AI: Threat or Opportunity for the MSP?
Knowledge blog
Every MSP will recognize this. A CEO or director who feels their organization is falling behind on AI. So the company looks for a partner who can help answer questions like: how do we introduce AI within our organization, at what level, for which employees, and what should our AI policy look like?
In theory, that’s a prime opportunity for MSPs: an advisory role carries real value in a still-uncharted area like AI. In practice, though, claiming that advisory role turns out to be difficult. Not because MSPs are wary of AI, but because they lack the knowledge and a concrete plan to sell it as a service. Some MSPs bring in an adoption consultant to help build that plan.
You’d expect customers to go straight to a hyperscaler instead. Hyperscalers offer generic adoption programs and documentation, but no advice tailored to an organization’s specific situation: which process is suited to AI, which employees need to be brought along, and what risks come with it? That exact gap is what makes MSPs relevant, provided they can independently take on a genuine advisory role.
The biggest misconception among MSPs
That advisory role is easier said than done. It comes down to the old way of working: fixed contracts, predictable work, a clear scope, usually priced per user or per device. That background creates the idea that offering AI services is mainly complicated, without a clear revenue model to match.
But the revenue model isn’t in the technology itself. You can set up a simple AI agent within minutes. The real work lies in what comes after: AI adoption. The agent also needs to become secure, reliable, and widely accepted. It takes time to get people to genuinely embrace a new way of working. That’s exactly where the MSP’s advisory role comes in: in my experience with MSPs, even a small SMB typically needs around two hundred hours of consulting just to get a single agent to land well.
That gap between building and actually making it work also shows up in what an AI agent can do today. The promise sounds big: a system that picks up, categorizes, and resolves a ticket entirely on its own, without any human action in the process. In practice, agents still mostly support processes, they don’t yet carry out tasks independently.
Governance as the differentiator
Adoption also doesn’t move at the same pace everywhere: even within a single organization, there are often already big differences. Some employees are already working with AI on their own, sometimes with their own tools alongside the AI solutions the organization officially provides, a small-scale version of bring your own AI. That uneven adoption is a governance risk: who decides which data can go to which model, who can override an AI decision, and how do you demonstrate that to an auditor? For an MSP, that’s not a side issue, it’s an opening to stand out. Whoever takes ownership of governance offers customers exactly what a hyperscaler can never deliver.
AI is not a threat to MSPs: they don’t distrust the technology, their hesitation mainly comes down to lacking the knowledge and the plan to sell it as a service. That’s the exact same hesitation their customers are wrestling with.
The next step
The difference isn’t about who hesitates the most, it’s about who gets their governance in order first and turns that into a concrete revenue model. As an MSP, get your own AI policy in order and invest in knowledge, adoption, and governance. Whoever takes that on now takes an important step toward a new revenue stream, and becomes exactly the partner that removes customers’ uncertainty about AI and gives them direction.