What is conversational AI?
Conversational artificial intelligence or conversational AI is a technology that enables computers to understand human language and engage in natural conversations with users. Using natural language processing (NLP), large volumes of data, and machine learning (ML), conversational AI recognizes user inputs and generates an appropriate response.
Some other sophisticated techniques, like user profiling, context awareness, adaptive learning, and user data integration, allow conversational AI platforms like Midjourney, IBM Watson Assistant, and Oracle digital assistants to provide an interactive and personalized user experience.
AI chatbot software leverages this power of conversational AI to understand user input and respond with the desired information. This feature is helpful in retail, banking, and healthcare industries for customer support functions.
Components of conversational AI technology
Conversational AI has the ability to extract the underlying meaning and intent of a given question. After analyzing the input, it generates relevant and accurate replies using some predefined rules and the system’s knowledge base. The technologies that make this possible can be divided into five components.
- Natural language processing combines computational linguistics with other technologies to process a large amount of unstructured human language data to generate structured data - or simply, interpret and generate human language. The entire process can be simplified as natural language understanding (NLU) and natural language generation (NLG), where NLU processes the input in terms of context, intent, syntax, and semantics and NLG generates the output in human-understandable language.
- Large volumes of data are used in training computers in human language using machine learning. The training data can be anything from numbers, images, texts, reports, and records that helps in acquiring new information for future interactions. The more data there is, the more a machine can train itself, and gradually generate better responses.
- Text analysis tools are used to extract meaningful information from text data. The process includes sentiment analysis, topic modeling, summarization, and intent recognition. The text is broken into subjects, verbs, adjectives, and relationships between different words to understand user query, draw relevant information, and generate appropriate responses.
- Although not directly related to conversational AI, computer vision helps analyze and interpret visual information. It enables computers to understand images or videos, recognize objects, understand the context, carry out optical character recognition (OCR), and generate relevant responses based on visual input.
- Speech recognition technology facilitates the conversion of human speech into text. It involves converting audio signals into written words and is used for voice-based interactions. Speech recognition systems use acoustic modeling, language modeling, and statistical algorithms to transcribe spoken words accurately.
How conversational AI works
Conversational AI works by employing a combination of technologies mentioned above to enable natural and meaningful interactions. The four steps that drive human-AI conversations are:
- Input generation initiates the conversation by asking the user to provide a text or a voice message through an app or a website. The input can be a question, a prompt, a command, or any other type of query.
- Input analysis uses NLU to understand the given command. However, if the prompt is voice-based, it uses automatic speech recognition (ASR) technology, along with NLU. Input analysis involves syntactic parsing, sentiment analysis, and intent recognition.
- Output generation utilizes NLG to give an appropriate response based on the analyzed input. The output is also influenced by techniques such as template-based responses, retrieval-based responses, and language generation models. The response can be in the form of text or speech.
- Using ML, the machine trains itself with experience, user data, and feedback. The system uses reinforcement learning algorithms to refine dialogue management and response generation over time.
Conversational AI examples
Conversational AI is a big part of everyday technologies like advanced chatbots and virtual assistants. The versatility of this technology allows it to improve user experience and enhance efficiency for businesses of all sizes. Today, different organizations are leveraging the power of conversational AI platforms in multiple sectors to increase profits and productivity, like the examples mentioned below.
- Customer service. Conversational AI chatbots are essentially static chatbots trained for human-like conversations that work great for quickly resolving customer queries and increasing customer satisfaction. Common issues, such as order tracking, can be handled by AI chatbots, whereas complex problems can be escalated to human agents.
- Healthcare. AI solutions have the ability to make the life of patients, doctors, and nurses easier. Things such as appointment booking, emergency case escalation, health tracking, symptom identification, and patient assistance are made easier with conversational AI.
- Financial Services. Processes in banking and finances, such as balance inquiries, fund transfers, and bill payments, are easily automated using conversational AI.
- Sales. Conversational AI can be integrated with business management platforms like customer relationship management (CRM) software. It helps update lead and customer information automatically, make product recommendations, identify opportunities for upselling and cross-selling, and pre-qualify leads before passing them to the sales team.
- Voice assistants. Conversational AI powers the well-known Apple Siri, Google Assistant, and Amazon's Alexa. These assistants understand commands and perform tasks like setting reminders, searching the internet, controlling smart home devices, playing music, and having a human-like conversation.
- AI-powered search engines. Google’s Bard and Bing AI can quickly generate results that best cater to the user's search query by using conversational AI technologies.
Benefits of conversational AI
There are several benefits to having conversational AI integrated into the business plan, especially in the customer support domain. Scalability, faster response time, increased efficiency, and productivity are some common benefits across industries.
- Proactive customer service. Businesses can respond to any questions or queries around the clock without the intervention of sales or customer service personnel. The AI model can be trained on frequently asked questions to ensure hot leads stay on the website and get their answers.
- Save time. Having AI customer support answer repetitive questions, such as order details and tracking, ensures customer support teams focus on tasks that require a human touch.
- Consumer insights. Data collected through customer conversations is critical in understanding what people actually want from the business. Informed decisions about increasing customer satisfaction can be made based on the insights
- Enhanced user engagement. Conversational AI has the potential to increase user engagement by delivering personalized experiences and tailored responses to customer queries.
Conversational AI challenges
Conversational AI is still growing, and multiple areas, like security and language nuances, may pose a challenge to the adoption of the technology.
- Privacy. Security is a big concern when it comes to the use of AI. Businesses that employ conversational AI should ensure that sensitive information like customers' contact details is stored securely to avoid data breaches.
- Native languages. Notably, only a small percentage of the global population has English as their first language. Training the chatbots and voice assistants on different languages, dialects, and cultural differences is essential in creating a good user experience for a diverse population.
- Linguistic nuances. Unlike human communication, AI conversations are more straightforward. Comprehending and adding nuances like humor, sarcasm, and emotions may be difficult for computers.
- Discovery and adoption. Even with AI becoming increasingly popular and easy to use, a section of the audience may need to be more comfortable with the technology. It is a good practice to educate the customers on the available opportunities.
Chatbot vs. conversational AI
Although chatbots and conversational AI seem like the same concept, there is a significant difference in the set of rules they work on.
Chatbots are static bots that use predefined rules to give fixed scripted answers. They may or may not run on conversational AI. These bots are commonly found on business websites and navigate users from one place to another. Chatbots require manual updates to the predetermined script for any desired changes. Text input is the only way to interact with a chatbot.
Conversational AI incorporates all the tools and technologies that have the ability to run a machine-to-human conversation. The conversations here are context-based and more dialogue focused. Found on multiple channels like websites, apps, and assistants, the technology grows with data and refines responses using machine learning. A conversational AI chatbot goes beyond traditional chatbots in terms of processing voice and text inputs.
Need help finding the right software for any business needs? Chat with G2’s AI chatbot Monty.
Harshita Tewari
Harshita is a Content Marketing Specialist at G2. She holds a Master’s degree in Biotechnology and has worked in the sales and marketing sector for food tech and travel startups. Currently, she specializes in writing content for the ERP persona, covering topics like energy management, IP management, process ERP, and vendor management. In her free time, she can be found snuggled up with her pets, writing poetry, or in the middle of a Netflix binge.
