Conversational AI technology is going to be transformational as the possibilities seem to be growing with the spurt in the reach of digital devices, soon augmented with AI-enabled conversational interfaces. From fetching data to answering questions, conversational AI can mimic all that a human agent does but in quicker time, giving users immediate access to information or providing immediate responses.
By definition, Conversational AI is a subfield of artificial intelligence focused on producing natural and seamless conversations between humans and computers. It has seen several incredible advances in recent years, with significant improvements in automatic speech recognition (ASR), text to speech (TTS), and intent recognition (Source: Internet)
Technology components of Conversational AI include Natural Language Processing, Intent Recognition, Entity Recognition, Voice Optimized Responses, Dynamic Text to Speech, Machine Learning, and Contextual Awareness.
Conversational AI is essentially about a computer program that attempts to maintain a conversation with a person (end-user). The communication process is the method by which a sender transfers information to the receiver, which implies that a sender wants to transmit information to the receiver. The receiver needs to decode the message and then send a message back to the sender.
A computer program or bot to understand the human language requires the ability to extract the information from a user’s message to respond appropriately. And, this is the area where conversational AI is in play through AI chatbots and assistants. These assistants can converse in natural language with humans. Based on a technique called natural language processing, AI chatbots can identify the intention in a person’s query and estimate the best possible answer, creating an interactive base for humans and computers.
A difference between a simple chatbot and an AI chatbot is identifiably user interaction. Users would be able to tell you that it’s more comfortable talking to AI chatbots as they would understand you and, more often than not, respond as a human would in natural language.
The evolution of conversational interfaces will change the world around us. As technology improves, ideas evolve and begs the question as to why we still have touse traditional technology because interacting with digital technology could provide the same experience as talking to a human.
AI and Machine learning are both usually interchanged in usage. ML is, in fact, a subset of AI, which includes components like Deep Learning and Reinforcement Learning. Deep learning can extract intricate patterns and sequences in a data set and is a primary technique employed in NLP engines.
For a computer program or bot to understand the human language, it requires the ability to extract the information from a user’s message to be able to respond appropriately. The component of AI that enables this process is NLP.
NLP (Natural language processing) is the science of extracting the intention of text input and relevant information from text through Intents and Entities. Natural Language Understanding (NLU) is a subset of NLP that turns natural language into structured data. NLU can do two things — intent classification and entity extraction. Intent classification and entity extraction are the primary drivers of conversational AI.
An ‘intent’ simply means, the intention of the end-user — what is the user trying to convey or accomplish. An entity provides the context for this intent. An ‘entity’ refers to the information that helps answer the user’s request.
Conversational AI is a welcome initiative for businesses as it can be used for both customers and employees alike to improve communication with the brand for customers and heighten customer and employee user satisfaction.
Businesses can create a system that eliminates long search times by the users and time-consuming methods for getting information. Companies can flip to the new by helping end-users get what they need through simple conversation.
Providing on-time assistance is crucial for businesses. The underpinning metrics for the performance of support agents lean on how quickly they can respond to users and get a better graph on positive feedback. For businesses, a proper understanding of their end-user needs is what is essential in increasing positive feedback. With ‘Time to resolution’ being a core KPI for support teams, there is a full possibility of impatience, bias, and assumption when interacting with end-users. The relief with AI from both the business side and user side is that such shortcomings in support can be eliminated, thereby improving the graph in user satisfaction.
Users could engage with an intelligent, interactive chatbot to achieve expected results and how many of us wouldn’t prefer a simple conversational interface to a clunky application that we’d have to navigate and learn to get results. Any application we access requires a bit of time to study the interface, which, simply put, has a bit of a learning curve for all individuals. With conversational AI, the learning curve can get flatlined because users wouldn’t require learning multiple interfaces, making it so much more user-friendly, thereby greatly improving the user experience.
Going through too many apps to get things done is not very desirable for users. With intelligent AI chatbots, you have a single conversational interface to get what you need. AI comes into the picture by getting various apps to work in sync through conversations, eliminating the need to use separate applications. For instance, if a user needs their travel insurance to become reactivated and tickets booked, it can all get completed through a conversational assistant.
When there’s a spike in queries, there’s no need to increase the team size to handle questions when a chatbot can complete the job at scale.
An improvised helpdesk provides numerous cost benefits and savings
Reduce costs of delivering HR & IT support with Conversational AI Chatbot
People look for ways to communicate directly, and messaging is a preferred form of communication to interact with a brand, and users always look for a chat interface to send out a message.
There are reports that users can’t distinguish between human and chatbot in many instances. Conversational chatbot almost becomes human when engaging with the end-user.
The dependability on a chatbot increases when users can instantly communicate and get responses without having to wait. It helps you move towards more happy customers and good feedback on your service.
There are no sick days and time off for a chatbot. Having a team of support agents on board to assist at all times is not a worry anymore.
The interactions with users get easily captured, and the data used to improve your support and service.
AI chatbots aren’t just for customers. They can provide useful support throughout a business, including your help desk.
An intelligent conversation with end-users requires multiple functions to come together for an interactive support system (through chat). The Workativ platform has four main components: The AI engine, the Conversation Designer, the Automation Designer, Analytics, and Reporting.
They come together to become a robust chatbot builder, allowing admins to design conversations and pull in automations to automate specific use cases in the workplace and the data from conversations and automations to help in learning from past interactions.
The AI-powered engine is the brain in this system, and the intelligent chatbot leverages the AI technology to understand the user input and respond to the user. It accomplishes this with NLP.
The NLP technology enables the chatbot to understand the text from a user in conversation with the chatbot. NLP can interpret the meaning of the sentence and identify the input’s corresponding data to return an answer.
The intents and entities that are part of the NLP system can train the chatbot to respond in a certain way. The training model in Workativ Assistant is especially useful in building the chatbot to get better at responses. Using the data from interactions, you can train the intelligent chatbot by improving theintents and entities stack to identify different words or terms with which a user interacts with the chatbot that means the same thing. For instance, there are many ways how we greet each other. Some examples being hello, hi, hola, howdy, etc. Adding these terms to the Greeting intent helps the chatbot identify that the user wants to initiate a conversation.
The Conversation designer is the core of the interactive conversations between the chatbot and user. This core component in chatbot building helps design conversations specific to each use case, like Unlocking an account or Reset Password. It helps to define the path of a conversation, depending on user input. When modeling a conversation, using options, you can easily set how a user moves from one step in a conversation to another. Intents and Entities support the interactions. For instance, if a user wants to apply Time Off, the ‘intent’ can be ‘ApplyTimeOff, ‘and the entity can be TimeOffRequest.
A part of the core system is the Automation designer, designed by integrating with popular workplace applications. Automations can be called into a dialog to execute actions as per the user inputs in a conversation with a chatbot. The automation engine is imperative in completing a request for a user. For instance, the user can ask the chatbot for their salary breakup, and the chatbot would be able to fetch the information instantly.
The Analytics and Logs component captures user interactions, analyzes the conversations, and captures metrics such as the number of times a bot provided satisfactory responses and when it failed to respond. Based on these reports, the training module can be modified to improve the chatbot’s performance.
Delivering an intelligent conversation to the end-user requires one major component, which is the ‘Channel.’ The channel is the interface where the user communicates with the chatbot. On the chat interface, the queries are received, processed, and the chatbot responds. The channel can be your workplace communication chat channel like Slack or Teams, to deploy the chatbot.
Conversational AI can help you deliver exceptional user experience in the workplace. Talk to us and start your journey with AI, to transform your workplace support. Contact us at sales@workativ.com or just pick a time of here.