6 AI Myths MSPs Can’t Afford to Believe

The hype around artificial intelligence (AI) has many MSPs wondering whether-and when-to dive in. While some are already leveraging AI for everything from help desk automation to marketing, others remain hesitant, held back by misconceptions about the technology's accessibility and practicality.

So, what's the reality? And how can you ensure your AI investments actually contribute to business growth? Members of CompTIA's AI Advisory Council shared key misconceptions holding providers back, and guidelines for implementation and ROI.

Myth: AI Is Too Expensive

Reality: Smart Implementation Pays Off

Many MSPs look at licensing fees for large language models (LLMS) and assume AI is too expensive to implement. However, those with experience say this view misses the reality of today's AI landscape. "As long as you have a process and procedures in place, it's no more expensive than other software," said David Tan, CTO at CrushBank. In fact, focused models often work better than the largest, most expensive options. "Foundation models that are fine-tuned for your business work way better and are infinitely less expensive," Tan said.

Plus, prices are trending downward. Karen Powell, founder and CEO of Omnipresence Group, noted, "The cost of computing is starting to drop. The more LLMs there are, the more that we're able to get things online." She advises MSPs to implement cost controls and leverage pay-as-you-go options rather than fixed contracts.

Myth: AI Will Eliminate Jobs

Reality: Evolution, Not Extinction

AI will automate certain tasks, which experts say creates opportunities rather than eliminates jobs entirely. "AI will start to replace human jobs. That doesn't mean it will replace humans and make them obsolete," Tan notes. "There will be ample opportunity for what I call human-AI collaboration and other skills and opportunities."

He points to common MSP workflows as an example. "You probably have someone whose job it is to look at every incoming ticket, categorize it, triage it, route it, prioritize it. You're probably paying a lot of money to do that, and they're probably pretty technical. How cool would it be if you could train a machine to do that? Then have that person do something that delivers much more value to the organization."

Myth: AI Isn't Secure

Reality: Security By Design

"Sure AI isn't secure-if you don't secure it," Tan said. "Like anything else, your data's not secure if you don't have proper controls in place."

Powell added that securing AI follows familiar patterns. "It's the same as when you're looking at anything from a cloud perspective and you're setting up different instances and different tenants. You're going to still have those same parameters that you would put in place, the same data lakes." She typically builds within one of the five major trusted ecosystems, leveraging their protection layers alongside her team's security measures.

Myth: AI Is Only for Large Enterprises

Reality: Small Players Win Big

Perhaps the most persistent myth is that AI primarily benefits large enterprises. But the reality is quite the opposite.

"I think AI will benefit SMB and enterprise more [than large enterprises] because enterprise moves slow," Powell said. "They've got a lot of barriers in their way for change. Whereas when you are smaller and in mid-market, you are a challenger brand. You are hungry to be able to disrupt."

This advantage extends to MSPs' clients as well. Corey Kirkendoll, CEO of 5K Technical Services, works extensively with smaller organizations and has witnessed client benefits firsthand. He concurred SMBs are now able to compete with larger organizations they couldn't before AI adoption.

Making AI Pay Off for Your Business

MSPs ready to move past the myths should start examining your existing tech stack. "We found that 60% of existing platforms already have AI baked in," Powell said. "So a lot of what you're using already has it, it's just that you're not necessarily aware that it is active and nuanced."

For MSPs ready to monetize AI, experts recommend starting with assessments. Kirkendoll said his team works with departments from HR to sales to identify specific use cases and challenges. These initial projects often lead to recurring revenue because the MSP then needs to maintain whatever technology is implemented.

Tan suggests putting together an offering around data discovery. "What applications do they have? Who owns the application? Where's the data? How do you get access to it? These are the building blocks foundational for AI solutions in any typical business." This practical approach helps clients understand their AI readiness while creating immediate value.

When it comes to measuring ROI, Powell emphasizes that traditional metrics still apply: Increased revenue, increased margin, increased productivity, decreased cost, increased customer satisfaction.

Tan added that ROI should be measured solution by solution. "Can you write more code? Can your marketing team process more newsletters or create more content for your website? Can you do more with less, or can you just do more outside of your previous capabilities?"

The key is starting with specific, measurable improvements rather than trying to calculate the holistic impact of AI adoption. As Kirkendoll noted, "Look at what's happening with [people's] time, and look at what's happening from a money perspective."

Even starting with just one application and a few KPIs, this focused approach allows MSPs to demonstrate value while building expertise in AI implementation and support.

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