Key takeaways
Most support agents spend the bulk of their day working tickets. But a surprising amount of that time isn’t actually spent solving customer problems.
Agents spend these large chunks of their day on administrative and investigative tasks, meaning average resolution time slows down, average handle time spikes, and support teams feel underwater, even when ticket volume itself isn’t particularly high.
It’s taken as normal in B2B support, but all this time adds up quickly. And it’s the hidden challenge behind customer support workflow optimization and overall business process efficiency.
Non-ticket work: the biggest workflow bottlenecks in B2B support
In most B2B support teams, a ticket rarely moves from open to resolved in a straight line. That’s common in B2C support, but in the B2B world there’s usually a lot of work that happens around the ticket before the customer gets a reply.
That work is what we call non-ticket work. They’re the administrative and investigative tasks that support agents handle alongside customer interactions:
- Looking up context
- Filing bug reports
- Checking product logs
- Replicating issues
- Documenting information
All these steps are necessary, but when they’re handled manually, they create significant operational drag and make scaling hard.
Certain non-ticket work items are common across every B2B support team I’ve seen
- Tool switching and context gathering
In most B2B organizations, it’s not unusual for agents to jump between several systems to get the full picture of what’s happening with one customer.
An agent might need to check the CRM for account details, the billing system for subscription status, a product analytics dashboard for usage data, and the support platform for ticket history.
Some teams also rely on separate tools for logs, monitoring, or customer health data.
Each context switch only takes ten seconds, but multiply these repetitive tasks across dozens of tickets and it adds up quickly and significantly slows support team productivity.
- Manual bug reporting and issue escalation
When support identifies a product bug, the next step is usually to escalate the issue to engineering, which is often a completely manual process.
Agents copy information from the ticket into JIRA or Linear. They try to replicate the issue and take screenshots. They document the steps to reproduce. They summarize the back-and-forth from the original support ticket.
Typically all of this documentation work happens outside your support ticketing platform, and agents are basically just recreating all the context that already exists in the ticket. It’s a perfect example of your current workflows including unnecessary steps.
- Pulling logs and technical diagnostics
Technical and complex B2B products often require logs or system diagnostics to troubleshoot issues. But those logs rarely live inside the support platform.
Instead, agents must log into a separate tool to retrieve them, or ask the customer to export and send the data manually.
Both approaches add friction. Agents spend time navigating different tools or waiting for customer replies, or customers are forced to spend their time gathering information before the investigation can continue.
Either way, resolution time increases and support workflow optimization becomes harder to achieve.
- Filing feature requests
Support teams are often the first to hear about product gaps or missing functionality. But getting that feedback to the product team usually requires another manual process.
Agents might log feature requests in a JIRA, use a separate product management tool, submit them through a Slack form, or send them directly to a product manager.
Whatever the process looks like, these small tasks accumulate, especially in organizations where feature requests are frequent. What originally started as a quick note becomes another piece of administrative work layered on top of customer support.
- Internal knowledge searches
Even experienced agents run into situations where they need help from someone else on the team.
If the answer isn’t clearly documented or if your knowledge management isn’t up to par, agents end up combing through old Slack threads or old Zendesk tickets. When that fails, they’re forced to ask a subject matter expert and hope for a quick reply.
While this approach works, it’s unnecessarily slow and creates two problems:
- Customers and agents have to wait for a response before moving the ticket forward.
- The eventual answer often stays buried in a Slack thread instead of becoming searchable knowledge for the rest of the team, recreating the problem next time it occurs.
The hidden cost of non-ticket work in B2B support
None of this non-ticket busywork is unexpected, or even unnecessary. Sometimes deep context gathering or troubleshooting is required to solve complex B2B issues, especially technical ones.
The problem is how much time they consume.
Because support teams are often relatively low on the priority list for internal tooling and product enhancements, most non-ticket work is done manually, requiring additional time and effort.
Imagine an agent handling 25 tickets in a day. Even if they only spend 5 minutes per ticket doing non-ticket work, that adds up to over an hour of their shift that isn’t directly helping customers.
