The How, What and Why Behind Conversational AI




PHOTO:
Shutterstock

Conversational AI, which uses Natural Language Processing (NLP), Automatic Speech Recognition (ASR), Advanced Dialog management, and Machine Learning (ML), are likely to pass the Turing Test and provide a more realistic experience than traditional chatbots. Most of us have had interactions on websites with chatbots that were less than satisfactory, leaving us to resolve our issues some other way. Today’s AI-based chatbots are able to have full blown conversations that leave people feeling like they just finished a conversation with a living person.

According to a report from MIT Technology Review, nearly 90% of those polled reported that they have recorded measurable improvements in the resolution speed for complaints, and over 80% reported enhanced call volume processing using AI. 80% also reported measurable improvements in service delivery, customer satisfaction, and contact center performance. A report from RobertHalf on the Future of Work revealed that 39% of IT leaders are currently using AI or machine learning, and 33% indicated that they plan to use AI within the next three years.

Chatbots and AI Have Evolved

The chatbots we have all interacted with on websites are just one example of “old school” chatbots. Another example is the Interactive call routing Voice Response systems we have all been forced to use when we call a doctor’s office, or other business with several departments. Most of us do whatever we have to in order to route the call to a real person. We do that because those types of chatbots are largely inefficient, tedious, repetitive, and slow. 

Those legacy chatbots are only useful for getting basic, predictable information out, such as the hours a business is open, an address, or a website domain. Anything beyond that is almost painful for the user to go through. 

Conversational AI chatbots have the ability to be predictive and highly personalized, with more complex, fluid responses that are very similar to human decision-making. Aside from having access to a customer’s previous interactions with a brand through a CRM or CDP, conversational AI is able to observe user-specific traits (location, age, mood, gender), learn conversational styles from past conversations, and take actions using tools such as Robotic Process Automation (RPA).

According to Chris Radanovic, a conversational AI expert at LivePerson, conversational AI can help consumers connect with brands directly in the channels they use the most. “Intelligent virtual concierges and bots instantly greet them, answer their questions and carry out transactions, and if needed, connect them to agents with all of the contextual data they’ve collected over the course of the conversation,” he said.

Conversational AI is a key for many brands who wish to improve the customer experience. Radanovic explained that consumers and brands are embracing conversational AI because it can be used to provide personalized experiences that are quicker and more convenient than traditional ways of interacting with brands. ”Think waiting on hold for a phone call or clicking through tons of pages to find the right info. Along with a more personalized experience, AI can also help to eliminate the pain points in the customer journey.”

Related Article: What’s Next for Conversational AI?

Types of Conversational AI

Conversational AI is typically used two ways: actively and passively. It is used actively during communications between humans and machines, and passively when it observes communications between one human and another. 

Digital personal assistants, such as Alexa, Siri, and Google Assistant are an example of the active use of conversational AI. Digital customer assistants are another example of active conversational AI, and they can be found on business websites, built into apps, and used for ordering food or responding to customer service tickets. Finally, digital employee assistants allow employees to quickly access customer information during customer service calls, and are also used to obtain vital information during conferences and meetings, or perform tasks that would otherwise require interaction with another employee.

The sheer speed at which conversational AI and machine learning are capable of operating at is very effective at making decisions based on actionable data, said Erik Duffield, GM of Deloitte Digital’s Experience Management Practice. He thinks the competitive ground in digital experiences has moved to a massive number of small decisions and interactions. “We are now seeing digital experiences shift from human to machine interactions, with AI and NLP enabling companies to execute their strategies at the speed and volume required to deliver the experiences that are expected by customers,” said Duffield.

Some great examples of conversational AI and what it could eventually become can be found in science-fiction. Though we are still decades away from HAL in the movie 2001: A Space Odyssey, or Jarvis from Iron Man, it is conversational AI that makes those characters so believable. 

Related Article: Conversational AI Needs Conversation Design

Conversational AI Uses Predictive Analytics To Make Decisions

Predictive analytics is defined as the use of data, statistical algorithms and machine learning to discover the likelihood of future outcomes using historical data and statistical modeling. Conversational AI uses predictive analytics to determine the next “best step” in the customer or employee journey. Aside from using AI and predictive analytics for responding to humans, it is also used for fraud detection, managing resources, and reducing risk. 

