2026 IT Help Desk Benchmark Report

Help desk benchmarks are surprisingly hard to come by. There are plenty of generic IT surveys out there — the kind where respondents self-report their performance and round their numbers — but very few that start with actual ticket data and work up from there.

This means that IT leaders are often working in the dark. They're trying to optimize and scale their teams, take on new challenges (like supporting an explosion of new AI applications and top-down initiatives aimed at using those apps to increase efficiency), figure out where and how to add automation, and make sure their end users feel seen and supported so they can do their best work. And they're doing it all without the data they need. That's where Fixify's 2026 Help Desk Benchmark report comes in.

We analyzed more than 50,000 help desk tickets submitted across a range of organizations over fourteen months. Every number in what follows is derived from real ticket records: timestamps, categories, use cases, sentiment scores, response times, and resolution times. The goal was to answer the questions that every IT leader eventually asks: What does normal look like? How does our mix compare? How can we make our team more effective, and make the help desk experience even better for our end users?

As we dug into the data, six big themes about the current state of IT emerged. Here's what we saw — and what we think these insights mean for IT teams.

Demand is predictable — if you know where to look.

While managing incoming tickets can sometimes feel chaotic, we found that help desk volume follows consistent organizational rhythms. Eighty-two percent of tickets arrive during business hours, peaking at 11am. Tuesday is the busiest day of the week, carrying nearly a quarter of all volume. July is the busiest month of the year, running 29% above average, driven by summer hiring and mid-year access reviews. February through April are the quietest months. Knowing these patterns can give IT organizations the opportunity to plan for the surges and lulls.

The ticket mix is more concentrated than you might expect.

The use-case data helps us see even more of the picture. At the use-case level, the most frustrated tickets are those involving technical failures that stop work in progress — network troubleshooting (39%), hardware troubleshooting (38%), OS troubleshooting (37%), and application troubleshooting (31%) all exceed 30%. These are problems that interrupt the workday mid-stride.

More than 1 in 5 tickets is a work stoppage.

Here’s what we found.

Twenty-two percent of tickets are productivity-blocking — meaning the employee can’t do their job until the issue is resolved. That rate climbs with company size, reaching nearly one-third of all tickets for organizations with 1,000+ employees. Knowing which tickets stop work, and how to speed resolution for those tickets, can help IT teams make the most of their resources.

Most users aren't frustrated — but when they are, it's usually fixable.

The sweet spot for a happy user resolution is 15 minutes to 4 hours. Tickets resolved in this window convert frustrated users at 93–97%. The 15-minute-to-1-hour band is particularly striking: tickets resolved in this time yield a 97% improvement rate, and more than a third of frustrated users become actively positive. Their feelings about the interaction aren’t just neutral. They feel genuinely appreciative.

Hardware and connectivity generate the most frustration — by far.

Our sentiment analysis also found that 1 in 4 hardware tickets arrives with negative sentiment — roughly 3x the average for all ticket types combined. Connectivity is a close second at 22%. Every other category falls well below these two. Yet more than 80% of negative users convert to satisfied users when their issue is resolved. This means that user frustration is often acute but fixable: fast-tracking these categories is the highest-leverage intervention for moving satisfaction numbers.

Company context shapes your benchmarks.

Fixify’s analysis shows that industry, size, and growth rate shape the ticket mix more than anything your IT team does. Fast-growth companies (>20% employee growth) face a 30% productivity-blocking rate and 13% negative sentiment — significantly more negative than stable companies. Enterprise organizations with 1,000+ employees show 3x the negative sentiment rate of small companies. Those differences are one big reason the IT benchmarks in this report can be so useful — they allow organizations to compare themselves to their peers rather than to the whole world of IT. We offer a breakdown by industry, size, and growth rate in Section 4.

