Contact Center Pipeline September 2025(clone) | Page 27

In this application, response generation has been largely static, relying on pre-written responses that guarantee consistency but often lack the nuance and adaptability required for truly personalized interactions.

In contrast, GenAI has fundamentally transformed this paradigm. Leveraging advanced natural language generation( NLG), GenAI dynamically crafts responses in real time by considering the context of the entire conversation. This shift enables a previously unattainable level of personalization, addressing a vast variety of customer interactions with greater fluidity and intelligence.

However, this power also introduces a challenge: ensuring that the conversation remains on scope. To address this, companies must guide and guardrail GenAI’ s output by employing natural language briefings based on prompt engineering.

A natural language briefing is a report prepared by humans that trains the AI agent to interact with customers. It covers a wide range of knowledge, such as company policies and processes, as well as the preferred tone the agent should use when engaging with customers. It’ s important that the briefing contains clear, concise, non-technical language.

These methods ensure that every generated AI response is both verified and compliant with the company’ s predefined rules. Thus, they deliver dynamic yet controlled customer interactions.

These two types of AI( GenAI and conversational AI) can therefore work together to create a more human experience for customers and with little intervention by humans.

THE NEW AI ROLE

Because of these advancements, contact center managers are now looking at the role of AI agents differently. Rather than limiting AI agents to being assistive tools, it’ s now possible to train them to handle customer interactions from start to finish.

FIGURE 1:

The new goal is to leverage AI agents to scale, especially in high-volume call situations. And in cases where a human needs to be involved, AI can make the handoff more seamless for a better customer experience( CX).

The timing for all of these AI advancements couldn’ t be better, as the ability to quickly scale up a contact center is becoming more paramount.

The call center industry standard for service level is to answer 80 % of calls in 20 seconds, but that is easier said than done when an incident causes an unexpected influx of customer calls. Every customer knows that pre-recorded disclosure:“ We’ re currently experiencing higher than expected call volume.” They know that means they’ re in for a long wait, even if they stay on the line.

Handling twice— or even 10 times— the usual volume of customer conversations was once prohibitively expensive and complex. But in the AI era, rapid scalability is not only achievable, it’ s transforming the way contact centers operate.

ARTIFICIAL INTELLIGENCE

SOURCE: PARLOA

GETTING RID OF THE SCRIPT

So, how do these two AI application types work? Traditional conversational AI relies on clearly defined, rule-based decision-making. This approach guarantees consistent responses and meets strict compliance requirements, making it ideal for routine, regulated interactions that support human agents.

In contrast, GenAI fundamentally transforms agent design. It does so by using natural language briefings to generate dynamic, context-driven responses in real time. Also known as agentic AI, this type of artificial intelligence has the ability to act independently, learn faster, and solve problems with less human intervention.

By eliminating the constraints of rigid scripts, GenAI-powered agents can tackle a broader variety of customer inquiries with greater personalization. This allows contact centers to automate more complex interactions, reserving highly-skilled human agents for situations that demand empathy and nuanced problem-solving, thus ensuring both efficiency and regulatory integrity in customer service.

This means in case of a large-scale incident, deploying a fast, scalable reaction is possible by quickly adopting AI agents. These simulate the correct behavior and put it into action within hours.

This was not possible before. Instead of a scenario where people in urgent need of help are left waiting for hours for their calls to be picked up, imagine that an army of trained AI agents can quickly come online and begin picking up calls within minutes.

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