What is Conversational AI

The digital transformation continues to evolve and become prevalent in business. As a result, companies use conversational AI to communicate with their customers using computer-generated voice and text systems.

People cannot distinguish between the best conversational AI applications from human-delivered communications.

Many people confuse conversational AI with chatbots, but they are not the same. Chatbots are text-only communications that simulate texting. Chatbots use conversational AI, but not all conversational AI uses chatbots.

While chatbots continue to grow in popularity, conversational AI offers text and voice modalities, and organizations can use them over any device or channel.

How Conversation AI Works

Conversational AI uses automated messaging along with speech-enabled technology so that humans and computers can communicate. It responds by mimicking human conversation. The application does this by:

  • Recognizing text and speech
  • Comprehending intent
  • Interpreting various languages
  • Responding accordingly

For example, when a person contacts their bank, a voice answers and asks how it can help them. Using a few words, they tell the system what they need.

A person can then complete the task they called for without any human interaction. That is a high-quality conversational AI.

The Components of Conversational AI

  • Human initiation: A person will contact an organization by prompting the AI to initiate communication. It does this either written text or spoken word. Voice and text recognition translates this into a machine-readable format.
  • Text/Voice deciphering: The system will decipher the text. It does this using Natural Language Understanding (NLU), also known as Natural Language Processing (NLP), to determine the intent.
  • Dialog management: It then renders an AI-generated response using dialog management. It produces the responses and converts them into a format a human can understand.
  • Response delivery: AI delivers the message through text or speech synthesis to the human initiator.
  • Machine Learning adaptation: Over time, the AI will improve the application by learning from each interaction and adapting accordingly.

The Challenges of Conversational AI

These application use a technology known as Automatic Speech Recognition (ASR) along with Advanced Dialog management, and NLP. It also uses machine learning (ML). That allows it to adapt and react, to learn from each communication.

There are some significant challenges with Conversational AI. Advanced technology is necessary to overcome these challenges. These include:

1. Change in Communications

Languages, accents, and dialects impact the efficacy of the system. Slang, sarcasm, and even emojis are factors that affect the communication between a machine and a human.

Conversational AI systems must adapt to what is considered a normal human voice. Also, it must learn what individual humans consider "normal" language.

For example, in the South, people use the word "y'all" in their vernacular. The conversational AI must decipher this as "you all."  Other parts of the country use the term "fixing to," which the AI must interpret as "about to."

These are a few examples of different language patterns people consider normal who speak English fluently. What happens when the AI must decipher what the people communicate who don't speak English fluently?

Conversational AI applications must learn these differences. Many struggles to adapt.

2. Privacy and Security

Conversational AI systems must operate with security in mind when dealing with sensitive personal data. It is the only way to ensure this information does not end up in the hands of fraudsters.

Companies must ensure the privacy of their customers. All personal details must be kept confidential. The organization must also redact data depending on what channel they use.

For example, when a person must verify a social security number, the system should only ask them to enter the last four digits.

3. Discovery and Adoptions

While Conversational AI systems are generally easy to use, some people still struggle with the technology. Most people understand how to use the systems.

Yet, as conversational AI adapts, people must overcome the challenges of learning the latest applications.

Companies have found some workarounds to this challenge. They set up the applications to offer prompts that assist their customers. The systems do this either verbally or with pop-up instruction bubbles.

Unfortunately, there are still people who cannot use these systems. They become frustrated because they want to interact with a human and not a "robot." Some companies advertise they don't use conversational AI. They do this because of these challenges.

The Role of Automatic Speech Recognition in Conversational AI

ASR is an essential component of conversational AI. It enables the AI application to identify the spoken language.

The ASR overcomes many of the challenges of these applications. It paves the way to ensure a positive customers experience.

For that reason, if an organization adopts conversational AI for customer communications, they must make sure they invest in a high-quality application. The more advanced models that use ASR continue to improve these interactions.

Language scientists must properly train and tune these applications. If a company chooses to adopt conversational AI, it should research the one that best meets organizational needs and goals.