Everything around AI Chatbots – Challenges and Opportunities
The increasing use of AI chatbots across service lines is one of the most extensive and prominent use cases for adopting Artificial Intelligence in the industry. Chatbots have become essential to all large organizations’ internal and external communication strategies. They are a human substitute for first-level query resolution across various industries, and end users interact directly with chatbots in all cases.
Table of Content
- What is a chatbot?
- How AI Chatbots work?
- Types of chatbots
- Advantages of using AI chatbots
- Best AI chatbots
What is a chatbot?
A chatbot is a rules-based computer program, which simulates human interaction with end-users via a chat interface. In other words, a chatbot can have a conversation with you just like a real person, ask questions and answer queries based on pre-defined rules and logic.
Powered by complex Machine Learning algorithms, Chatbots allow computer programs to mimic human conversations and react to written or spoken queries to deliver a service. Because chatbots are powered by AI, they are self-learning and can comprehend human language, not just computer commands. The efficiency, accuracy and overall intelligence of chatbots increase with the number of conversations they have and the unique situations they are exposed to.
How AI Chatbots work?
Chatbots are similar to a messaging interface where bots respond to users’ queries instead of human beings. They look like other apps. But its UI layer works differently. Machine Learning Algorithms power the conversation between a human being and a chatbot.
ML algorithms break down your queries or messages into human-understandable natural languages with NLP techniques and send a response similar to what you expect from a human on the other side.
Let us share an example of how Chatbot works.
Suppose you have a smart AI-based conversational chatbot app on your phone or computer and you want to travel from LA to New York. You can open the chatbot app and write a message:
“Book a flight from LA to New York.”
You may get a response like this:
“How many people are traveling with you?
Once you send a response, the bot will respond with all possible flight details in seconds. Sounds amazing? Right?
The response sent back by the bot looks so natural, the way you expect from a real human being. But, do you know a lot of work goes behind providing you with such experience?
First of all, a bot has to understand what input has been provided by a human being. Chatbots achieve this understanding via parameters like Artificial Neural Networks, Text Classifiers, and Natural Language Understanding. Human beings need to respond with an appropriate message, which should look like a natural reply. It is done using Natural Language Generation (NLG).
Let’s understand all these techniques in more depth.
- Text Classifiers:
In this technique, words and sentences are divided into significant intent. Chatbots understand the intent and respond accordingly.
Text classification is the process of assigning a set of predefined categories to the content. With Natural Language Processing (NLP), text classifiers can analyze text and create a set of pre-defined tags or replies based on the input text.
- Natural Language Processing:
Bots depend a lot on Natural Language Processing techniques. Human language may get chaotic and NLP has the capability to handle all the mess. Made up of various libraries, the NLP engine identifies and extracts entities, which are essential pieces of information provided by the user.
Chatbots are classified into two types:
- Chatbots based on fixed rules
- Chatbots based on machine learning
Chatbots based on fixed rules only respond to specific commands and represent a fixed smartness level. If it is given some command that it does not understand, it won’t be able to perform appropriately.
However, a chatbot based on machine learning incorporates artificial intelligence and can understand the language, not only commands. It can learn with more information or interactions.
Machine Learning is the system’s ability to learn from past experiences without human involvement and use what they have learned.
Computer systems learn by getting exposed to various examples with machine learning. The approach to learn from examples is based on how the brain learns and is called neural networks. Machine learning uses algorithms that are sequences of instructions commanding computers what to do. Algorithms can be arranged and combined in complicated ways.
When a chatbot gets an input prompt, it must identify the prompt and create context so that it can evaluate the required output. Since the chatbot is trained with data input, it finds patterns that it can store for reference.
Also, deep learning is a type of machine learning that employs layered algorithms called artificial neural networks. Instead of task-specific algorithms, deep learning uses techniques where the system explores representations in the data that enable it to make the context of the raw data. Every layer of algorithms contains interconnected artificial neurons. The prior learning patterns and events measure the relationship between neurons. Algorithms can search for patterns in huge quantities of data and conclude how to respond to new data.
Therefore, this approach works in AI chatbots, where a predefined set of responses is not workable or appropriate.
LeewayHertz AI Development Services
Transform your business processes with Smart AI applications
What are the significant advantages of using AI chatbots?
Let’s look at some of the key advantages of deploying AI chatbots in various business processes.
- Improved End-User Experience: Chatbots provide end-user support on a real-time basis in any setting, be it in a retail sales store, product support center/website or a business front or back office. Because these interfaces are readily available to end-users, there is no specific wait time. This means, customers or end-users can readily have the answers to their queries, which significantly enhances the user experience. Based on the query, chatbots can present users with rich content with documentation, videos and so on to help resolve queries.