Section 5 • Resolution Benchmarks — Chart 5.3

The resolution time gap

Median help desk ticket resolution time by automation level

Fixify · 2026 IT Help Desk Benchmark Report50,000+ tickets · Jan 2025–Feb 2026

Tickets with AI automation get resolved in a median of 4.4 hours. Without automation, when a human agent is performing the vast majority of the work, the median is 71 hours. (That's nearly three whole days!) So, while first response times are essentially identical whether or not you're using automation (about 5 minutes), we see a big gap in everything that happens after. Figuring out when automation makes the biggest difference in resolution time — which we know is critical for user happiness — can help IT teams decide how to add automation to their processes.

With our findings in mind, here are the top five things IT teams can do now to help maximize resources and reduce chaos, meet end users where they need you most, and plan for what’s next.

1. Staff for Tuesday mornings and the summer surge.

Weight your coverage according to predictable patterns. For example, consider heavier staffing on Tuesdays, and use the February–April lull for infrastructure work and training.

2. Benchmark against your peer group, not the overall dataset.

A healthcare organization with 35% productivity-blocking tickets is running normally; a cybersecurity firm at the same rate would warrant investigation. Check Section 4 for benchmarks that let you see your IT metrics in your peer group context.

3. Build category-specific resolution SLAs.

A single SLA for all tickets doesn't match the reality of ticket diversity, so it can't give you the full picture of how your team is doing. For example, IAM and connectivity can be resolved in 3–4 hours with AI automation. Hardware and collaboration take 3–5 hours. Onboarding and offboarding workflows take 25–75+ hours structurally — not because they're mismanaged, but because they involve multi-step approvals and third-party coordination. SLAs for each category can give you a better picture of where you're succeeding, and where you may need to add more resources.

4. Target the 15-minute-to-4-hour window to turn frustrated users into satisfied ones.

This is the resolution speed sweet spot where most frustrated users become much happier. In fact, a third of them become actively positive. Hardware and connectivity tickets deserve priority here — they arrive with the most frustration and convert at 80%+ when resolved.

5. Add automation to improve resolution time, not response time.

IT teams can achieve a 5-minute first-response benchmark with or without AI automation. But they won’t achieve a 16x faster resolution time without automation. So, if you’re evaluating whether automation tools are worth the investment, the question to ask isn’t “Will this help us respond faster?” but, “Will this help us resolve our most common issues faster?” It’s also useful to evaluate the cost of slower resolution in terms of user satisfaction and lost productivity time.

If you had to guess the peak hour for help desk tickets, you'd probably say 9am. Users get to work, fire up their laptops, discover problems — that's when the tickets pour in, right?

Not quite.

The actual peak hour across the dataset is 11am. Volume builds steadily from 8am, climbs through the morning, and crests just before lunch — then gradually tapers through the afternoon and drops sharply after 5pm. The 9am hour is busy, but it's the 10am–1pm window that accounts for 31% of all daily volume — nearly one in three tickets arrives in those three hours.

In total, 76% of tickets arrive between 9am to 6pm Monday through Friday. The other 24% arrives after hours or on weekends. For companies that don't staff their help desk overnight, this means there's often a long queue of work waiting each morning. Those off-hours requests skew heavily toward provisioning and access requests rather than break-fix issues.

Here's the practical implication: while business-hours coverage handles the overwhelming majority of demand, help desk teams that staff symmetrically across the 9-5 window are misaligned with demand. Instead, consider front-loading coverage toward mid-morning when employees are hitting their first friction points of the day. This maximizes responsiveness when it matters most.

97.5% of tickets arrive on weekdays. That's not a surprise — but the intraweek shape is worth understanding, because it's not what most people assume.

Tuesday is the busiest day of the week, accounting for 23.5% of all tickets. Monday is close behind at 21.1%. Together, Monday and Tuesday capture nearly 45% of weekly volume — almost half the week's demand compressed into two days. Volume declines steadily from there: Wednesday sits at 20.5%, Thursday drops to 17.8%, and Friday falls off a cliff at 14.6% — roughly 30% below the Monday-through-Thursday average.

The Tuesday peak isn't just a statistical quirk. Monday brings its own surge — users returning from the weekend find new issues — but many of those issues don't become tickets until Tuesday morning, after the initial triage and attempts to self-resolve. The result is a front-loaded week where the first two days carry an outsized share of demand.

