AI is changing customer service as we know it. In this video we explore how.
Illusion of conversation with no substance
There has been a serious investment in customer service AI chatbots for the past several years. A lot of them did not work out, not because the idea was wrong, but because the implementation was. This included hard-coded flows, button trees, and an illusion of conversation that had no substance. What happened was that the user started to distrust them because of their static nature.
Now the actual difference is that these chatbots get real-time context data, they reason with the data sources, and actually give a much more dynamic response. However, the brand risk is still real. If the chatbot does not give a good user experience or does not respond with helpful answers, when they think about the chatbot, they also think about the company, which can cause damage to their reputation.
Agentic customer service
We're in the agentic phase right now. The bot doesn't just respond, it acts. So when a user asks about their refund status, the bot pulls out the user's details, their account details, and purchase history, checks whether the item they purchased is refundable, and starts the process. No humans involved, no ticket created, just a seamless user experience.
There's a useful parallel here. Before, when computers came, we controlled everything through the terminals. We typed commands, then we started clicking buttons. Now we just simply ask. And there's this light shift here where the chat is the new interface. And now we have moved from systems that we control and operate to systems that act on our behalf.
How to get started
The AI architecture behind this is generally complex. This includes AI models, system integrations, data source integrations, and workflow automation. However, human oversight is still a big part of the system. Edge cases get flagged, sensitive cases get escalated, and the whole interaction makes the system better.
The first question to ask is what the system should do. Not in abstract, but specifically, which interaction should it have, what kind of behavior should it follow, and what kind of human oversight layer should it have? Once that has been decided, it's time to think about the foundation. This includes the AI architecture, data architecture, and workflow automation. With the right foundations, it will make sure that the system will be scalable and actually useful in the long term.
We have done this across industries. Starting from highly sensitive healthcare clients to global companies with massive scale. Even though domains might differ, the underlying idea is the same. Build something that works when it matters and actually serves the customers.