Hospitality industry players such as restaurants and hotels are able to use predictive analytics to determine the number of guests on any given night, which allows them to maximize occupancy and ROI. Retailers are able to use predictive analytics to forecast inventory requirements, configure the store layout to maximize sales, and manage shipping. By analyzing past travel trends, airlines are able to more appropriately set ticket prices. 

“Machine learning programs and projects have existed in organizations for a number of years now, advancing from statistics and analytics toward data science. Out of that proving ground emerged a future goal (or requirement) that companies will not only have a few ML models, but dozens, if not hundreds, operationalized and embedded into consumer experiences,” said Duffield. “This shift is leading to a new class of technology known as MLOps, which appears to be following the same maturity path as application development: Continuous Integration/Continuous Deployment, deployment automation, testing automation, etc. Leaders will build capabilities in this space and machine driven decisions will be automated, validated, tested and measured.”

Related Article: Designing Effective Conversational AI

How Does Conversational AI Work?

There are several components that enable conversational AI to have human-like conversations through voice or chat:

  • Automatic Speech Recognition (ASR)
  • Natural Language Understanding (NLU)
  • Dialog Management 
  • Natural Language Generation (NLG)
  • Text to Speech (TTS)

Although AI enables applications to quickly make decisions based on actionable insights gathered from data, there are several steps involved in the process. The initial step occurs when the AI application receives the data from a human through either text or voice input. By using Automatic Speech Recognition (ASR), the AI application is able to understand spoken words and translate them into text. 

Next, the AI app has to determine what that text means by using Natural Language Understanding (NLU). The next part of the process is when the AI app formulates a response to the text. This is accomplished using Dialog Management, which creates a response that is understandable by using Natural Language Generation (NLG). The response may be delivered as text, like an AI chatbot would do, or voice (using Text to Speech), such as Alexa would do. 

Finally, the AI app uses machine learning to accept corrections and learn from each experience, which enables it to produce better and more accurate responses in the future. 

The Challenges of Conversational AI

Conversational AI applications rely on conversation data and are typically trained through the use of a Maximum Likelihood Estimation (MLE) objective and/or Reinforcement Learning (RL). Retraining is often required, even if there has only been a small change in the conversation. Data preparation and training can become an expensive endeavor. Additionally, conversation responses are based around business logic, much of which is industry specific and challenging to describe. Decoding such logic using text data alone as input is practically impossible at this point. 

With voice-based conversational AI, there are many other challenges that come into play. When people are speaking to one another, voice itself is only one way that they are communicating. Fluctuations in tone, hesitation, and volume can be detected by AI and interpreted appropriately. Other non-verbal cues, such as facial expression, eye movements, and hand gestures, are impossible for AI to detect unless the medium is video. This increases the importance of voice interpretation immensely.

Other challenges include the varying degree of knowledge of each person that is communicating with the AI application. Children are limited in their level of knowledge, and have to be spoken to in an age appropriate manner. Adults with different levels of education or experience in a given industry also must be “spoken” to with a response that is likely to be understood. There are also differences when it comes to location, language, sentiment, etc. Feelings and sarcasm are also difficult for AI to interpret. Voice input also has the added challenge of background noises and dialects to deal with. Another issue is that often, when one person is speaking, there are others in the area who are also talking simultaneously, which then requires the AI application to differentiate identical voices from one other. 

Additionally, complexity often becomes a pain point in the customer journey, and is a valid reason why the customer experience is less than exceptional. Tasks such as purchasing an item online take more time because there are so many options, as well as opportunities to compare other items before completing a purchase. Exceptional customer experiences can only occur when complex information is presented in a simplified, easy to use, uncomplicated manner. There is no “one size fits all” solution when it comes to conversational AI.

Final Thoughts

Conversational AI enables people to use natural language to communicate with machines. It’s being used in the call center, in chatbots for customer and employee queries, in kiosks, in automobiles, and in digital personal assistants, all of which are now able to have personalized, highly specific conversations that are all but indistinguishable from human conversations. 





Source link

We will be happy to hear your thoughts

Leave a reply

Logo
Shopping cart