More organizations are continuing to invest in new customer service technologies, yet consumers still encounter rising effort and communication friction when trying to resolve issues.
Speed and accuracy are now among the top priorities for customers when contacting a business, with quick connection now regarded as empathetic to the caller’s needs.
A recent Avaya report has revealed that 60% of US customers now expect to reach a live representative within six minutes or less before frustration and disengagement take place.
While support channels are becoming more sophisticated, the path to a human is often slowed by the design of service systems and the way tools connect to one another.
When Speed Expectations Meet System Delays
With modern day tools now creating an assumption of immediate access and smooth resolutions, delays can increase the between gap between service expectations and reality.
As a result, many organizations add new tools without redesigning workflows, meaning chatbots, messaging apps, IVR systems, and agent desktops may not share data efficiently.
And with these tools often sitting in front of live support, this can slow escalation when the issue requires a human, having the customer spend more time navigating automation before reaching the person who can resolve the problem.
From here, this can cause customers to repeat information, move through long menus, or get redirected, increasing effort despite the tools being newer.
The Growing Gap Between Speed and Experience
Rising customer expectations now include near-immediate access to help, as other areas of digital services begin to offer quicker responses.
These delays that once felt normal now feel slow, raising customer sensitivity to friction, with even small delays feeling like an enterprise failure.
Indirect escalation paths also delay access to resolution, with customers often being routed through automated layers before reaching a human.
Whilst these layers are designed to reduce load on agents, they also introduce decision points, retries, and loops, meaning when a system cannot correctly classify an issue, customers get redirected instead of escalated, extending the time before meaningful help begins.
Furthermore, many support environments are built from separate systems, with a chatbot handling one layer, an IVR handling another, and agents working in a different interface.
Amy McDonnell, CCO at Flip, argues that contact centers were originally designed to minimize queue contact rather than solve customer problems, leading to overly complex systems that prioritize deflection over resolution.
“We designed most contact centers around avoidance, not assistance. For years, the KPI wasn’t “did we solve the problem?” It was “did we keep them out of the queue?,” she explained.
“That’s how you end up with the current state of service: a maze engineered to feel less like support and more like a digital purgatory designed to exhaust a customer’s patience until they either surrender to a dead-end FAQ or end up shouting “Agent” at a machine.
“Customers aren’t struggling because we lack technology. They’re struggling because we’ve deployed that technology in service of the wrong goal. Deflection became the strategy, instead of resolution.”
Higher expectations increase the likelihood that customers notice friction, meaning when fragmented workflows create more friction points and indirect escalation paths add time before human intervention, they amplify each other.
As a result, wait times increase in practice, and effort perception increases, so even when organizations deploy more advanced tools, the system improves at individual tasks, the end-to-end experience becomes more complex.
Even if each component is individually efficient, the full journey becomes longer and more repetitive.
Automation Slows Down Access to Humans
As a result, the six-minute gap comes from friction built into how modern service systems route customers before they reach a human.
With many systems placing automated chat at the front of the journey, chatbots are designed to resolve simple requests and reduce agent load, often requiring multiple prompts, repeated inputs, and confirmation steps before escalation, meaning if the bot fails to classify the issue correctly, the customer stays in the loop longer than necessary.
IVR systems that create long routing paths are also creating this gap in wait times in phone-based support, often relying on layered menu structures and customer category selections, repeating options, and confirming choices if a system misroutes, adds seconds or minutes to the journey, with repeated loops increasing the total time to reach a human agent.
Furthermore, even when AI is used to assist routing or pre-fill information, that context does not always carry cleanly into the agent workspace, meaning customers may still repeat details, and agents may need to verify information again, extending both waiting and handling time.
