Conversational AI will continue its growth trajectory

As conversational AI systems continue to evolve, they hold promise to help overcome the barrier of interaction and achieve simpler human-machine collaboration. Future iterations will enhance accessibility, communication with visual data interpretations.

People have been talking to technology for years – from scolding the iron for burning their favorite shirt to offering words of encouragement when starting the 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 like Siri and Alexa are now smart assistants with whom we communicate regularly helping us keep up with whatever information we ask for. So, what exactly is this technology that enables communication between humans and machines?

Conversational artificial intelligence 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.

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Conversational AI primarily works on the power of its main driver – Natural Language Processing (NLP). NLP is a sub-discipline within artificial intelligence that enables the synthesis and analysis of speech and text and, in so doing, equips computers with the ability to understand and communicate with humans and other machines. Since human language is largely unstructured, NLP is what helps computers understand user requests and extract contextual information.

Using advanced NLP, conversational AI attempts to understand all the different ways of expressing a statement without being explicitly trained on all of the possible variables and the many ways the same statement can mean different things, given the context of the conversation. NLP divides user statements into requests or commands. Once user requests/commands are identified, machine learning – a subset of artificial intelligence 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 dialogue that looks as human as possible.

See also: Conversational AI: Improved service at lower cost

Conversational AI is set up to open up many business opportunities

Back in 2020, with Covid restrictions in full force, chatbots were one of the higher Artificial intelligence applications in organizations. They compensated for the closure of call centers and the absence of staff. Conversational AI adoption is still in the post-pandemic stage, with the global AI conversation market expected grow by $15.7 billion by 2025.

Recently, many companies have come to rely on conversational AI to improve customer engagement, hiring processes, and overall business efficiency. Messaging apps and AI-led bots on e-commerce websites make it easy to support online customers and answer common questions, even offering personalized advice. HR processes, such as employee recruitment, onboarding and training, are now AI-enhanced with conversational solutions. AI-powered chatbots and apps reduce time and improve cost efficiency in routine customer support interactions. The technology also helps companies collect and analyze data such as call durations, average calls per day and call outcomes, allowing them to discover areas for improvement, if any.

according to GartnerBy this year, 70% of white collar employees will be using conversational AI daily. Because of its convenience in many sectors, it is a great way to save costs for businesses as round-the-clock automation reduces human input.

in selling by piecesChatbots conduct personalized conversations with customers and direct them to make appropriate purchases. Some chatbots have the ability to Understanding Customer intent by analyzing conversation tone and context, allowing businesses to navigate conversations based on customer sentiment. For repeat buyers, the chatbot also knows each customer’s purchase history, allowing businesses to make personalized recommendations that ensure high-quality customer engagement and foster stronger relationships. These tools also provide better experiences for shoppers and retail employees by removing harmful pain points. It helps reduce operational delays through inventory control and narrow queues with contactless payments.

in FinanceIt helps consumers monitor their finances and make transactions, all with simple commands. Conversational AI tools are deployed to handle the huge volume of customer inquiries by answering common customer questions. Chatbot interactions help employees save time as the most complex queries that require human attention are directed to designated administrators.

suitable in Health CareThis 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 critical time in some urgent cases. Amidst the potential shortage of doctors, which Accenture Expect Doubled in the next nine years, conversational AI has the real potential to enhance operational power. Conversational AI also helps with Strengthen Mental wellbeing as its applications helps measure users’ moods, provides assistance to patients in the initial stages and assigns more complex cases to qualified professionals.

Another important contribution is virtual education. Customized learning experience, artificial teaching assistants, quick support, structured learning schedules, and study buddies are some of the features brought by AI to conversation. At Georgia Tech Jill Watson, an IBM AI chatbot, Servants As one of nine teaching assistants of 300 students, they answered 10,000 inquiries with a 97 percent success rate.

Current Limitations of Conversational AI

Being able to ask a bunch of questions is one thing, but actually speaking is an entirely different ball game. Conversational AI systems are certainly chatting, but they have not yet reached the level of language comprehension required for a natural, human-like conversation. Understanding Natural Language (NLU) is very difficult and is one of the biggest challenges many AI researchers are working on. Besides the NLU, they lack empathy, emotional intelligence, and other nuances. AI chatbots are heavily trained on language models where previous conversation data becomes the main driver in getting machines to make new words. These systems have no connection to the real world besides the language they were trained in.

Despite improvements aimed at making them more human-like, conversational AI systems are still mechanical. Making these systems more human-like ensures that customers are retained where they can bypass commands with which they are programmed. Lacking emotions and decision-making skills, chatbots fail to empathize or engage users as well as human conversation does. Providing the human-like nuances of conversational AI tools helps win customers’ trust. With more moral awareness and less bias, chatbots can become more friendly and trustworthy. Efforts are being made to create chatbots with personality that depict exclusivity and empathy. But achieving this feat is still a long way off.

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A lot of progress has been made over the past decade in the field of conversational artificial intelligence. As these systems continue to evolve, they hold promise to help overcome the barrier of interaction and offer simpler human-machine collaboration. Future iterations of conversational AI will enhance accessibility and communication with visual data interpretations as well. All this ensures that conversational AI will play an important role in the future of work.

However, we need to be cautiously realistic and optimistic about the full scope of conversational AI, which is still in its infancy. Technology is still very limited in simpler forms of dialogue, role-sharing and answering questions in a limited context. However, considering its increased use among companies and industries in recent times, with the upcoming innovation, we can expect it to be adopted more widely. Additionally, with more concerns about the ethics of AI, innovators will inevitably direct their efforts toward creating fair AI products through a human-centred approach.

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