The data suggests weighting your staffing coverage toward Tuesday morning. Monday gets the reputation, but Tuesday gets the tickets.

Monthly comparisons are tricky when your customer base is growing — more customers in December than January means raw counts go up regardless of seasonal effects. To isolate the seasonal signal, we normalized each organization's monthly ticket volume against its own average monthly rate, then aggregated across the dataset. An index of 100 represents a typical month; above 100 means busier than average.

July is the busiest month of the year — running 29% above average. The mid-summer spike reflects several dynamics: summer onboarding cycles as new hires start before the fall, system reconfigurations as teams prepare for the back half of the year, and a general uptick in hardware issues as employees work from different locations. September and October are also elevated (12–13% above average), consistent with the broader back-to-work pattern following summer slowdowns.

The flip side: February and March are consistently the quietest period — February at 24% below average, March at 20% below. The post-holiday and post-January-rush slowdown is real, and it persists through early spring.

One finding from this dataset that challenges conventional wisdom: business-hours concentration is remarkably consistent across company sizes.

You might expect enterprise organizations to concentrate their tickets more tightly in business hours because of more structured IT processes, clearer support windows, and fewer after-hours requests. But the data doesn't support that. Every size category falls in the 77–80% range for business-hours tickets. Small companies, mid-size companies, and enterprises all generate about one-fifth to one-quarter of their tickets outside standard hours.

What does vary is peak hour: organizations under 100 employees and the 100–499 bucket peak at 10am, while the 500–999 bucket peaks at 11am. This one-hour shift is modest, but it may reflect that larger organizations have more layers of morning triage (stand-ups, team syncs) that delay the moment an issue becomes a ticket.

The practical takeaway: you don't need to over-engineer your after-hours coverage model based on company size assumptions. The demand signal is consistent — roughly 77–80% during business hours, 20–23% during off-hours — regardless of how many employees you have.

Our dataset falls into nine ticket categories. One of them — Software & Applications — accounts for more than a third of all volume. Add Onboarding & Offboarding and IAM and you’ve covered more than 70%. The remaining six categories share the rest.

The frequency of Software & Applications tickets reflects how much of modern work runs through software, and how often that software needs tending, from new access to configuration changes, integrations, and troubleshooting. Onboarding & Offboarding is the story of growth-stage companies managing the employee lifecycle at scale: every new hire needs to be provisioned and every departure needs to be unwound. IAM shows the identity management burden of cloud-native environments: locked accounts, wrong permissions, MFA issues.

The long tail — Security, Connectivity, Other, Custom — makes up less than 10% combined. This isn’t trivial, but it’s not where the volume is.

Within these categories, the concentration is even more striking at the use-case level. App assignment — an employee requesting access to an application — accounts for more than 1 in 4 tickets. That overshadows everything else on the list.

The top three use cases — app assignment, permissions management, and employee offboarding — together account for 40% of all tickets. The remaining 40+ use cases share the rest. That kind of concentration is an opportunity for IT teams. If you can streamline or automate the top two or three use cases, it can make a big difference for the entire help desk.

One thing worth noting: employee lifecycle management — onboarding and offboarding of both employees and contractors — accounts for 16% of all tickets when you combine the four related use cases. For growth-stage companies actively hiring, this share is even larger.

The overall category breakdown of ticket types is a useful benchmark, but there is also significant variation by company size. The mix of what employees need help with changes substantially as organizations grow — and understanding that shift helps predict where your own help desk load is heading.

A few patterns are worth highlighting:

The category mix is broadly stable across weekdays, but a few weekly and seasonal patterns stand out.

Not all tickets are created equal. Some are routine requests. An employee may need a new app or want a Slack channel created. Other tickets represent a genuine work stoppage — the employee can’t do their job until the issue is resolved.

We classified tickets into these two types based on use case. More than 1 in 5 tickets (22%) is productivity-blocking. For every 100 tickets your help desk receives, roughly 22 represent an employee sitting idle.