Furthermore, chatbots can provide 24/7 assistance and support to customers and end-users. They can be programmed to provide automated answers to common queries immediately and forward the request to a real person when a more comprehensive action is required. This has a significant positive impact on customer and user experience.
- Increased Face-time with Customers: Businesses can use chatbots to increase their face time with customers. Research suggests that modern customers expect a personalized experience with their favorite brands through increased interaction times and more personalized communication channels. Chatbots enable just that and more by providing easier and faster access both ways. Moreover, chatbots can be readily integrated into popular platforms such as Facebook or Instagram, thus enabling a seamless experience for customers and end-users.
- Analytics and Insights: Chatbots serve as a great communication channel and a medium to gather insights into customer preferences and behavior. Businesses can collect instant feedback from customers and end-users through chatbots and then analyze the data to gather insights around their habits and preferences.
Besides, chatbots can also be leveraged to identify purchasing patterns and consumer behavior. It can help businesses make critical decisions around product marketing and launch strategies.
- Lead Generation and Conversion: With all the customer and end-user information that a chatbot aggregates, it is possible to help customers in their purchasing journey through focused messaging using a chatbot. Chatbots can be programmed to persuade and influence user decisions and increase conversion rates.
- Cost Savings and Scalability: Developing and implementing a fully functional chatbot is faster and cheaper than developing a cross-platform app or hiring employees to handle many incoming queries. Thus, businesses can make significant savings in terms of hiring, training and payroll costs. A typical chatbot would only involve the initial development cost and a nominal runtime cost, which is potentially lesser than the costs spent on actual human resources.
Furthermore, multi-lingual chatbots can be used to scale up businesses in new geographies and linguistic areas relatively faster. Businesses can program the chatbot to easily handle incoming queries without having to augment their staff readily.
Let’s explore some of the best AI chatbots used across different industries.
Here are some of the best smart AI chatbots you should explore to experience the power of AI:
- Watson Assistant
Built by IBM (one of the leaders in the AI space), Watson Assistant is the most advanced AI-powered chatbot in the market. It is pre-trained with data from your particular industry so that it can understand your historical call logs, chat, ask customers for clarity, connect them with human representatives, search for an answer in your knowledge base and provide you with training recommendations to enhance its conversational abilities.
Powered with deep learning-based natural language understanding and multitasking capabilities, Rulai is an AI chatbot for enterprise brands. It can predict user behavior, analyze the context of the conversation, take actions, move to different tasks, ask customers to get more clarity, and understand customer preferences.
Designed explicitly for enterprise brands, Inbenta leverages its own NLP (Natural Language Processing) engine and machine learning to discover the context of each customer conversation and respond to their questions accurately. It has a dialog manager that allows you to design custom conversation flows.
When Inbenta chatbot feels that any of your customers should talk to a human for a specific concern, it escalates the conversation to the right support agent.
By gathering over 20 years of messaging transcript data and feeding it to the AI Chatbot, LivePerson, it can automate messaging for every industry and integrate with messaging channels such as mobile apps, websites, text messaging, Apple business chat, Line, Whatsapp, Google, Facebook Messenger, Google AdLingo and Google Rich Business messaging.
Bold360 patented its own NLP engine to allow brands to develop chatbots that can understand the customer’s intent without the requirement of keyword matching and know how to provide the most accurate answers. It can interpret complicated language, respond to customers with natural responses and remember the context of the whole conversation.
It is evident that chatbots provide unique benefits for businesses and can be a trusted backup for employees for relatively basic and repetitive tasks. The biggest challenge with AI chatbots at present will still be the need to train them with Machine Learning to efficiently handle queries and situations of varying levels of complexity.
If you are looking to build an AI-based chatbot, consult our team of AI experts and get started.
Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.
Start a conversation by filling the form
Once you let us know your requirement, our technical expert will schedule a call and discuss your idea in detail post sign of an NDA.
All information will be kept confidential.
What are neural networks? How do they work?
Neural networks, referred to as artificial neural networks (ANNs), are computational models that mimic the structure and operations of the human brain.
Bridging the AI-human communication gap: A comprehensive guide to prompt engineering
Prompt engineering is the practice of designing and refining specific text prompts to guide transformer-based language models, such as Large Language Models (LLMs), in generating desired outputs.
Streamlining document workflows: The power of Intelligent Document Processing (IDP)
IDP is an AI powered document processing technique that not just scans and captures structured, unstructured and semi-structured data, but also understands it deeply.