From Virtual Agent to High-Performing Agent: The AI-Powered CX Shift Explained

Alexis Noelle Jimenea at Zoom explores how leading CX teams are turning AI-driven insights into faster resolutions and delivering experiences that customers remember.

Every contact centre leader knows the tension: customers want fast answers, but great service still hinges on human judgment.

In Zoom’s recent webinar From Virtual Agent to High-Performing Agent: Unlocking Better CX with AI, the team explored how AI-powered tools are changing the way organisations approach this balance.

AI-powered virtual agent solution can help automate voice and digital customer service conversations — supporting end-to-end resolution, intelligent escalation, and continuous improvement through interaction data.

What Are AI-Powered Virtual Agents?

AI-powered virtual agents are designed to support customer service teams by automating voice and digital conversations across channels, helping resolve customer enquiries, complete multi-step tasks, and transfer conversations to human agents when additional support is needed.

Unlike basic chatbots that rely primarily on keyword matching and scripted responses, more advanced virtual agent solutions use AI to understand customer intent, interact with connected systems, and adapt when conversations change direction.

Solutions, such as Zoom Virtual Agent, can integrate with contact centre platforms to provide a more connected experience, allowing virtual agents to support self-service while human agents handle more complex interactions with relevant context available.

For contact centre managers, the distinction is important: the focus is not simply on reducing contact volumes, but on helping customers reach a resolution.

AI-powered virtual agents can support this by handling suitable interactions independently and enabling smoother transitions to human agents when required.

What Can AI-Powered Virtual Agents Do?

AI-powered virtual agents can support a broad range of capabilities across voice and digital channels. The specific features available will vary depending on the solution, but common capabilities include:

• Order and account lookups – connecting to systems such as order management, CRM, and billing platforms to retrieve and surface customer information during a conversation.
• Multi-step task completion – supporting more complex, context-aware interactions, such as processing returns, checking loyalty points, or updating account details without requiring human involvement.
• Intelligent escalation – recognising when a query requires additional support and enabling a smooth transfer to a live agent, with relevant conversation context and summaries available to support the handover.
• Voice and chat support from a single platform – enabling organisations to manage virtual voice and chat experiences through shared configurations, knowledge bases, and AI capabilities.
• Multimodal input support – allowing customers to share additional information, such as images or documents, to provide more context during interactions. This can be particularly useful for scenarios such as billing queries or order-related issues.
• Multi-language support – supporting customer conversations across multiple languages, with some solutions offering real-time language detection and response switching.
• AI custom skills – connecting virtual agents with backend systems through configurable integrations, allowing automated processes to support both virtual and human agent workflows.
• Knowledge base gap management – identifying questions that cannot be answered and highlighting areas where knowledge resources could be improved.

When evaluating virtual agent solutions, a useful question to ask is: “If a customer asks a question that the knowledge base does not cover, what happens next, and how does the platform help improve that gap over time?”

Why Self-Service Challenges Can Impact Escalation Volume, Handle Time, And Agent Workload

Self-service has become an increasingly important part of the customer experience, but the effectiveness of automated experiences depends on their ability to address customer needs.

When self-service tools cannot resolve customer queries, this can contribute to higher escalation volumes, longer handling times, and increased pressure on frontline teams.

During the conversation, Arvind Rangarajan, PMM Lead for Zoom Virtual Agent, discussed the changing role of self-service and the importance of creating automated experiences that can support customers effectively.

Ram Rajagopalan, Senior Product Manager at Zoom, explored where organisations should begin when developing virtual agent capabilities.

Building an effective virtual agent requires a strong foundation, including the ability to test experiences before launch, learn from gaps over time, and provide human agents with relevant context when a conversation requires additional support.

Here’s a closer look at the key moments that stood out.

How to Test Virtual Agents Before Deployment

Justin Steinberg, Senior Technical Sales Architect at Zoom, discussed the importance of testing virtual agents before they move from pilot stages into wider production use.

A common challenge for contact centre teams is that, no matter how carefully an automated experience is designed, teams can typically only test the scenarios they have already considered.

Testing capabilities can help address this by generating additional test cases based on the tasks and interactions a virtual agent is designed to support. AI-powered testing tools can simulate a wider range of customer scenarios, including less predictable situations that may not have been considered during initial configuration.

For example, a generated test case might involve a customer attempting to return an item that has not yet been shipped. These types of scenarios can help teams assess how a virtual agent responds from the customer’s perspective, rather than only testing the expected journey.

Another important consideration is how integrations and AI capabilities can support both virtual and human agents.

Some platforms allow custom skills or integrations created for virtual agents — such as accessing loyalty systems, checking order status, or retrieving account information — to also support human agents through connected tools.

This approach enables organisations to create shared capabilities across different parts of the contact centre, helping reduce duplicated development work and simplify ongoing maintenance.

AI Virtual Agent Knowledge Base Management: How Tools Can Help Identify and Address Gaps

AI-powered virtual agents can help organisations identify areas where their knowledge resources may need improvement.

