Planning to implement conversational AI in your organization? Read this comprehensive guide to get a full understanding of conversational AI, how it works, and its capabilities and use cases.
Conversational AI is a set of powerful technologies that empower computers to comprehend, process, and respond to human utterances and text/voice inputs naturally. Used in conjunction with chatbots or voice assistants, it helps organizations deliver meaningful and personalized customer and employee engagement economically on a large scale.
The conversational AI market size is expected to grow from AUD 6 billion in 2019 to AUD 22.6 billion by 2024, at a CAGR of 30.2%, during 2019-2024.
As per Gartner,
With Conversational AI, an organization can benefit from personalized, context-aware, and differentiated customer and employee experiences. Global organizations leveraging conversational AI technologies like chatbots, virtual assistants and voice bots are significantly reducing support costs, streamlining internal operations, improving agent productivity, and delivering powerful customer service. The scope of conversational AI is vast; the channels are rapidly expanding. You may experience conversational AI through the following means-
Additionally, advances in NLP and machine learning, the availability of vast amounts of data, flexible app integrations, and low-code bot-building platforms have made conversational AI a force to reckon with.
Conversational AI uses a plethora of advanced technology components, including natural language processing (NLP), intent and entity recognition, machine learning, natural language generation, dynamic text to speech capabilities. Below we present a walkthrough of how these technologies make conversational AI a reality –
Natural Language Processing (NLP) is a component of AI and is the ability of a computer program to understand human or natural language as it is spoken/written. NLP helps a bot/virtual assistant understand the semantics of the language being used, including synonyms, canonical word forms, grammar, slang, and logically respond using natural language, consistent with the user’s query.
Natural Language Understanding (NLU) is a technology that deciphers the context and meaning behind the user’s words. With NLU, the AI assistant can easily understand the user’s query, even overlooking grammatical errors, shortcuts, etc., and remember the context throughout the conversation.
Contextual awareness is necessary to recall information over interactions and hold natural, human-like back and forth conversations.
NLU goes above and beyond scripted conversational technology that involves giving a pre-programmed response to a particular phrase or keyword.
Natural Language Understanding extracts intent and entities – precisely what the user is trying to achieve (intent) and elements that define what is required to accomplish the task, such as dates, time, numbers, and objects, also known as entities.
Example – I am trying to find a restaurant that sells blueberry cheesecake.
In the above query, the intent is to “find.” The relevant information (entities) required to fulfill the user’s request are “restaurant” and “blueberry cheesecake.”
Machine Learning is a subset of AI that studies algorithms and statistical models, giving computers the ability to perform a specific task without being explicitly trained to do so. With machine learning, bots rely on patterns, inferences, human-agent conversations, and historical interactions to learn and improve their performance.
Fundamental Learning ensures input information always produces the same output. It determines intent from user utterance using semantic rules, such as grammar, sentence structure, word match, language context, etc.
Knowledge Graph groups key domain terms according to similarities and differences. The model then associates them with context-specific questions, synonyms, and ML classes.
After understanding the user’s intent, the conversational AI assistant uses natural language generation to respond in a textual or voice output that is easily understood by the user.
Through conversational AI, conversations truly feel human-like. Just as humans remember context throughout the conversation, a conversational AI chatbot retains context from one response to the next. Because of its conversational ability, interactions don’t feel scripted. You can hold conversations about anything – as long as the bot has the data to build the conversation.
Below we take you through the various capabilities of conversational AI –
Conversational AI can take one or both of the following approaches –
By incorporating conversational AI into everyday organizational functioning, you can reap the following benefits –
The IT helpdesk is often inundated with routine questions. As soon as a request is raised, IT chatbots help the user do basic troubleshooting and in most cases fix the issue and thereby reduce the employee downtime.
If the issue isn’t resolved or the user isn’t satisfied with the outcome, bots provide the option to connect with a support agent – thereby leaving the more complex queries to human agents. This leads to faster resolution times, improved incident management, improved security, better handling of outages and ensuring that employees are kept informed with steady and timely alerts.
Some of the use cases include:
AI-enabled sales virtual assistants can integrate with data warehouses, CRM, BI and LOB Systems to perform tasks such as creating new leads, updating lead status, getting visual reports in multimedia formats, updating CRM records etc.
Use cases include:
Conversational AI marketing assistants can gather data about potential customers that equips marketers with essential information to design their products and advertising strategies. They can be integrated with various social media channels and used to reach out to customers of various demographics.
Following are the use cases:
Employees can use the company’s intranet chatbot to perform simple actions such as checking on internal company updates, accessing documents, applying for leaves etc.
Use cases include:
AI-enabled virtual assistants can be used at different stages of an employee’s life cycle – right from recruitment and onboarding to engaging the employee and fostering retention, in order to optimize the whole process.
Use cases include:
Conversational AI in banking aims at delivering personalized customer services to improve customer satisfaction and engagement. Some of the use cases include:
CPG and retail companies are increasingly using conversational AI to transform customer experience. Following are the use cases in the retail sector –