What is the difference between conversational AI and chatbot?
The difference between conversational AI and chatbots is mainly tied to the way in which they handle inputs. Chatbots are a type of conversational AI but not all chatbots are conversational AI.
Rule based chatbots use keywords and other language identifiers to trigger pre-written responses and are not built on conversational AI technology. This means these types of chatbots are limited in response to what has been added into the chatbot’s rules. Non-conversational AI chatbots require continual maintenance with text-only commands and inputs to remain up to date and effective, while conversational AI uses inputs and sources such as websites, databases, and APIs to learn.
There are a few application examples of conversational AI technology:
Chatbots: often used in customer service applications to answer questions and provide support
Virtual assistants: often voice-activated and can be used on mobile devices and smart speakers
Speech recognition software: used to transcribe lectures, create transcripts of phone calls, or generate automatic captions for videos
Conversational AI for the Digital Workplace
Workgrid's AI Work Assistant connects employees with the information they need, when they need it, through a conversational interface. With robust intent understanding, exception handling, and contextualized answers, the assistant can do far more than a rule-based chatbot.
The assistant searches across multiple data sources - including enterprise systems, documents, and knowledgebases - to answer employee questions. In addition to answers, the assistant integrates with business applications to surface real-time notifications, tasks, and alerts, allowing employees to take action right within the conversational interface.
The AI Work Assistant supports information discovery for employees by:
Delivering answers with relevant recommendations and suggestions using advanced query analyzer and semantic similarity analysis
Ranking algorithms to showcase results based on relevance
Summarizing data points from multiple sources into one cohesive answer using Retrieval Automation Generation (RAG)
Prompting employees for more information if an intent is understood but the question cannot be fulfilled
Learn more about the Workgrid platform.