Manual Ticket Routing Emerges as Customer Support’s Biggest Bottleneck; 57% Favour AI-Led Automation, Finds Kapture CX

 Every support team has a version of the same story. A ticket comes in. It sits in a queue while someone figures out who should handle it. That person opens three different tools to find the customer’s history. By the time an agent actually starts working on the problem, the customer has already sent two follow-up messages asking for an update.

This is not a people problem. It is a process problem. And a recent poll conducted by Kapture CX asked customer support professionals to name it directly: what is actually standing between a ticket and its resolution? The answers were specific, honest, and point clearly toward where the industry needs to move next.

The Bottleneck Is Earlier Than Most Teams Realise

When asked to identify the single biggest bottleneck in resolving customer support tickets today, 34 percent of respondents pointed to manual triaging and routing. This is the moment before the real work even begins: a ticket arrives, and a human has to read it, categorise it, decide who it belongs to, and send it there. In high-volume environments, this step alone can add meaningful time to every interaction, and it scales poorly. The more tickets that come in, the more this step becomes a ceiling on how fast the team can move.

Close behind, 28 percent cited switching between multiple tools as their primary bottleneck. This one is easy to underestimate from the outside. An agent who has to toggle between a CRM, a ticketing system, an order management platform, and a communication tool to piece together a single customer’s situation is not being inefficient. They are working within a system that was never designed for the complexity of modern customer interactions. The context is scattered, and the agent pays the cost of reassembling it every single time.

Twenty-four percent identified lack of customer context as the core problem. This connects directly to the tool-switching issue but goes deeper. It is not just that the information lives in multiple places. It is that by the time an agent gets a ticket, they often have an incomplete picture of who this customer is, what they have already tried, and what they actually need. Resolutions built on incomplete context tend to be partial solutions that generate follow-up tickets rather than closing the loop.

Fourteen percent pointed to human dependency as the primary constraint, meaning tickets that cannot progress until a specific person reviews or approves something. In workflows built around sequential human involvement, a single unavailable team member can stall a ticket entirely.

Taken together, the four bottlenecks form a clear picture: the support process is too manual at entry, too fragmented in the middle, and too dependent on individual availability at key decision points.

What Teams Believe Would Actually Make the Difference

The second question asked which capability would have the greatest impact on reducing ticket resolution times. The response here was decisive.

Fifty-seven percent of respondents chose AI-powered ticket routing. That is not a slim majority. It is a clear signal from the people doing this work every day that the first step, getting a ticket to the right place, is where they believe AI can deliver the most immediate and tangible value.

Thirty-six percent selected autonomous issue resolution, which represents an even more ambitious expectation from AI: not just routing a ticket to the right person, but resolving the routine, repetitive, and predictable ones end to end, so that human agents are freed for the interactions that genuinely need them.

Seven percent chose better agent productivity tools, reflecting an expectation that the improvement needs to come upstream rather than making agents faster at a process that is already fundamentally broken.

Notably, zero percent selected unified customer data as the capability that would have the greatest impact. This is an interesting finding given that 24 percent identified lack of context as a major bottleneck. The gap suggests that while fragmented data is a real problem, respondents do not believe that solving it alone would be transformative. The data needs to be connected, but it also needs to be acted upon intelligently, which is where AI enters.

What the Data Is Really Saying

Read together, the two questions tell a coherent story. The support process is losing time before it really starts, in the manual work of figuring out where each ticket belongs. And the people working inside that process believe that AI-powered routing is the most direct path to fixing it.

What makes this finding significant for the broader CX industry is not just the percentage. It is the specificity. Support teams are not asking for AI in the abstract. They are pointing at a particular moment in the workflow where automation would make an immediate, measurable difference.

“These findings reflect what we hear consistently from enterprise support teams,” Sanchit Sood, Chief AI Officer at Kapture CX. “The appetite for AI in customer support is no longer about exploration. Teams know where the friction is, and they know what they need. The question now is how quickly the right infrastructure can be put in place to act on it.”

The poll results suggest that customer support teams have done the thinking. They understand their own bottlenecks, they have a clear preference for where AI should intervene first, and they are ready for the tools to catch up with the urgency they already feel.