Copilots & Virtual Assistants: Emerging Use Cases, Trends, & Strategies 

Copilots and virtual assistants are continuing to drive efficiency across customer-facing teams.

In doing so, they are drafting customer responses in service, automating lead-gen initiatives in sales, supporting copy generation in marketing, and so much more.

However, with generative AI still early into its hype cycle, what comes next for copilots and virtual assistants?

In this roundtable, our panelists answer that question. They include:

• Felix Winstone, Co-Founder & CEO at Talkative
• Sebastian Glock, Director of Product Marketing at Cognigy
• Peter van der Putten, Director of the AI Lab at Pegasystems
• Sam Richardson, Executive Engagement Director, EMEA & APJ at Twilio

Below, each participant shares emerging use cases for copilots and virtual assistants, trends for 2025, and best practices for deploying them.

Emerging Use Cases for Copilots & Virtual Assistants

Supervisor Copilots

Winstone: Today, we think of CX copilots as primarily agent-facing. An emerging use case in 2025 will be supervisor copilots.

These copilots can expedite and eventually automate the functions of the classic contact center supervisor role.

Rather than having supervisors sift through countless transcripts and calls, the AI will detect anomalies in real-time, surfacing issues only when human oversight is truly needed.

They will also deliver far richer reporting by synthesizing large volumes of data - for example, summarizing thousands of transcripts to reveal trends instantly.

Beyond real-time supervision and reporting, supervisor copilots enhance knowledge bases, facilitate AI training and feedback loops, and support compliance monitoring.

In doing so, they free supervisors to focus on strategic improvements that ultimately drive better customer experiences.

Personalized Outbound Calling

Glock: A breakthrough use case for AI Agents in 2025 is proactive, personalized outbound calling that feels helpful rather than disruptive.

Previous bot generations, relying on NLU-driven, deterministic approaches, struggled to anticipate customer context or sentiment, often leading to generic, poorly-timed calls that frustrate customers.

Agentic AI transforms this by enabling AI Agents to adapt flexibly to each customer's situation in real time.

For example, when contacting a customer about a contract renewal, the AI Agent can consider recent interactions, service usage, and sentiment to personalize the offer—whether it's a retention package, upgrade suggestion, or proactive check-in.

With low-latency responses and natural, humanlike voices, these interactions feel smooth and personal, increasing customer trust and acceptance of AI-driven service.

The ability to reason and adapt dynamically makes outbound engagement more effective, turning it into a powerful tool for customer satisfaction and revenue growth.

Research Agents

van der Putten: Generative AI is quickly transforming from a passive service being called once with a simple dynamic prompt to agentic systems that can take actions themselves.

These agents use the power of generative AI to make sense of a user request and translate it into context and goals, understand what tools it has available, and then generate and execute dynamic plans to reach these objectives.

As it is a safe testing ground, the first agents that will be successfully adopted at scale in customer service and experience will be ‘research agents’ who will research a particular issue or question iteratively and, when they conclude, synthesize it into a compact answer.

For instance, Pegasystems has introduced an AI-based ‘intern’ called Iris, who researches various data sources and systems to respond to up to a thousand inbound email requests daily.

Effective IVR

Richardson: In 2025, we’re likely to see further automation of many of the day-to-day functions of contact centers and frequent sales and support activities such as pre-sales conversations, lead qualification, and self-service product assistance.

While self-service channels for customer service are now commonplace, Gartner has found that - despite as many as 70 percent of customers using virtual assistants and other self-service channels to resolve their queries - only around nine percent are successful in fully resolving them purely by these means. As the technology develops, we’re likely to see it become more flexible and adaptable.

According to McKinsey's research, this will help boost efficiency and save costs. They found that improving the effectiveness of the IVR systems commonly used in contact centers by 5-20 percent can help reduce total call center costs by 10-30 percent within three to six months because customers get the help they need more quickly.

Ultimately, there’s less circling back and a smaller workload for contact center staff to pick up.

Key Copilot & Virtual Assistants Trends

Conversational Knowledge Curation

Winstone: We see a new role as key to the success of copilots: the "Conversational Knowledge Curator."

Typically, an experienced agent with deep organizational expertise, this individual will manage the data and workflows that copilots consume.

They ensure virtual assistants deliver context-rich, accurate responses by building and maintaining a curated knowledge base encompassing explicit and tacit information.

As contact centers grow increasingly complex, the conversational knowledge curator will coordinate with teams to update and optimize insights, bridging any knowledge gaps that arise.

As organizations scale their use of copilots, this role becomes a linchpin, ensuring virtual assistants remain relevant, compliant, and consistently aligned with business objectives.

We’ll reach a point where human supervisors do one percent of the work, and the AI does 99 percent.

You don't want it to be 100 percent AI. But you want it to be a human-machine collaboration that continually refines the CX function.

Managing Entire Interactions With AI Agents

Glock: Businesses are moving away from managing multiple disconnected service channels as customer expectations shift toward more straightforward, more unified experiences.

AI Agents can now engage customers consistently across voice, chat, and text, ensuring seamless conversations without forcing customers to repeat themselves or switch channels to get help.

These agents can handle inquiries and service tasks fluidly and in a context-aware manner.