longer dependent on a single type of buyer to get a full number.
A note on the numbers
The framework, the phase model, and the pilot figures come from Top Down Ventures' 2025 report, "The State of MSP Capital in the Age of AI," published November 2025. Top Down is a venture fund invested in this thesis, so I have treated its own forward estimates, including the 15 to 25 percent governance uplift and the phase weightings, as an informed projection rather than settled fact, and attributed the market data to the named analysts where the report names them (Canalys, CIBC Capital Markets, CompTIA, GTIA, PitchBook). Where the report made near-term calls for 2026, I checked them against how the year is actually playing out rather than repeating them as forecast. The valuation basics underneath all this, the multiples and the classic value drivers, live in the companion pieces linked above. If you want the full working, ask me on LinkedIn.