Are Your Virtual Agents Escalating Far Too Many Queries?

Virtual Agents can improve speed and efficiency, but when escalation rates are too high, any promised benefits quickly come undone – and fast! Customers get frustrated and advisors are left handling everything from simple requests to complex issues.

So where is it all going wrong? We asked our panel for the most common challenges CX leaders are facing right now and their top tips for bringing your escalation rates down!

Align Your Strategy With User Preferences – Rather Than Just Business Cost

Minimize friction by asking the question most companies ignore: what do your customers actually want to self-serve? High volume doesn’t always justify automation.

By identifying the specific tasks users prefer to handle independently, you avoid automating complex interactions that trigger immediate “speak to agent” demands.

Aligning strategy with user preference rather than just business cost ensures customers stay in-channel by choice.

Contributed by: Matthew Clare, VP, Product Marketing, UJET

Explain What Your Virtual Agents Can and Can’t Do to Better Manage Expectations

Reducing escalations starts with aligning capabilities to real-world customer needs. Virtual Agents should be context-aware, integrated with back-end systems and able to deliver actual outcomes not just more output.

Virtual Agents also need to understand who the customer is, what their intent is and context of what the customer has already tried.

Clear expectations also matter, so explaining what the Virtual Agent can and cannot do will build trust and reduce frustration.

Contributed by: Lewis Gallagher, Senior Solutions Consultant, Netcall

Review Root Causes at Scale to Spot Patterns and Opportunities

Take a systematic look at the conversations behind the escalations! There’s a reason for every escalation.

Maybe the Virtual Agent misread the query or contradicted the knowledge base it’s supposed to pull from. It could’ve been a tone problem, pushing the customer to request a human conversation instead.

When you’re reviewing these root causes at scale, consistently, patterns will begin to emerge that might have otherwise stayed hidden behind a solitary metric like escalation rate.

This is a huge opportunity for any organization deploying chatbots or Virtual Agents for customer support.

You can feed your findings back into knowledge base updates, adding or tweaking content, and refine the workflows guiding the bots as well.

Contributed by: Derek Corcoran, CEO, Scorebuddy

Connect Your Virtual Agent to Your CRM, Billing, and Order Systems So It Can Finish the Job

Most escalations aren’t an AI problem. They’re a design problem.

Here’s where I’d focus:

• Give your Virtual Agent reach, not just answers – A bot that can explain a refund but can’t process one will always escalate. Connect it to your CRM, billing, and order systems so it can finish the job.
• Learn from every escalation – Insights from advisor-resolved cases can help improve future workflows and responses over time.
• Measure resolution, not containment – A high deflection rate means little if customers come back unhappy.

The goal isn’t fewer escalations at any cost. It’s making sure that when a human steps in, it’s because the customer genuinely needs one.

Contributed by: Ben Neo, Head of Zoom Contact Centre and CX Sales, EMEA, Zoom

Stop to Ask Yourself, “Is This a Training Gap or a Design Problem?”

If your automation is escalating frustrated customers at scale, you have a design problem – not a training gap. Fix the handover. Fix the context transfer. Fix the failure modes that send people sideways before a human ever picks up.

That being said, no design is perfect, and agents will always inherit some mess. So yes, train your agents. But brief them on the end-to-end automation and intended goals, not just the customer. Give them clean handover summaries.

Tell them the common or potential failure points. And track every escalation back to its origin so you can close the loop on design.

After all, the goal isn’t teaching agents to recover broken experiences. It’s building a system that rarely breaks and having agents good enough to handle it when it does.

Contributed by: Michael Clark, Co-Founder and Principal Consultant of CXTT Consulting

View the Challenge as a Pyramid – With High-Volume, Low-Complexity Interactions at the Bottom

The challenge can be viewed as a pyramid, with high-volume, low-complexity interactions at the bottom: the enquiries that traditionally clog contact centres.

Many are already automated; for example, UK Power Networks handles 94% of power outage communications automatically.

The next step is moving progressively up the pyramid. Agentic AI, or Virtual Agents, can now manage increasingly complex interactions, although some still require escalation when customers hit AI guardrails or request a human. Crucially, escalation does not mean failure.

At the very least, Virtual Agents significantly reduce the repetitive tasks traditionally handled by human advisors, such as collecting names, verifying details, or identifying the reason for contact.

They can also transfer their entire automated interaction, alongside relevant context and interaction history, in order to facilitate rapid resolution by a human agent.

Contributed by: Martin Taylor, Co-Founder and Deputy CEO, Content Guru

Be Smart Enough to Know What Should Never Be Automated in the First Place

There’s a growing myth in our industry that Voice AI will soon automate 40–50% of contact centre calls with 80–90% containment – but this rarely survives contact with real operations.

In most live environments, fully automated resolution sits somewhere between 12% and 18% of calls. And that’s not out-of-the-box performance. That’s after months of tuning, refining intents, and fixing edge cases.

The real problem is that customer conversations are messy. Around 30% of inbound calls contain more than one intent. That’s based on analysing 20,000 agents and >200 million calls at a leading BPO.

And the moment a second intent appears? Automation systems fail. I’ve tested all leading conversational AI systems and almost all of them fail drastically when it’s a multiple-intent conversation.

This is where a new capability is emerging: Conversational Decision Intelligence.

