For years, many companies believed that simply offering a "chat" was enough to serve customers. In practice, what existed was a FAQ with a conversational interface, repetitive and limited. The user typed a question and always received the same answer, regardless of the context. No learning curve, no adaptation, no fluidity.
This is the logic behind traditional bots, built on predefined flows. They operate within rigid menus and inflexible text blocks. They are easy to deploy and quick to get up and running, but even quicker to generate frustration. After all, a simple deviation from the planned route is enough for the user to encounter generic responses or, worse, the dreaded error message: "Sorry, I didn't understand."
With the arrival of Large-Scale Language Models (LLMs), this paradigm has changed. Instead of following fixed paths, AI has begun processing natural language in real time. This means that it understands variations in intent, adapts its response to the context, and maintains coherence even when the user decides to change the subject or go back to previous stages of the conversation.
There's no need to restart the flow. There's no data loss. There's no freezing on the first exception. With each interaction, the model reorganizes the information and keeps the dialogue alive, fluid, and intelligent.
This capability translates into three key points: same input data, multiple possible outputs; same business objective, multiple language strategies; and same attention span, resulting in less friction and more conversion.
The difference in practice
In critical areas such as customer service, collections, and sales, this change is crucial. The difference between closing a deal or missing the timing lies in the AI's ability to sustain its reasoning without breaking the flow.
Imagine a customer inquiring about an installment payment. In a traditional chatbot, any change in value forces the user to restart the process. An LLM (Loadable Lifetime Management) system, however, understands the change, adjusts the offer, and continues the negotiation. Every minute saved increases the chance of closing the deal.
Furthermore, while fixed flows sound mechanical and repetitive, advanced models deliver unique responses in each conversation. The user doesn't feel like they're listening to a script, but engaging in a real dialogue. Even though the numbers and information remain consistent, the way of communicating varies. This humanization of discourse is what differentiates AI from simple automation.
The truth is that many businesses still operate with "menus" disguised as AI. However, consumers quickly realize when they are talking to something that simply repeats pre-programmed responses. In contrast, interactions based on LLMs deliver dynamism, flexibility, and measurable conversion results.
What the market needs to understand is simple: customer service can no longer be repetitive; it needs to be intelligent.
This means abandoning the "quick shortcut" logic that only serves to give the appearance of innovation but does not generate real value. Today's consumer can already tell when they are faced with a rigid interaction and no longer accepts wasting time navigating endless menus. They expect fluidity, clarity, and, above all, answers that make sense in their specific context.
Companies that still insist on operating with static chatbots, based on fixed flows, are not only technologically behind: they are missing business opportunities. Every frustrated customer is an interrupted negotiation, a lost payment, a delayed sale. On the other hand, those that adopt LLMs transform each interaction into a chance to build rapport, reduce friction, and increase conversion in real time.
Ultimately, it's not just about adopting more modern technology. It's about deciding whether the company wants to offer an experience that respects the customer's time and intelligence. And on this point, there is no middle ground: either customer service evolves towards intelligent conversations, or it will remain stuck in a past of repetitive answers and limited results.
The question remains: has your customer service moved beyond the workflow, or is it still stuck in menus?
Danielle Francis is COO of Fintalk, a leading conversational AI company in Brazil. Email: finatalk@nbpress.com.br
