How businesses can benefit from conversational AI applications

As is the case with other emerging branches of AI, the capabilities of conversational AI tools are steadily improving, ensuring that these tools are no longer relegated to simplistic requests and responses with consumers.

Conversational AI is making its way into more types of applications, like getting answers to customer service questions or checking HR policies. The most obvious use cases for conversational AI are chatbots or digital assistants that answer repetitive questions. However, augmented analytics platforms also leverage conversational AI to enable more people in an organization to interact with the data. Instead of typing an SQL query, a business user can simply type a natural language query and answer it in natural language.

Conversational AI is enabled by natural language processing (NLP), specifically natural language understanding (NLU) to determine what a user is trying to communicate and natural language generation (NLG) to respond to that person . It also uses machine learning to constantly improve its accuracy.

One of the main criticisms of conversational AI is that it does not understand human emotion, although this is changing. The more human AI appears and behaves, the less likely an interaction is to escalate.

Understand the human factor

Frustrated customers often change the volume of their speech to help convey their current state of mind. For example, a typed message may contain the use of all capitals and exclamation points to express anger. By using semantics to understand relationships between words and reinforcement learning, conversational AI improves.

“Conversational AI can understand human characteristics such as pauses, repetition, tone and even sarcasm,” said Jason McMahon, digital strategist at Australian digital marketing agency Bambrick. “These are fundamental tools of human communication that conversational AI can quickly leverage to make encounters more engaging for customers and businesses.”

Digital assistants, such as Siri and Alexa, show how commonplace conversational AI has become, though the results of these interactions can be misleading. While errors are usually only slightly inconvenient from a consumer perspective, errors in a business context can be very costly, both financially and reputationally.

McMahon said conversational AI apps can use NLP to understand sentiment by classifying customer behavior as positive, negative or neutral, allowing a chatbot to respond appropriately to the user. Yet many emotions are just positive, negative, or neutral undertones and humans expect AI to understand those undertones.

Context is key

What a person says or types can be easily misunderstood without knowledge of the user’s context. However, one can infer a set of potential interactions depending on whether the person is using a retail app or a travel booking app, for example.

Computer vision is also used to identify behaviors and body language that provide additional context. While words are central to conversational AI, non-verbal cues can emphasize or even contradict what a person is saying. In short, the more information the AI ​​has about a user at a specific time, the more accurately it can determine what action to take.

AI platforms also collect data that can be used for training. For example, sports and entertainment event ticketing company AXS realized that its call center staff could no longer handle the huge backlog of questions, so the company decided to implement the platform. Conversational AI from Satisfi Labs. Satisfi’s AI assistants leverage shared insights gathered from 350 client organizations using the platform. In the end, AXS managed to reduce live escalations to 6% of all traffic and saved around $900,000.

Conversational AI tailored to the use case

Authenticx, another conversational AI platform provider, analyzed 50,000 customer interactions to better understand and improve customer experiences in healthcare. He discovered the following:

  • On a daily basis, 25% of healthcare customers are stuck in their customer journey.
  • It costs an average of $323,000 per month to resolve these disruptions.
  • Healthcare organizations face average annual costs of $3.8 million related to agent time and resources.
  • A single customer was responsible for 60% of status check calls for claims and general processing.

With this information, costs related to complaints and billing statements can be significantly reduced.

In cybersecurity use cases, security and IT analysts rely “heavily” on conversational AI applications, according to Anurag Gurtu, chief product officer at StrikeReady. Specifically, conversational AI is used to process alerts and incidents.

“Conversational AI in cybersecurity goes beyond knowledge retrieval to include hundreds of tasks and operations,” Gurtu said. “Some of the benefits customers can expect include increased ROI on IT and security investments; improved security; reduced operating costs; and increased speed, accuracy, and scalability.”

As the world becomes increasingly digital, conversational AI applications will become even more common. The more human and precise an interaction with a machine, the more humans are likely to trust it.

Lance B. Holton