The top drivers of productivity-blocking tickets are permissions management (28% of all productivity-blocking tickets), login troubleshooting (22%), and application troubleshooting (16%). Together, they account for two-thirds of all work stoppages.

The productivity-blocking rate rises with company size — and the jump at enterprise scale is pronounced:

For small companies, 12.5% of tickets are productivity-blocking. The mix is dominated by Collaboration and Onboarding requests, which are largely administrative. At 1,000+ employees, nearly one-third of all tickets represent a work stoppage, reflecting the greater complexity of identity management and technology environments at scale.

Companies with 500–999 employees have a lower productivity-blocking rate than the 100–499 category. This is a composition effect: the 500–999 range is dominated by organizations whose ticket mix is weighted toward app assignment — routine requests rather than troubleshooting. Adjusting for that effect, the rate follows a consistent upward curve.

Productivity-blocking tickets also arrive with significantly more frustration. Their negative sentiment rate is nearly 5x that of non-blocking tickets — 21% negative versus 4%. When employees can't work, they say so.

For the subset of organizations with application-level data (8% of tickets), we can see which specific tools are generating support volume. Okta leads by a wide margin, appearing in nearly 13% of all app-tagged tickets — roughly 2x the volume of the next most common application.

The list is a recognizable modern enterprise stack: identity management (Okta, 1Password), productivity suites (Microsoft 365, Google Workspace, Slack), project tools (JIRA, Confluence, Wrike), device management (Mosyle, Intune), and an emerging layer of AI applications.

Across the dataset, 483 distinct applications appear — nearly 500 different tools generating support requests. App diversity grows with company size: organizations under 100 employees average ~5 distinct apps in their ticket data, while those over 1,000 employees average ~63.

On the sentiment side, not all apps generate equal frustration. File sync, email, and security tools generate the most negative sentiment: OneDrive at 39%, Outlook at 37%, PolicyPak at 31%, and GlobalProtect at 26%. When a VPN won’t connect, a file won’t sync, or a security policy blocks work, users feel it immediately and submit tickets that show their frustration. At the other end, AI tools and password managers skew notably positive. These tend to be proactive requests for access rather than complaints.

The vast majority of tickets (82%) arrive with no emotional charge at all. Employees submit a request, describe their issue, and move on matter-of-factly, without visible frustration or enthusiasm. The remaining 18% are split roughly evenly between positive and negative.

For IT teams looking to level up, the 8.7% negative baseline is the number to benchmark against. Roughly 1 in 11 tickets arrives with a frustrated or unhappy user on the other end. That adds up. At a company processing 500 tickets a month, that’s roughly 44 frustrated employees who are telling you, through the tone of their submission, that something has gone wrong enough to generate emotion.

Here’s the most important finding in this section, and one of the most important findings for IT leaders overall. It shows how impactful IT work is when it comes to supporting the wellbeing and productivity of an entire organization.

Of the tickets that begin with negative sentiment, 81.6% improve by the time the ticket is resolved — users move to either a neutral (63.1%) or positive (18.4%) sentiment. Only 18.4% of frustrated users are still negative at close.

Looking at the full picture across all tickets — not just negative starters — 23.5% improved, 63.2% stayed the same, and 13.2% worsened by resolution. The worsening figure deserves a closer look: it’s driven almost entirely by neutral tickets that slipped to negative by close (6.3% of the large neutral base), not by positive tickets collapsing. Only 4.5% of tickets that started positive ended negative.

One pattern worth flagging: positive sentiment is harder to maintain than negative sentiment is to fix. Only 31.6% of tickets that started positive held onto that sentiment through resolution — nearly two-thirds drifted to neutral. This suggests that even when users arrive enthusiastic, slow or complex resolutions can dampen that initial goodwill. The practical implication: even people who submit positive-start tickets need care to get to a good outcome.

Not all ticket types arrive equally charged. The category and use case mix matters enormously — and the data shows a clear pattern: frustration correlates (almost perfectly) with disruption of the most basic tools users need to get their jobs done.

