Conversational AI

Role of conversational AI in the digital age

Continuous innovation continues to advance the digital age in uncharted territories. In a tech-driven world, conversational AI has become integral to many industries. While the evolution of conversational AI is far from over – it is helping businesses improve customer communication and generate quality leads.

In the next three years, you can expect the market size of conversational AI to grow at 21.9% CAGR. The total valuation of the conversational AI market will also cross the threshold of $30 billion by 2030. The swift adoption of conversational AI is one of the main reasons the technology continues to be in the spotlight. In the quest to improve customer communication and engagement, businesses want to leverage conversational AI.

In a short time, it has become highly competitive and helping enterprises meet the diverse and unique needs of customers. People send and receive billions of messages each month on social media platforms, and conversational AI is at the center. And that’s because conversational AI ensures customers get prompt service and boost the team’s efficiency at the same time.

Common use cases

The use cases of conversational AI have become common in online stores, social media channels, and websites. AI-powered technology streamlines and accelerates the process of answering customer inquiries or questions. Remember, conversational AI is one of the elements of artificial intelligence that involves dealing with intelligence agents.

In layman’s terms, conversational AI is about using natural language processes to communicate and engage with humans. At its core, it allows machines to understand, respond, and process human language. You can see conversational AI through the lens of synthetic brainpower that allows virtual intelligent agents to comprehend the nature and style of a conversation and communicate. Depending on the intent and context of the conversation, conversational AI can automate communication in the form of speech or text that mimics human-like behavior.

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A deeper look at Conversational AI

Conversational AI or artificial intelligence revolves around virtual assistants that can communicate with people. Conversational AI solutions are the direct result of language processing, automated responses, and machine learning. And making a conversational AI tool work involves recognizing communication patterns and language that leads to imitation and finally creating a human-like interaction experience.

The use cases of conversational AI have become common in online stores, social media channels, and websites. AI-powered technology streamlines and accelerates the process of answering customer inquiries or questions. Remember, conversational AI is one of the elements of artificial intelligence that involves dealing with intelligence agents.

In layman’s terms, conversational AI is about using natural language processes to communicate and engage with humans. At its core, conversational AI allows machines to understand, respond, and process human language.

You can see conversational AI through the lens of synthetic brainpower that allows virtual intelligent agents to comprehend the nature and style of a conversation and communicate. Depending on the intent and context of the conversation, it AI can automate communication in the form of speech or text that mimics human-like behavior.

How Does Conversational AI Work?

The mechanics of conversational AI are more straightforward than people realize. However, understand that there are many technologies and elements that make it work.

1. For starters, AI-based technology receives information in the form of spoken phrases or written text.
2. After that, ASR or Automated Speech Recognition technology processes the spoken input for the machine-readable text format.
3. Next, AI technology deciphers the input information. Next, NLU, or Natural Language Understanding, reads the intent of the text and forms a suitable response.
4. In the dialogue, the AI application sends responses that are converted into human-understandable text format using NLG or Natural Language Generation.
5. Ultimately, the conversational AI tool delivers the processed response from text to speech or in plain text format.
6. When it comes to conversational AI tools – reinforced learning is crucial to learn and re-learn user experiences and deliver the best responses.

Building your first conversational AI: The Rasa approach

Importance of Conversational AI in 2022

Amazon, Google, IBM, Microsoft, SAP SE, Oracle, and Salesforce are major players in the conversational AI market. In the last three years, the use of conversational AI tools has significantly increased. Since AI continues to get smarter, companies want to utilize it to take care of human conversations on a large scale.

Like AI technology, consumer expectations are at an all-time high. Whether it’s mobile apps, texts, or chats, consumers want a delightful and seamless communicative and customizable experience. Intelligent automation is on the rise, and AI-powered conversations allow you to create human-like conversations.

One of the hallmark aspects of conversational AI tools is that they allow companies to improve customer satisfaction, customer loyalty, and customer experience. In fact, conversational AI solutions help companies extend their customer lifecycle.

Since conversational AI automates communication across multiple channels, companies can save a lot of costs and optimize their customer support system to avoid human mistakes. With a conversational AI tool, a company can build a dedicated virtual customer service agent to process customer inquiries 24/7.

Conversational AI Vs. Traditional Chatbots

You can program a conversational AI tool with a high or moderate degree of complexity. But the higher the complexity – the more dramatic results you can see in the end product. Conversational AI is advanced personal assistants that automate interactions between businesses and customers.

