These days, you get greeted by a chatbot on every website you visit,“Hi, how may I help you?”
The growing popularity of chatbots has become a fact. Since the early 1960s, people have been interested in developing a robot that can converse with humans. From phone calls and texts to messaging applications, we’ve come a long way. Companies have come to realize the importance of chatbots in assisting numerous industries to reach and engage with customers in ways never seen before.
Going one step ahead, the evolution of Artificial intelligence (AI) has helped step up the game in the world of chatbots. It is now possible to create conversational virtual agents that can understand and respond to a variety of questions, commonly referred to as Conversational AI or Conversational Artificial Intelligence.
Chatbots and Conversational AI are frequently used in the context of Artificial Intelligence (AI) interchangeably. They might sound the same, but they are not.
Most individuals and enterprises are confused between these two buzz words.
So, let us get down to understanding them.
What is a Chatbot?
At the most basic level,
A chatbot is a computer program that allows humans to interact with the computer using a variety of input methods like voice, text, gesture, and touch, 24/7/365.
Chatbots can be extremely basic Q&A-type bots that are programmed to respond to preset queries. Most of us are familiar with bots for customer service in our personal lives, and also with popular chat and messaging platforms like SMS, Facebook/LN Messenger, WhatsApp, banking help bots, etc.
So far, chatbots have gained popularity to enhance customer experience and business efficiencies, now they are even being utilized for employee assistance.
Chatbots are broadly classified into two types, rules-driven and AI-driven.
Rules-based chatbots follow a predefined workflow, a simple tool capable of understanding and responding to text-based commands. It can only work on a single channel and operates as a chat interface. Usually navigational, it works on a pre-determined chat flow.
AI-driven chatbots leverage NLP (Natural Language Processing) and ML (Machine Learning) to understand what the user is asking or looking for. It is more conversational, performs complicated multi-turn conversations, and executes judgment-intensive tasks, just like us- humans.
What is a Conversational AI?
Conversational AI is a higher-level concept of a chatbot.
Conversational AI is the communication automation technology based on advanced robotic solutions. Unlike chatbots, conversational AI is a digital assistant that can lead natural conversations with humans.
It combines AI technology with other technologies (natural language processing (NLP), machine learning (ML), secure integration, process workflows, dialogue state management (DSM), speech recognition, etc.) to deliver a more personalized user experience.
Think of conversational AI as the ‘brain’ that powers a virtual assistant or advanced chatbot.
How does Conversational AI work?
To assist someone, you basically need to understand what help they need. A machine should first identify the “need” or “intent”. In conversational AI, the brain has to translate human language into something a machine can understand and respond to in a humanlike manner.
Let’s take an example where this can be applied.
In a scenario where a sales rep makes a query to a digital assistant to find out his performance:- What is my sales (intent) in April ’21 (entity)?
Imagine a robot built to mimic humans.
Same as us, the technology uses Automatic Speech Recognition (ASR) to function as humans’ ears to hear and convert voice into text.
Natural Language Understanding (NLU) functions as a brain to interpret what’s being said.
Natural Language Processing (NLP) helps understand and assimilate text and spoken words in much the same way human beings can.
Output is generated through Dialogue Management to serve as brain cells. During this step, the application formulates a response based on intent. Natural Language Generation (NLG), a part of NLP, orchestrates and converts the response into a human-understandable format like speech.
Proprietary algorithms also play an important role in enhancing the overall NLU capabilities of a digital assistant. In the case of the Ariya, the conversational AI interface, phamax has trained the supporting framework to understand business nuances and terms used in the context of healthcare and can be scalable to train the assistant to understand organizations context (product names for instance).
Conversational AI still needs humans to train them.
Conversational AI-based digital assistants can work their best only if they have both a trained team and implementers who can make the most of these technologies behind them. As digital assistants answer more questions and AI trainers assist in increasing their knowledge, conversational AI becomes smarter. Conversational AI learns new variations for each intent and how to improve its responses.
How is a Conversational AI different from a Chatbot?
To make it easier to understand here is a summary of some of the typical features* of a conversational AI application versus a basic chatbot.
*not all these features are necessarily part of a conversational AI solution
The business value of conversational AI
There is a wide range of conversational AI applications currently being used across all industries. From customer service to marketing and security, these intelligent programs are helping businesses connect with customers and employees in innovative ways. In fact, conversational AI has become an integral part of many organizations’ digital transformation in the wake of the global pandemic.
1. Customer service support:
Conversational AI for Customer Service, such as online chatbots and voice assistants (VAs), is increasingly being employed by contact centers to mimic human employees as part of customer support service automation. The customer service digital assistants can be deployed across channels, providing brands with the opportunity to create an omnichannel customer experience. AI-powered digital assistants are capable of delivering service, 24/7 which means that there’s always someone to respond to customers’ queries!
If the customer support query is complex or beyond the scope of the digital assistant, there is a seamless process to hand off the query to a live agent based on their skill sets and current workload.
2. Communication channel:
Chatbots have become a vital part of messaging platforms’ ecosystems. In 2015, the top 4 messaging platforms exceeded the 4 biggest social networking apps in the number of monthly active users, according to Business Insider. Since then, AI-based digital assistance has become an integral offering at every platform, we land, aiming to create more personalized and meaningful conversations.
3. Enterprise support:
The difference between chatbots and an AI-based conversational experience lies in the integration of back-end systems to provide information to users. When you use AI to ingest systems like your SAP, CRM, or ERP, you may have direct transactional dialogues with customers without the need for a business person.
Advanced AI chatbots can integrate with an organization’s data source or back-end systems, such as inventory management or customer relationship management (CRM). Today, Conversational AI-enabled digital assistants like Ariya have the potential to support the sales force to drive greater efficiency. The AI-enabled digital assistant interface is designed and domain trained to assist sales reps to quickly access phone numbers, extract competitive and performance updates, even make a CRM entry instantly. The potential of conversational AI to simplify processes is enormous.
4. Everyday digital assistants at work:
With information and data overload, it is quite impossible and unfair to expect your employees to remember or have enough time to extract the information they need. An everyday assistant who can be present with all the information one needs can motivate them to perform better.
In customer service, for example, reps don’t have to waste time seeking information, or depending on tools and colleagues to help them, which cuts down the waiting time to respond to customers due to a knowledge gap. Additionally, it enables reps to locate answers significantly faster, resulting in shorter encounters and higher first-contact resolution rates. All of these are well-known markers of consumer satisfaction.
5. Unlocking knowledge:
Most businesses now keep massive amounts of data and information which go unused. Organizations are aware of their accumulating data, but their processes and systems for accessing and utilizing the data are hampered by outdated or less efficient practices.
If a decision-maker is looking for sales figures, for example, the first step is to approach a member of the sales team, who may have to pull from multiple systems and reports to answer a single, yet complex question. Which might be stressful also consumes a lot of his time.
With the new advancements in technology, AI provides the scope to extract unused data while handling massive data warehouses effortlessly. Conversational AI can act as an assistant that can quickly extract and process data in a meaningful format. Organizations worldwide have started leveraging these across functions and hierarchies.
Conversational AI is the next evolutionary step:
With increasing popularity, Conservational AI is here to stay. Training the technology with domain knowledge can open greater opportunities towards enhancing organizational and employee efficiency. Eliminating all tedious steps throughout the information journey is a thing of the past. Gone are those days when you had to depend on your colleague or have the technical skills to get information.
Conversational AI assistant like Ariya is one of the revolutionary examples of transforming the traditional ways to process and access information.