Hardware and connectivity are, by far, the highest-frustration categories. One in four hardware tickets arrives with negative sentiment — roughly 3x the overall average. Connectivity follows at 22%. When something physical breaks, or the network goes down, users feel it immediately and their frustration shows through.

The use-case data helps us see even more of the picture. At the use-case level, the most frustrated tickets are those involving technical failures that stop work in progress — network troubleshooting (39%), hardware troubleshooting (38%), OS troubleshooting (37%), and application troubleshooting (31%) all exceed 30%. These are problems that interrupt the workday mid-stride.

The contrast at the extremes is striking. Network, OS, and hardware troubleshooting each approach or exceed 40% negative — but they’re relatively lower-volume categories. Login troubleshooting is the most consequential frustration driver in practice: it’s the fifth-most common use case in the dataset, and 21% of those tickets arrive negative. That combination of meaningful volume and elevated frustration puts it in a category of its own.

At the other end, app assignment, email management, employee onboarding, and group management are all below 3% negative. These are expected administrative workflows. Users know what they’re asking for, they’re not in crisis, and the submissions reflect it.

Larger organizations generate more frustrated tickets. The relationship is consistent and directional — and the jump at enterprise scale is pronounced:

The 1,000+ organizations' 17.2% negative rate is more than three times that of sub-100-employee companies. The primary driver is the ticket mix: larger organizations have more login troubleshooting, hardware issues, and application troubleshooting tickets — all categories that carry elevated frustration. At scale, the help desk increasingly absorbs the problems that genuinely block people from working, and those problems come with emotion attached.

Small companies (<100) are notable for a different reason: they show the highest positive sentiment rate at 16.6%. This reflects a ticket mix dominated by administrative requests. And it’s likely that smaller organizations promote a closer, more personal relationship between employees and their IT teams.

Speed and satisfaction aren’t the same thing — but the data shows that they’re closely related.

First response times are essentially identical across all three groups — all sit at a median of roughly 5 minutes. The speed difference doesn’t emerge at initial pickup; it emerges during the resolution process itself.

Tickets submitted with positive sentiment resolve more than twice as fast as tickets submitted with negative sentiment. The median resolution time for positive-start tickets is 8.8 hours. For neutral tickets, it’s 16.6 hours. For negative tickets, it’s 21.0 hours.

Section 3 • End User Sentiment — Chart 3.6

How does resolution speed affect satisfaction?

Median resolution time by initial sentiment · First response ~5 min across all groups

First response times are identical across all three groups (~5 minutes) — the gap is entirely in resolution, not pickup.

Fixify · 2026 IT Help Desk Benchmark Report50,000+ tickets · Jan 2025–Feb 2026

View data — Three-column comparison block — Median resolution time by initial sentiment

Initial sentiment	Median resolution
Positive	8.8 hours
Neutral	16.6 hours
Negative	21.0 hours

Frustrated users may be submitting harder problems that take longer. Or the extended back-and-forth of a slow resolution may cause frustration in users who started neutral. What the data makes clear is that speed and satisfaction move together — and that fast resolution is the most reliable path from frustration to satisfaction.

The improvement rate data makes this concrete — and it shows an area where IT teams can really improve their users’ experiences:

The sweet spot for a happy user resolution is 15 minutes to 4 hours. Tickets resolved in this window convert frustrated users at 93–97%. The 15-minute-to-1-hour band is particularly striking: tickets resolved in this time yield a 97% improvement rate, and more than a third of frustrated users become actively positive. Their feelings about the interaction aren’t just neutral. They feel genuinely appreciative.

Under 15 minutes performs somewhat less well (82%), likely because very fast closures sometimes reflect auto-closures or triage rather than genuine resolution, leaving some frustrated users still unresolved.

The data suggests that what frustrated users value most isn’t raw speed but attentive speed — a meaningful resolution delivered quickly.

After 3 days, the sentiment conversion rate drops to 68%. The frustrated user who waited a week is substantially less likely to walk away satisfied than the one who got a resolution in an hour — even if the eventual resolution is the same.

