COMMENTARY: I think it is time for MSPs to think bigger about AI. MSPs should not look at AI only as a way to close tickets faster or reduce technician workload. That helps, but it does not solve the bigger problem. Customers are already testing AI inside their businesses, often without their MSP involved. That creates risk, but it also creates a clear opening. MSPs should be asking clients what they are doing with AI, where they need help, and how they can use it safely. The real value is using AI to stay closer to customers, spot risks earlier, connect planning with support data, and give better guidance before something breaks. MSPs that do this will look less like vendors and more like real partners.
Despite all the activity, something is not adding up. MSPs are running pilots, deploying tools, and experimenting with automation across their stacks. And yet, the operational outcomes most operators expect still feel uneven. Some are pulling ahead. Most are not. I don’t think that gap is random. It comes down to whether the tools are actually connected to how the business runs.
The Operator Mindset Trap
Most MSPs were built by technical people. That’s a genuine strength — but it’s also the thing that keeps operators locked into a narrow view of what AI is actually for.
When I talk to MSPs about where they are applying AI, the conversation almost always goes the same direction: support automation, ticket deflection, and reducing technician load. And I get it. That is the business they know. But that is also working inside the business, not on it. The ones getting somewhere are asking a different question entirely. Not, “What existing workflow can AI automate for me,” but, “What can AI help me do to scale my MSP that was previously impossible to do at scale?”
The MSPs getting the most traction with AI are not simply using it to reduce technician workload; they are using it to increase the amount of meaningful customer engagement they can deliver consistently across their client base. That is a very different operational mindset.
The Anti-Pattern Nobody Is Talking About
There is another pattern I am seeing that concerns me just as much, and it goes in the opposite direction.
AI has made it exciting to build things from scratch. The ability to spin up tools that would have taken a dev team months is remarkable. But I am watching MSPs disappear into rabbit holes because of it: vibe-coding internal tools they could subscribe to cheaply, rebuilding systems from scratch to save marginal fees, and chasing every interesting use case that comes across their feed.
We have had to consciously address this ourselves. The question we keep coming back to is simple — does this bring in clients or keep them? If the answer is no, we stop. The discipline to walk away from interesting rabbit holes is becoming one of the most important leadership skills in this environment. If it’s not bringing in clients or keeping them, it’s probably a distraction.
The Threat You May Not Be Seeing
Here is what keeps me up at night as an operator: Almost every business owner I talk to is actively running AI experiments inside their business. Buying hardware. Deploying bots. Automating processes that used to require dedicated teams. And they are doing it without their MSP involved at all.
That is shadow AI at scale. And it's happening because MSPs aren't even broaching the subject with their clients yet. If you walk in and ask, "What are you doing with AI and how can we help you do it safely?" the majority of customers will accept your help. You just have to ask.
Where the Real Opportunity Is
Customer success has always been the hardest part of running an MSP. Most clients will tell you honestly that they do not feel like they have a strategic partner. They feel like they have a vendor who shows up when something breaks.
That’s the problem worth solving. Most MSPs operate with 90% of their time eaten up by overhead and 10% actually spent in front of clients. That ratio can flip. The overhead that used to make consistent strategic engagement impossible is gone. You can do this at scale now without a dedicated team. The MSPs who get this are going to look completely different to their clients very soon. Not because they have better technicians, but because they finally show up as a partner instead of a vendor. And that momentum builds on itself.
The Layer That Makes This Possible
Here’s the biggest barrier to becoming a partner to your clients: disconnected tools. A great PSA, a solid RMM, a CRM — if each requires a separate login and a separate context switch, your AI can only be useful in one tool at a time. You are not running an AI-powered operation. You are running the same disconnected operation with an AI layer on top of each silo.
That is why most MSPs are still getting incremental gains from AI instead of transformational ones. Faster ticket summaries, better reporting, smarter scripting — those things matter, but they are still improvements happening inside disconnected workflows.
The bigger opportunity for MSPs is using AI to do things they previously could not do consistently at scale: engage clients more proactively, strengthen customer relationships, deliver strategic guidance continuously, and operate more like a true partner instead of a reactive vendor.
The concept getting traction right now is headless operations: running your entire business through a connected AI interface without logging into individual tools. This is what Model Context Protocol, or MCP, enables. I tested this recently, and what would have taken days happened in minutes.
But the real value was not the speed itself. It was what the speed unlocked.
When systems can exchange operational context fluidly, AI stops acting like a feature attached to a single platform and starts becoming part of how the business operates. Lifecycle planning can connect directly to budgeting conversations. Support trends can surface customer risk earlier. Strategic reviews can become proactive instead of reactive.
That is the shift from AI as a product feature into AI as the engine behind a scalable services model that compounds over time. The operators who build this now won't just operate faster. They are going to show up differently to clients — more proactive, more strategic, and far more embedded in customer decision-making.
That is the problem worth solving.
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