WHAT’S THE DIFFERENCE between Robotic Process Automation and Conversational AI?

Robotic Process Automation applies to processes that do not include any type of interaction via chat and is aimed at eliminating inefficiencies, increasing the speed and performance of business processes.

RPA involves automation bots, while conversational AI uses chatbots.

RPA bots therefore do not manage conversations unlike chatbots which are based on NLP (Natural Language Processing) technology to emulate human conversations and interaction in natural language.

The growing level of attention towards Large Language Models requires further clarification regarding the difference between Conversational AI and LLM.

Conversational AI is an application of LLMs that has attracted a lot of interest and attention due to its scalability across many industries and use cases. Although conversational systems have been around for decades, LLMs have provided the qualitative push needed for their widespread adoption.

conversational AI features

Conversational AI is applicable to any user interaction activated via voice (phone or voice-activated interface) or textual message (text, chat, email, web, etc).

It has a conversation-focused approach, is mainly based on the automation of user interactions through digital channels, simulates human conversation and understands its intent to produce automated tasks.

A chatbot is able to respond to customer requests by interpreting their intent, retrieve the necessary data and present the information in response to the customer.

Whether we are talking about RPA or Conversational AI, the technology can range from basic rule-based automation to complex solutions based on machine learning and generative AI.


Rule-based bots are based on a so-called “tree” decision-making process, which uses a series of predefined rules and conditions.

These rules constitute the knowledge base through which the bot recognizes the cases and provides an answer.

Just like a flowchart, rule-based bots map out conversations, anticipating what the customer might ask and how the bot should respond.

However, going outside this default flow means sending the bot into trouble, resulting in disappointment on the part of the user who is using it.

Rule bots can use very simple or very complicated rules, but they cannot escape the context thus outlined. They do not learn through interactions and only act within the conversational scenario for which they have been trained.

Chatbots instead use machine learning to understand the context and intent of questions.

These chatbots are able to provide answers to complicated questions using natural language and increase their level of expertise over time, learning from interactions with users.

While rule-based bots are easier to train and are more predictable in their response behavior, they lack flexibility and scalability.

Machine learning-based chatbots may require a longer training time than rule-based bots, but they have more advanced performance levels and allow you to optimize processes and resources.

This because:

They keep learning
They include user behavior paths
They have a greater range of decision making skills
They can understand several languages

Added to these are generative AI chatbots that use Large Language Models to generate new Q&As and coherent answers to user questions.

In conclusion, RPA and Conversational AI are two complementary technologies, where Conversational AI allows organizations to automate interactions with customers and employees and RPA can significantly reduce the need for human intervention in end-to-end business processes.

In the field of Conversational AI, the progress of Large Language Models allows for increasingly fluid and natural interaction.