QUICK ANSWER What is MSP profitability? MSP profitability refers to the margin remaining after all costs of service delivery—including normalized owner compensation—are accounted for. Industry benchmarks suggest healthy MSPs target 10–15% net profit margins. Businesses operating below 5% are at elevated risk.
The MSP industry is in a crisis that few are willing to confront. Many industry observers believe that roughly 40–50% of MSPs generate 5% EBITDA or less after normalization. A significant number of those businesses are actually losing money once owner compensation is normalized. On paper, a significant number of MSP owners would be financially better off shutting down and taking a job. That should create urgency, desperation in some cases. It should drive owners to aggressively seek help and fix what isn’t working. Instead, many are chasing AI.
The problem is simple:
No MSP has ever automated its way out of a broken business model.
AI might make technicians faster. It won’t fix pricing.
AI isn’t going to save struggling MSPs. But it may accelerate their decline.
Why AI Will Widen the Gap, Not Close It
The MSPs best positioned to benefit from AI already have strong foundations. They are:
- Profitable enough to invest in R&D
- Mature enough to operate standardized environments
- Organized enough to maintain strong documentation
- Strategic enough to focus beyond daily firefighting
These businesses will use AI to become even more efficient and scalable, widening the gap between best-in-class MSPs and those barely getting by. At the same time, major players are betting on the channel. OpenAI recently invested $100M in Thrive Holdings, a spin-off corporation of a large venture capital firm. What is Thrive doing with that capital? They are executing an aggressive MSP roll-up, effectively giving OpenAI a sandbox in which to introduce AI-driven solutions for MSPs. This signals confidence that MSPs will play a key role in bringing AI solutions to market. It’s not so much that OpenAI sees the MSP channel as a huge market. It’s that they see the MSP channel as an effective vehicle for implementing and supporting AI-driven solutions to all of their clients.
The opportunity is real. But it will not be evenly distributed.
This Isn't a Technology Problem
Many struggling MSPs are led by highly capable engineers. They understand technology deeply. They have kept up with every major shift in tools and platforms. That hasn’t translated into business success. The issue isn’t a lack of technical expertise. It’s a lack of business know-how and discipline.
What's Really Holding MSPs Back: Business Fundamentals
At the core of MSP profitability is a simple equation: Revenue – Costs = Profit. Yet many MSPs don’t fully understand their cost of service delivery. Without that clarity, pricing becomes guesswork. Margin becomes accidental. Growth becomes risky. Beyond the numbers, leadership gaps compound the issue. In many organizations:
- Roles are unclear
- Decision-making is centralized
- Owners remain buried in day-to-day operations
A more sustainable approach requires building a real business, not just delivering good technical work.
>> Assess your AI readiness: GTIA members, get the steps to prepare, package and profit from AI in the channel. Download the guide on the Member Portal.
How to Build a Profitable MSP: The Fundamentals That Matter
- Get clear on the true cost to deliver service
- Price services based on reality, not assumptions
- Build structured service delivery, not heroics
- Define roles and push decision-making down
- Clarify the owner’s role and protect time for it
None of this is new. None of them are flashy. But this is the work that drives MSP profitability, scalability and long-term success.
How MSPs Can Turn Around Profitability
For many MSP owners, this may feel uncomfortably familiar. That’s not a criticism, it’s an opportunity. The path forward doesn’t require new technology; it requires better execution of fundamentals. Businesses that make these changes put themselves in a position to benefit from AI—rather than being disrupted by it.
What To Do Next
If these challenges feel overwhelming, don’t try to solve them in isolation. Join a peer group. Surround yourself with operators who understand the realities of running an MSP and are willing to share what works. The fastest way to improve MSP profitability isn’t another tool—it’s better perspective, accountability, and execution.
AI will reshape the MSP industry. That much is certain. The question is whether it becomes a tailwind—or the force that exposes everything that isn’t working. Chances are it will be some of both. The MSPs that win won’t be the ones who chased AI first, they’ll be the ones who built a real business—and then used AI to accelerate it.
Frequently Asked Questions
Why are so many MSPs unprofitable? Most MSP profitability challenges stem from unclear cost of service delivery, pricing based on assumptions rather than data and leadership structures where owners remain embedded in day-to-day operations rather than working on the business.
Will AI help struggling MSPs become more profitable? Not on its own. AI amplifies existing business strength—MSPs with solid foundations, standardized environments and strong documentation will benefit most. For businesses without those fundamentals in place, AI adoption is more likely to accelerate existing problems than solve them.
What should MSPs focus on before investing in AI? Before layering in AI tools, MSPs should get clear on their true cost to deliver service, establish value-based pricing, define roles and reduce owner dependency on daily operations. These fundamentals create the conditions where AI investment pays off.
GTIA members have access to a variety of AI readiness resources in the Member Portal.
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About the Author: Dave Cava is the owner of PeopleSharp and a member of the GTIA Workforce Advisory Council. The GTIA Workforce Advisory Council focuses on addressing IT workforce challenges and raising awareness of workforce development and the career opportunities available in the tech industry, particularly among underrepresented groups.
