What is Conversational AI?
What’s a Rich Text element?
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
Static and dynamic content editing
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
How to customize formatting for each rich text
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
Conversational Artificial intelligence is the technology that powers automated messaging applications, which allows them to offer human-like interactions. Chatbots and virtual agents use conversational AI technology to have human-like conversations with people. This technology uses a combination of machine learning trained by large volumes of data, natural language processing technology, text recognition, and translations to interpret and replicate human interactions.
Not only can conversational AI recognize text and speech. It can also understand the intents behind queries and respond in a way that is as close to human conversations as possible. Conversational AI applications are built in a way that incorporates context and personalization to human-computer interactions.
Although this technology has become more popular with chatbots, it is also available in invoice format. In its most advanced form, you can expect conversation AI to deliver results that are barely distinguishable from human conversation.
How does Conversational Artificial Intelligence work?
Conversational AI is a complex suite of various AI technologies built into one. The components of a conversation AI system include Automatic Speech Recognition (ASR) technology, Natural Language Processing (NLP), Machine Learning, and Advanced Dialog Management system. A conversational AI system uses these technologies to understand conversations and provide human-like responses accordingly. It also takes data from these interactions to learn and improve.
So how exactly does this work?
First, a Conversation AI application (think a chatbot) will receive input from a customer either as written text or spoken phrases. In the case of speech input, the automated speech recognition technology will translate the speech to text format to make it readable for the system.
Next, the application will try to decipher the meaning of the input. For this, it uses Natural Language Understanding (an aspect of Natural Language Processing) to read and understand the intent of the query. The application forms a response based on its understanding of the text input. For this, it uses Dialog Management and Natural Language Generation (another aspect of NLP).
The response is delivered to the end-user as a text (in the case of a voiced response, speech synthesis or text to speech is used to deliver the response in a voiced format). After the response has been delivered, the machine learning component of the system responsible for improving it over time will accept corrections. The AI system will learn from further interactions to provide better responses with future interactions.
Conversational AI Use Cases
When people think of conversational AI applications, online chatbots typically come to mind. But they can also be used to power voice assistants and for omnichannel deployment. Although conversation AI applications have advanced technology built into their backend to ensure human-like experiences, experts consider conversational AI a form of weak AI. This is mainly because they are restricted to solving a narrow field of problems.
But their narrow focus notwithstanding, conversational AI has proven valuable for a broad range of use cases for business enterprises. Some of the everyday use cases of conversational AI across various businesses and industries include:
Online customer support
These days, online chatbots have become quite popular and are gradually replacing human agents. This is one of the most popular applications of conversational AI technology. Online chatbots provide answers to frequently asked questions from customers. They may also cross-sell products, provide personalized recommendations or suggest products on websites and social media platforms. You'll probably interact with bots like this as virtual agents on e-commerce website.
Companies can use conversational AI bots to reduce entry barriers for their products. This group of conversational AI bots assists users who rely on assistive technology through the use of text-to-speech dictation, language translation, and similar tools.
Conversational AI has found application in the healthcare industry as one of the ways to make healthcare readily accessible to people. It can also be used to manage administrative processes in hospitals, like claims processing in healthcare facilities.
Many software tools now incorporate some elements of conversational AI. A typical example of this is the autocomplete and spell check feature on many software.
Automating HR Processes
Human resources departments can optimize employee training. It can also be used to onboard new workers. An AI-powered HR assistant can also manage the process of collecting employee information.
Internet of things (IoT) devices
A more advanced use case of conversational AI is with IoT applications like Amazon Alexa, Google Home, and Apple Siri. These tools interpret commands from users and take the required action using conversational AI tech.
Common types of conversational AI applications
Conversational AI is an advanced piece of technology that can be programmed in various ways to varying degrees of complexity. The end products are different types of applications, including chatbots and personal assistants that can be used to facilitate and automate conversation.
On the simpler end of the spectrum, we have Chatbots or FAQ bots which are basically query and response machines. Users type in their queries to interact with them, and the chatbot relies on keywords to process and deliver appropriate responses. It should be noted that not all FAQ bots use conversational AI technology. Many of these bots don't use NLP to understand speech, neither can they improve over time through the aid of machine learning technology.
Virtual Personal Assistants
Virtual Personal Assistants like Siri, Alexa, and Google Home are a more advanced form of conversational AI. They are linear with a very rudimentary dialog management system. Virtual personal assistants are unable to interpret conversations or carry context from one conversation to another. But they use ASR and NLP technology.
Virtual Customer Assistants
As their name implies, virtual customer assistants serve a more specific role in customer service. Hence, their dialog management system is more advanced. They have become quite popular in the customer service and sales industry for enhancing user experience. One prominent feature of virtual customer assistants is that they can interpret context and carry information from one interaction to the other.
