Difference between chatbots and bots – The competitive advantage of conversational AI is clear, but so are not the differences between chatbots and bots.
In order to use a chatbot, the processes to be automated should involve a conversation; otherwise it is better to opt for traditional RPA.
which is the difference between Robotic Process Automation and Conversational AI?
Robotic Process Automation is applied to processes that do not involve any chat interaction and it is finalized to eliminate unefficiency and increase business processes’ speed and performance.
RPA involves bots, while conversational AI involves chatbots.
RPA bots do not handle conversations, while chatbots use NLP (Natural Language Processing) technology to emulate human conversation and natural language interaction.
conversational AI features
Conversational AI is applied to every user interaction that is voice or text activated. It is focused on conversation and it is based on user interactions through digital channels. Chatbots simulate human conversation and uderstand the conversation’s intent before producing a response.
A chatbot can reply to customers’ requests interpreting the intent of each question, retrieving data to respond and giving back a reply to the customer.
Whether we are talking about RPA or Conversational AI the technology may vary from basic automation, based on rules and conditions to complex solutions, based on machine learning.
which the difference between rule-based bots and chatbots?
Difference between chatbots and bots – Rule-based bots are also referred to as “decision-tree” bots because they use predefined rules and conditions in their decision making.
The rules define the knowledge base that these bot use to recognize different cases and provide an answer.
Like a flowchart, rule-based bots map conversations, anticipating what the customer may ask for and how to respond.
Anyway, if you ask something that is not included in the pre-defined rule set this means the bot to fail.
Rule-based bots cannot understand out-of-context intents, they do not learn from interactions and act only in their pre-defined territory.
On the other hand, chatbots use machine learning to understand context and intent of queries.
Machine-learning based chatbots can provide answers to complicated questions using natural language and increase their capabilities over time, by learning through interactions.
While rule-based bots are easier to train and their behaviour is predictable, they are not scalable nor flexible.
Chatbots may require longer training times, but have high performance levels and can really optimize processes and resources.
This is because:
- chatbots keep learning
- chatbots understand users behaviour paths
- chatbots have a wider decision making capabilities
- chatbots can understand many languages
crafter.ai chatbots, for example, can independently handle over 98% of conversations, bringing the average error margin under 2%, thus contributing to an augmented user experience.
In conclusion, RPA and Conversational AI are complementary technologies: Conversational AI allows organizations to automate conversations with users and employees, where machine-learning bases chatbots represent the most performing solution; while RPA can significantly reduce the need of human interventions in end-to-end business processes.
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