The AI use cases MSPs keep avoiding, and why that's probably the right call

Not every AI use case is one you should be chasing. A recent survey of 100+ MSPs found resistance clusters in a few specific places. AI-generated client churn scores. Managing email campaigns. Building custom AI applications. Generating client budgets. Analyzing insurance coverage.

Look at that list for a second. None of it is random.

Every item needs expertise most MSPs don't carry in-house. Data science. Software engineering. Marketing. Or it touches something where a mistake costs real money. That's the pattern. MSPs deploy AI. They don't build it. Where AI shows up inside a platform you already use, adoption's high. Where it means building something from scratch, or wandering outside your actual lane, it stalls. Doesn't matter how good the use case sounds on a slide.

Here's the thing, though. That's not a gap to feel bad about. It's a signal. The AI worth having is the kind that shows up in the tools you're already running, not the kind that asks you to become a software company on the side.

What's an AI use case you tried, or almost tried, that just wasn't worth it?