For tickets that started negative, the improvement rate once an issue is resolved is consistently high across all categories — a strong signal that resolution genuinely shifts user experience regardless of what went wrong.

Every category shows an improvement rate of 80% or better for tickets that started negative. Hardware and Connectivity are particularly notable. Despite generating the most frustration at submission (25% and 22% negative respectively), they convert at 80–82%, meaning that even the most frustrated users are largely satisfied by resolution. The problem isn’t that these issues are frustrating to users long-term; it’s that they’re acutely disruptive in the moment.

Most of the companies in our dataset run hybrid or remote-first workforces — the majority of organizations with remote work data are above 75% remote. That distribution matters: not for total ticket volume, but for what ends up in the queue.

The clearest finding is that IAM tickets rise steadily with remote work percentage — from 8.6% of the ticket mix at low-remote organizations to 17.9% at fully distributed ones. This is because remote workers are more dependent on identity systems and have more opportunities for access issues when they’re not on corporate networks.

Hardware and security tickets follow a counterintuitive U-shaped pattern — elevated at both the least-remote and most-remote organizations. For highly remote companies, when physical devices fail, there’s no on-site IT desk to help. Issues that might be resolved informally in an office escalate to a ticket when you’re working from home.

The expected “VPN tax” — connectivity tickets climbing with remote percentage — is present but modest: 3.5% at low-remote versus 3.1% at very-high-remote.

The overall takeaway is that, while industry and company size are stronger predictors of ticket patterns than remote work percentage alone, the IAM gradient is real and worth planning for.

Size, among all the variables in our dataset, plays the biggest role in shaping help desk demand. Category mix, productivity-blocking rate, sentiment, and ticket composition all shift meaningfully as organizations grow. We’ve touched on several of these patterns in earlier sections. Here we’ve brought them together.

Section 4 • Company Context — Chart 4.2

How does company size shape help desk demand?

Key metrics compared across company size buckets

S&A = Software & Applications · O&O = Onboarding & Offboarding · Collab. = Collaboration

Fixify · 2026 IT Help Desk Benchmark Report50,000+ tickets · Jan 2025–Feb 2026

View data — Multi-metric comparison table — Key help desk metrics by company size

Metric	<100	100–499	500–999	1,000+
Top ticket category	Collab.	S&A	S&A	S&A
#2 category	Security	O&O	IAM	O&O
Productivity-blocking rate	12.5%	21.6%	17.4%	32.5%
Negative sentiment rate	5.1%	5.7%	7.9%	17.2%

A few of these progressions are worth a closer look:

How fast a company is growing turns out to be a meaningful predictor of its help desk profile — sometimes more meaningful than size alone.

Fast-growth companies (>20% employee growth) show the highest productivity-blocking rate (30.5%) and negative sentiment (12.5%). Rapid headcount expansion creates sustained pressure on identity management, access provisioning, and hardware deployment — all of which translate to more productivity-blocking and more frustrated users.

Stable companies show the best overall profile. These companies have the lowest productivity-blocking rate (13.3%), the lowest negative sentiment (6.8%), and the lowest onboarding burden (7.6%). These organizations have established routines and manageable ticket loads.

Growing companies (5–20%) have the highest onboarding share at 27%. This is where hiring volume is significant, but organizations may not yet have automated provisioning workflows to absorb it.

An unexpected finding: Shrinking companies still show elevated productivity-blocking rates (23.3%) along with meaningful onboarding/offboarding volume (17.6%). Workforce transitions in either direction create help desk volume.

Different industries face genuinely different IT environments — and their help desks feel that difference.

Three industry-driven patterns stand out:

It’s one of the most common questions in IT planning, and one of the hardest because the honest answer is, “It depends.” Our dataset offers a practical reference range.

Among organizations with IT/security headcount data, the median is 1.6 IT and security staff per 100 employees, with a practical range of 0.7 to 4.3 per 100.

IT staffing ratios decline with company size from a median of 2.2% at companies under 100 employees to 1.2% at 1,000+ — reflecting economies of scale. The ratio is also higher at cybersecurity and IT services firms, which treat security operations as a core function rather than support overhead.

