Conversational AI (artificial intelligence) is technology that simulates the experience of person-to-person communication for users, either through text-based or speech-based inputs.
The AI component is crucial. Unlike traditional conversational technologies, which deliver pre-written scripts and dialogues to users when prompted by specific keywords, conversational AI recognizes and responds to the content of a user’s query by leveraging two complementary artificial intelligence technologies: natural language processing (NLP) and machine learning.
Like most AI systems, NLP and machine learning operate by analyzing massive datasets in order to continuously yield more sophisticated outputs. In the case of conversational AI, these outputs are the responses it provides to users.
NLP takes place across four distinct stages:
- Input generation is when the user initiates the conversation, interacting with the software through a written or spoken query.
- Input analysis starts the process of interpretation, with the system deploying natural language understanding (NLU), or a combination of NLU and automatic speech recognition (ASR), to decipher the user’s query.
- Dialogue management applies a different component of NLP technology, natural language generation (NLG), to compile a rapid, lucid response to the query.
- Reinforcement learning directly follows every exchange, with the system automatically assessing the success of the interaction to further refine its accuracy in the future.
Machine learning is the ability for AI algorithms to continuously and automatically improve.
“Many companies are already employing conversational AI”
As AI technologies are exposed to more inputs and interactions, their capacity for recognizing patterns and making predictions increases. Because this functionality is built into NLP, technology experts broadly consider it to be a subset of machine learning.
What is an example of conversational AI?
Today, many companies are already employing conversational AI, and users are engaging with it regularly in both their personal and professional lives. Some common examples include:
- Chatbots, which perform tasks such as greeting website visitors, starting new conversations, asking follow-up questions, offering product tips, capturing sales lead data, and routing customers to the correct support channels. These bots can communicate in outbound efforts, or they can reply to inbound conversations.
- Virtual personal assistants, which use NLP and ASR to perform a number of tasks, ranging from providing information when prompted by human questions to performing basic tasks such as setting reminders or playing music.
Why use conversational AI?
There are many advantages to conversational AI. For example, website chatbots can provide round-the-clock basic customer support. This helps companies give their customers speedy answers to common inquiries, while freeing up customer support teams to handle more complex issues.
In some cases, conversational AI solutions can be trained on FAQs or knowledge base articles to resolve customer issues, and can even be programmed to give instant responses using the brand’s preferred voice and tone.
Conversational AI can also capture and qualify sales leads in real time while a prospective customer is browsing a web page, which helps marketing and sales teams convert more website visitors into actual customers.
Whether assisting with inbound inquiries or outbound marketing efforts, conversational AI is an efficient and cost-effective way to optimize and scale customer success initiatives.