Virtual Employee assistants
These are similar to virtual customer assistants but are explicitly used for streamlining and automating enterprise operations. They often feature robotic process automation and more advanced conversational AI technologies. Virtual employee assistants can be integrated deep into an enterprise system to provide contextual assistance to employees in a wide range of situations.
Benefits of conversational AI
Conversational AI is a valuable solution for managing various business processes. While most chatbots are currently being used to solve rudimentary problems, they still do an excellent job of managing repetitive customer support interactions. Conversational AI can help free up a lot of personnel resources for business. So far, many conversational AI apps have been perfected to be able to replicate human-like conversations. This leads to an improved rate of customer satisfaction. The following are some of the significant benefits of using conversational AI.
Conversational AI can help cut your customer service budget without reducing efficiency. Not only do you get to reduce the staffing of your customer service department, but conversational AI also makes it possible to provide service to customers beyond regular service hours.
Chatbots can provide instant responses to customer queries on a 24-hour basis. Since most customer service interactions are repetitive, conversational AI can be easily programmed to handle these use cases and provide consistent and comprehensive support. This frees up your customer service team to handle more complex cases.
Improved customer engagement
Conversational AI can make it easier for brands to engage with customers faster and more frequently. Customers no longer have to deal with long wait times when trying to get information or answers to their queries. What's more, conversational AI apps can be further customized to provide personalized recommendations leading to faster product discovery for customers. This overall improvement in customer engagement can boost customer satisfaction, loyalty and consequently lead to revenue improvement.
Conversational AI can grow with your business. Adding infrastructure to improve the capabilities of your conversational AI is typically cheaper and easier than the process of hiring and onboarding new employees to serve your business. You will find this ease of scalability particularly useful when entering new markets or during short demand spikes like during holiday seasons.
Efficient human support
The goal of conversational AI is not to replace or eradicate human customer service. In fact, while the technology continues to grow and improve, human service agents will continue to remain relevant. Human agents will be needed to attend to more high-level queries and navigate problems that the AI system cannot handle satisfactorily. But using AI will improve the efficiency of human support agents as it allows a more consistent flow of information and faster resolution of issues.
How to Build Conversational AI
The process of building your conversational AI begins with you thinking like your potential users. This would include imagining how the users might interact with the AI product and the common questions they are likely to ask. The AI tools can then be designed to allow them to route relevant information to answer user queries. There are a few simple steps to creating your conversational AI application.
Defining your goals
The first step is to determine the purpose of your conversational AI application. Are you trying to automate mundane tasks for your agents or increase customer satisfaction or solve customer issues faster? The goal of the AI application will determine how it will be designed.
Training your AI
Once the goal has been established, the next stage is to train your AI based on likely scenarios where users may phrase questions or request information. For instance, if you're building a use case of a user requesting for help with accessing their account, you can come up with variants of the query like
- "How to login"
- "How to signup for an account"
- “How to reset password"
- and so on...
This stage of the process will rely heavily on historical data from past customer support tickets. You will also partner with your analytics team to set the conversational AI tool appropriately. In addition to using data from your system, queries from web searches and other sources may also be used.
Design user journeys
The next stage is to design the conversation flow or user journey. This gives your conversational AI some personality. You must ensure that your conversation workflow covers all the likely scenarios of conversations with the user. The goal is to create a system that allows the AI machine to decipher the user's intent and determine the most relevant response to give at any given time.
The next step is to integrate your conversation AI into your backend systems. The type of integration depends on the specific use case of the software. For instance, a conversational Ai for resolving customer service issues may need to draw data from CRM systems without human intervention.
Measure and optimize
One of the hallmarks of a conversational AI system is that it is continuously improving. To do this, you have to put systems in place to determine the impact of the AI systems on your KPIs.
Information such as average handle time, the accuracy of responses, first-rate responses, and more will be valuable for measuring the performance of your AI system.
Over time, you will uncover new ways to train your AI and empower it to solve new problems or perform better at solving current problems. Retraining your AI can be automatic via the aid of machine learning or manual for a specific scenario.
Conversational AI encompasses a variety of technologies that makes efficient and automated human-like communication possible.
This technology which has become the brainpower behind many virtual chatbots and online assistance systems, has become quite popular in recent times for improving customer support, sales, marketing, and overall customer experience.
With the help of a robust conversational AI application, businesses can provide quick and efficient resolutions to everyday customer questions and seamlessly resolve issues.
While Conversational AI still faces some challenges in the area of discovery, adoption, and ever-evolving communication, advancements in AI will help solve these problems with time.