Unlocking the Power of AI for Small MSPs

As artificial intelligence (AI) continues to evolve at a rapid pace, the opportunities for managed service providers (MSPs) of all sizes are becoming clearer. Once considered a tool for large enterprises with vast data reserves and deep pockets, AI has leveled the playing field, creating a range of possibilities even for the smallest MSPs.

This transformation has empowered small teams to achieve efficiencies that once seemed out of reach. In a recent podcast, I spoke with Nicole Reineke, AI Strategist at N‑able, to explore how smaller MSPs can navigate this new terrain and use AI to their advantage. With the right approach, any MSP—regardless of size—can unlock the potential of AI to better serve their clients and optimize internal operations.

A Paradigm Shift in AI Accessibility

Gone are the days when building AI capabilities required large, dedicated teams of developers. Today, with the emergence of tools like Google Vertex, AWS Bedrock, and Microsoft’s Copilot Studio, the barriers to entry are lower than ever before. According to Nicole, “even just one or two-person organizations can create AI solutions without having to do a lot of coding.” This development has democratized AI, making it accessible to smaller organizations, and enabling them to enhance their operations in ways that were previously unimaginable.

The real game-changer here is the ability to leverage AI without needing a massive team or advanced technical expertise. Nicole adds, “There’s a lot of tools that have evened the playing field. It’s whether or not it makes sense to do that kind of an investment and whether or not you have the data to support it.” For smaller MSPs, that’s an exciting prospect—AI is no longer just for the “big guys.”

Data: The Key to AI Success

While AI tools are now widely accessible, the true power of AI lies in the data that fuels it. In the simplest terms, AI thrives on data to generate insights, automate tasks, and make intelligent recommendations. The more accurate and extensive the data, the better the results. This is where some MSPs may face a challenge. As Nicole emphasizes, “In order for you to do anything like building your own machine learning or AI, you’re looking at requiring hundreds of thousands to millions of pieces of data that’s well-labeled, clean, and well understood.”

For smaller MSPs, who may not have the luxury of enormous datasets, this might seem like a barrier. However, there are alternatives. Smaller data sets can be used for simpler AI applications, like tuning off-the-shelf tools to meet specific needs. Nicole gives an example: “If you want to have a ticket system that looks at your existing tickets and suggests your next likely solution, that can be done with just a few thousand records.” Even with limited data, MSPs can start small and grow their capabilities over time.

And for those lacking data entirely, there are other options available. In some cases, purchasing data sets from trusted sources is a viable route. Nicole points MSPs toward sites like Huggingface, a popular community for machine learning specialists. “They sell datasets,” she shares, “so you could actually use somebody else’s dataset if it’s trustworthy and clean and it aligns to your use case.”

How Small MSPs Can Start Their AI Journey

The potential for AI to enhance productivity and improve service offerings is substantial, but where should smaller MSPs begin? Nicole suggests starting with simple generative AI solutions, like chatbots or content generation tools, which can be implemented with minimal setup and low cost. “Everybody should be trying this,” she says. “Even if you’re not putting it into production, it would be a shame if you didn’t even dip your toe, especially into the generative AI aspects.”

Starting small not only offers immediate benefits but also allows organizations to get comfortable with AI before diving into more complex projects. This phased approach helps MSPs build confidence in their AI strategies, laying the groundwork for larger investments down the road.

Another critical first step is determining how to communicate the use of AI with customers. Transparency is essential when handling customer data. Nicole advises that MSPs decide upfront how they will inform customers of AI-driven processes. Whether it’s through obtaining explicit consent or providing notification, MSPs must ensure that customers are aware of how their data is being used. This transparency is key to fostering trust and minimizing any regulatory or legal risks.

With AI tools becoming more accessible, there’s no reason for smaller MSPs to feel left out. In fact, there are clear advantages for small businesses willing to take the plunge into AI. Nicole suggests that the future of AI is about to get even more interesting: “Our edge devices will end up being AI devices.” AI will become smaller, faster, and more localized, meaning that even individual devices will have built-in AI capabilities, further expanding the scope for MSPs to optimize operations and improve customer service.

For any MSP looking to differentiate themselves in an increasingly competitive landscape, AI is not just a passing trend—it’s a necessity. By investing in the right tools and leveraging the right data, MSPs can stay ahead of the curve and deliver enhanced value to their customers.

Take the Next Step Toward AI Adoption

Whether you’re a one-person MSP or a larger enterprise, now is the time to explore the possibilities that AI brings. As Nicole concludes, “It can dramatically improve the interaction that you have with your customers, streamline processes internally, and take away a lot of the things that create human error.”

Tune into the full podcast discussion with Nicole Reineke for an in-depth look at how AI is reshaping the industry and how even smaller MSPs can take advantage of these opportunities. Whether you’re just starting out or looking to refine your AI strategy, there’s valuable insight waiting for you.

Beyond the Horizon Episode 3: Navigating AI Adoption in MSPs

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Pete Roythorne is Senior Brand Editor at N‑able