How AI is transforming customer service interactions [Q&A]

If you’ve contacted a company recently it’s more than likely that you have encountered some form of AI either online or over the phone.

We spoke to Priya Vijayarajendran, CEO of ASAAP to find out how AI is transforming real-time customer service interactions in the contact center, and what it means for the evolving relationship between humans and machines.

BN: How is the human-AI relationship evolving in live customer interactions?

PV: How AI enhances live customer service is quickly evolving. Traditionally, contact centers focused automation efforts on deflecting tickets by addressing low-level, routine questions such as answering FAQs, tracking orders, and authentication. Helpful as these solutions were, their constraints ensured that they did most of their work quietly to guide the question or process pre-defined routes. That too is changing quickly.

AI is now assuming a much more active role in customer service. Modern systems can recognize intent, make the right suggestions, and act in real-time. Generative AI and advanced natural language models are becoming the frontline of customer support, with human agents stepping in to support these systems by guiding, refining, and escalating when needed to ensure every customer receives fast, accurate, and responsive service.

This evolution is a turning point. AI is not just a back-office productivity add-on; it is turning into an inherent aspect of the customer experience. AI can automate low-volume, high-value tasks so agents can take on challenging, emotive, or high-value problems requiring empathy, imagination, and human reasoning. These are the moments that stick with people and make them feel not only supported but really heard.

BN: What are some best practices for designing escalation flows that feel natural to customers?

PV: Escalation is a critical point in the customer experience, and also where service experiences can begin to break down. If the handoff between AI and a human agent is clumsy or incomplete, it introduces unnecessary friction. Customers will quickly become frustrated if they have to repeat themselves or re-explain an issue already explained to an AI agent. This repetition has the potential to eliminate efficiency and goodwill built up front in the interaction.

To prevent this, escalation flows must be designed around the customer experience. AI systems should collect and communicate the full context of the interaction -- including what was said, what was accomplished, and any emotional signals or preferences -- so that human agents can step in to support the AI when needed. The goal is a seamless continuation of service, not a frustrating restart.

Equally crucial is knowing when to escalate. While large language models are improving rapidly and can increasingly detect customer sentiment, there are still moments where human involvement is essential -- especially in situations involving heightened emotion, ambiguity, or regulatory complexity, where deeper context and judgment are required. When escalation happens at the right moment and with the right context, it turns a potential frustration point into a trust-building and substantive support opportunity.

BN: What do enterprises need to do to ensure compliance and build trust with users and regulators?

PV: As more decisions are being made by AI, companies must be intentional about how these systems are designed, regulated, and understood. Trust can't be an afterthought. It must be baked in from the start as a core design principle.

First, organizations need to have insight into how their AI systems operate. What that means is having visibility into what was used as data, how it was decided, and whether or not those decisions are regulatory compliant and meet internal policy. With more developments globally into data privacy law, that visibility has increasingly become table-stakes.

No less important is the ability to explain AI-generated outcomes, not just to regulators but also to customers. When people find a decision incomprehensible or unfair, trust is quickly lost. Transparent and clear explanations are essential to maintaining credibility and loyalty.

The businesses that put ethics, transparency, safety, and strong governance at the forefront of their AI strategies will not only stay in compliance but will also build the long-term trust that sets them apart.

BN: Why does AI explainability matter in customer service and how do you strike the right balance?

PV: Customer service is inherently a trust-building function. People reach out when something has gone wrong -- or when they need special treatment, a fix, or an exception. If AI is involved in those interactions, explainability becomes non-negotiable.

But explainability doesn’t mean exposing the math behind the model. Most customers don’t care about technical details. What they want is to understand the why behind a decision. Why was their refund denied? Why were they escalated to a particular team? Why did the system recommend a certain solution?

While large language models are improving rapidly and can increasingly detect customer sentiment, there are still moments where human involvement is essential -- especially in situations involving heightened emotion, ambiguity, or regulatory complexity, where deeper context and judgment are required. For agents, this means having tools that help them guide and optimize AI-driven interactions -- interpreting AI outputs, filling in context, and ensuring the customer experience stays clear, fair, and human. For customers, it means receiving answers that feel intentional and well-supported, not mechanical or confusing.

The right level of explainability balances clarity with usability. If it feels human and reasonable, you’re doing it right.

BN: What skills do agents need in order to thrive as interactions shift from voice to digital?

PV: As more and more customer interactions move into messaging, chat, and asynchronous channels, the role of the human agent is evolving extremely fast. Routine inquiries are being addressed by AI increasingly, so the conversations that do reach a human are more complex, emotional, or sensitive. This shift needs the agent's skill set to expand.

Written communication is a must-have skill. Agents need to convey information clearly and sympathetically through channels -- sometimes managing multiple conversations at once. Multitasking, managing time, and emotional intelligence are all a necessity. Just as crucial is the ability to work with AI: utilizing AI-driven insights, prompts, and tools to streamline workflows and customize service in real time.

The agent of today is more than a friendly support rep. They are part interpreter, part brand representative, and part human in the loop -- ensuring that AI systems operate effectively and empathetically in real-world customer scenarios. The agents who can balance tech savvy with emotional intelligence are the ones defining what great service is in an age of AI.

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