Why IT is moving beyond mean time to resolution

For decades, IT and security teams have measured success by mean time to resolution (MTTR). The logic was simple: things will break, and success is measured by how fast human teams can fix them. But this approach has a flaw: it focuses on how well companies respond to failure, not whether it could have been prevented in the first place.

"For everybody that logs a ticket, there are nine or 10 people who had the same problem who didn't log a ticket," says Jason Keogh, Vice President of Solutions at TeamViewer. Those unreported issues are invisible productivity losses that MTTR never captures, as people waste hours self-troubleshooting instead of doing their core work.

Therefore, the question IT teams need to ask has changed. Instead of "How fast can we fix this?" organizations should be asking "How quickly can we learn from patterns and prevent recurrence?"

The limits of mean time to resolution

The downside of MTTR is that it measures speed, not risk. A ticket resolved in two hours might look successful in your dashboard, but that metric tells you nothing about what happened before the ticket was logged or what damage occurred during those two hours.

Keogh experienced this firsthand. He spent three hours across two days trying to fix a failed Windows Update on his machine. "I logged the ticket at the end of day two, which was Tuesday, and on Wednesday morning, they picked that up," he explains.

The system showed a resolution time of about two hours, from Tuesday evening to Wednesday morning. However, those three invisible hours of lost productivity never appeared in any report.  

This gap between reality and metrics shows up at scale, too. When Keogh asked a customer to review their ticketing system to identify opportunities for automation, they struggled to prepare. The reason was twofold: support staff often don’t document what they do to fix a problem; it’s simply marked as resolved. And even when they do document thoroughly, extracting consistent, usable data from tickets written by different people proves nearly impossible.

"Most ticketing systems don’t have details about what actions are being taken to resolve an issue," Keogh notes. Without those insights, you can't identify patterns, can't build automations, and can't prevent the same issue from recurring next week.

The shift from reactive to proactive to predictive

If tickets don't reflect reality, the problem stops being response time and starts being visibility. Traditional monitoring doesn't close that gap because it relies on predefined thresholds and known failure modes. It can only surface issues you already expect.

"Even having a tool in place that's monitoring something, what is it monitoring?" Keogh asks. "You're not necessarily going to capture the actual issue that's happening for the actual user." If emerging patterns sit outside those definitions, they remain invisible until users are already affected.

The constraint isn't effort or skill; it's human capacity. No team can continuously observe thousands of endpoints, connect weak signals across systems, and decide what matters quickly enough to intervene early. "Monitoring is about admiring problems, really," Keogh says. "But we're not here to admire the problem. We're here to fix the problem."  

You can automate responses to known problems, but you can’t prevent issues you don’t see forming. “You might have an automation tool,” Keogh explains, “or a monitoring tool that isn’t even tracking the right signals. Automation can deliver real value, but only if you’re automating the right things. And the challenge is knowing what those are.”

How agentic AI changes the prevention equation

The answer lies in systems that learn from what actually happens during incident resolution, not just from predefined monitoring conditions. Traditional tools are designed to react: a metric crosses a threshold; a ticket is created, a human steps in. 

Agentic AI works differently. 

Agentic AI systems don’t simply respond to alerts or execute predefined workflows. They observe how environments behave, how humans intervene, and how issues unfold over time. Rather than waiting for a ticket or a handoff, these systems build their own understanding of risk by watching what actually happens during day-to-day operations.

This shifts the question from “is something broken?” to “are conditions moving in a direction that usually leads to failure?” Risk becomes something that can be recognized early, while there’s still time to act.

In practice, this means systems can surface patterns that would never appear in ticket logs. For instance, TeamViewer Session Insights uses AI to observe the concrete actions taken during a remote session. This means every manual fix becomes a signal. When multiple agents resolve similar issues, patterns emerge that rarely appear in ticket logs, revealing what should be automated and what risks are starting to develop.

“When we have multiple people solving problems for multiple users, multiple times, we start to understand what’s being done manually,” Keogh explains. “Those actions show us what should become the automation of tomorrow.”

The result is a continuous feedback loop where known problems get automated away, and emerging issues surface earlier. As a result, IT work moves from reacting to incidents after they occur, to preventing them from reaching users in the first place.

Moving from MTTR to predictive operations

If the goal is predicting and preventing incidents rather than just resolving them quickly, you need different metrics. Mean time between failures (MTBF) becomes your primary indicator because it measures how long systems run without problems. A rising MTBF proves your predictive strategy is working. MTTR still matters for containment when issues do occur, but it's no longer the headline number.

"Meantime to resolve becomes fairly meaningless in that world because we're preventing the tickets from ever occurring," Keogh says. "What we want to look at is how many issues we're solving autonomously. How many tickets are we avoiding? How much time are we giving back to our users?"

This shift needs more than new tools. It requires building four interconnected capabilities that feed into each other, creating a system that gets smarter over time.

Here’s how teams can move from MTTR to more predictive operations.

Streamline initial response and recovery

You still need to fix things when they break. Standardize incident response procedures and use tools for rapid remote diagnosis and recovery. This minimizes business impact and preserves valuable IT staff time. Fast resolution matters, but it shouldn't be where improvement efforts stop.

Formalize systematic failure diagnosis

Treat every significant incident as a learning opportunity. Implement a mandatory, blame-free review process to identify the underlying system's weakness that allowed the incident to occur. The question isn't just "what broke?" but "why was this possible, and how do we make sure it doesn't happen again?"

Dedicate resources to permanent fixes

Establish a formal, high-priority backlog for identified root causes. This ensures that maintenance and automation projects, such as system updates, security patching, and configuration standardization, are prioritized over new, non-essential feature requests. If you're constantly building new things while your foundation stays fragile, you'll never break the reactive cycle.

Shift your KPIs to stability

Change your primary measure of success from how fast you fix problems (MTTR) to how long your systems run without problems (MTBF). This aligns operational goals with business continuity and service quality. When stability becomes the headline metric, prediction becomes the priority.

When systems fail less often, the value shows up in time that people never lose in the first place. In environments where minutes matter, that difference compounds quickly.

For instance, at one healthcare organization, nurses and doctors log into PCs in patient rooms throughout the day, each creating a user profile. Over time, dozens of profiles accumulate on each machine, causing boot times to climb from 10 seconds to 2-3 minutes.

"Every time another nurse or doctor walks into that room, they lose 2 minutes," Keogh explains. "When they do that across days, they end up losing an hour or two hours a day." By identifying and eliminating those profiles automatically, you hand medical staff back hours of their day. "That's like handing the hospital dozens of additional nurses and doctors. When we're talking about productivity, we are also talking about the sort of gain that leads to you having the equivalent of more staff."

That's not just faster incident resolution. That's prediction creating measurable business value.