Nobody thinks much about field service until they are the one standing next to a machine that is not running.
Field service organizations (OEMs, third-party service providers, and dealer networks) keep critical equipment operating in factories, hospitals, and industrial plants. When that equipment fails, the consequences are immediate. A machining center goes down and production stops. A medical imaging system fails and care is delayed. A food processing line stalls and costs start piling up by the hour.
That is what makes field service different from many other service environments. Customers are not calling with a minor issue or a general complaint. They are calling because something essential is broken, and the cost of that failure is already accumulating.
In that environment, first-time-fix has earned its place as one of the most important metrics in service. It matters because every repeat visit adds downtime, increases cost, and puts more strain on the customer relationship. But for all its value, first-time-fix should not be treated as the end-all, be-all measure of service performance.
On its own, FTF is a partial view. It tells you whether a problem was solved on the first visit. It does not tell you how long unresolved cases linger, how much effort they consume, how often teams avoid dispatch entirely, or how the customer experience changes as time passes. A service organization can post a respectable FTF number and still create a frustrating experience if too many cases drag on, bounce between teams, or require repeated follow-up.
That is why service leaders need to stop treating FTF as the whole story and start treating it as one signal within a broader view of service health.
The Limits of a Static KPI
According to Aquant’s 2026 Field Service Benchmark Report, which analyzed nearly 30 million service events across 161 service organizations, the industry average first-time-fix rate is 77%. Top performers reach 88%, while bottom performers sit at 60%.
Those are meaningful differences. But even that useful benchmark leaves out something critical: time.
A single first-time-fix percentage gives leaders a snapshot. It does not show what customers actually experience as a case remains unresolved. And in field service, the experience of an open case often gets worse with every passing day.
On Day 1 of a service case, even lower-performing organizations can look relatively strong. The difference between the top 20% and bottom 20% is only five percentage points: 99% versus 94%.
But the gap does not stay small. It widens as cases age. By Day 30, the first-time-fix rate of bottom-tier organizations drops to 54%, while top performers remain at 87%. What starts as a narrow performance gap becomes a 33-point divide over the course of a month.
That is a much more revealing picture of service performance. It shows not only whether an organization resolves issues, but how quickly it loses control when problems are not solved early.
The Customer Experience Curve Tells a Fuller Story
This is the idea behind the Customer Experience Curve: service quality is not static. It degrades over time, and that degradation maps directly to what customers feel.
For the customer, a 30-day open case is not just a long service ticket. It is a month of uncertainty, repeat visits, production disruption, and declining confidence that the issue will ever be fixed correctly. Even if the equipment is not down every hour of those 30 days, the disruption is still real. And as Ken Creech, Director of Customer Support at Makino, points out, the clock starts when the machine fails, not when the customer finally calls support.
Seen this way, first-time-fix is still important, but it becomes part of a larger operational picture. Leaders also need to understand case age, time to resolution, repeat effort, remote-resolution opportunities, and where expertise is or is not being applied early enough.
Makino’s Transformation Was About More Than FTF
Makino is a strong example not just because it improved results, but because it appears to have broadened the way it thought about performance in the first place.
Eighteen months ago, Makino’s first-time-fix rate stood at 65%, meaning most cases required follow-up visits. Only 56% of cases were resolved within two days. After changing how service knowledge was captured, shared, and applied, Makino raised first-time-fix to 88%. Cases resolved within two days rose from 32% to 53%, and remote resolution increased by 15%.
But the bigger lesson is that Makino’s change was not limited to chasing a single KPI. Creech says the company realized it had not been measuring what it should have been measuring. Instead of focusing primarily on response-time metrics (how fast teams answered the phone) Makino began paying closer attention to measures that better reflected the actual customer outcome, including mean-time-to-resolve. That shift helped the organization focus less on activity and more on whether the customer’s issue was actually being solved.
The company also used its data to uncover broader operational patterns: internal cost leakage, differences in how long engineers took to complete similar jobs, and where greater remote resolution or better technician support could improve performance. In other words, Makino did not just improve first-time-fix. It started looking at service performance more holistically.
That distinction matters. The story is stronger when positioned this way. Makino is not simply an example of a company that raised FTF. It is an example of a company that recognized that service quality cannot be understood through a single metric alone.
Knowledge Distribution Changes the Curve
Makino’s experience also highlights why some cases age poorly in the first place.
The cases most likely to drag on are usually the most complex ones. They require deep diagnostic expertise, not just responsiveness. In organizations where that expertise lives only in the heads of a few senior technicians, performance becomes fragile. If the right person is not involved early, the case lingers, repeat visits multiply, and the customer experience deteriorates.
That is why knowledge distribution matters so much. Makino found that senior technicians were among the earliest adopters of AI-assisted service tools because they recognized the value of surfacing and scaling the expertise they had built over decades. The goal was not simply to make service faster. It was to make expert knowledge more available across the organization, so more technicians could resolve more cases correctly the first time.
Adoption from end customers also took off as this gave them the opportunity to fix issues themselves, instead of waiting for support. Junior technicians were more hesitant at first, worrying the system was there to evaluate them. Over time, they came to see it as support rather than surveillance.
The Real Question for Service Leaders
The most important shift is simple: replace the static KPI view with a more dynamic and holistic one.
Service leaders should treat first-time-fix as one indicator within a broader operational system, not the headline measure of service quality. The organizations improving customer outcomes most consistently are measuring how performance changes over the life of a case: how long issues remain unresolved, how often repeat effort occurs, which cases escalate unnecessarily, where remote resolution succeeds, and where expertise is not reaching the problem early enough.
That requires shifting attention away from activity metrics alone and toward outcome-based measures that reflect the customer experience more directly. Mean-time-to-resolve, case aging, repeat dispatch rates, remote-resolution rates, and variance in technician performance often reveal operational weaknesses that a single FTF number cannot. Leaders should also examine how knowledge moves through the organization: whether frontline technicians can access proven diagnostic approaches quickly, whether expert knowledge is concentrated in too few individuals, and which types of cases consistently deteriorate as they age.
The goal is not simply to improve one KPI. It is to build a service operation that maintains performance as complexity increases, resolves issues before they spiral into prolonged disruptions, and gives customers confidence that problems will be handled quickly and correctly from the outset.