For instance, a FAQ-based bot or an online chatbot is a conversational AI tool that allows online users to cross-reference and interact with specific information. In the FAQ bot, the response machine responds to basic questions. Once you input the right keywords, it triggers a suitable response.

Chatbots are basic and don’t fall under the umbrella of conversational AI. And that’s because traditional chatbots don’t use Natural Language Processing, machine learning, or dialog management to improve conversations over time. Traditional chatbots work as tree-bots that process information based on predefined rules.

Amazon Alexa, Google Home, and Apple’s Siri are famous virtual assistant solutions. And these personal assistant products represent the highest level of maturity by conversational AI tools. However, these conversational tools are designed to be linear and don’t necessarily retain the context of the information for every inquiry. While these modern-day assistant products use NLP and ASR, they have straightforward dialog management.

Similarly, virtual employee assistants and virtual customer assistants are built to support automated and specialized processes. In fact, these conversational AI applications function as Robotic Process Automation that helps companies optimize and streamline operations.

What are the Applications of Conversational AI?

With conversational AI applications, sales departments can generate better leads. Conversational AI tools also help companies focus on potential customers that are somewhat familiar with your products or services. Similarly, you can use conversational AI tools to launch and promote products.

You can make custom changes within AI-based chatbots to promote your product 24/7. But contrary to naïve misconception, the software engages with potential customers in a human-like conversation to present an attractive offer. And the best part is that the application can process clients’ requests and preferences on your site in real-time.

Many enterprises use conversational AI tools as purchase assistants to help customers guide throughout the shopping journey and render high customer satisfaction. Similarly, a conversational AI application can work as a uniform sales representative to accommodate customers, show media content, and finalize orders.

Conversational AI by Industry

• Banking, Financial Services, FinTech
  BFSI or banking, financial services, and insurance market is expected to hit $7 billion by 2030. And conversational AI-based helps insurance firms and banks analyze unstructured and structured data collected in real-time.
• Telecommunications Sector
  Like the BFSI market, telecommunication companies have a large user database and receive a plethora of inquiries. AI-powered bots using cognitive technology provide human-like support to customers.
• Airline Industry
  The airline industry suffered more than $2.4 trillion in losses during the pandemic crisis. But it bounced back using AI-powered automation solutions. With conversational AI, traveling has become more efficient. It allows millennials and digital natives to book travel arrangements in an instant and get claim responses on the go.
• Automobile Industry
  Major brands in the automobile sector can use conversational AI to improve customer engagement and provide human-like interactions. Automobile players are now using conversational AI tools to guide customers choose a vehicle based on their unique needs.
• Retail and eCommerce Space
  Most brick-and-mortar retailers and online merchants don’t make an active effort to streamline their communications. With conversational AI in place, traditional retailers and eCommerce players can provide customers with 24/7 access to information. Customers can also check and track orders in real time.
• Real Estate Sector
  The real estate market has started to benefit from conversational AI tools to deliver proactive and consistent customer service. Conversational AI applications help potential homebuyers, or property investors get instant answers. Intelligent AI bots can also maintain customer records, generate high-quality leads, and help real estate agencies close deals faster.
• SaaS
  SaaS providers can use conversational AI to deliver a personalized and heightened user experience. Conversational AI helps SaaS providers better understand the unique requirements of users and create a tailored experience. It increases engagement and propels users to make the best use of software.

Key Components

Conversational AI pairs machine learning with natural language processing. In order to make sense of the information, conversational AI uses NLP to contextualize input and interact with people in an intuitive manner.

Here are the critical components of conversational AI:

• Machine Learning
  

  Machine Learning consists of algorithms, datasets, and unique features that speed up the learning process to respond to various users by reviewing the responses of human agents.

• Natural Language Processing
  

  NLP makes it possible for an AI tool to read human-written text. Natural Language Processing helps AI form a better understanding of sentence structure as opposed to focusing on trigger keywords.

• Automatic Speech Recognition
  

  ASR is an important element of artificial intelligence that works to process voice conversations. Typically, contextualizing human speech or audio is complicated. However, ASR guides AI bots to process human speech and filter out the noise. ASR renders a human-like response by deducing speech-to-text queries. ASR software solutions fall into natural language conversations and directed dialog.

• Data Mining
  

  Data mining is a quintessential element of artificial intelligence that involves data analysis via unsupervised learning. Data mining is not so different from machine learning, but it works as a technique to find new attributes rather than make predictions like machine learning.

Why is Automatic Speech Recognition Important in Conversational AI?

