Conversational artificial intelligence (AI) refers to a set of technologies behind automated messaging and online text to speech applications that provide human-like interactions between humans and computers. These applications use large volumes of data, natural language processing, and machine learning to help intimate humans’ interactions such as recognizing speech and text inputs, translating them into various languages.
Use Cases Of Conversational (AI) artificial Intelligence
When we think about conversational artificial intelligence, online chatbots and voice assistance are the things that come to our minds for customer support and omnichannel deployment. Most conversational AI applications have extensive analytics built in their backend programs to ensure human-like conversational experiences.
Experts take conversational AI’s current applications as weak AI because they focus on performing a narrow field of tasks. Whereas, strong AI, which is based on a theoretical concept, focuses on human-like conciseness which is able to solve numerous tasks and solve a wide range of problems.
No matter how narrow the conversational AI is, it is a lucrative technology that can help different enterprises to become more profitable. The online AI chatbot is one of the most popular types of conversational AI; there are many other use cases as well. Some of them are as follows:
Online customer support
When it comes to online customer support, online chatbots have replaced human agents along the customer journey. They provide answers for frequently asked questions (FAQs) from visitors about different topics such as shipping, personalized advice, cross-selling products, size suggestions, and more. For instance, different messaging bots on e-commerce sites use virtual agents, messaging apps such as Facebook Messenger or Slack, and their tasks are usually done by virtual assistance and voice assistants.
Accessibility
If companies use assistive technologies for users, they can reduce entry barriers and can become more accessible. Frequently used features of conversational AI for such group of companies are language translation and text-to-speech dictation.
HR processes
With the help of conversational AI, you can optimize many human resource processes. For instance, you can manage employee training, updating employee information, and maintaining onboarding processes.
Health care
Using conversational AIs can make health care services more feasible and affordable for patients. Furthermore, it can also improve performance efficiency, and administrative processes of health care centers, such as claiming processes, can be more streamlined.
Internet of things IoT devices
Most households use IoT devices, whether it is Alexa speakers, smartwatches, or smart cell phones. These devices have an automated speech recognition system installed in them that helps them to interact with end-users. Some of the most popular applications used for the Internet of Things include Amazon Alexa, Google Home, and Apple Siri.
Computer software
When it comes to managing multiple tasks in the office environment, they simply use conversational AI is to improve efficiency and time management. For instance, we all use search autocomplete when we are required to search about a particular thing on Google, and most importantly, we use spell check for every little detail.
Most conversational AI chatbots and applications have rudimentary problem-solving skills which help them to reduce time and improve cost efficiency on customer support interactions. Conversational AIs also help to free up personnel resources so that they can focus more on customer interactions.
In conclusion, conversational AI apps can replicate human conversational experiences in the best manner, which leads to a higher rate of customer satisfaction.
Components of Conversational AI
Conversational AI combines machine learning with natural language processing (NLP) which flows into a constant feedback loop to continuously improve the AI algorithms. Besides, conversational AI comprises of some principles that allow it to process, understand, and generate a response in some natural way.
Let’s discuss machine learning and natural language processing;
Machine Learning
Machine learning can be defined as a sub-field of artificial intelligence that is made up of using a set of different algorithms, features, and sets of data that helps in continuously improving experiences. When the input of machine learning grows, the AI platform machine works better in recognizing patterns as well as making predictions.
Natural Language Processing
Natural language processing is the current method used for the analysis of language by using machine learning that is used in conversational AI. NLP comprises four steps that can be broken down further as Input generation, Input analysis, Dialogue management, and reinforcement learning.
