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.
Moreover, best practices should always include continuously monitoring and fine-tuning AI models to meet evolving business goals and customer expectations.
By promoting trust and transparency within the virtual agent’s functionality, contact centers not only ensure regulatory compliance but also drive higher adoption rates and better overall outcomes.
Give the Virtual Agent a Personality
Maas: A best practice is to train the virtual agent with a voice that echoes the brand’s personality.
It should also be able to analyze historical customer service conversations with AI to discover what priorities the brand should address.
In doing so, the contact center’s virtual agent will create data-driven value and eliminate the need to guess what customers are most likely reaching out about.
Contact Center Virtual Agents: Providers
Avaamo
Menon: Deploying virtual agents is a mix of technology, integration to existing enterprise data, and providing a seamless flow at the user’s existing access node.
As conversational AI goes mainstream, the focus of contact center buyers has shifted to value capture instead of debating features.
Avaamo has been doing this successfully for a long time for the world’s largest companies at scale.
Cognigy
Glock: With proven experience and trusted by global leaders like Lufthansa, TechStyle, Aramark, and Mercedes-Benz, Cognigy’s AI Agents are built to deliver real impact.
They don’t just automate; they integrate seamlessly into any contact center, offering multilingual support, real-time agent assistance, and pre-trained industry knowledge.
Cognigy focuses on quick, measurable results, helping its customers scale efficiently without overhauling existing systems.
When you choose Cognigy, you get an AI workforce that drives operational excellence and exceptional customer experiences (and a partner you’ll love to work with!).
Kore.ai
Schreffler: AI is already a core component of all contact center systems and applications. However, with the rapid pace of innovation, contact centers often use 45-50 different systems and applications, creating challenges in achieving seamless integration.
Enterprises looking for best-of-breed solutions must be flexible to augment existing ecosystems rather than just rip and replace.
To address these challenges, investing in future-proof, agnostic solutions is crucial. Kore.ai offers Contact Center AI solutions with cutting-edge capabilities while providing the flexibility to choose from various options (for deployment, integrations, etc.).
Kore.ai also offers robust security measures, which is imperative in an era where serious data breaches pose a real threat.
Additionally, the platform’s architecture makes it easily scalable, allowing businesses to efficiently manage demanding workloads and customer interactions as they grow.
Lastly, it even offers a range of integration capabilities, streamlining the process for reporting, surveys, and other user-friendly contact center functions.
Sprinklr
Maas: Sprinklr helps contact centers build a virtual agent once and deploy it across 25+ channels (including voice) in 100+ languages using an intuitive drag-and-drop UI for discovery, building, testing, deployment, and KPI measurement.
It also helps reduce contact center costs by making it easier to deploy unified AI models tailored to specific industries — and scale them across use cases, channels, and functions to enhance contact center productivity.
Sprinklr also claims its performance tracking capabilities are unmatched. Contact centers can identify future bot topics and track key KPIs to continuously improve bots.
Lastly, Sprinklr helps contact centers connect virtual agents with existing CRM, CDP, and knowledge base systems to provide agents with critical customer information in real-time.
Brands can use Sprinklr AI to identify leading contact drivers and create bot responses that are accurate, human, and helpful.
Miss out on our previous CX Today roundtable? Catch up here: Contact Center WFM: A Best Practice Guide
