What is Conversational AI?
Definition: Conversational AI is a set of technologies that enable computers to understand, process, and generate natural human-like conversations through text or voice-based interactions.
Conversational AI is used in numerous software, like chatbots, virtual agents, and voice-enabled devices like smart speakers.
These systems are used in customer support and service, sales and marketing, internal communications, and other domains where natural language interactions are valuable with the goal of increasing efficiency and reducing errors and costs.
Conversational AI vs. machine-learning chatbots
Conversational AI is broader and more complex than machine-learning chatbots because it is a set of systems with multiple technologies and algorithms such as Natural Language Processing, Natural Language Understanding, and dialog management.
Due to the use of these technologies, Conversational AI systems can understand human input better and provide a more relevant, human-like response. They have unlimited conversational abilities and can learn & store patterns when interacting with humans.
Conversational AI systems can take the role of customer support or voice-enabled devices because of their ability to maintain the context. A popular example of conversational AI in 2023 is Chat GPT.
Machine-learning chatbots are a subset of conversational AI, with fewer algorithms and features to maintain the context and dialog with humans.
Machine-learning chatbots have a text-based interface, so they react to text-based input and provide an answer from the pre-established database but can’t go beyond simple interactions. These chatbots can also learn from interactions over time but don’t understand more complex questions and user intent at the moment.
They can assist users with answering FAQs, sending links to help articles, and instructing users on solving minor technical issues. An example of a machine-learning chatbot is a pre-programmed bot that answers customer questions on Messenger on behalf of the company.
Elements of conversational AI
- UI (User Interface)
In the context of conversational AI, UI enables users to engage with a machine and facilitates the dialog between the two. Examples of User Interfaces are chatbots, virtual agents and voice assistants, all of which take the information they receive, understand, and respond to it.
- ML (Machine Learning)
Machine learning is a technology that enables machines to learn from data and interactions by themselves. With machine learning, computers are trained to understand, recognize and store this data as they are exposed to new data, patterns, and interactions.
- Speech Recognition
Speech recognition refers to the ability of conversational AI to notice and recognize spoken input. Voice assistants use this technology to understand non-text-based user input.
- NLP & NLU (Natural Language Processing/Understanding)
NLP refers to the machine’s ability to understand and process user input. To understand the meaning of words, sentence structure and the context, NLU algorithms refer to large sets of data.
- NLG (Natural Language Generation)
Based on the user’s intent and the AI’s data, a conversational AI system uses NLG to form a relevant response.
- Integrations and APIs
Conversational AI is integrated with a database to provide personalized information to users, while it can also be integrated with chatbots, CRM and voice assistants.APIs are used to retrieve data and create and delete entries.
How does conversational AI work?
User input & processing
Conversational AI reacts to user input in written or spoken form. The computer can “read” the text input, while for spoken input, it uses ASR (automatic speech recognition) to convert it into a text input.
Natural Language Understanding
Conversational AI uses Natural Language Understanding algorithm to decipher the meaning, intent, and context of the input by referring back to the database.
Dialog management
Dialog management is in charge of the overall structure of the conversation, and it uses intent recognition and dialog policies to maintain the flow of the conversation, keep the context, and predict questions.
Response generation
During the response or output generation phase, the machine crafts words, phrases, and grammatical structures to formulate a relevant response for users. NLG formulates a response in a format humans can understand through sentiment analysis and text summarization.
Output processing
Output processing refers to response selection and personalization. During this stage, conversational AI systems choose the most relevant response to a user query.
Machine learning and optimization
Conversational AI uses Deep Learning and Reinforcement Learning algorithms to learn and improve on their own. Conversational AI learns from experience, stores patterns in the database, and refines future responses.
Uses cases of conversational AI
- Customer service and support
The most widespread use of conversational AI is automating customer service by letting the chatbot answer questions, process customer requests, and provide other technical support.
Example:
Intercom is a messaging platform that automates customer support for businesses that own websites. Their chatbot is integrated into the company’s website and helps with basic tasks such as resolving minor issues and scheduling appointments.
- Human resources
Conversational AI can be used in the human resources sector to automate recruitment, start onboarding, and increase employee engagement. Businesses can use AI chatbots to schedule interviews, answer HR-related FAQs, and gather feedback by surveying employees.
Examples:
Mya systems (now acquired by StepStone) is a conversational AI platform and chatbot that helps companies replace old and long traditional recruitment methods by automating the hiring process. The chatbot schedules interviews, reviews applications, and answers questions.
Unilever is a consumer goods company with a chatbox called U-First. U-First helps candidates prepare for interviews by answering FAQs and providing tips and advice based on the conversation with the candidate. Unilever benefits from the chatbot by attracting and highlighting the best candidates for their programs.
- Logistics and operations
As for the sector of logistics and operations, conversational AI is widely used for helping customer track packages, estimate delivery costs or reschedule delivery.
Examples:
Locus Robotics has a software solution with integrated conversational AI that helps warehouses and storage spaces manage and track inventory. The workers can communicate with the platform and get information regarding all of the operations in the warehouse.
UPS bot is a chatbot on the UPS (a logistics and delivery company) website and mobile app. The company uses conversational AI to answer customer needs in terms of package cost, location, or delivery.
Common challenges of conversational AI
- Lack of personalization and limited capabilities – Conversational AI usually can’t solve more complex and unique customer problems it hasn’t come across before, leading to unresolved issues and displeased customers. Every business has a unique tone of voice, message, and set of values that CA can’t fully replicate.
- Technical glitches and privacy issues – Since NLP is based on learning and recycling patterns users use, the privacy issue with data collection and user information is still unresolved.
- Language barriers – Conversational AI has problems with comprehending jargon, irony, and misspelled words, often leading to misunderstanding customer requests.
- Integration and adoption challenges – Many businesses have complex CRM or legal systems that make integrating conversational AI impossible. Switching to newer technologies can be challenging for older or not tech-savvy customers.
