As conversational AI systems evolve, they promise to break down an interaction barrier and enable easier human-machine collaboration. Future iterations will improve accessibility and communicate with visual data interpretations.
People have been talking about technology for years – from admonishing an iron for burning their favorite shirt to words of encouragement when you start a car on a cold morning. However, these words were not used to establish effective communication because these devices were not responding. But the world has changed. Today, with the advent of conversational artificial intelligence (AI), machines actually listen and react when we try to interact with them.
Smart speakers and virtual assistants have become popular in recent years. Today, thanks to Conversational AI, systems like Siri and Alexa are our intelligent assistants, with whom we communicate regularly and who help us to stay up to date with all the information we need. So what exactly is this technology that enables human-machine communication?
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 decode context to respond in a human-like manner.
Conversational AI works mainly on the basis of one important driver – Natural Language Processing (NLP). NLP is a sub-discipline within AI that enables the synthesis and analysis of speech and text, empowering computers with the ability to understand and communicate with humans and other machines. Since human language is very unstructured, NLP helps computers understand users’ queries and extract contextual information.
With advanced NLP, the 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 in which the same statement can have different meanings depending on 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 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 one Answer. So Conversational AI tries to create an easy-to-understand dialogue that is as human-like as possible.
See also: Conversational AI: Improved service at a lower cost
Conversational AI is designed to unlock a variety of business opportunities
In 2020, when Covid restrictions were in full effect, chatbots were one of the most important uses of AI in companies. They compensated for the closure of contact centers and the absence of employees. Post-pandemic, adoption of conversational AI continues to surge, with the global conversational AI market expected to grow by $15.7 billion by 2025.
Recently, many companies are relying on conversational AI to improve customer engagement, hiring processes, and overall work efficiency. AI-powered messaging apps and bots on e-commerce websites make it easy to support customers online, answering frequently asked questions and even offering personalized advice. HR processes like hiring, onboarding and training are now AI-optimized using conversational solutions. AI chatbots and apps reduce time and increase cost-efficiency in routine customer support interactions. The technology also helps businesses collect and analyze data such as call duration, average calls per day, and call outcomes so they can identify areas for improvement, if any.
According to Gartner, by this year 70 percent of employees will be using conversational AI on a daily basis. Given its convenience in many areas, it’s a great way to save costs for businesses as 24/7 automation reduces human effort.
in the retail tradeChatbots engage in personalized conversations with customers and guide them to appropriate purchases. Some chatbots are able to understand the customer’s intent by analyzing their conversation tone and context, allowing businesses to direct conversations based on the customer’s emotions. For repeat buyers, the chatbot also knows each customer’s purchase history, allowing businesses to provide personalized recommendations that ensure quality customer retention and foster stronger relationships. Such tools also ensure better experiences for shoppers and retail workers by eliminating adverse pain points. They help reduce operational delays with inventory monitoring and limit queues with contactless payments.
in the finance, it helps consumers monitor their finances and conduct transactions, all with simple commands. Conversational AI tools are deployed to handle the sheer volume of customer inquiries by answering customer FAQs. These chatbot interactions help employees save time as only more complex queries that require human attention are routed to the appropriate officers.
Appropriately in healthcare, the technology helps patients track health metrics and register symptoms using data. As in other industries, conversational AI helps doctors, nurses and patients access data faster, saving crucial time in some urgent cases. With physician shortages predicted by Accenture to double over the next nine years, conversational AI has the real potential to boost operational robustness. Conversational AI also helps promote mental well-being, as its applications help assess user moods, support patients in the initial stages, and assign more complex cases to qualified professionals.
Another important contribution is virtual education. Personalized learning experience, artificial teaching assistants, quick support, structured study plans, and study friends are some features achieved through conversational AI. At Georgia Technical University, Jill Watson, an IBM AI chatbot, served as one of nine teaching assistants for 300 students and answered 10,000 queries with a 97 percent success rate.
Current Conversational AI limitations
It’s one thing to be able to ask a series of questions, but actually having a conversation is quite another. Conversational AI systems are definitely chatty, but they still haven’t reached the level of language understanding required for a natural, human-like conversation. Natural Language Understanding (NLU) is extremely difficult and one of the biggest challenges many AI researchers are working on. Aside from NLU, they lack empathy, emotional intelligence, and other nuances. AI chatbots are heavily trained on language models, where previous conversation data becomes the key factor in getting machines to create new utterances. These systems have no connection to the real world other than the language in which they were trained.
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 emotion and decision-making skills, chatbots cannot empathize with users or charm them like human conversations can. Providing human-like nuances to conversational AI tools helps earn customer trust. With more ethical awareness and less bias, chatbots can become more affable and trustworthy. Efforts are made to create chatbots with a personality that reflects uniqueness and empathy. But there is still a long way to go before this feat.
In the last decade, great strides have been made in conversational AI. As these systems evolve, they promise to break down an interaction barrier and enable easier human-machine collaboration. Future iterations of conversational AI will improve accessibility and also communicate with visual data interpretations. All of this guarantees that conversational AI will play a major role in the future of work.
However, we must be realistic and cautiously optimistic about the full scope of Conversational AI, which is still in its early stages. The technology is still very much limited to simpler forms of dialogue and taking turns and answering questions in a limited context. However, with recent growth in corporate and industry use and imminent innovation, we can expect it to become even more widespread. Additionally, as concerns about AI ethics grow, innovators will inevitably direct their efforts toward creating equitable AI products through a human-centric approach.