What is Conversational AI?

Artificial intelligence (AI) that can engage in conversation refers to tools that allow users to communicate with virtual assistants or Chatbots. They mimic human interactions by identifying speech and text inputs and translating their contents into other languages using massive amounts of data, machine learning, and natural language processing.
Conversational AI is revolutionizing human-computer interaction by combining technologies including machine learning (ML), natural language processing (NLP), speech-to-text (and vice versa) recognition, user authentication, and intent and domain prediction (HCI). The most significant advantage that conversational AI has over rule-based bots is the identification of user contexts and intentions. They can thus decipher a user’s query and deliver a personalized experience.
Conversations can be a short one-off request/response or part of longer-running customer engagement.Conversational AI empowers brands to deliver intelligent, superior and personalized customer experience.

Examples of Conversational AI:

1. Amazon – Prompted questions: Amazon’s first point of contact with customers is a virtual assistant. Prompted queries, like the one in the picture above, make up a big portion of the Amazon experience. In order to determine what the clients would be interested in, it also incorporates information on recent orders.
2. Schedule Appointments: Smart Action: Intelligent appointment booking allows companies to save time and money. It also saves time for the customers. Moreover, an easy, seamless interaction can improve the customer experience (CX) and boost satisfaction. Everything from making reservations and sending confirmation emails to giving directions may be done through conversational commerce. Additionally, they continuously compile information from consumer encounters to optimize and enhance dialogues.
3. Execute Basic Transactions: Watson Assistant: This technology may be used in a variety of fields, from fashion to healthcare, and it essentially frees up customer support representatives to handle more difficult problems. One example of software with a strong track record for answering queries, carrying out straightforward transactions, and connecting users with agents when necessary is IBM’s Watson Assistant.
4. Recommend Products and Services:: Automate.ai: One great example of a conversational AI that does just that is Automate.ai. Automate is a conversational AI software with exceptional listening skills that “understands every customer” and has a built-in product recommendation engine that serves up personalized suggestions and engages customers to provide superior shopping experiences.
5. Resolve Customers’ Problems_Cognigy: Conversational AI is the perfect tool for efficient customer service. Not only does it allow companies to offer support 24/7, but your customers will also appreciate quick, round-the-clock access to the answers to their questions. Conversational AI bots allow companies to reduce their customer service costs and optimize the time of their human agents. Popular software options include Live Engage, Bold360, Mobile Monkey, and Cognigy.
6. Boost Customer Engagement _ Boost.ai: Engaging customers is essential for every business. Companies can use conversational AI-powered Chatbots to engage with leads in real-time and reach out to customers who are at risk of defecting to a competitor. They can also send customers personalized offers and targeted messages to retain them.

Components of  Conversational AI:

Natural language processing (NLP) and machine learning are combined in conversational AI. To keep the AI algorithms up-to-date, these NLP operations interact with machine learning processes in a continual feedback loop. The fundamental elements of conversational AI enable it to process, comprehend, and produce responses in a natural manner.

1. Machine Learning: An area of artificial intelligence called machine learning (ML) consists of algorithms, features, and data sets that constantly get better with use. The AI platform machine gets better at identifying patterns and employing them to create predictions as the input increases.
2. Natural Language Processing: Conversational AI currently uses natural language processing to analyze language with the help of machine learning. Before machine learning, linguistics, computational linguistics, and statistical natural language processing were stages in the development of language processing techniques. Deep learning will enhance conversational AI’s capacity for natural language understanding in the future.

NLP consists of four steps:

1. Input generation: Users provide input through a website or an app; the format of the input can either be voice or text.
2. Input analysis:If the input is text-based, the conversational AI solution app will use natural language understanding (NLU) to decipher the meaning of the input and derive its intention. However, if the input is speech-based, it’ll leverage a combination of automatic speech recognition (ASR) and NLU to analyze the data.
3. Dialogue management: During this stage, Natural Language Generation (NLG), a component of NLP, formulates a response.
4. Reinforcement learning: Finally, machine learning algorithms refine responses over time to ensure accuracy

How to create conversational AI?

Thinking about how your potential customers might want to connect with your product and the key queries they might have is the first step towards conversational AI.After that, you can direct them to pertinent information with the aid of conversational AI tools. We’ll go over how to start thinking about and developing conversational AI in this section.

1. Find the list of frequently asked questions (FAQs) for your end users: Conversational AI is being built on frequently asked questions. They assist you in identifying the primary requirements and issues of your end users, which will reduce the number of calls your support team receives. If your product doesn’t already have a FAQ list, consult your customer success team to come up with a list of queries that your conversational AI can help with.
2. Use FAQs to develop goals in your conversational AI tool: Form the foundation of objectives, or intents, conveyed through user input, like accessing an account. Your conversational AI needs to be taught how to ask for this kind of information in different situations. You may consider additional terms that customers might use in chat with a support agent in addition to “how to access my account,” such as “how to log in,” “how to reset my password,” “sign up for an account,” and so forth. Consult your analytics and support teams if you’re unclear of any more terms that your consumers might use.If your chatbot analytics tools are configured properly, your analytics team can mine online data and look into additional site search queries.
3. Use goals to understand and build out relevant nouns and keywords: This type of information can include values like “username,” “password,” “account number,” and others. The same data that was gathered from tools or supporting teams to build objectives or intents can be used to comprehend the entities that surround particular user intents. These words will either come before or after the main question.
4. Put it all together to create a meaningful dialogue with your user:All of these elements work together to create a conversation with your end user. The intents allow a machine to decipher what the user is asking for and entities act as a way to provide relevant responses.

Benefits of Conversational AI System:

Apart from the fact that customers find conversational AI solutions more friendly and easy to use, there are various other reasons for companies to dive into this technology.

1. Saves time: Conversational AI provides quick responses and fast customer service. This is especially attractive to customers who do not want to go through the tedious process of connecting with a customer service desk for generic queries.
2. Easy real-time access: Customers can easily communicate with a chatbot on their preferred channel. The conversations are also simultaneous, lowering the possibility of information contradiction.
3. Increases efficiency: One of the key benefits of implementing a conversational AI solution includes increased operational and customer support efficiency. The high number of calls and emails no longer takes a tool on the customer service teams. With automated operations and lowered customer acquisition costs (CAC), companies can focus on other business functions.
4. Handles the entire customer cycle: A customer can complete a transaction, receive detailed purchase information, and receive after-sales information. It covers the complete loop – from seeking product information to sharing product feedback.

Conclusion:

AI is at the center of a new enterprise to build computational models of intelligence. The main assumption is that intelligence (human or otherwise) can be represented in terms of symbol structures and symbolic operations which can be programmed in a digital computer. There is much debate as to whether such an appropriately programmed computer would be a mind, or would merely simulate one, but AI researchers need not wait for the conclusion to that debate, nor for the hypothetical computer that could model all of human intelligence.