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 McDonnel, 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.
This gap is largely universal across service-heavy industries, with the pattern appearing across telecoms, banking, retail, travel, and public services due to the underlying structure being similar.
When most organizations use layered support models that combine self-service, automated triage, and human escalation, this creates a multi-industry-wide issue.
These design choices, where automation is used to filter demand instead of completing tasks, extend both time to human and total effort.
Nikola Mrkšić, CEO & Co-Founder of PolyAI, suggests customer frustration comes from service systems designed to deflect demand rather than resolve issues, treating human agents as scarce resources and using automation that often understands requests but cannot complete actions.
“Customers struggle to reach a human quickly because the system is designed to deflect, not to resolve,” he said.
“The architecture of most contact centers still assumes that human time is the scarce resource to be protected at all costs. So everything upstream (IVRs, chatbots, routing logic) is optimized to contain demand rather than actually complete the job.
“If the AI can understand the request but can’t take action (issue a refund, rebook the service, update the account), then all you’ve done is insert another step into the journey.”
Redesigning Service for Resolution
By recognizing the design flaw in architecture, organizations can begin to close the six minute gap by shifting from fragmented, delay-driven systems to integrated, resolution-focused service design.
Redesigning escalation architecture to be direct and intentional rather than a byproduct of failure will help reduce unnecessary steps, shorten IVR paths, and allow earlier exits from chatbots.
Customers with complex or high-value issues should be routed quickly to a human or to a system that can fully resolve the issue, having escalation logic based on intent and outcome, not just containment.
Time to human should also be treated as a service reliability indicator, measuring how long it takes for a customer to reach meaningful help highlights friction in the journey, helping balance efficiency with customer experience rather than optimizing only for deflection.
Organizations should also integrate services end-to-end as systems need to share context across channels and stages, enabling customer data, interaction history, and intent to move seamlessly from chatbot to agent, or from digital to voice, reducing repetition and enabling faster resolution, with integration also allowing AI tools to take action, not just provide information.
This also includes shifting automation from gatekeeping to resolution, allowing the tool to complete tasks where possible, such as processing requests or updating accounts, and removing steps when it can both understand and act, reducing the need for escalation and shortens the overall journey.
From a customer perspective, this means higher expectations around a clear continuous experience, allowing them to move seamlessly across channels without repeating themselves, understand what is happening at each stage, and reach resolution quickly.
At the same time, access to a human should be available when needed, but the priority is that the issue is resolved with minimal effort, whether by AI or an agent.
Why Technology Alone is Not Enough
The six-minute gap reflects a structural issue in how service systems are designed.
Avaya’s report reveals that whilst organizations have invested heavily in AI, automation, and new channels, these tools are still often layered onto fragmented workflows that prioritise deflection over resolution.
As a result, customers face longer paths, repeated interactions, and delayed access to meaningful help.
Closing this gap requires a shift in service architecture, having systems built around end-to-end resolution, with connected channels, clear escalation paths, and tools that can both understand and act.
Measuring outcomes such as time to human and resolution rate helps align performance with customer expectations.
While technology remains essential to closing the gap, it is not sufficient on its own, meaning without integration and intentional design, more tools can increase complexity rather than reduce it, as connectivity comes from how systems work together, not just from how many are deployed.