Instead of analysing calls after they finish, like the traditional speech analytics approach, it evaluates intent, context, and customer signals in real time to decide whether a conversation should be automated, escalated, or routed to a specialist.

The future of AI in contact centres isn’t automating everything. It’s being smart enough to know what should never be automated in the first place.

Contributed by: Andrew Charles Moorhouse, Executive Strategy Advisor – Conversational AI, CX & Growth

Identify What’s Behind Each Escalation

Understand why customers are being transferred in the first place. Identify whether the escalation is due to a knowledge gap, Virtual Agents struggling with intent recognition, or failing to adapt to new customer queries and behaviours. Analyse the underlying patterns to reveal areas of improvement.

Another key thing is to offer transparency. When customers know early on whether they’re talking to a Virtual Agent, it helps to reduce frustration and build trust.

Also, ensure your Virtual Agents are operating in a connected omnichannel environment, where customer context carries across channels and escalation paths are seamless.

Ultimately, the goal of AI agents should be to resolve simple queries and not to block access to humans. The customers should have trust that for the complex or emotional interactions, they will be able to reach the right advisor quickly and with full context intact.

Contributed by: Zaineb Ahmed, Marketing Manager, Peopleware

Give Virtual Agents Smarter Escalation Rules

Reducing escalations does not mean trapping customers in automation. It means creating smarter rules for when escalation is actually necessary.

A strong Virtual Agent strategy defines which issues the bot should fully resolve, which issues it should partially handle before transfer, and which issues should go directly to a human.

For example, the Virtual Agent can authenticate the customer, collect context, identify the issue, and suggest next steps before involving an advisor.

Escalation should be based on signals like customer frustration, repeated failed attempts, high-value accounts, or complex situations. This reduces unnecessary transfers while still protecting the customer experience.

Contributed by: Jonathan Kenu Escobedo, Customer Success Manager, MiaRec

Design Around Real Customer Behaviour and Clearly Defined Automation Goals

High escalation rates from AI agents are often the result of poor design, weak knowledge grounding, or insufficient operational tuning rather than the AI itself.

Yet AI agents and Virtual Assistants perform best when they are designed around real customer behaviour, real conversation data, and clearly defined automation goals.

Start by regularly analysing escalation triggers to identify the interactions the AI Agent struggles to resolve. These insights can then be used to improve conversational flows, expand knowledge coverage, refine automation logic, and optimize backend integrations.

Escalations should not simply be treated as failures – they are one of the most valuable sources of optimization data available.

Contributed by: Rodney Hassard, Head of Product, Applications, Vonage

…But Don’t Strive to Totally Eliminate Escalations

Ensure Vulnerable Individuals Are Still Identified Early and Escalated Quickly

Advances in LLMs and NLUs have improved intent recognition, context and sentiment, but technology alone isn’t enough.

True impact comes from combining this with strong backend integration and well-governed knowledge, enabling customers to complete tasks accurately without human intervention.

Effective self-service is critical, giving customers seamless access to knowledge, troubleshooting resources, and digital channels such as SMS, reducing reliance on advisors.

At the same time, knowing your customer base is key, ensuring vulnerable individuals are identified early and escalated quickly when needed.

Ultimately, lower escalation rates come from continuous optimization, refining journeys and tuning experiences to customer needs. The best Virtual Agents don’t avoid escalation, they make it the exception, not the rule.

Contributed by: Nick Sargeant, Programme Manager, Route 101  

Lower the Virtual Agent’s Pride Threshold With a “Graceful Surrender” Trigger

The industry instinct is to tune Virtual Agents to “try harder” before handing off, with more clarifications, more retries, more containment.

The evidence points the other way. Once the virtual agent has failed twice the customer is already negative, and the human picks up a hostile conversation that’s much harder and slower to resolve (if it can be resolved at all).

So, lower the Virtual Agent’s pride threshold. Build a “graceful surrender” trigger that hands off the moment certain signals appear: a customer rephrasing, sentiment dipping, or high-stakes intents like fraud, complaints, or vulnerability.

Then add a polite “let me get someone who can help properly” rather than making another attempt. Yes, you may see escalations rise slightly, but AHT will fall and CSAT will jump because the human inherits a conversation that’s actually still salvageable.

Stop Treating Containment as Success

Most organizations are quietly optimizing the wrong number. A “low escalation rate” looks like a win on the dashboard, but it often masks customers who simply gave up, closed the chat, abandoned the call, or quietly switched to a competitor. The virtual agent didn’t resolve anything; it just outlasted them.

The fix isn’t to reduce escalations. It’s to stop treating containment as success. Measure verified resolution and escalation quality instead. A well-handled escalation, with full context passed across, is a better outcome than a “contained” customer who churns next week.

Counter-intuitive but true, the contact centres with the healthiest Virtual Agent strategies often have slightly higher escalation rates, because their AI is honest about its limits.

Contributed by: Steve Nattress, VP Product, Enghouse

What Have You Tried to Reduce Your Escalation Rate?

Click here to join our Readers Panel to share your experiences and feature in future Call Centre Helper articles.

For more great insights and advice from our panel of experts, read these articles next:

• How to Centralize Your Data – Before Scaling AI
• 12 Amazing Things You Can Now Do With Customer Preferences
• How to Use AI to Connect the Dots – Not Create More Silos

Author: Megan Jones
Reviewed by: Jo Robinson