Today, advances in automation, artificial intelligence (AI) and natural language processing (NLP) make it possible to design cost-efficient digital experiences. Now, where information can be purposeful, simple, and natural, customer conversations with organizations increasingly resemble conversations with employees in-person.
According to Deloitte report, embellished with such innovative capabilities a programmatic and intelligent way of offering a conversational experience to mimic conversations with real people, through digital and telecommunication technologies, informed by rich data sets 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, is what we call Conversational AI.
The Conversational AI market size is expected to grow from US$ 4.2 billion in 2019 to 15.7 billion by 2024, at a CAGR of 30.2%, during 2019-2024.
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.
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.