When a customer asks a question that an agent cannot resolve, some solutions can capture and surface those queries alongside similar interactions, creating a clearer view of where knowledge gaps may exist.

Addressing these gaps can then become part of an ongoing improvement process. Administrators can review flagged questions, add relevant information, and update the knowledge base so future interactions can benefit from the additional content.

The workflow typically involves:

• The virtual agent identifies questions it could not resolve
• The knowledge base interface highlights these queries as potential improvement areas
• Administrators review the gaps and add the appropriate information
• The updated knowledge can then support future customer interactions

For contact centre managers, this changes the operating model entirely. Instead of proactively auditing the knowledge base on a schedule, the agent surfaces its own training priorities based on real call volume.

The questions callers are actually asking, not the ones the team anticipated, drive the improvement roadmap.

Solutions such as Zoom Virtual Agent provide examples of how this type of capability can be applied within a wider customer service environment.

How Quality Assurance Can Be Applied Across Virtual and Human Agent Interactions

Quality assurance frameworks can increasingly be applied across both virtual agent and human agent interactions, including escalations that span both.

Some contact centre platforms allow QA teams to play back the full conversation, review the transcript in sync, monitor sentiment across the arc of the call, and view automatic scoring against configured QA criteria.

For escalated calls, that visibility is continuous: your QA team reviews the virtual agent portion and the human agent continuation in a single interface, without switching tools.

This matters because many contact center environments don’t work this way. Virtual agent calls and human agent calls typically live in separate reporting and QA systems, which means accountability effectively stops at the point of escalation.

With some platforms, you can review what the human agent did, but not how the virtual agent set up the interaction that preceded it.

Solutions, like Zoom CX, treat the entire call as one continuous experience, regardless of which type of agent handled each segment.

A high-performing virtual agent isn’t one you launch and forget. It’s one you test relentlessly, refine with every gap it surfaces, and pair with human agents who inherit its context the moment they answer.

Quality management is the mechanism that makes that loop accountable — running across all interactions in Zoom CX, not just the ones a human agent handled.

How Can Virtual Agents Be Deployed in a Contact Centre?

Deploying a virtual agent in your contact centre can be more flexible than most teams expect.

Existing customers already using a contact centre platform that offers integrated virtual agent capabilities, such as Zoom Contact Center, may be able to add these features through their existing administration interface, reducing the need for separate management tools.

Organisations using contact centre platforms with integrated virtual agent capabilities may be able to add virtual agents through their existing administration portal. Depending on the deployment and configuration, voice and chat automation can be live in minutes

Enterprise contact centres using SIP-based systems, including platforms built on standards-based telephony, can integrate virtual agent solutions, such as Zoom Virtual Agent, via SIP, regardless of the underlying platform.

Chat deployment requirements will vary depending on the solution. For example, If your team already uses Zoom Virtual Agent for voice, the voice agent configuration can be imported directly to the chat agent — no rebuild from scratch.

Knowledge base quality sets your starting resolution ceiling. Prioritize the top 20% of inquiry types by inbound volume when building the initial knowledge base. These drive the majority of contacts.

Plan for at least two to three testing iterations before go-live. AI-powered testing tools, such as Zoom’s Agent Performance Suite, can generate test cases automatically, but reviewing results and refining the configuration takes time. Build that cycle into your deployment timeline.

Connect backend systems via AI custom skills early. Integrations with order management, CRM, and loyalty systems can help virtual agents go beyond answering basic FAQs.

Zoom Virtual Agent Use Cases: From Contact Center Automation to Intelligent Escalation

Zoom Virtual Agent is built for the inquiry types that drive the highest inbound volume across industries:

Order and shipment inquiries: Customers calling to check order status or delivery timelines get a specific, accurate answer pulled directly from the order management system. No wait time, no human agent required.

Loyalty and account management: Customers checking point balances, tier status, or rewards eligibility get real-time answers without being transferred. The same skill is available to human agents if the call escalates.

Intelligent escalation: When a customer asks something outside the knowledge base, the virtual agent recognizes the gap and transfers the call with the customer’s order details, conversation summary, and escalation reason already on the agent’s screen before they answer.

Billing disputes: Customers can upload a bill or screenshot directly in the conversation. Zoom Virtual Agent processes the document and uses it as context for the next step, reducing the back-and-forth that typically extends these calls.

Multilingual support: A single deployment handles more than 20 languages, with real-time detection when a conversation shifts. No separate configuration required per language.

How to Measure Virtual Agent ROI: The Value of Testing, Knowledge Management, and QA

AI-powered testing tools, such as Zoom’s Agent Performance Suite, can help identify gaps before launch. Knowledge base management helps surface areas for improvement after customer interactions, while quality management can provide visibility across both virtual and human agent interactions.

Some platforms also allow AI custom skills built for virtual agents to support human agents when a call escalates.

The takeaway: A high-performing virtual agent isn’t one you launch and forget. It’s one you test relentlessly, refine with every gap it surfaces, and pair with human agents who inherit its context the moment they answer.

This blog post has been re-published by kind permission of Zoom – View the Original Article

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Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: Zoom
Reviewed by: Jo Robinson