As conversational AI systems continue to evolve, they hold the promise to help overcome an interaction barrier and bring in more simplified human-machine collaboration. Future iterations will bolster accessibility, communicating with visual data interpretations.
People have been conversing with technology for years– from reprimanding a clothing iron for burning their favorite shirt to offering words of encouragement when starting a car on a cold morning. However, these words served no purpose in establishing effective communication as these devices would not respond. But the world has changed. With the introduction of conversational artificial intelligence (AI), today, when we try to interact with machines, they actually listen and respond.
Smart speakers and virtual assistants have become popular in recent years. Thanks to conversational AI, systems such as Siri and Alexa are now our intelligent assistants that we regularly communicate with that help us stay up to date with any information that we require. So, what exactly is this technology that enables communication between humans and machines?
Conversational AI is an umbrella term that describes how machines understand, process, and respond to human language. It is the brain that powers virtual assistants or chatbots to understand human speech and decipher context to respond in a human-like manner.
Conversational AI functions primarily on the strength of a major driver – natural language processing (NLP). NLP is a sub-discipline within AI that enables the synthesis and analysis of speech and text, and in doing so, arms computers with the ability to understand and communicate with humans and other machines. Since human language is highly unstructured, NLP is what helps computers comprehend users’ requests and extract contextual information.
With advanced NLP, conversational AI attempts to understand all the different ways of expressing a statement without being explicitly trained on each of its possible variants and the many ways the same statement can mean different things, given the context of the conversation. NLP breaks down a user’s utterances into requests or commands. Once a user’s requests/commands are identified, machine learning– an AI subset that enables systems to learn and improve from experience without being explicitly programmed– evaluates the request in the context of the conversation and determines the appropriate response. This is how conversational AI tries to create an easy-to-understand dialog that’s as human-like as possible.
Conversational AI is primed to unlock a multitude of business opportunities
Back in 2020, with Covid restrictions in full force, chatbots were one of the top applications of AI in enterprises. They made up for the closing of contact centers and the absence of employees. Post-pandemic, conversational AI adoption is still soaring, with the global conversational AI market expected to grow by $15.7 billion by 2025.
In recent times, many corporations have come to rely on conversational AI to improve customer engagement, hiring processes, and overall work efficiency. AI-led messaging apps and bots on e-commerce sites facilitate customer support online and answer FAQs, even offering personalized advice. HR processes such as employee hiring, onboarding, and training are now AI-optimized using conversational solutions. AI chatbots and apps reduce time and enhance cost efficiency on routine customer support interactions. The technology also helps businesses collect and analyze data such as call durations, average calls per day, and call outcomes, allowing them to detect areas of improvement, if any.
According to Gartner, 70 percent of white-collar workers will make daily use of conversational AI by this year. Given its convenience in many sectors, it is a great way to save costs for businesses as round-the-clock automation reduces human input.
In retail, chatbots hold personalized conversations with customers and guide them to make fitting purchases. Some chatbots have the ability to comprehend customer intent by analyzing their conversational tone and context, allowing businesses to navigate conversations based on the customer’s emotions. For repeat buyers, the chatbot also knows the purchase history of each customer, which allows businesses to make personalized recommendations ensuring quality customer engagement and fostering stronger relationships. Such tools also curate better experiences for shoppers and retail employees by removing detrimental pain points. They help reduce operational delays through stock monitoring and narrowing down queues with contactless payments.
In finance, it helps consumers monitor their finances and make transactions, all with simple commands. Conversational AI tools are deployed to tend to the sheer volume of customer queries by answering customer FAQs. These chatbot interactions help employees save time as only more complex queries requiring human attention are directed to designated officials.
Aptly pertinent in healthcare, the technology helps patients track health metrics and register symptoms through data. Like in other industries, conversational AI helps doctors, nurses, and patients access data quicker, saving crucial time in some urgent instances. Amidst prospects of physician shortages, which Accenture predicts will double in the next nine years, conversational AI holds the true potential to reinforce operational robustness. Conversational AI also assists in promoting mental well-being as its applications help gauge users’ moods, provide assistance to patients in preliminary stages and assign more complex cases to qualified professionals.
Another important contribution is virtual education. Personalized learning experience, artificial teaching assistants, quick support, structured learning schedules, and study buddies are some features brought on through conversational AI. At Georgia Technical University, Jill Watson, an IBM AI chatbot, served as one of nine teaching assistants to 300 students, responding to 10,000 queries with a 97 percent success rate.
Current Limitations of Conversational AI
It’s one thing to be able to ask a set of questions but to actually converse is an entirely different ballgame. Conversational AI systems are definitely chatty, but they still have not reached that level of language understanding needed to have a natural human-like conversation. Natural Language Understanding (NLU) is extremely hard and is one of the biggest challenges that many AI researchers are working on. Besides NLU, they lack empathy, emotional intelligence, and other nuances. AI chatbots are heavily trained on language models where previous conversational data becomes the key driver in getting machines to construct new utterances. These systems have no connection to the real world besides the language that it’s been trained on.
Despite improvements aimed at making them more human-like, conversational AI systems are still mechanical. Making these systems more human-like ensures customer retention as they could go beyond the commands they are programmed with. Due to their lack of emotions and decision-making skills, chatbots fail to empathize with or charm users the way human conversation does. Providing human-like nuances to conversational AI tools help win customer trust. With more ethical cognizance and reduction of bias, chatbots can become more affable and trustworthy. Efforts are being made to create chatbots with a personality that portrays uniqueness and empathy. But, achieving this feat is still a long way off.
A lot of progress has been made over the last decade in conversational AI. As these systems continue to evolve, they hold the promise to help overcome an interaction barrier and bring in more simplified human-machine collaboration. Future iterations of conversational AI will bolster accessibility, communicating with visual data interpretations as well. All of this guarantees that conversational AI will play an important role in the future of work.
However, we need to be realistic and cautiously optimistic about the full scope of conversational AI, which is still in a nascent stage. The technology is still very much limited to simpler forms of dialog and turn-taking and answering questions in a limited context. Nonetheless, keeping in mind its increase in use amongst enterprises and industries in recent times, with forthcoming innovation, we can expect it to be even more widely adopted. Additionally, with more concerns about AI ethics, innovators will inevitably steer efforts towards creating equitable AI products through a human-centered approach.