ASR solutions complement conversational AI and bring added value for businesses. Combining ASR software with conversational AI tools makes it easier to contextualize human speed. It increases the accuracy and speed with which AI tools can interpret human interactions.

In hindsight, Automatic Speech Recognition is integral to conversational AI tools to receive voice input. And that’s because ASR directly helps conversational applications to identify spoken human language. High-quality ASR technology can significantly improve user experience.

So, when choosing or reviewing conversational AI tools, use ASR models to process voice inquiries accurately. But whether it’s understanding speech, handling complicated audio, improving accuracy, ensuring searchable data, customizing model training, or creating call transcription, ASR is foundational to conversational AI tools.

What are the Benefits of Conversational AI?

• Increase Information Accessibility
  Sales agents can’t be present 24/7 and every day of the week. But when you integrate your social media channel with a dedicated conversational AI tool, you can depend on an AI bot to tend to customer issues and queries in real-time. It also gives businesses a chance to sell outside of standard hours.
• Save Time
  A conversational AI tool like a virtual assistant is an effective way to meet customers’ needs and save valuable time of your time. Conversational AI bots can handle simple customer service queries and give teams more room to deal with complex issues.
• Eliminate Language Barriers
  One of the highlights of conversational AI applications is that they cut out language hurdles. Today, most AI-powered virtual assistants and bots support more than one language translation. In fact, these applications can detect, contextualize, and efficiently generate a translation of most languages.
• Improve Customers’ Purchase decisions
  Since machine learning renders a personalized customer experience, conversational AI applications can recommend add-ons and products to customers they ordinarily would not have selected. In short, conversational AI tools speed up the decision-making process of customers.

What is Trained Conversational AI?

Like other AI solutions, conversational artificial intelligence tools have to be trained. Sure, conversational AI tools are developed not too differently from standard AI applications. But when you’re playing with human-written texts and audio speeches to create prompt responses, it is crucial to train conversational AI for high accuracy.

In fact, before deploying any conversational AI tool, it is crucial to ensure whether or not it can retrieve a high volume of data and make quick impressions. You can feed the information directly into the conversational AI tool to test its strengths and limitations. Some conversational AI tools are designed to continue learning throughout their usage. In time, more interactions with people improve the natural language processing capabilities of the software.

Challenges of Conversational AI Technologies

Conversational AI tools still have a long way to go and face common challenges in interpreting human speech and text.

• Lack of Safety
  

  While there is no grave risk to use conversational AI tools, it takes proactive effort to store and process data securely to ensure it does not slip through the cracks.

• Difficulty Understanding Human Emotions
  

  Semantic understanding of voice assistants has become strong, but they often fail to understand basic human emotions. Fundamentally, many conversational AI tools struggle to differentiate between “what” and “how” a piece of information is delivered to maintain communication successfully.

• Consistent Changes in Communication
  

  Different dialects, accents, languages, and sarcastic connotations make it difficult for contemporary conversational AI tools to keep up with the communication.

How does it Transform the Future of Apps?

In the era of the smartphone boom, businesses want to adopt new tools to get the most insightful information to improve customer connectivity. But connectivity is more than just delivering messages – it’s the ability to maintain back-and-forth communication with customers.

Today, users can build a solid relationship with apps, and AI is consistently helping developers improve mobile apps’ communication protocols. The future of app development is more nuanced and revolves around human-like communication and relationship. And this is where conversational AI tools come to the rescue.

Whether it’s recognizing text and speech inputs and imitating human-like interactions, conversational AI tools offer multiple use cases. For instance, Lyft mobile app is a perfect example of personalized, graphical, and consistent conversation. Broadly, conversational AI applications extend beyond text and voice commands. In fact, the future potential of conversational AI ties together with how fast mobile applications advance.

Conclusion

A decade ago, the use cases and applications that stem from conversational AI would have been unfathomable. Of course, the dynamics around conversational AI will continue to evolve over time. In fact, with the advent of new tech innovations, conversational AI is bound to get more sophisticated and seamless to optimize customer communication.

When conversational AI solutions interpret “how” humans behave, react, and respond in oral and written languages, it makes it easier for companies to build capabilities and offer services that meet their unique needs. In a broad sense, implementing conversational AI can help companies reach out to more customers, generate more leads, retain loyal customers, and provide a solid customer support system.

Experts predict that the future conversational AI will naturally detect customer sentiment and emotions. In retrospect, an optimistic drive in the tech space makes a strong case for conversational AI. While it is difficult to anticipate the specific direction conversational AI will take in the next decade, companies have a unique opportunity to provide a reliable support system and garner the interest of more people.