Across a team of 100+ support agents, the impact becomes massive. If an agent makes $60k per year and you’re wasting one hour per day on busywork, across 100 agents that equates to roughly $1.1 million in salary costs per year.
Investing in better tooling and support workflow automation suddenly seems wise (and inexpensive!), doesn’t it?
In addition to driving up average handle time (AHT), non-ticket work also contributes to agent fatigue. When large portions of the day are spent switching tools, documenting issues, or repeating manual processes, support work starts to feel more like administrative work than problem solving. The core of the job becomes monotonous and frustrating.
For a deeper look at how support leaders tackle this challenge of scaling without adding headcount, see our post on scaling B2B customer support without scaling headcount.
How can you measure where your support team’s time actually goes?
Most support leaders know their team is busy, but it’s not always easy to see what agents are actually doing with their time.
And let’s be candid: traditional time tracking doesn’t work well in support environments. Asking agents to log every task they perform quickly becomes just more busywork, making the problem worse.
A better approach is to approximate where time is going. A few simple methods can reveal workflow inefficiencies quickly.
- Shadow a few agents for a day
Spend a full day observing how agents handle tickets. Pay attention to how often they:
- Switch between tools
- Copy information between systems
- Look up customer data in external dashboards
- Ask internal teams for context or approval
- Hunt for knowledge that should be available
Shadowing can highlight how much work is happening outside of your core support platform. An agent may only spend a few minutes replying to the customer, but much longer gathering the information needed to write that response.
- Ask your team directly
Your team usually knows exactly which tasks slow them down, so ask them this simple question directly:
“What tasks take up time but don’t feel like they move tickets forward?”
Your team may flag how they classify and route cases, how frequently they’re hunting through your internal knowledge base, or how much copying and pasting they have to do between systems.
All these are things that hurt the customer experience and the agent experience. In my experience, the things your team mentions often are pretty great areas for you to focus your workflow optimization and process improvement efforts.
- Track time-in-status across your support tickets
Some customer service KPIs can also reveal where non-ticket work exists, like high AHT.
While it’s not a typical metric, most support platforms today can track how long a ticket spends in each status. Within Zendesk, for instance, you can look at time in open, pending, and on-hold states.
That data is worth pulling.
If tickets are consistently sitting in an "agent working" related state for long stretches without any customer-visible activity (no reply sent, no status change, no resolution), that's a strong signal that work is happening behind the scenes that isn't moving the ticket forward.
Unlike AHT, time-in-status is hard to explain away.. If an agent is actively working a ticket for 25 minutes but the customer only sees one reply, something in that gap is consuming time. That's usually where the tool-switching, log-pulling, and manual documentation live.
Most B2B support teams already have access to this data — they just aren't using it to diagnose workflow friction.
Three ways you can eliminate non-ticket work in B2B support workflows
Once you identify where agents are losing time, the next step is to fix the workflows that create that friction.
Remember, the goal of support workflow optimization isn’t to push agents to work faster. You don’t want them doing the same thing they’re doing today, just 10x faster. That’ll lead to burnout and frustration, and ultimately to upset customers and churn. Better capacity planning for support can help, but you’re still making the best of a bad situation.
The real goal is to remove the existing bottlenecks that slow them down and to streamline processes.
If you can do that, then they’re able to work at a sustainable pace while providing a better customer experience at the same time.
For most B2B support teams, this comes down to three strategies:
- Automating repetitive internal tasks
- Giving agents unified access to customer data
- Reducing the number of repetitive tickets agents need to handle
1. Automate repetitive internal workflows
Many of the tasks that consume agent time follow predictable patterns. When those workflows are automated, agents can focus on resolving the issue instead of managing the process around it.
Here are some ideas:
- One-click bug reporting. Instead of copying ticket details into JIRA, an agent clicks a button that triggers an AI-powered workflow to automatically create the bug report with the ticket summary, screenshots, and relevant metadata already attached.
