The evolution of chatbot capabilities: from scripted to GenAI flows

The chatbot landscape is evolving from purely scripted flows to dynamically generated AI-driven conversations. This shift not only enhances the customer experience but also minimizes the maintenance effort required to update these flows while maximizing their robustness and flexibility.

What are scripted flows?

In scripted flows, the path of the conversation is manually defined. You can think of it as a decision tree where the chatbot follows a predefined script based on the user’s responses.

Below we illustrate a simple example of an appointment flow:

This approach comes with some advantages. Since each response is predefined, we can guarantee consistency across multiple conversations and ensure 100% accuracy in responses within the set framework.

However, there are also notable limitations. Each scripted flow must be manually created, meaning every modification requires human intervention. Additionally, scripted flows are inherently rigid, capable of handling only those scenarios explicitly programmed during their development. This limitation often necessitates human handover when deviations occur. Another significant drawback is their inability to effectively extract and retain relevant information from previous interactions. While scripted flows can store predefined attributes, this method fails to capture sufficient context, forcing users through every step of the flow. Consequently, this leads to repetitive questioning and a frustrating user experience.

How could this be solved?

The solution lies in transitioning from scripted flows to AI-generated conversational flows. Here’s how:

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1. Flexibility and Context adjusting
   Unlike scripted flows, AI-powered chatbots can dynamically adjust based on the context of a conversation. They do not rely on fixed decision trees but instead use LLM’s to interpret user inputs and determine the most relevant responses in real time.
   
   
2. Context Retention and Personalization
   Conversational LLMs can retain and recall context from previous interactions, reducing redundant questions and allowing for more seamless conversations. This enhances the user experience by making interactions feel more natural. Additionally you can give specific instructions to a LLM to tailor it to the user’s needs.
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3. Automated Flow Generation
   ‍To structure a generative AI chatbot, a flow blueprint can be used as a conversation directive. GenAI enables the automatic generation of these conversation flows, eliminating manual construction. This involves describing the desired flow, which an LLM then generates for execution by another LLM.
   

4. Action Execution and Real-Time Information Retrieval
   AI-driven chatbots can go beyond answering queries—they can execute tasks, make decisions, and pull real-time information from databases or APIs. This allows them to assist customers more effectively without requiring human intervention. To achieve this, we suggest leveraging an agentic framework. More information can be found here.

Conclusion

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The optimal chatbot approach depends on the specific conversation. Scripted flows remain valuable when strict control and structure are paramount. However, for a truly superior customer experience, AI-generated flows are the future. Generative AI empowes chatbots to engage in more human-like interactions, capturing and utilizing context for seamless, natural conversations. This capability reduces friction for the user, avoiding the frustration of repetitive questioning and ensuring that interactions are both efficient and effective. By embracing this transition, businesses can achieve several key customer experience advantages: reduced wait times, faster issue resolution through intelligent automation, personalized interactions tailored to individual user needs and past history, and an overall enhanced satisfaction level that translates into greater customer loyalty and advocacy.

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