The term conversational artificial intelligence frequently pops up in articles, YouTube videos, and social media posts. People sometimes talk about it in terms bordering on science fiction, which makes them sound like they are discussing Skynet or that evil AI computer from Robopocalypse.
No matter how amazing this technology seems, there’s nothing otherworldly or inexplicable about it. It’s just a highly elaborate and complex form of programming with more power than goes into building common software applications So, what does this term include precisely, and how does it work? Let’s sort this out together!
What’s Conversational AI?
This term means AI-powered software that people interact with through text and voice input. All conversational products use natural language processing, machine learning, or a mix of these technologies. They analyze vast volumes of data and scenarios to talk to human users naturally. It’s possible to encounter this type of AI almost everywhere.
Many websites and mobile apps heavily rely on this technology for seamless communication and data gathering. Chatbots are some of the most common examples, but conversational AI also includes voice and virtual assistants. Let’s see how each component plays a part in its internal and external processes.
Components of Conversational AI Technology
- Natural Language Processing (NLP).
To talk to someone, one needs to understand the context behind their speech. Sometimes, a lack of understanding occurs due to limited knowledge of the recent slang and idioms. Our brains also analyze speech patterns to know if someone is hostile, playful, sarcastic, or sincere. When it comes to conversational AI tech, it also needs a way to understand the intricacies of human speech.
This is where natural language processing comes into play. It gives many conversational AI examples the means to analyze and produce human language. NLP heavily uses machine learning to understand how words relate to each other and their different meanings in various scenarios. This brings up another critical component - machine learning.
- Machine Learning.
In the past, programmers had to teach software how to behave in different situations through direct editions of requests and responses. Nowadays, machine learning in conversational applications allows them to learn independently and constantly improve their capacity to understand intent and nuance.
This is accomplished by exposing the software to new data. And what can be a more significant source of information than thousands of users asking for help daily? Each interaction teaches conversational AI solutions about human behavior, needs, and speech patterns. You end up with a machine that constantly fine-tunes itself without outside influence.
- Text analysis.
Conversational solutions don’t blindly process data in the form of disconnected words. They use the same approach as grammatical checkers such as Grammarly.com. When analyzing sentences, they identify subjects, verbs, and objects. These products also determine whether a word is used as a noun, a verb, or an adjective.
Once text analysis is over, it’s possible to understand the meaning of a sentence, how it relates to the larger body of the text or the previous conversations with the user. This component uncovers the main topic and lets the conversational AI technology understand the sentiment.
- Speech recognition.
The best conversational AI products are capable of more than text recognition. Speech recognition lets them comprehend, analyze, and respond to the spoken word. It follows the same pattern as text analysis, uncovering the grammatical structure and meaning behind what’s being said.
Conversational AI products that support this tech respond to voice requests similarly. It’s also possible to teach them how to convert speech into text. This is considered more sophisticated because the component also recognizes the speaker’s tone of voice and emotions.
Common Conversational AI Products
Currently, three major types of conversational solutions are used for many purposes. These include chatbots, IVR, and voice assistants. Here, we’ll talk about each variant at some length to give you a rough idea of their work and purpose.
Chatbots
There’s probably not a single web app that doesn’t use them, from social media platforms to online jewelry stores and flower shops. They serve as virtual assistants that help clients. Chatbots provide a cost-effective and timely way to deal with complaints, sort our order mistakes, and help choose the right items to buy. Aside from web apps, you can find chatbots in Android and iOS software products.
In the past, chatbots could only make pre-programmed responses. But, the developments in conversational AI made their capabilities practically limitless. Thanks to the power of ML and NLP, they produce more nuanced reactions, make suggestions, understand intent, and can help with many issues surrounding a particular industry.
Interactive voice response (IVR)
If you ever called a bank or your ISP, you might have had the experience of working with an IVR. Companies heavily invest in them to avoid maintaining a large customer support staff. These conversational AI examples answer callers and provide them with menu options.
With the help of these robotic assistants, it's possible to request support, check account status, and perform other actions via keypad or voice. Of course, their efficiency varies from one product to another. Some IVRs take forever to understand a request, leading to more frustration for the user. However, fine-tuned products better direct people to the correct departments, offer self-service and answer various questions.
Voice Assistants
As their name suggests, voice assistants use speech recognition to execute tasks and comprehend voice commands. They receive audio input, convert it into text, and process the information. This conversational tech is heavily used in many commercial appliances, such as smart speakers and assistants.
It’s also present in operating systems and search engines. Google has had a voice input option on its platform for many years, and who doesn’t know about Siri and Alexa? The main perk of this technology is that it works with different languages. Of course, it doesn’t work perfectly every time, but makes an excellent addition to traditional chatbots.
Ten Practical Applications Of Conversational AI
This technology is highly versatile, making it a shoo-in for many purposes.
- Customer support.
This field currently sees the most conversational AI technology products. Companies of all industries rush to get their hands on chatbots and assistants to help support agents handle the daily stream of clients. These solutions answer common questions, track orders, provide order information, and check account data. Chatbots also automatically direct people to live support agents if things get too hard for them to solve.
- Banking
Conversational AI is also being steadily introduced in the financial and banking sectors. More institutions implement them to improve client satisfaction. They help customers with fund transfers, balance information, and account management. More advanced models assist with applying for loans, investing, and discussing financial services.
- Commerce.
In the current market, e-commerce companies build personal relations with customers to keep them interested in buying more. The best conversational AI tools increase the chances of people shopping at particular locations through targeted product recommendations. These solutions also answer questions about different items, potentially increasing the sales volume.
- Human Resources.
Conversational tech helps organize many HR practices. For example, they train employees about different aspects of the job and working with the company. This allows applicants to understand their responsibilities, dress code, and rules of conduct. After onboarding, these products update employee information by themselves.
- Internet Of Things (IoT) products.
It’s arguably the most significant niche for conversational AI technology, as it’s present in many retail devices, including smartwatches, speakers, and smartphones. Many people use the likes of Google Home, Siri, and Alexa in their homes despite the security concerns voiced by many users.
Benefits Of Conversational AI
The past couple of years saw a boom in providers and firms looking to improve their processes with this technology. It’s no surprise, as CAI offers great benefits.
- Better customer experience and engagement.
This technology is irreplaceable for improving customer satisfaction. An integrated conversational solution instantly answers their questions with a drastically decreased waiting time. Gathered data is further implemented in targeted marketing, leading to higher sales.
- Scalability and sustainability.
Unlike employees, conversational solutions are always available for instant communication. This is especially helpful with an international worker and client base. These solutions are scalable and sustainable, allowing them to handle many interactions simultaneously, drastically reducing response time.
- Personalized interactions.
Everybody has their individual preferences. AI adapts to this by providing personalized solutions and recommendations. The technology remembers details from previous interactions and better responds to clients’ current situations. It acts just like natural support agents genuinely interested in helping people.
- Improved productivity.
Conversational AI allows businesses to reduce the workload on support team agents and improve operational efficiency. This gives them more time and opportunity to focus on issues that can’t be addressed via chatbots.
- Deep data analysis.
AI-powered solutions use data obtained from customer interaction to extract valuable information about their behaviors, sentiments, and preferences. Companies use this to understand and cater to their customer base.
Final Thoughts
The power of conversational AI exceeds the examples presented in our article. You can experience them firsthand whether you’re a business owner or someone who likes to play around with conversational products.
