vCAIO is showing up more and more in my conversations with MSPs. Peer Group meetings, AI Community Group meetings, LinkedIn posts, conference hallway tracks. And like a lot of buzzwords in this space, it sounds compelling on the surface. But before you go build a vCAIO offering and start pitching it to clients, it’s worth asking a simple question: do you actually know what that looks like in practice?
I’ve been thinking about this a lot. Let’s talk about vCAIO and ongoing AI related business today.
The Problem With How MSPs Are Approaching This
The most common thing I’m hearing from MSP owners is some version of: “How do I generate MRR from AI?” I get it. MRR is the lifeblood of a Managed Services business. Of course you want to turn AI into a recurring revenue line.
The problem is that most MSPs are trying to build the pricing model before they’ve proven the value. They want to figure out the per-user fee, the monthly retainer structure, the invoice line item, before they’ve actually delivered a successful AI project for a single client. That’s backwards.
You don’t get to charge for ongoing AI advisory work because you put it on a price sheet. You get to charge for it because you earned that conversation by delivering real results.
What vCAIO Actually Requires
Here’s the thing about vCAIO, or vCTO, or any of these fractional technology leadership offerings. They’re not really about technology. They’re about positioning yourself (and your company) as a technology leader for your clients.
The MSPs that can pull this off are the ones their clients already see as business advisors, not IT vendors. If your clients call you when something breaks, that’s a very different relationship than if your clients call you when they’re thinking through a new business process or a growth initiative. Both relationships are valid. But only one of them leads naturally to a vCAIO conversation.
This is why so many MSPs struggle with the idea. They’ve built their client relationships on being the tech people. Their clients like them, and trust them to fix the broken stuff. But when an AI opportunity comes up, those clients either DIY it, hire a consultant, or ask you to enable a tool and get out of the way. That’s not a failure of the client. That’s a reflection of how the relationship was built and maintained.
Starting With the Right Conversation
If you want to position yourself for AI advisory work, it starts in the sales and discovery process, well before a client signs a contract.
The old version of discovery sounds like: what’s your tech stack, what’s your budget, what problems are you having with your current provider? That gets you the contract. It doesn’t get you the advisor relationship.
The new version sounds different. How do you make money? What does your team spend most of their time on? What happens when a key process breaks down? What keeps you up at night? These questions signal that you care about their business, not just their infrastructure. Over time, that’s what earns you a seat at the table when the bigger strategic conversations happen.
If you’re trying to shift the thinking of your existing clients, asking these questions and probing into their use of AI in your SBR meetings (you’re having those, right?) is what I’d do if I were in your shoes.
The other thing you need to understand before recommending any AI solution is the process the client currently has in place. AI doesn’t fix broken processes. It automates them, faster and at scale. If you skip the process discovery work and jump straight to recommending a tool, you’re either going to burn a lot of hours on something that doesn’t work, or you’re going to get lucky. Getting lucky is not a repeatable business model.
An Order of Operations for AI Projects
Once you’ve done the discovery work and you understand the business, you’ll likely find two types of AI opportunities:
- Cost-saving projects
- Revenue-generating projects.
Cost-saving projects are things like automating a manual workflow, reducing time spent on repetitive tasks, or cutting down on administrative overhead. Revenue-generating projects are things like helping a client’s sales team close faster, improving the customer experience in ways that drive retention, or building a process that opens up a new revenue stream.
I’d recommend that you lead with a cost-saving project as your proof of concept. It’s lower risk for the client and lower stakes for you. You get to test your process, demonstrate competence, and build trust before you go after the bigger opportunity. Don’t burn your best revenue-generation opportunity on an unproven engagement.
Once you’ve delivered real results on the cost-save side and the client has seen what you’re capable of, the revenue-generating conversation becomes much easier to have. That’s the one that actually justifies ongoing advisory fees. It’s much easier to take a victory lap with your client for a project that is literally putting more money in their pockets.
Cost-saving projects are harder to convert to ongoing revenue. When you save a client money, they tend to think of it as a one-time win. Revenue-generating projects are a different story. When you help a client make money, they’re far more willing to keep paying because they’re seeing a return every month. Neither type of project is off the table. Just understand the difference and plan accordingly.
What the Ongoing Revenue Actually Looks Like
Let’s talk pricing for a minute, because this is where the vCAIO conversation usually falls apart.
There’s no established per-user or per-endpoint model for AI advisory work. Not yet, anyway. The MSPs pulling this off are doing one of two things. Either they’re pricing based on outcomes, tying their ongoing fee to maintaining and optimizing a result they’ve already delivered. Or they’re doing a simple strategic retainer, reserving a set number of hours each month for technology planning and advisory work. Both models are honest. Both are things clients can understand and justify.
What doesn’t work is inventing a monthly AI fee before you’ve proven anything. Clients aren’t going to pay ongoing fees for something that hasn’t delivered results. And they shouldn’t.
One thing to understand is that the resources required to do this work for your clients are expensive. Often, this is an owner or highly compensated person within the organization. It’s unlikely that a Tier 2 tech making $65k per year can do this with any real regularity. Higher dollar resources = higher rates. You MUST anchor your AI / Business Consulting rate at something much higher than your standard hourly fee. As you build out your AI / MIP practice you will need to ensure you aren’t leaving margin on the table.
The Bottom Line
vCAIO isn’t a product you build. It’s a position you earn. It comes from showing up as a business advisor, understanding your clients’ processes, and delivering results that actually move the needle for their business.
If that’s not where you are yet with your clients, that’s okay. Start with the discovery conversation. Ask better questions. Build your understanding of their processes before you recommend any tool. Deliver a project that proves you know what you’re doing.
The MRR conversation follows. But only after the work is done.
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