Will AI replace customer service agents?
It’s the question behind many conversations about AI in customer service. For CX leaders investing in automation, it’s also a practical concern: How will the work change, what skills will teams need, and where should human support fit?
AI will automate a significant amount of customer service work. It can answer common questions, retrieve information, and help customers complete predictable tasks. That creates real opportunities to reduce repetitive work and make support more accessible.
But automating tasks doesn’t eliminate every need behind a customer interaction. Sometimes the customer needs someone to untangle conflicting information, recognize an exception, or help them feel confident about what comes next.
As routine requests move to AI, those moments become a larger part of the human agent’s job.
AI doesn’t make human expertise less valuable. It changes where that expertise creates the most value.
AI handles routine work. Humans handle complexity.
Customer service teams spend time on questions with straightforward answers: Where can I find this document? How do I update my information? What’s the status of my request?
When connected to reliable information and appropriate workflows, AI customer support can help customers answer these questions without waiting for an agent. It can also support the work happening behind the scenes by detecting intent, routing conversations, retrieving relevant resources, and summarizing interaction history.
That’s useful customer service automation. Customers get help sooner, and agents have fewer repetitive tasks competing for their attention.
The challenge is what happens when a request stops being predictable.
Consider a patient completing an online registration form. AI might explain where to enter insurance information. But if the patient’s coverage details conflict with what the portal shows, repeating the instructions won’t resolve the uncertainty.
Or consider a banking customer asking about an unfamiliar transaction. Finding the transaction is one task. Understanding the customer’s concern, asking the right follow-up questions, and determining the appropriate next step requires a different level of support.
The same distinction applies when a software administrator encounters an unusual implementation problem or an insurance customer needs help navigating a claim after a stressful event.
These customers need information interpreted in context. An effective agent can recognize what’s unusual, adapt the conversation, and guide the customer toward a clear next step.
AI can assist with that process. Human judgment helps determine how to proceed when the answer depends on circumstances that don’t fit a standard workflow.
The agent’s role moves from answering to advising
As automation absorbs simpler requests, the mix of work reaching agents can change. A team may handle fewer routine questions while spending more of its time on complicated, sensitive, or consequential interactions.
That shift has implications for staffing, training, and performance expectations.
Agents need opportunities to develop diagnostic skills, explain complicated processes clearly, and respond to customers who may already be frustrated. They also need to understand when they can resolve an exception themselves and when specialist support is necessary.
A useful training exercise might involve a customer who received an accurate answer from AI but still couldn’t complete their task. What additional questions would uncover the obstacle? What would help the customer move forward?
Performance measures should reflect that work, too. If automation removes straightforward requests, comparing agents’ average handle time against their previous workload may give leaders an incomplete picture.
The goal should be to help agents resolve the interactions they’re now responsible for. That means giving them access to relevant history, usable knowledge, clear escalation paths, and visibility into the customer’s digital experience.
Expecting more judgment while leaving agents to reconstruct the situation puts unnecessary obstacles in their way.
The AI-to-human handoff is part of the experience
Even a capable agent can struggle to recover an interaction when the handoff loses everything the customer has already explained.
Imagine spending several minutes describing a problem to an AI assistant, trying its suggestions, and requesting human help. Then the agent opens with, “What can I help you with today?”
The customer has reached a person, but the experience has restarted.
In Glance’s 2026 Customer Experience Trends research, 44% of respondents said they almost always have to repeat themselves when switching channels. That finding highlights how easily progress can disappear between support touchpoints.
A better handoff should give the agent enough context to continue productively:
- What the customer is trying to accomplish
- What they’ve already asked and attempted
- What remains unresolved
- Where they’re encountering difficulty
AI-generated summaries can help, but agents should also be able to check the underlying conversation when details matter.
Customers need an accessible route to human support, too. Requiring them to repeat unsuccessful steps before they can reach someone adds effort at the point when they most need help.
Human-in-the-loop AI should make intervention a planned part of the journey. Leaders should define when escalation is appropriate, what information travels with it, and how the agent continues the interaction.
Give agents visibility into the problem
Conversation history explains what’s been discussed. It doesn’t always show why the customer is stuck.
An agent might know that a customer can’t submit an application without knowing which field is causing the problem. The customer may struggle to describe the page, especially if they’re using unfamiliar terminology or navigating a complicated form.
With visual engagement, the agent can understand the digital obstacle more directly.
Glance’s Guided CX Platform brings human guidance into digital experiences through capabilities such as cobrowsing, screen sharing, and video. Rather than relying entirely on verbal descriptions, agents can see relevant on-screen context and help customers navigate the next step.
For example, an insurance customer might understand which documents a claim requires but struggle to upload them. With shared visibility, the agent can identify where the process has stalled and guide the customer through it.
That’s where Guided Customer Support can make human expertise more effective: It gives agents a practical way to help within the digital journey.
The operational benefits can be measurable. In a Total Economic Impact™ study commissioned by Glance, Forrester Consulting modeled a composite organization based on customer interviews. The model included a 20% reduction in average handle time and an improvement in first-time interaction resolution from 88% to 90%.
Those modeled results aren’t a guarantee for every organization, but they illustrate how better visibility can support efficiency and resolution together.
Personalize the path to support
Two customers with the same question may need different kinds of help.
One may want a quick answer and feel comfortable completing the task independently. Another may need guidance because the process is unfamiliar, the stakes feel high, or an earlier attempt failed.
McKinsey research found that 71% of consumers expect personalized interactions, and 76% become frustrated when they don’t receive them.
For service leaders, that suggests an opportunity to think about personalization beyond messages and recommendations. The support path itself can respond to the customer’s situation.
AI might handle the initial request, organize relevant information, and identify the appropriate team. A human can then clarify the remaining issue and help the customer act with confidence.
Effective human AI collaboration depends on making that transition useful and timely. A customer shouldn’t have to exhaust every automated option before receiving the help their situation requires.
Measure AI by resolution, not just deflection
Deflection can show how many interactions avoid a human agent. It can’t, by itself, tell leaders whether customers accomplished their goals.
A customer who leaves an automated conversation may have solved their problem. They may also have given up.
To evaluate AI customer experience, leaders need to look beyond containment and ask:
- Did the customer complete the task or solve the problem?
- Was the issue resolved on the first interaction?
- How much effort did the experience require?
- Did the handoff preserve useful context?
- Did the customer feel understood?
These measures help distinguish successful automation from work that’s simply been pushed back onto the customer.
They also give leaders a clearer way to evaluate AI and customer service agents together. AI can create efficiency by handling repeatable work. Human experts can concentrate on the situations where judgment, empathy, and explanation make the greatest difference.
The opportunity is to use AI to give people more time for the work humans are uniquely equipped to do—and the tools to do it well.
Explore how Glance brings human guidance into AI-powered customer journeys.
