Conversational AI embellishes several innovative capabilities while being a programmatic and intelligent way of offering a conversational experience to mimic conversations with real people, through digital and telecommunication technologies, informed by rich datasets and intents, providing customers with informal, engaging experiences that mirror everyday language, digitally enabled products, platforms, and experiences relating to communication, sales and service consultations, as well as other customer services. Using conversational AI, organizations can provide personalized and differentiated experiences that build relationships with their customers. Each interaction can feel like a 1:1 conversation that is context-aware and informed by past interactions.

Ever wondered, what inbred technologies drive such innovation. According to Deloitte's report, Conversational AI brings together eight technology components, including Natural Language Processing, Intent Recognition, Entity Recognition, Fulfilment, Voice Optimized Responses, Dynamic Text to Speech, Machine Learning, and Contextual Awareness. NLP is the ability to "read" or parse human language text. It is a pre-requisite for understanding natural sentence structures versus simple keyword "triggers". Intent Recognition is the ability of a system to understand what the user is requesting, even if phrased unexpectedly. A good intent recognition is vital if you don't want to annoy your users with roadblocks in the experience.

Furthermore, Entity Recognition stands for understanding that some text refers to informative abstract categories (entities) such as "February 2" = Date. It is necessary for more complex commands and analysis. Where Fulfilment is the ability to pull data from web services or databases using APIs, run conditions, and inform the Dialog Manager, Voice Optimized Responses is the ability of a system to engage in conversation in a humanlike manner and show emotions to deliver an optimized experience.

Dynamic Text to Speech converts a written text to natural-sounding speech, supporting various languages, voices, and accents. It allows for emphasizing capital letters and tonal inflection. Contextual Awareness is the ability to follow conversation history, translate, recall, and memorize information over conversations. It is necessary for natural, human-like back, and forth conversation. Machine Learning is about learning how to better respond to the user by analyzing human agent responses. ML is necessary to improve intent recognition.

Reporting & Monitoring, and Security & Compliance are the other supporting elements of Conversational AI. Where the ability to tell you how your conversational agent is performing by providing insights and analytics is termed as Reporting & Monitoring, the ability to mitigate security risks, security & logging capabilities vary amongst platforms is considered as Security & Compliance.

How does Conversational AI Work in Practice?

As we saw above that Conversational AI is a collection of AI-related technologies that enable human-like interactions between computers and customers. Individually based on NLP, the technology has 3 distinct components: Input, Analysis, and Response.

Input

As with human-to-human conversation, everything begins with recognizing/hearing human speech and/or text, then understanding the intent behind the words. In this process "natural language understanding" or NLU, a part of NLP is being used which helps the self-service tool recognize and understand what a human customer wants (intent).

Analysis

Having understood the human's intent, Machine Learning enters the picture to analyze all the potential responses, using all available data, pattern recognition, and algorithms. As the ML tool goes through these response options, it determines the right response to the customer in each particular context.

Response

Now comes the "natural language generation" or NLG which enables the computer program to generate an appropriate response to the human in conversational language.

Through this, we get a better understanding that Conversational AI dynamically incorporates context, personalization, and relevance within the human-to-computer engagement.