What the data can’t tell us is whether a higher IT staffing ratio drives better outcomes. The correlation between IT staffing ratio and ticket volume is weak (r = 0.21), and correlations with sentiment and productivity-blocking rate are negligible. Both are better explained by industry type and company complexity. More IT staff doesn't automatically mean fewer or faster-resolved tickets. What it means depends entirely on how those staff are deployed.

The median first response time across our dataset is 5 minutes. Three-quarters of tickets receive a response within 8 minutes. Nine in ten are responded to within 15 minutes of submission.

Section 5 • Resolution Benchmarks — Chart 5.1

How fast should the first response to a help desk ticket be?

First response time percentile benchmarks across the dataset

9 in 10 tickets receive a first response within 15 minutes of submission.

Fixify · 2026 IT Help Desk Benchmark Report50,000+ tickets · Jan 2025–Feb 2026

View data — Percentile reference callout — First response time benchmarks

Percentile	First response time
25th	3 minutes
50th	5 minutes
75th	8 minutes
90th	15 minutes

The distribution is concentrated heavily at the fast end. The long tail is modest and concentrated almost entirely in onboarding and offboarding workflows. In these cases, batch processing creates structurally longer initial windows.

For most tickets, across most ticket types, the pickup is fast. When a bottleneck exists, it isn’t in getting to the ticket. It’s in resolving it.

Across virtually every ticket category and use case, first response times land in a narrow band of 4–6 minutes. App assignment, login troubleshooting, hardware failures, application troubleshooting, and network issues are all handled at roughly the same initial speed regardless of complexity or category. We can see that triage and initial pickup aren’t a bottleneck. The variability in help desk performance lives downstream.

The one clear exception is employee offboarding, with a median first response of 11 minutes. This isn’t a slow pickup — it’s a scheduled workflow. Offboarding requests are typically queued and processed in batches, so the “first response” clock starts before the workflow begins. It’s a structural difference, not a performance gap.

When we separated tickets to study the impacts of AI automation, we discovered that automation didn’t change everything about support performance, but the changes it did make were dramatic.

Resolution time: a 16x gap

Section 5 • Resolution Benchmarks — Chart 5.3

The resolution time gap

Median help desk ticket resolution time by automation level

Fixify · 2026 IT Help Desk Benchmark Report50,000+ tickets · Jan 2025–Feb 2026

Tickets with AI automation close in a median of 4.4 hours. Tickets without automation take a median of 71 hours. That's roughly three full days — and a 16x gap in the core metric that determines whether an employee gets back to work today, or sometime next week.

Part of this gap is because of genuine workflow differences. Non-automated tickets are often more complicated and involve human collaboration, multi-step coordination, vendor dependencies, and physical actions that inherently take time. But the magnitude of the 16x gap suggests that the tickets that are resolved with assistance from AI automation close at a fundamentally different pace than the human-dependent portion.

The gap holds at every time benchmark:

Section 5 • Resolution Benchmarks — Chart 5.4

How quickly do tickets resolve at each automation level?

Percentage of tickets resolved within each time benchmark

8-hour row highlighted as a natural SLA benchmark.

Fixify · 2026 IT Help Desk Benchmark Report50,000+ tickets · Jan 2025–Feb 2026

View data — Comparison table — % resolved within time benchmarks by automation level

Benchmark	With AI automation	Without automation
Within 1 hour	29.5%	3.6%
Within 4 hours	48.9%	5.7%
Within 8 hours	55.0%	9.0%
Within 24 hours	65.9%	24.2%
Within 3 days	80.5%	50.3%
Within 7 days	95.2%	84.4%

Nearly half of tickets with AI automation resolve within 4 hours. At that same 4-hour mark, only 6% of tickets without automation are done. It takes 3 full days for non-automated tickets to reach the completion rate that automated tickets achieve in 8 hours.

Category by category, the gap is consistent.

