Contact Center Virtual Agents: Trends, Best Practices, & Providers

Over the past 12 months, contact center virtual agents have proved to be the talk of the CX town.

In May, the head of Tata Consultancy Services, K Krithivasan, predicted that AI and virtual agents will "make call centers obsolete".

Yet, Gartner research suggests that the future Tata touts – if it ever comes – is a long way away.

In August, it found that – across the contact center space - only 14 percent of customer service issues are fully resolved by a company’s self-service channel.

With the disparity between this reality and the ambitions for AI, this month's CX Today roundtable aims to get under the skin of what's happening in the contact center virtual agent market.

Four industry specialists have taken part, sharing the latest market trends, best practices for deploying a virtual agent, and more. Those experts are:

• Ram Menon, Founder & CEO of Avaamo
• Sebastian Glock, Director of Product Marketing at Cognigy
• David Schreffler, GM International at Kore.ai
• Michael Maas, SVP Europe at Sprinklr

Here’s what they had to contribute.

Contact Center Virtual Agents: Trends

Virtual Agents Automate the Workflows Behind the Conversations, too

Schreffler: Conversational AI for hyper-automation is a trend that goes beyond simple chatbot interactions by integrating virtual agents with advanced automation technologies to manage entire workflows from start to finish.

Hyper-automation leverages AI, machine learning, and robotic process automation (RPA) to automate complex, repetitive processes across multiple systems without human intervention.

In this approach, virtual agents not only handle customer queries but also trigger and manage backend processes across different platforms.

For example, when a customer requests a service change, the virtual agent can autonomously authenticate the customer, update databases, initiate workflows like payment processing or scheduling, and provide real-time updates – all within one interaction.

This integration results in faster, more accurate resolutions, reduces the need for human intervention, and boosts operational efficiency.

Sentiment-Aware Virtual Agents Pick Up Steam

Maas: AI-powered sentiment analysis to create personalized customer experiences – particularly in retail – is hot right now.

For example, a customer messages a company's support chatbot and is upset about a delayed refund for shoes that the customer returned. The chatbot would recognize the negative sentiment, gather relevant information on the message, and initiate an expedited refund process for the shoes.

During the interaction, the chatbot will explain the steps being taken to resolve the issue promptly and reassure the customer that the company is committed to excellent service.

Virtual Agents Support Employees, In Addition to Customers

Glock: Real-time contact center agent assistance is rapidly increasing adoption.

Rather than just automating tasks, AI actively supports human agents by suggesting next-best actions, providing real-time translation, and instantly retrieving knowledge. That enables faster, more accurate responses while elevating the quality of customer conversations.

It’s a clear shift toward making human agents more effective without adding additional staff.

Voice Automation Catches Up with Digital Automation

Menon: Digital interactions were the first port of call for virtual agent deployment. Yet, as voice technology has improved over the last 18 months, more enterprises are using voice-driven virtual agents to answer calls upfront, guide the user through, and complete an end-to-end workflow.

Contact Center Virtual Agents: Best Practices

Don’t Implement Virtual Agents Like Traditional Software

Menon: The biggest mistake is assuming that a virtual agent deployment is like implementing traditional software.

Some think it should be perfect on the first turn, but it doesn’t happen that way.

Monitoring unhandled queries and adjusting content, variations, and edge cases should be a best practice, and expectation management around this is paramount.

Ongoing efforts to improve accuracy are also a best practice that customers almost always trip up on.

Avoiding Using Generative AI for Everything

Glock: Generative AI (GenAI) is powerful, but using it for everything can backfire - wasting resources and creating an orchestration problem.

Instead, treat it like a precision tool, targeting high-impact areas that deliver clear ROI.

Additionally, focus on game-changers like knowledge retrieval or automatic summarization.

GenAI has enormous potential to boost efficiency and elevate CX when applied correctly. But, if misapplied or non-integrated, it can become a costly distraction rather than a driver of real value.

Make Sure That AI Use Is Responsible and Transparent

Schreffler: As AI adoption accelerates, it's crucial to build virtual agents that prioritize fairness, security, and transparency.

Implementing guardrails that monitor the behavior and responses of AI systems to prevent biases, misinformation, or harmful outputs is critical here.

Part of that means clearly informing users when they are interacting with a virtual agent, maintaining data privacy, and ensuring compliance with regulatory standards.

Enterprises need to increasingly use a balanced mix of virtual and live agents, with controls in place to prevent issues like hallucination, toxicity, and bias.

Incorporating the "human-in-the-loop" approach can further enhance AI performance by combining AI automation with human oversight and reducing errors like hallucinations or biased outputs.