AI has been evolving over the years and is becoming more valuable by the day. As a result, plenty of businesses are now investing in AI.
AI is being used by businesses to improve customer experience as well as the employees working at the company. Not only does AI cut down the amount of time it takes a customer to get help, especially when it is something that can quickly be sorted by an AI, like a booking change or cancellation, but AI can also save your employees valuable time that can be spent on other tasks.
By 2026, the conversational AI market is expected to reach 18.6 billion dollars. Not just is it rapidly growing, but more than half of companies believe that conversational AI is disrupting industries and believe that their competitors will most likely implement such technology.
As you can see, conversational AI is becoming a crucial part of many businesses marketing strategies and customer service.
Getting the hang of conversational AI and implementing it in your business is essential, which is why today we’ll be looking at the ultimate guide to conversational AI in 2022.
What is Conversational AI
Conversational AI is like the upgraded version of a simple chatbot. It’s used to send automated messages and have conversations between computers and humans. It’s still a chatbot but can have a more human-like conversation.
They can communicate like a human by understanding the intent of sentences and then replying in a text that imitates that of a human. The idea is to use these conversational chatbots to engage with customers and make them feel like they’re talking to a real human.
This allows them to feel more important and that their experience is personalized.
A chatbot is also faster and can deal with smaller issues that might take a human longer to respond to and fix.
Chatbots: Who invented them?
ELIZA was the first chatbot recorded in the history of computer science in 1994. It was made by Joseph Weizenbaum at MIT. It was here that the term “Chatterbox” was created.
ELIZA worked by recognizing keywords or phrases from the input and then used those keywords to send a pre-programmed response back. Obviously, this means that ELIZA wasn’t very personalized and would often give the same reaction to different phrases or sentences.
For example, if you mention your family, like, “My father is a fisherman,” ELIZA would reply, “tell me more about your father.”
ELIZA recognizes the word “father” and has an automated response linked to that word. So anytime the word “father” or “dad” is written, it will offer the same answer.
Tell me the difference between conversational AI and a traditional chatbot.
It’s easy to confuse conversational Ai with an average chatbot, but there are enough differences to separate them from each other.
Conversational AI is at the core of what makes chatbots and virtual assistants tick.
Conversational AI uses machine learning to allow it to analyze and comprehend what humans are writing. From there, it can generate a response that correlates to the user’s writing.
Chatbots can use conversational AI, but there is plenty that doesn’t. For example, basic chatbots usually use pre-determined responses or are programmed with rules instead of an AI deciding what to answer.
Conversational AI isn’t rule-based and chooses how to respond according to the context and intent of the user’s response.
A recent study suggests that by 2030 the conversational AI market will reach 32 billion dollars. It’s currently being invested in by plenty of companies with no end.
How does conversational AI work?
Conversational AI uses a platform of structures that can send individual outputs depending on the input.
Using machine learning, conversational AI can keep learning and widen its range of queries to which it can successfully answer or respond. This is because each time a user talks to the AI, it can examine the context and intent of the user’s response, thus learning new questions that might need the same answer.
It might seem simple initially, but machine learning is much more complicated than questions and responses. Therefore, having the right AI structure is crucial.
Here are some of the primary components that make up the natural language processing of conversational AI.
- Machine Learning (ML). Machine learning is part of AI built around algorithms and data sets constantly developing and improving. These algorithms learn from previous messages with humans, learning what a human’s response is to specific questions and answers and what the correct response is to the human response.
- Natural Language Processing (NLP). This is a method of language learning that works together with machine learning. It’s currently being used, but with deep learning around the corner, most conversational AI will switch to deep learning to help AI understand the language better.
- Analyzing Received Input. This is the part where AI analyzes the text sent in by the user and scans to figure out the context and intent of the message.
- Dialogue Management: After NLP is done and the input has been analyzed, the AI needs to reply with an appropriate response. Dialogue management is where the AI decides which answer is best suited to send to the user, using the previous processes to choose the response.
- Reinforcement Learning: Lastly, the user’s and the AI’s response is stored. Then, machine learning analyzes the input and output and whether they match correctly. From there, machine learning can check whether the user’s intent and the AI’s answer matched and better learn to answer the following similar input.
What is conversational AI used for?
Most people have encountered some form of conversational AI before and might not even have known they were talking to AI instead of an actual human. Some chatbots are easy to spot, but some aren’t.
There are plenty of uses for conversational AI. For example, if you’ve ever spoken to customer service using a messenger on their website, there is a high chance that it was a chatbot. It’s regularly used for customer service at this point since FAQs are easily programmed as responses for the chatbot, as well as managing bookings, schedules, and cancellations.
IT desk service
Conversational AI can also be used for IT desk service, helping with basic IT queries and fixes. Instead of keeping IT personnel busy all day with simple fixes, chatbots can help people who might have simple fixes and solutions. Chatbots can still send users through to a real person if the problem cannot be fixed.
Conversational AI can also be used to advertise and sell products. These bots can be set up to offer promotions or just sales and send them to a target audience. If you have a well-set-up chatbot, it should be able to address the person by their name and possibly know some basic information about them.
These bots can get users to sign up for subscriptions or down the funnel toward your product page.
Many businesses forget that conversational AI can be used to collect data.
With countless interactions a day, your conversational AI program should be able to store all the information gathered throughout the day and offered specific analytics about the day’s activities and messages.
- Record all messages and customer calls.
- Make all conversations searchable, so you can identify issues customers might be having.
- Track specific keywords related to issues on all calls and messages and look for customer replies.
- Collect essential data like call times, how many daily responses, and the outcomes of the reactions for the day.
Conversational AI examples across industries
Conversational AI is used across plenty of different industries for different uses. Here are three examples of conversational AI used in various industries.
SmarAction is scheduling automation software with built-in conversational AI that can understand queries about bookings, which we all know can be more complicated than just giving a date and time and booking it.
This AI excels at understanding natural language and can handle any scheduling issues or requests a user might have.
IBM created Watson Assistant, and who better create a conversational AI that can take care of customers’ transactions?
This AI assistant can work in many industries, including fashion and healthcare.
It’s able to answer simple questions, execute transactions, and contact agents when the need is there.
A study showed that companies using Watson Assistant could reduce handle time by 10%, improving customer satisfaction.
Cognigy is an excellent conversational AI tool that allows for efficient customer service 24 hours a day.
Cognigy is best used for customer service, optimizing the time it takes for a customer with queries to get the necessary answers.
Plenty of airlines use this software. This became especially the case after Covid when airlines had to deal with numerous customer service-related issues due to cancellations and refunds. Here, AI like Cognigy can be utilized to reschedule or refund customers that qualify without contacting a customer service representative.
If you’re looking for more conversational AI tools, look at this list of the best conversational AI tools.
With plenty of uses for conversational AI, it’s no wonder it’s been slowly taking over specific business sectors. Of course, it’s not to say that you’d never need to speak to a real person, but with simple tasks, conversational AI can speed things up when real humans are too busy with other, more important things.
Featured Image Credit: Photo by Andrea Piacquadio; Pexels; Thank you!
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