AI is no longer something MSPs choose to switch on. It’s already running inside the PSA, RMM, security, and productivity tools they and their clients use every day. Work is getting done faster. Whether it’s getting done well is much harder to tell. This is the new success metrics challenge facing MSPs and their clients.
Most MSP scorecards still revolve around uptime, MTTR, and ticket closure rates. Those numbers aren’t going anywhere, but the job behind them is changing. MSPs are now responsible for more than keeping client systems running. They also have to make sure the AI inside those systems stays reliable, explainable, and effective.
AI you can’t see is AI you can’t measure
Much of today’s AI is designed to fade into the background. Agentic features sit inside existing tools rather than demanding a separate interface, which is usually the right call. The side effect is a widening gap between how work actually gets done and how MSPs measure it.
“Traditional success frameworks that center on speed, volume, completion and efficiency were useful indicators of value when human judgment and oversight were inherent to execution,” says Douglas Murray, CEO of Auvik.
Take that human judgment out of the loop and the familiar numbers start to mislead. Viewed on their own, volume and velocity metrics can:
- Penalize the people who take time to review, validate, and refine AI output
- Make engineers who lean heavily on AI look like the top performers
- Inflate ticket closure rates and deployment frequency without saying anything about quality
What matters now is whether the results hold up over time, and whether AI use stayed within the boundaries the MSP and its client agreed on.
Shadow AI widens the blind spot
Sanctioned AI is only part of the picture. Employees will reach for whatever tool helps them get the job done, and a policy on paper rarely stops them. Auvik’s 2026 IT Trends Report found that 61% of IT professionals discover unauthorized SaaS in their organizations every month. AI tools are hungry for data by design, so every unvetted app is another place confidential client information could end up.
The channel already knows this is a problem. Auvik’s data shows that 20% of MSPs rank shadow IT as the number one issue business leaders aren’t paying enough attention to. Without insight into which tools are in use and how, MSPs are left reacting, spotting risks only once they’ve turned into incidents. Until that happens, end users only experience the upside of their unsanctioned tools, which encourages them to keep using them.
There’s a positive side to closing that gap. Visibility into shadow AI shows how employees are already getting value from AI and where the approved toolset falls short. That’s a useful starting point for the next client review.
“Visibility is the foundation for effective governance, and the ability to maintain it should be monitored as a measure of IT health alongside traditional metrics,” Murray adds.
Adding context to the scorecard
None of this means ripping up output metrics. It means giving them the context they need to be read accurately, so they reflect whether MSPs and their clients are still in control as AI spreads. A more context-aware scorecard could include:
Visibility and control
- Volume of unauthorized AI use in a client environment, including by department and user, plus reason for use
- Average time to remediate it once found
- The share of AI outputs that meet agreed explainability and traceability standards
What happens after the work is done
- Reopen rates for tickets resolved with AI support
- Change failure and rollback rates for AI-assisted vs. unassisted changes
- Long-term outcomes of work completed with and without AI
Comparing AI-assisted and unassisted work over time is the clearest way to see whether AI is improving quality and resilience or simply generating more activity. MSPs that let their metrics evolve alongside their business strategy will be in a much better position to scale AI securely, without it turning into an operational burden.
Outcomes over output
Measure the wrong things and you’ll get the wrong behavior. One (extreme) example is “tokenmaxxing”, where teams push their AI token usage as high as possible because heavy consumption is treated as a sign of productivity.
The better approach is to choose metrics that reward responsible AI use and long-term value. Speed and volume still count, but success increasingly depends on an MSP’s ability to surface, map, and govern AI across every environment it manages. Those are also the results clients will remember at renewal time.
Rethink your metrics at MSP GLOBAL
Want to hear how other MSPs are measuring and governing AI across their client base?
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Thanks to Auvik and CEO Douglas Murray for their expertise and support in creating this article.