The monthly view: stable vs. volatile

Our monthly comparison makes another pattern visible: it’s not just that tickets with AI automation resolve faster — they resolve at a consistent speed regardless of the time of year, ticket volume, or other external factors.

The time to resolution for tickets that used AI automation ranges from 2.4 to 6.3 hours across a five-month window — a narrow band that held even as ticket volume grew. The time to resolution for partially automated tickets swings from 49 hours to 102 hours, with no trend toward improvement.

The consistency story is almost as important as the speed story. Resolution with AI automation doesn't just deliver faster outcomes — it delivers predictable outcomes. For IT leaders setting SLAs and managing expectations, predictability matters almost as much as speed.

First response: where automation level doesn't change things

Here’s a counterintuitive finding: first response time is nearly identical regardless of automation level.

Tickets with AI automation receive a median first response of 5 minutes. Tickets without automation: 4 minutes.

It turns out that more automation doesn’t make triage faster, even though it speeds up the time for a ticket to be resolved.

Section 5 • Resolution Benchmarks — Chart 5.7

First response: where automation level doesn't change things

Median first response time for help desk tickets by automation level

The value of automation isn't in getting to the ticket. It's in what happens next.

Fixify · 2026 IT Help Desk Benchmark Report50,000+ tickets · Jan 2025–Feb 2026

Sentiment: automation’s downstream effect

We considered one more element when it comes to automation. How do users feel when they submit tickets that end up at different automation levels?

When users first submit a ticket, sentiment is nearly identical. About 11% of tickets that are resolved with AI automation carry negative sentiment at submission. For non-automated tickets, it’s about 12%. So we can see that users don’t feel differently about their issue based on how it will be handled — frustration is driven by the problem itself, not the process by which it will be fixed.

But automation’s effect on sentiment shows up downstream, through resolution speed. In Section 3, we saw that when tickets are resolved within 4 hours, IT teams convert frustrated users to satisfied users at 93%+. When tickets take more than 3 days to see improvement, conversion rates fall to 68%. This means that the automated median of 4.4 hours puts most tickets squarely in the high-conversion zone, while a 71-hour non-automated resolution time means user satisfaction is more likely to decline.

The use-case resolution reference

For teams setting category-specific SLAs or benchmarking their own performance, our use-case-level data provides the most granular reference points available. The figures below reflect ticket resolution time with AI automation.

Section 5 • Resolution Benchmarks — Chart 5.8

Use case resolution benchmarks

Median ticket resolution time by use case · Heavily automated (75–100% AI) · Sorted fast to slow

Fixify · 2026 IT Help Desk Benchmark Report50,000+ tickets · Jan 2025–Feb 2026

View data — Two-column reference table — Median resolution time by use case (heavily automated, sorted fast to slow)

Use case	Median resolution
Group management	0.8 hours
OS troubleshooting	2.2 hours
Email management	2.4 hours
VPN management	2.4 hours
License management	2.6 hours
Login troubleshooting	3.3 hours
Channel management	3.8 hours
MFA management	3.8 hours
Secrets management	3.9 hours
Configure app	3.9 hours
Hardware troubleshooting	4.3 hours
Application install	4.5 hours
Account management	4.8 hours
KB management	4.8 hours
Network troubleshooting	4.9 hours
App assignment	5.2 hours
Application troubleshooting	5.3 hours
Shared folder management	6.5 hours
Permissions management	9.6 hours
Video conferencing troubleshooting	18.8 hours
Email troubleshooting	19.0 hours
Contractor offboarding	20.6 hours
Employee offboarding	25.1 hours
Calendar management	25.0 hours
App integration	26.9 hours
Onboarding & Offboarding (overall)	41.1 hours
Contractor onboarding	44.4 hours
Employee onboarding	76.3 hours

Most troubleshooting use cases resolve in the 2–5-hour range with AI automation. Identity-related use cases, including login troubleshooting, MFA management, and permissions management, range from 3-10 hours because of the coordination and verification steps involved. Even with heavy automation, many of these workflows require human approval or sign-off, which extends the clock. Onboarding and offboarding workflows live in their own tier and should be benchmarked separately.