Bigger Platforms, Unwatched AI
Two sets of numbers moved across the channel, and on the surface they belong to completely different conversations.
Start with NinjaOne. The RMM platform announced a secondary funding round that more than doubled its private valuation to twelve point three billion dollars. The company is still founder-led, still profitable, still debt-free — and it reported roughly seventy percent year-over-year growth. A platform built to serve MSPs is now worth more than twelve billion dollars, and it got there while making money, not burning it.
Pax8 moved in the same direction. The distributor expanded its Marketplace with a batch of new tooling for partners — Subscription Insights, a Partner Analytics Hub, customizable finance-ready reports — all of it built to pull more of how an MSP actually runs its business onto Pax8’s rails. And alongside that, Island — the enterprise browser company — launched its small-business platform directly on the Pax8 Marketplace. One more vendor, landing on the same rails, reaching partners and their SMB clients through the same front door.
Now the second set of numbers, and they point somewhere else entirely. Glean — the enterprise AI company — surveyed six thousand full-time digital workers across the US, UK, and Australia, and found AI is saving them around eleven hours a week. But it’s quietly taking most of that back. Workers now spend an average of six point four hours a week — nearly a full working day — on what Glean calls ‘botsitting’: feeding AI the context it’s missing, checking its outputs, and cleaning up the confident-but-wrong answers it leaves behind. And when that work goes untracked, people stop doing it — more than two-thirds admitted to shipping AI work they hadn’t checked, didn’t fully understand, and couldn’t defend if asked.
And then Ivanti put a number on the control problem. Eighty-five percent of IT teams say every AI agent in their environment has a named owner. Only forty-two percent actually do — a forty-three-point gap between who’s supposedly accountable and who actually is.
So that’s the picture. The platforms underneath the MSP are getting bigger, richer, and more concentrated. And the AI running on top of them still can’t be left alone in the room.
The Vendor Walks Into the Channel
Underneath both of those is one shift: the model — the actual engine every one of these tools is built on — is turning into a commodity. And when the engine becomes a commodity, the value doesn’t vanish — it migrates to the layer that wraps around it. The companies that make the model have figured that out before the channel has.
Start with the share numbers. TechCrunch, citing Sensor Tower, reported that ChatGPT’s share of the AI market slipped below fifty percent for the first time — down to forty-six point four percent — as users spread out across Google’s Gemini, Anthropic’s Claude, and a widening field of competitors. No single model owns the market anymore. They’re becoming substitutes for one another, which is another way of saying the model itself is becoming interchangeable.
ALSO Group named the shift directly. One of Europe’s largest technology distributors, through its group lead for vendor ecosystem development, Mark Appleton, argued that AI success won’t be decided by how sophisticated the model is. It’ll be decided by ecosystem preparedness — the skills, the governance, the wiring built around the model. Strip that down and it’s a simple claim. The model is no longer the thing that separates a winner from a loser.
Here’s where it turns. If the model is becoming a commodity, the people who own it can’t sell it to enterprises one deal at a time and call that a business — they need volume, and the channel is where volume lives. So OpenAI did exactly what Anthropic had already done. It launched a partner network — a hundred-and-fifty-million-dollar program aimed at consultants, systems integrators, and managed service providers — to push its models out through partners instead of around them. Read that carefully. The company that makes the model just walked into the reseller’s spot and sat down.
And that’s the move that quietly rewrites the math for the MSP. When the model is interchangeable, and the company that makes it is now your channel competitor, reselling the model is the one position that’s already gone. Whatever is still worth paying for has to live above it.
Govern It or Absorb It
So what does living above the model actually look like inside a client environment — and what does it cost the MSP who skips it? Start with the bill. Asana surveyed UK IT leaders and found that more than eight in ten had been hit with unexpected AI costs — spend nobody forecast, surfacing after the fact. And the same survey found more than half had already seen an AI tool or agent take an action that caused financial, legal, reputational, or compliance harm — the AI making a call, or touching data, in a way nobody authorized. That’s AI running inside a business with no one deciding what it’s allowed to do and no one checking what it produces — and the bill, financial and legal, landing on whoever’s responsible for the environment. For most SMBs, that’s the MSP. Govern none of it, and you’re not avoiding the work. You’re absorbing it for free, after it’s gone wrong.
Now the size of the opening. Deloitte, in its State of AI in the Enterprise report, found that only one in five companies has a mature way to govern autonomous AI agents — which means roughly four in five are running software that acts on its own with little real control over what it does. Sit with that. The agents are showing up in client environments faster than anyone has built a way to watch them. That gap — between the AI that’s deployed and the AI that’s actually governed — isn’t a problem to point at. It’s the exact width of the work the MSP can step into: deciding what each agent is allowed to do, and standing behind whether its output can be trusted.
So the choice is clean. You can be the MSP that owns that layer on purpose — orchestrating which AI does what inside each client environment, verifying the output before it reaches the client, and pricing that judgment as the service it is. Or you can keep reselling tools whose maker is now your competitor, and absorb the cost, the cleanup, and the compliance questions the day the ungoverned stack produces something nobody checked — without ever having been paid to carry it.
Why Do We Care?
Because your client is never going to walk in and ask who’s orchestrating their AI or checking its work — they don’t yet know that’s the question. So you bring it to them. In the next review, put one thing on the table: here’s every place AI is now making a call inside your business, and here’s exactly who verifies it before it reaches your customers. The first time a client hears that from you — instead of finding the gap the hard way — you’ve stopped being the shop that sells them tools and become the one they trust to govern them.
What to Consider
Build the map before you book the meeting. The conversation only works if you can actually produce it, so walk each client environment first and list every place AI is now making a decision or generating output that reaches a person — Copilot drafting their client emails, a support tool auto-resolving tickets, an agent touching their data — and note beside each one who or what checks it before it goes out. Most of those “who verifies” cells will come back blank. That blank is the conversation.
Lead with the output that reaches their customer, not the tool. Don’t open the review with an inventory of platforms — open with the one or two AI outputs that touch their customers, because that’s where a wrong answer costs them a relationship, not just a workflow. Pick the highest-stakes path — the AI that answers a customer, prices a quote, or flags a transaction — and make the verification question concrete there first. That’s how a governance pitch becomes a business-risk conversation the client actually leans into.
Turn the gap you surface into a named, priced service. The point of bringing the map isn’t to hand over a free audit — it’s to make a service the client now wants and put a number on it. When the blank “who verifies” cells become visible, set a defined offering against them: you decide which model does what in their environment, you stand behind the outputs that reach their customers, and that judgment is a line item, not a favor folded into the base rate. A client who sees the gap alongside you is far more willing to pay you to close it than one you pitch cold.
If this trend continues, within the next year the MSPs who bring clients that map first will have turned “which AI runs here, and who verifies it” into a standing review question and a billable governance line — while the shops that wait will be answering it reactively, after a client’s AI has already put a wrong answer in front of their customer.