For much of the past two years, the AI conversation for MSPs has focused on a logical set of questions. Which tools should we use? How quickly can we automate? Will clients pay for AI services? Is Copilot the answer?
For sure, these questions still matter. But, as AI becomes more deeply embedded in daily work, the may not be the most important ones. There’s no question whether AI will become part of the client environment because it already has. Employees are using public AI tools, executives are putting in requests for copilots and agents, and, by and large, clients now expect their technology partners to have an informed position on security, governance, productivity and risk.
That changes the dynamic for MSPs. The opportunity in front of them is no longer simply to resell another license bundle or deploy another cloud service. Rather, the have the ability to help clients make informed decisions before unmanaged AI use becomes another hidden operational problem. In other words, take on the same kind of consultative role as they have with other IT needs.
There’s been a lot of conjecture about how MSPs are handling AI — and how they should handle it. But, Cynomi’s new report, What MSPs Are Actually Asking About AI, is useful because it’s not about predictions, but looks at questions practitioners are raising in peer discussions and service-provider communities. Its key finding, not surprisingly, is that MSPs are not wondering whether AI matters, but are trying to determine how to govern it, secure it, operationalize it and turn it into a service opportunity without becoming a bottleneck for their clients.
That’s important because AI adoption is often moving faster than clients’ ability to establish rules around it. There’s a gap between enthusiasm and readiness, which creates opportunity for MSPs.
The AI Request May Not Be the Real Request
One of the most interesting questions the report raises is how MSPs can say “no” to a client’s AI request without jeopardizing the relationship. It’s a reasonable question, knowing that many clients hear about AI but haven’t done the requisite homework to make an informed decision.
For starters, the answer shouldn’t be simply, “no.” Rather, MSPs should flip it around and ask their clients, “What problem are you trying to solve?”
That shifts the conversation from product fulfillment to advisory services and gives MSPs a way to guide clients in the right direction based on their goals.
A client asking for an AI chatbot, Microsoft Copilot deployment or an autonomous agent may really be trying to reduce response times, improve employee productivity, make institutional knowledge easier to find or eliminate repetitive administrative work. These are all legitimate goals, but the requested tool may not be the right starting point.
If the MSP just says, “yes,” they could open themselves up to unnecessary risk. On the other hand, the MSP that simply says, “no,” may be viewed as resistant to change or innovation. The more valuable position is one that helps the client identify the intended business outcome and most appropriate technology, assess the data and security implications, and establish the right guardrails before deployment.
In addition to likely providing a solution that actually benefits the client, this approach also creates a more durable relationship because the MSP isn’t competing on product, but on trusted advice.
Now, what happens as AI requests become more urgent and less defined? It’s reasonable to expect that businesses may not actually have a clear use case, but they hear everyone talking about AI, including their competitors. They also know employees are using it to some degree. Collectively, that creates pressure to act, something MSPs should be ready to acknowledge without allowing it to dictate the conversation.
A practical response might be to build an AI readiness assessment into the process, which can examine data classification, user permissions, approved tools, regulatory requirements, existing Microsoft 365/Google Workspace hygiene, shadow AI usage, and the client’s actual business goals. That gives the MSP a foundation from which to recommend an adoption plan. It’s likely to be much more productive than a one-size-fits-all model.
AI Governance Is Becoming a Core Managed Service Conversation
There’s a second important theme in the report, which also is not surprising: Clients are already putting sensitive information into AI tools.
To be clear, this is not necessarily malicious by any means. It’s happening when employees want a faster answer, a cleaner document, a better email or help summarizing a long file. In other words, it’s a byproduct of productivity gains. But, an employee pasting contract details, customer records, HR information, source code or internal strategy into an unapproved AI tool can certainly create exposure that existing controls may not address, making AI governance an immediate operational issue.
AI governance works best when it’s visible in day-to-day work. Employees need approved options that are usable enough to compete with the convenience of public tools, and they need clear guidance about which data can be used, which data cannot, and what to do when they are unsure. MSPs also need to look a little deeper, because even a secure AI platform can create risk if the underlying permissions aren’t appropriately applied.
This is where MSPs have another opportunity to conduct permission reviews, data discovery, AI usage policies, security awareness training, approved tool selection, monitoring, and incident response planning. It also highlights how different facets of an MSP business are becoming increasingly intertwined, including traditional IT, cybersecurity, AI, and advisory services.
The Service Desk Opportunity Is Real, but It Is Not Fully Autonomous
It’s easy to think that the most immediate AI value for MSPs come from the service desk. But, that value doesn’t necessarily come from fully autonomous AI. Instead, some of the strongest use cases are assistive, not fully autonomous.
There’s no question that ticket triage, categorization, knowledge-base search, response drafting, documentation cleanup and issue summarization can help technicians work faster and make escalations more useful. These are not glamorous applications, but they address some of the most persistent sources of MSP friction.
A well-structured ticket is easier to route. A useful summary reduces or even eliminates time needed to reconstruct what happened. A current knowledge base can shorten resolution time. Better documentation improves handoffs between Tier 1, Tier 2 and senior engineering teams.
But, the temptation to automate everything is where MSPs could run into problems. Service desks handle sensitive systems, client communications, and business-critical changes. In many of these cases, human review matters, especially when an AI recommendation could affect access, security controls, customer data or production environments.
Given that, it’s often better to start with narrow, measurable use cases. To understand whether ticket quality actually improves, whether initial response time declines, whether escalations include better context., and whether technicians gain time for higher-value work.
The truth about AI is that it isn’t a shortcut around process maturity. It will accelerate processes that already exist, but it’s not a substitute for ensuring those processes exist and work.
The Competitive Threat Is Not AI, but the AI-Enabled MSP
The last question in the report is certainly one that many MSPs have probably asked: Will AI replace MSPs?
It probably won’t, but it will raise expectations. Clients already have higher expectations around AI, and that will only increase. MSPs will be expected to understand AI risk, recommend appropriate tools, secure data, improve workflows and provide practical guidance that will improve client-specific workflows. They will also expect their providers to use AI intelligently enough to become more responsive, efficient and strategic.
So, the real competition will come not from AI directly, but from MSPs who merge AI into their service delivery, not those who simply sell AI as another product. It’s about expertise and, as has long been the case with MSPs, it’s about trust. AI is no different and the most important lesson MSPs can learn is that the AI conversation isn’t about licenses or chatbots or features, but trust.
MSPs that help clients adopt AI safely, identify meaningful use cases and govern risks will become difficult to replace. It may, in fact, be the case that those MSPs will not be devalued at all and will actually climb higher up the value chain.
Edited by
Erik Linask
