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Understand How Conversational AI Works

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Joel Makhluf

What is Conversational AI?

It’s never been more important or challenging for businesses to communicate with customers demanding rapid issue resolution and seamless conversations during interactions. The impact of emerging technologies like chatbots, virtual assistants, and AI will be crucial to that communication for better customer experience strategies, says Gartner.

The nexus point of these technologies is conversational AI, which has emerged as the ideal means to support engaging customers across digital touch points. This set of technologies allows an application to communicate with humans via voice or text.

Most people will interact with an active conversational AI assistant like Alexa, Siri, or Google Assistant, or a chatbot. It can be to make online purchases, interact with a business, or resolve a service or product issue online or via smartphone. But to use this technology to communicate with customers, businesses must understand how conversational AI works to leverage it in the most effective way.

How Conversational AI Works

Conversational AI uses advanced speech recognition (ASR), natural language processing (NLP), and machine learning to understand, analyze, and effectively respond to the questions coming from humans. These technologies come together to help applications make fast decisions in a customer support conversation based on actionable insights gathered from data by using predictive analytics.

  1. The initial step in how conversational AI works occurs when the AI application receives the data from a human through either text or voice input.
  2. NLP then enables the application to analyze human language by breaking down sentences to identify actions or information. NLP also uses natural language understanding (NLU) to understand intent like how a human would in a conversation.
  3. The AI app then plans an understandable response by using Natural Language Generation (NLG). This technology makes the interaction more like a human-to-human conversation. In higher order conversational AI, sentiment analysis may be used to process and identify user emotions that convey the sentiment of the words being used by the speaker.
  4. Machine learning algorithms form the underlying technology that helps conversational AI learn and remember unfamiliar words, phrases, and contexts from conversational data so it gets better at recognizing speaker action, intent, words, and sentiment.

A machine learning algorithm is what data scientists will train with relevant conversational data to respond to a series of defined questions. This algorithm can continuously improve with every human-to-machine interaction. The larger the data sets to train the algorithm and the more interactions it has with humans, the better it becomes. But not all chatbots use conversational AI, so it’s important to understand how they differ.

What’s a Key Differentiator of Conversational AI from Chatbots?

While chatbots are basic software programs that can answer limited questions in a closed system, conversational AI is more context-based. The primary difference between the two is conversational AI is AI-based and chatbots are rule-based.
There are three types of conversational AI in use today:

  • Voice assistants like Alexa that respond to a verbal command or question
  • Mobile assistants like Siri or Google (no different from Alexa) but are designed for mobile phone interactions
  • Chatbots

First generation AI chatbots can only provide rudimentary assistance. Today’s conversational AI chatbots can be predictive and highly personalized. They can deliver more complex, fluid responses that are very similar to human decision-making. Many will have access to a business’s customer relationship management (CRM) or customer data management (CDM) systems to match historical client data.

The true power of a conversational AI platform or application is its ability to observe user-specific traits with each encounter and learn conversational styles that support better and more empathetic interactions. They can also leverage tools like robotic process automation (RPA) to streamline process fulfillment or historical data access to resolve customer needs faster and more accurately. These attributes show how conversational AI works and provide the clues to its importance for every business and organization moving forward in the digital age.

Why is Conversational AI Important?

Communication has always been how humans navigate all aspects of the socioeconomic landscape. But the ability to speed up that process and make it easier through technology has become even more crucial in the digital transformation age. Choices, products, services, and customer demands have exploded, which makes it more challenging for consumers and businesses to hear each other. This gets to the core of why conversational AI is important.

Humans will have the natural ability to communicate with empathy and understanding of intent that surpasses machines for some time to come. Today, it is imperative that humans have machines to support the communication between people. That means communication between those who run businesses and organizations and the customers and clients needing products, services, and support.

Most customers don’t need or want to understand how conversational AI works. They just need a communication experience that caters to their needs at any given moment. Businesses and organizations will only be able to compete and grow if they can understand how conversational AI applications can support humans. Those that choose the right conversational platforms or applications will see important benefits for them and their customers, including:

  • Increased customer understanding
  • An ability to always meet the customer’s needs across any communication channel they choose (omnichannel support)
  • Faster resolution of customer problems, needs, or questions involving products, services, and support
  • Delivering a personalized experience to customers closely mirroring human empathy and understanding of intent
  • Providing a seamless, self-service experience when and where appropriate to meet the digital customer’s demand for greater control
  • Increase cost efficiency through reduced human intervention to enable humans, such as call center agents, to deal with more complex customer challenges and have support in providing an end-to-end customer experience
  • Generating better insights via conversational AI to capture new data they can use for better customer understanding and employee/call center agent engagement

As conversational AI grows, these benefits will expand and grow deeper in ways that will further support human interactions and understanding for faster resolution and better customer experiences.

The Future of Conversational AI

The expected global Conversational AI market size will grow to USD 13.9 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 21.9%, according to Markets and Markets. That growth represents the competitive imperative of conversational AI as much as the acceptance of its benefits. And as conversational AI use grows, its capabilities will also expand.

Human to human conversation is complex, with compound questions, emotional nuance, and ambiguities where the words say one thing and the tone says another. The future of conversational AI will be in a better position to tackle these complexities by understanding the nuances of human conversational speech. It can then respond empathetically to give customers what they want: to be heard and understood.

This future of sentiment analysis affects far more than conversational AI and the human-to-machine conversation dynamic by supporting human-to-human conversations in marketing, sales, and customer service. Augmented intelligence leaders like Cogito are paving the way for greater customer sentiment analysis to augment call agent understanding. This shows how conversational AI and next generation responsive machine learning algorithms can effectively draw from larger data sets representing a broader set of customer sentiments.

The next stage of conversational AI will build on customer engagement through conversational experiences where complex queries and sentiment analysis are the foundation. This will provide businesses and organizations with the means to create advanced chatbots and assistant applications. It will be common for these conversational AI applications to draw on usage and profile data across voice, text, and chat to deliver faster and more accurate omni-channel customer support.

As the real-world data sets these conversational AI applications draw from get larger, the chatbots and virtual assistants will get better at listening, interpreting, and communicating with humans. There are few limits to what the future will bring for conversational AI as long as the goal is to augment human intelligence and empathy rather than mimic it as a substitute.To learn how Cogito uses sentiment analysis to pave the way for stronger CX and agent understanding through conversational AI, follow this link.

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