Turning AI Hype into a Viable MSP Business Strategy
For partners, AI is at a turning point. MSPs now face the real challenge of moving beyond the novelty and strategically embedding AI into operations, offerings and outcomes.
by Howard M. Cohen
Generative AI has enjoyed faster adoption than any previous new technology. GenAI achieved almost 40 percent adoption within two years of introduction. Meanwhile, the Internet took five years to achieve 20 percent. Personal computers took three to achieve 20 percent.
All three were initially introduced as consumer products. PCs were initially sold through retail stores. The popular adoption of the Internet began with the arrival of Pizza Hut and 800-Flowers on the World Wide Web. GenAI, though, was introduced as something of a toy. It could write your letters and e-mails for you. It could make pictures of you appear more rugged, more muscular, more beauteous or more adorable.
Over the years, we've talked about the consumerization of technologies, but each of these three examples actually experienced the opposite. Given that all businesspeople are also consumers, they met these technologies as consumers and transitioned them to business. With the arrival of VisiCalc spreadsheet software, people immediately saw business applications and began using them. Word processors, databases, communications and other applications followed to support business adoption of PCs. Even before the advent of the Web, the Internet was providing communication and data sharing among universities and a few large corporations. The Web made this functionality available to a broader business audience. If consumers could order pizza and flowers over the Web, what couldn't they do? It didn't take long for Jeff Bezos to explode Amazon into unprecedented success.
AI, however, is proving to be more of a challenge, with many MSPs scratching their heads as to how exactly to monetize this new phenomenon.
The Nature of AI
Many MSPs have been led to believe that AI is an application unto itself, and that they need to figure out how to "sell AI." But potential applications of AI are so broad in scope that AI should never be seen as a standalone entity -- it is an enabling technology. It is, therefore, a component of improved applications that can do far more with far greater granularity and specificity than before. Where classic digital technologies were only capable of "true or false," "yes or no," "zero or one" or "on or off," AI enables shades of meaning in between. Inference. Influence. Shades of grey between the black and the white.
One of the earliest implementations of AI was the "recommender engine," which took a product database filled with features, benefits, reviews and other data regarding every product in a company's inventory and compared it against a customer database that included information obtained through social media listening, survey responses and interaction transcripts, as well as purchase histories and more. Based on inference from all these sources, the engine identified products that would most likely be of interest to each customer and sent them the appropriate marketing materials. Salespeople were then notified by the engine of the need to follow up on materials sent. Imagine being a salesperson armed with that kind of assistance!
Strategically Incorporating AI into Applications
In a January article in my column Practical AI in Pure AI magazine titled "AI Changes Our Relationship with Applications," I reported on how Applied Information Sciences (AIS) was improving on traditional applications by enhancing them with generative AI technologies. In that article, their VP of Cloud Modernization and AI Solutions described how these new applications served more as assistants that were far more interactive. Importantly, he also stressed that building one's own large language model (LLM) was far too expensive and time-consuming to be practical. Instead, he recommended identifying an existing commercially available LLM that could be adapted to the customer's requirements and enhanced with retrieval augmented generation (RAG) or similar technologies.
AIS, which has been providing IT services since 1982, has successfully incorporated GenAI into their offerings as a strategic advantage for their customers. That same VP also informed us that English would be the next great programming language, which should offer incentive to those MSPs who don't see themselves transitioning into application development.
The Tactical Approach to AI
Many MSPs are finding GenAI solutions coming from the software developers who create the tools they use in the course of delivering their managed services. Perhaps the most well-known is AIOps, in which AI technologies are used to augment the capabilities of network operations with more automation. GenAI can deliver predictive analytics for better systems maintenance, automated remediation of many common issues, optimization of various resources, and also automation of service desk operations.
In some cases, AI agents are replacing human agents in answering and processing customer call requests for servicing. This usually leads to faster triage and faster issue resolution, which increases customer satisfaction. AI also personalizes the customer experience, providing chatbots and other virtual assistants that can be far more responsive than human operators.
Cybersecurity and threat management vendors are also integrating more GenAI into their platforms, enabling network traffic analysis, anomaly detection and identification of potential threats far faster than human operators ever could. AI-enabled platforms are responding automatically to a wider range of anomalies and threats, freeing human operators to focus on more strategic improvements.
Business intelligence (BI) and analytics are dramatically accelerated. The time to actionable insights is slashed when GenAI is used to analyze vast amounts of operational data to identify trends and predict potential outcomes. This leads to better-informed, far-more-effective decision-making. Even cloud operations, including better control over costs, can be achieved by incorporating GenAI tactically.
It All Begins with Proactive Decision-Making
MSPs are best served when they gather their team for a careful discussion of where GenAI fits into their practices and how their offerings can be augmented. This is not fit for a trial-and-error methodology. Instead, begin with an analysis of what your customer base really needs. How could you offer them greater strategic advantage and greater value by applying GenAI technologies? How can you align your GenAI offerings with your skills base and available resources? What investments are you willing to make?
More and more analysts agree that AI is the future of the MSP channel. How you embrace it, how you deploy it and how you leverage it for your own operations, will all determine your success and your growth going forward.
Posted by Howard M. Cohen on June 02, 2025