AI for MSPs: What You Need to Know Before You Start

Artificial intelligence is no longer a futuristic buzzword. AI is here, and MSPs that learn to use it effectively will create faster, more profitable service desks while those who wait risk being left behind. A recent survey shows 92% of MSPs plan to invest in AI in the coming year, yet 87% admit they lack the experience to deploy it effectively. If you feel uncertain about where to begin, you’re not alone.

But there’s also a lot of noise in the market. Vendors promise magic outcomes, headlines create confusion, and technicians often wonder whether AI is meant to help them or replace them. MSP leaders are caught in the middle, trying to separate hype from value while keeping their businesses running smoothly.

We were reminded of this recently at IT Nation Evolve, where the number one question from MSP leaders was: how do we get started? The curiosity is there, but so are the doubts. Here we’ll zero in on a few of the most pressing questions we heard—what tools make sense, how to approach best practices, whether messy data holds you back, and if hiring a full-time AI engineer is really necessary.

Choosing the Right Tools

The first question every MSP asks is, “Where do I even begin?” The key is to look at the daily pain points of your service desk. What slows your technicians down? What creates bottlenecks? Which tasks never seem to scale?

For many MSPs, the entry point is AI-powered ticket handling. Systems can now listen to a phone call or read an email and automatically generate a structured ticket with clear notes. This doesn’t just save time—it also eliminates the “telephone game” where critical details are lost between the end user, dispatcher, and technician. Tickets that once required manual sorting and retyping now appear ready to assign, with accurate summaries that speed up resolution.

Another area with immediate return is AI copilots for technicians. Instead of searching through scattered documentation, a technician can ask a question directly within the PSA and receive relevant steps, knowledge base entries, or even pre-written responses. That shift reduces frustration and improves first-call resolution, which in turn raises customer satisfaction scores.

Some MSPs also experiment with AI-powered self-service. Imagine a client calling in to reset a password, get the status of an onboarding request, or check whether a server is back online. An AI voice or chat agent can handle those basic needs without ever creating a ticket. That means fewer interruptions for your technicians and a smoother experience for your customers.

Finally, workflow automation powered by AI is a way to deal with tool sprawl. Many MSPs operate with a patchwork of PSA, RMM, security platforms, and documentation tools. Connecting those with automation prevents wasted motion. Even a single automation that removes repetitive “swivel chair” tasks between systems can free up hours every week.

The important thing to remember is that you don’t need to adopt everything at once. AI is not an all-or-nothing proposition. Start with one or two targeted tools that solve a real pain point for your team, prove the value, and then expand from there.

Best Practices for Adoption

Rolling out AI isn’t the same as rolling out another PSA module or RMM integration. Success depends less on the technology and more on how you introduce it to your business and your team.

• Clarity is critical. Pick a single use case and define what success looks like. Maybe you want to reduce the time it takes to create tickets from incoming calls. Maybe you want to cut down on password reset tickets by deploying self-service. Starting with a sharp problem statement gives you a north star to measure progress against.
• Trust is just as important. Technicians will naturally wonder whether AI is here to replace them. The answer needs to be clear: AI is here to offload the repetitive, low-value work so they can focus on complex problems and high-value service. Involve them early, give them a chance to test the tools, and show them how their work improves with AI support.
• And finally, discipline. AI thrives on repeatable processes. If your service desk is inconsistent, with tickets handled differently depending on the person or day, AI will only magnify the chaos. Standardize playbooks, align your team on workflows, and then automate. The investment pays dividends because automation amplifies structure, not disorder.

Leaders who treat AI adoption as a series of small, measured steps tend to see the best results. That perspective isn’t just ours—it’s been echoed in recent Channel Insider coverage of how MSPs are approaching AI adoption.

Working with Imperfect Data

One of the most common worries we hear is, “Our documentation is a mess. Won’t AI just make that worse?” The reality is that AI can be surprisingly resilient with less-than-perfect data.

Think about ticket notes. One technician writes, “Laptop won’t boot.” Another writes, “Device not starting.” A third just logs, “Won’t turn on.” To a human dispatcher, those might look inconsistent. To an AI trained on service desk patterns, they all mean the same thing—and it can normalize them into a single, clear category.

AI is also excellent at summarizing long, messy descriptions into something usable. An end user might write a three-paragraph email describing every step they took before their Wi-Fi stopped working. A technician doesn’t need to wade through all that. AI can distill it into a concise issue summary with the relevant details highlighted.

And here’s the important part: you don’t need perfect data to get started. As Matthew Linn explains in this video, the idea that “our data isn’t good enough” is an outdated perspective. Modern AI tools can triage and categorize tickets accurately without requiring you to first rebuild your knowledge base or scrub every PSA field.

That said, AI isn’t magic. If your knowledge base is completely out of date or your PSA fields are never filled in, there’s only so much it can do. The best way to think about it is as both a helper and a spotlight. It makes messy data more useful in the moment, while also showing you where you need to tighten up your data discipline.

Thread’s AI-Ready MSP resource frames this well: your data doesn’t need to be perfectly clean. What matters is picking the right workflows to automate, customizing proven processes, and iterating quickly.

Do You Need a Full-Time AI Engineer?

The short answer is no—not to start. Most MSPs can get value from the AI capabilities already baked into their PSA, RMM, or service desk platforms. These are designed to be turned on and used by existing staff without specialized programming skills.

As your usage grows, you may want to designate an internal champion. This doesn’t have to be a new hire. Often, it’s a technician or service desk manager who is curious, process-oriented, and willing to experiment. Their role is to try new features, document what works, and train the rest of the team. Having a point person builds consistency without requiring a dedicated engineering salary.

Only once AI becomes a core part of your service delivery—driving a significant percentage of ticket handling or customer interactions—does it make sense to consider a full-time automation or AI engineer. At that point, you might want to build custom models, train agents on proprietary data, or integrate AI more deeply across your tech stack. But for most MSPs, that stage is down the road. The right first step is to start using what you already have and build maturity from there.

Taking the First Step

AI is reshaping the MSP industry. It’s not about replacing people; it’s about giving your team the tools to focus on what matters. Customers want fast, accurate, and modern service. Technicians want less repetitive work and more time for meaningful problem-solving. Leaders want higher margins and predictable growth. AI, when applied with care, helps deliver all three.

The temptation is to wait until you feel fully ready. But the truth is, the MSPs seeing the most benefit are the ones who start small today and learn as they go. Pick one assistive workflow to automate this month, assign an AI champion internally, and set a clear 90-day goal with measurable KPIs. As Thread’s AI-Ready MSP framework shows, assistive automation builds trust, creates momentum, and lays the foundation for more advanced agentic AI later.

The future of the service desk won’t be defined by the companies that had the neatest documentation or the flashiest technology. It will be defined by the MSPs who chose to act—those who empowered their teams with AI to create a modern, scalable, and more profitable way of working.