As conversational AI systems continue to evolve, they promise to help overcome an interaction barrier and simplify human-machine collaboration. Future iterations will enhance accessibility, communicating with visual interpretations of data.
People have chatted with technology for years, whether it’s berating an iron for burning their favorite shirt or offering words of encouragement when starting a car on a cold morning. However, these words were of no use in establishing effective communication because these devices were not responding. But the world has changed. With the introduction of conversational artificial intelligence (AI), today when we try to interact with machines, they listen and respond.
Smart speakers and virtual assistants have become popular in recent years. Thanks to conversational AI, systems like Siri and Alexa are now our smart assistants that we communicate with regularly and help us stay up to date with all the information we need. 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 allows virtual assistants or chatbots to understand human speech and decipher the context to respond in a human way.
Conversational AI primarily runs on the strength of one major engine – natural language processing (NLP). NLP is a sub-discipline of AI that enables the synthesis and analysis of speech and text and, in doing so, gives computers the ability to understand and communicate with humans and other machines. Since human language is very loosely structured, NLP is what helps computers understand user requests and extract contextual information.
With advanced NLP, conversational AI tries to understand all the different ways to express a statement without being explicitly trained on each of its possible variations and the many ways the same statement can mean different things, given the context of the statement. conversation. NLP breaks down a user’s utterances into requests or commands. Once a user’s requests/commands are identified, machine learning – a subset of AI that allows 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 make dialogue easy to understand and as human as possible.
See also: Conversational AI: better service at lower cost
Conversational AI is poised to unlock a wealth of business opportunities
In 2020, with Covid restrictions in place, chatbots were one of the main applications of AI in business. They compensated for the closure of contact centers and the absence of employees. Post-pandemic conversational AI adoption is still booming, with the global conversational AI market expected to grow by $15.7 billion by 2025.
In recent times, many companies have come to rely on conversational AI to improve customer engagement, hiring processes, and overall work efficiency. Messaging apps and AI-driven bots on e-commerce sites facilitate online customer support and answer FAQs, even offering personalized advice. HR processes such as employee hiring, onboarding and training are now powered by AI using conversational solutions. Chatbots and AI applications reduce the time and improve the profitability of routine interactions with customer support. The technology also helps businesses collect and analyze data such as call duration, average calls per day, and call results, allowing them to detect areas for improvement, if any.
According to Gartner, 70% of white-collar workers will be using conversational AI daily by this year. Given its convenience in many industries, it’s a great way to reduce costs for businesses because round-the-clock automation reduces human intervention.
In detail, chatbots hold personalized conversations with customers and guide them to make appropriate purchases. Some chatbots have the ability to understand customer intent by analyzing their conversational tone and context, allowing businesses to navigate conversations based on customer emotions. For repeat shoppers, the chatbot also knows each customer’s purchase history, allowing businesses to make personalized recommendations that ensure quality customer engagement and foster stronger relationships. Such tools also curate better experiences for shoppers and retail employees by removing detrimental pain points. They help reduce operational delays by monitoring inventory and reducing queues with contactless payments.
In finance, it helps consumers monitor their finances and make transactions, all with simple commands. Conversational AI tools are being deployed to respond to the massive volume of customer queries by answering customer FAQs. These chatbot interactions help employees save time because only more complex queries that require human attention are directed to designated managers.
Relevant in Health care, the technology helps patients track health metrics and record symptoms through data. As in other industries, conversational AI helps doctors, nurses and patients access data faster, saving crucial time in some urgent cases. Amid the prospect of physician shortages, which Accenture predicts will double over the next nine years, conversational AI holds the true potential to build operational robustness. Conversational AI also helps promote mental well-being, as its applications help assess users’ moods, provide assistance to patients in preliminary stages, and assign more complex cases to qualified professionals.
Another important contribution is virtual education. A personalized learning experience, artificial teaching assistants, quick support, structured learning schedules, and study buddy are some of the features that conversational AI brings. At Georgia Technical University, Jill Watson, an IBM AI chatbot, was one of nine teaching assistants for 300 students, answering 10,000 queries with a 97% success rate.
Current limits of conversational AI
It’s one thing to be able to ask a series of questions, but conversing is a whole other ballgame. Conversational AI systems are definitely talkative, but they still haven’t reached the level of language understanding needed to have a natural, human-like conversation. Natural language understanding (NLU) is extremely difficult 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 main driver to get the machines to construct new utterances. These systems have no connection to the real world outside of the language on which they were trained.
Despite improvements to make them more human, conversational AI systems are still mechanical. Making these systems more human ensures customer loyalty as they could go beyond the orders they are programmed with. Due to their lack of emotions and decision-making abilities, chatbots fail to sympathize with users or charm them the way human conversation does. Providing human nuances to conversational AI tools helps build customer trust. With more ethical knowledge and reduced bias, chatbots can become more affable and trustworthy. Efforts are made to create chatbots with a personality that represents uniqueness and empathy. But achieving this feat is still a long way off.
Many advancements have been made over the past decade in conversational AI. As these systems evolve, they promise to help overcome an interaction barrier and simplify human-machine collaboration. Future iterations of conversational AI will enhance accessibility, also communicating with visual interpretations of data. All of this ensures 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 its infancy. The technology is still very much limited to simpler forms of dialogue and taking turns and answering questions in a limited context. Nevertheless, considering its increasing use by businesses and industries in recent times, with the innovations to come, we can expect it to be even more widely adopted. Moreover, with more concerns about the ethics of AI, innovators will inevitably direct their efforts towards creating fair AI products through a human-centered approach.
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