- Automated feature request logging. Customer requests can be captured directly from tickets, categorized by AI, and routed to the appropriate product team or submitted in their project management tool without manual data entry. Or, similar to the above example, you can create a one-click automated workflow that gathers all the necessary context, summarizes the ticket, and logs the feature request in your product team’s preferred tool.
- Zero-touch escalations. When a ticket requires engineering support, an AI agent can compile the escalation by automatically summarizing the issue, attaching logs, and including the relevant customer context. That can then get passed to a senior support engineer, a specialized team, or other stakeholders for intervention.
These types of workflows dramatically reduce the time spent on non-ticket work because agents no longer have to recreate information across multiple systems.
Companies like Yotpo have seen measurable results from streamlining internal workflows in this way. After improving how agents escalated issues and shared information across teams, Yotpo reduced ticket handling time by roughly 30% while maintaining customer satisfaction.
And since many support teams are now building no-code AI workflows and agents, it’s usually possible to do all of this without requiring engineering resources.
2. Give agents unified data access in one place
So many support teams I’ve worked with waste so much time on context switching.
When agents have to jump between several tools to understand a customer issue, even simple tickets become time-consuming. The fastest way to improve support efficiency is to bring that information together in one place.
With unified data access, agents can see everything they need from a single interface, including:
- CRM and account information
- Billing and subscription details
- Product usage and telemetry data
- Customer communication history
- Logs and diagnostic information
AI-powered agent assist tools take this a step further by allowing agents to query this data in natural language (among other things). Instead of navigating multiple dashboards, agents can ask questions and receive the relevant context instantly. Agent assist tools can also draft replies and recommend next steps to human team members, further increasing efficiency.
The result is a real-time, 360-degree view of the customer in one place that streamlines support operations by dramatically reducing context switching.
Another approach that accomplishes a similar end goal is to prioritize support tool consolidation, as the more you can consolidate your tech stack, the less context switching is required.
Unified data access has been a game change for companies like Monday.com. They’ve improved resolution speed by giving agents faster access to the information they need, resulting in 13.5% less ticket handling time.
3. Deflect repetitive tickets
The final lever for improving support team productivity is reducing the number of tickets agents need to handle from the start.
In a lot of support environments, a large percentage of tickets involve the same types of questions, password resets, configuration help, basic troubleshooting steps, or product navigation issues.
A B2B AI support platform can handle many of these repetitive requests automatically through self-service, knowledge automation, and intelligent ticket deflection. While deflection rate shouldn’t be a primary goal for B2B teams, resolving tickets through self-service and automation is still an important piece of optimizing workflows.
Every ticket that can be resolved without an agent removes work from the queue. That frees agents to focus on the issues that actually require human expertise—and the more you optimize your workflows, the more valuable that time and focus becomes. It often unlocks opportunities for continuous improvement and additional streamlining.
There are plenty of strategies for deflecting Tier 1 support tickets, but the key point is that when you combine automation, unified data access, and intelligent ticket deflection, you can eliminate a large portion of the non-ticket work that slows ticket resolution and limits capacity.
Think about it. If agents spend 40% of their time on administrative work, and automation removes just half of it, it creates a 20% capacity gain without adding headcount.
For support leaders under pressure to scale efficiently, that kind of improvement can make the difference between hiring more agents and unlocking the productivity already inside the team.
Support workflow optimization is the fastest way to scale your customer support
If you’re a support leader trying to optimize their team’s workflows and scale their customer service operation sustainably, support workflow optimization is one of the highest-impact investments you can make.
Non-ticket work is one of the biggest hidden costs preventing your team from serving more customers. Tickets take longer to resolve, queues grow, and teams feel like they’re constantly playing catch-up.
Automate those repetitive internal tasks, unify your customer data, and use AI-powered deflection to add capacity and serve more customers without increasing headcount. .
If you want to see how this works in practice, request a demo of Mosaic to learn how tools like Agent Builder and Assist help teams eliminate busywork, streamline support operations, and give agents more time to focus on helping customers.
