Table of contents
- Key Takeaways
- The Pragmatic MSP’s Approach to AI: Start Small. Ask for Proof.
- Start Small, or You’ll End Up With a Lot of Half-Built Automations
- Ask Every AI Vendor for the Data
- What a Credible Answer Sounds Like
- Measure It Yourself
- Know Where AI Shouldn’t Touch the Experience
- Frequently Asked Questions
- Don’t just manage IT. Secure it with Syncro.
Key Takeaways
- When it comes to automation, start with one or two small, repetitive tasks. Prove they’re reliable first, and keep the setup modular before you automate anything else.
- Before adopting any AI feature from a vendor, ask them for real numbers/data that prove the value or efficacy of said feature (e.g., specific metrics that detail how much time XYZ feature saves techs), and whether a human stays in the loop (hint: the answer should be ‘yes’).
- Before a trial, jot down your current numbers (like how long tickets wait to be assigned). Then compare those same numbers with the AI turned on. It’s the simplest way to see what a feature is really doing for you. How you adopt AI, more than which feature you pick, is what’s setting MSPs apart right now.
The Pragmatic MSP’s Approach to AI: Start Small. Ask for Proof.
AI is the biggest cost-reduction wave to hit the channel since RMM showed up in the early 2010s. That’s not just hype, it’s the pattern repeating itself. But a wave this big also draws a lot of vendors happy to say “AI-powered” without ever showing you a number. The MSPs pulling ahead right now aren’t the ones with the flashiest AI story. They’re the ones treating AI adoption the way they’d treat any other vendor decision: start small, demand proof, and keep a human where a human belongs.
Start Small, or You’ll End Up With a Lot of Half-Built Automations
Plenty of us have lived this mistake. The first time AI does something that used to be painful and time consuming, it’s exciting enough that you want to automate everything at once. What follows is predictable: a machine full of half-created agents and automations that all try to do too much.
A better approach is to choose a couple of repetitive tasks, the kind that take a few minutes but happen constantly. Solve those first. Confirm they’re reliable. Keep the setup modular so you can back out of a change without unwinding everything else. Then move to the next thing.
Ask Every AI Vendor for the Data
Before you adopt any AI feature or tool, ask the vendor for the actual data behind the claim, not just the demo and the pitch.
A few questions worth asking directly:
- What’s the specific metric? “Faster resolution” or “less manual work” means nothing without a number attached. Ask what improved, by how much, and over what time period.
- Whose data is it? Ask if the number comes from their own customer base, a pilot group, or a vendor-run benchmark. Ask to talk to a partner using the feature today.
- What happens when it’s wrong? Every AI feature makes mistakes. Ask how the vendor catches those, and who’s accountable when it does.
- Is a human still in the loop? Find out if the feature requires approval before it acts, or if it runs on its own. Neither answer is automatically wrong, but you should know which one you’re getting.
If a vendor can’t answer these, that’s worth noting on its own.
What a Credible Answer Sounds Like
Trustworthy answers have a shape: the feature is live and documented, the price is in writing, and there’s a customer who measured a result. The evasive answers have a shape, too.
| The claim | What a real answer sounds like | The red flag |
| “AI-powered triage” | Live and documented, with a dated release note and known limitations | A roadmap date presented as if it’s already shipped |
| An accuracy number | Measured on a stated sample of real tickets, methodology available | A percentage with no sample size attached |
| Customer proof | A named customer with a workflow and a measured result | A logo wall |
And the measured result should sound like a person, not a percentage floating in space. An example of what that might sound like, coming from the vendor:
One MSP owner worked through 15 tickets in an hour and a half, from the bleachers at his kid’s baseball game, and his shop’s ticket load dropped by a quarter in the first week.
You can interrogate a claim like that. There’s a workflow and a number attached to it.
Measure It Yourself
You don’t need anything formal for this. Before you start a trial, spend a few weeks noting how things run today: how long tickets wait before they’re assigned, how often they get reassigned, how many get escalated, and how many get resolved on the first touch. Thirty days is enough for a fair picture.
Then turn the AI features on and watch those same numbers through the trial. If they improve, you’ll know exactly what the feature earned. If they don’t, you’ll know that too, before you’ve committed to anything. Either way, you’re making the call on your own numbers, and most vendors will gladly help you set up that comparison.
Know Where AI Shouldn’t Touch the Experience
Pragmatic also means knowing the limits. When a client is dealing with a security incident, a payroll system down, or a suspected breach, they want a person on the phone, not a bot. That’s not a temporary gap in the technology. It’s where the human relationship still does the job AI can’t.
Adopting AI well isn’t about moving fast. It’s picking the small task that actually saves time, holding vendors to a real number, and keeping a person in the moments that call for one. That combination, more than any single AI feature, is what’s setting MSPs apart right now.
Frequently Asked Questions
Pick one or two repetitive tasks that take a few minutes but happen constantly. Prove the AI handles them reliably, keep the setup modular so you can back out of any change, and only then expand. Automating everything at once is how you end up with half-built agents.
Automation follows rules you write: if this, then that. AI makes a judgment from patterns, like summarizing a long ticket thread or suggesting the best-fit technician. Both are useful; they’re just different tools, and it helps to know which one a feature actually is.
That’s your call, and it can evolve. A responsible design starts with a human approving anything that creates or changes a record, and as specific features earn your trust, you can let the AI execute those on its own. The point is that you decide where that line sits, and you can move it. Ask every vendor exactly how this works in their product.
No. The practical wins today are in removing the work that never needed a skilled tech, e.g., rebuilding context, sorting queues, chasing data across tools. The judgment calls, and the moments when a client wants a person on the phone, stay human.
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