An interesting fact about us humans is that, for all our complexity and perceived modernity, our brain has remained essentially unchanged for the past 40,000 years. This means that the neurochemical reactions occurring inside the brain, the triggers we respond to, and the thinking patterns we develop are, by and large, the same as our ancestors living in the savannas and hunting for their daily sustenance.

It also means that while living in the Information Age has unequivocally changed the world in many positive ways, it has also exposed most of us to information overload and placed heavy demands on brain function.

As a frame of reference, Statista notes that the total amount of data created, captured, copied, and consumed globally is likely to have reached 64.2 zettabytes in 2020 (a zettabyte is a billion terabytes or, to put it more graphically, 250 billion DVDs). This amount of global data is estimated to effectively triple by 2025 to a size that would have been inconceivable at any other time in history. As far back as 2016, one UK-based study showed that one-third of people felt stressed because of the increasing amount of digital information they were expected to consume, make sense of, and act on in their daily lives. In the last few years, with rising levels of remote and hybrid work models, this stress has been compounded by the expectation to be always on, connected, and responsive – affecting people's productivity and workplace satisfaction.

Conversational AI Was Born From the Need For Convenience

In this increasingly complex environment, where the expectations of workers and consumers alike are growing incrementally, the need for technology-based solutions that can help us make sense of all the information anytime and anywhere, simplify our lives, and streamline processes is greater than ever before – as are the benefits of adopting such solutions.

Conversational AI is the perfect candidate for solving these 21st-century issues, not only because it answers the underlying questions but also because it does so in a human-like manner. That, in turn, greatly appeals to our ancient brains’ need for connection and makes us feel higher satisfaction levels than other potential tech solutions.

In the Beginning, There Were Chatbots

This was not always the case, however. Building, growing, creating, and seeing what we are capable of… pushed humankind to overcome unthought-of limits. To such a point that one day somebody accepted the challenge to create intelligence out of… a thing! ELIZA, the first chatbot invented in 1966 at MIT, was the starting point for groundbreaking innovation in artificial human language. ELIZA was one of the first chatterbots (later clipped to chatbot). It was also an early test of a machine's ability to produce intelligent behavior equivalent to a human's. So, in its earliest iterations, conversational AI took the form of basic chatbots.

Thus, a user would write down a question, and hopefully, the chatbot would be able to revert with a relevant answer if it had received the proper training to cover the situation described. The conversation flows, meaning what the chatbot would respond to each command it received from the user, needed to be designed manually, similar to a flowchart. Moreover, because they were so labor-intensive, these chatbots often took up to a year, or even more, to fully build, deploy, and train. They required the input of specialized teams with data science and engineering expertise.

The limitations of the intrinsic technology structure of these earlier versions of chatbots usually led to user experiences that felt unnatural, robotic, and extremely limited. The main issue was that, while chatbots could always follow and interpret simple commands, their inability to understand natural language made them less intuitive for the average person.

Then, the Chatbots Grew Intelligent Brains

The next step in the evolution of conversational AI was, thus, to make use of the combined capabilities of machine learning and natural language processing to improve the interaction supported and allow it to resemble human-like conversations more closely. In the first instance, conversational AI solutions employ machine learning to comprehend user input and maintain a human-like conversation. Machine learning allows systems and applications to learn from past experiences by analyzing substantial amounts of data and identifying patterns.

On the other hand, Natural Language Processing (NLP) seeks to equip machines with the ability to comprehend and manipulate human language. Based on machine learning, deep learning, and rule-based modeling, NLP enables software agents to process and summarize text and interact with humans via natural language generation, sentiment analysis, word sense disambiguation, and speech recognition. Embedding these most recent advances enables conversational AI-powered chatbots to comprehend the entire context of a sentence rather than just the individual words. This enables them to comprehend more complex questions and to respond to human dialogue more naturally. Thus, while simpler versions of chatbots serve a valuable purpose in and of themselves by freeing up time for support agents, the actual value lies in conversational AI solutions that understand and respond in natural language.

... And Became Ai-Powered Virtual Assistants

The conversational AI assistants are intelligent enough to gain knowledge from each interaction, growing independently to offer increased levels of support without requiring resource-intensive teams to train them continuously. There is no need for AI training, complex manuals, or professional services, and no preparatory work such as data cleansing. Deploying AI chatbots does not have to take months; instead, the solution can be implemented in a few short days or weeks and, especially in the case of low-code/no-code technology versions, this can be done by non-IT specialists working in a very intuitive graphic designer platform.

Conversational AI Is Part of Our Lives

Incorporating this compelling ability into business operations can provide significant benefits. Technology is changing at a faster scale than ever before. The environment of digital tools that we are expected to work or engage with daily is becoming increasingly complex. The quantity of data we need to absorb and deal with is growing daily – and, in the context of large organizations, just finding it and ensuring that it is correct can become a challenge. In this context, conversational AI can support human users to locate information faster and more intuitively or process this information differently. Thus, the end user gains time back and is relieved of mundane, repetitive tasks that drain his morale at work. On the other side of the spectrum, the business achieves overall efficiency as the automated tasks are performed with the highest degree of accuracy, decreasing costs.

Conversational AI, at its best, produces results indistinguishable from those a human would. As a result, organizations will look to AI in the future to open new revenue, cost-cutting, and business-growth opportunities, adapt faster to market conditions and adopt more innovative operational models.

The swift and smooth transition to Conversational Business Applications (CBA) - creating a single, unified user experience through the seamless interaction of multiple technologies brought to life by a conversational AI assistant - will be the next step in conversational AI applicability. This evolution of conversational technology will bring people, systems, data, and processes together in a symbiotic relationship to maximize productivity, efficiency, and usability.

Businesses have struggled for years to strike a balance between technological innovation and automation while preserving the human experience. Moving forward, conversational AI will provide users with a balance between the speed and efficiency technology provides and the depth, breadth, and expressivity of human language, revolutionizing interactions between humans and businesses at all levels. Businesses can streamline processes, leverage accurate data, facilitate human-like interactions with internal and external audiences, and much more by choosing to go the conversational AI route.