Conversational AI Assistants: The Dynamic Engine Driving Today’s AI Expansion

The ability to communicate information in a meaningful, useful way is the fuel of human civilization. It’s no wonder that Artificial Intelligent communication evokes awe and curiosity—while spurring boundless business opportunities. Until very recently, people believed that only physical brains could store and convey information. Yet, we are now building Artificial Intelligence technology that not only stores but communicates ingeniously to detect, recognize, analyze, intuit, and learn language. 

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Like the human brain, AI solutions take in data, draw relevant conclusions from it, and create new insights from that cache of information. While it took humans countless millennia to acquire language, it has taken a remarkably short time to analyze and understand language structure and put it to use in new applications. 

When we created Artificial Intelligence, it was a certainty that Conversational Artificial Intelligence would follow on closely. Our ability and range of uses for Conversational AI Assistants is galloping ahead at impressive speed. Gartner prophecies that “Innovations in NLT, the proliferation of virtual assistants, and increasing usage of neural machine translation will transform business, social and human-machine interactions…”

“Transform” is an understatement! According to Gartner, by 2023, 40 percent of enterprise applications will have embedded conversational AI capability, up from less than five percent a mere year ago. By 2023, 25 percent of employee interactions with applications will take place via voice—again up from five percent in 2020. Progress is actually racing ahead of visionary predictions. 

The acronyms that define AI conversational technology have become part of our normal technology business lexicon, but it’s worth noting that these simple, three-letter abbreviations represent a revolution in human communication: NLP or Natural Language Processing grew out of classic verbal linguistics and defines the ability of software to manipulate speech and text—a momentous computational leap. 

NLU or Natural Language Understanding encompasses syntax, semantics, knowledge about the world and abstract elements of language that we take for granted, even when casually speaking. NLU opened the door to NLG or Natural Language Generation. A further advance, Emotion AI, can actually comprehend the nuances and feelings that people infuse into words, which is vital to interpretation and understanding.  

Aisera, a leading AI innovator, has seized on these advances to build a rich Conversational AI Platform, a new productivity resource with a wide range of communications capabilities. As companies digitally transform, the ability of the Conversational AI Platform to improve business processes and propel operational efficiency is boundless. For example, Aisera technology now uses conversational AI Assistants to automate and enable employee self-service across the enterprise to improve manually weighted processes in HR, IT, customer service and more, all of this driving an AISM revolution. 

Conversational AI Assistants: Closing the Communication Gap

Language is the ultimate human communication tool. Initially, AI communication took the form of the now-familiar supervised chatbot. But primitive chatbots had a number of limitations which often frustrated users. They lacked the versatility and agility of a true Conversational AI Assistant—one that could derive meaningful context, support business applications, and improve the all-important user experience. 

How did conversational AI Assistants grow to eclipse the basic chatbot? The answer is that chatbots were limited from the start by their fundamental NLP/NLU architecture, which is supervised (as opposed to unsupervised). That difference became the key factor in digital transformation and future-proofing AI applications.

The Limits of Supervised Chatbot Functionality

Rule-based virtual assistants or chatbots rely on Supervised Learning and need human oversight of that process. This approach calls upon data scientists, who use labeled or tagged data to build, scale, deploy, and manage supervised learning models. They match user input to a rule, pattern, or decision tree, and choose answers from predefined responses via algorithms. They can’t transcend those decision trees to gain insight into user requests or respond to questions that are not pre-coded. So additional rules and responses must be fed manually into their decision trees to expand their utility. While the customer or employee waits—and waits. 

Unsupervised learning, by contrast, is the mechanism behind the Conversational AI Virtual Assistant. Unsupervised Learning is more computationally complex but also far more efficient, needing no labeled data or human oversight, but instead training itself through machine learning. Unsupervised Learning is equipped with the inherent intelligence to automatically discover information, structure, and patterns from the data itself. It leverages Machine Learning algorithms to extract and process content from user interactions and comprehends abstract dialog context. Conversational AI Assistants don’t look for a predefined response but rather use NLU to understand the intent of a user request. Then they use NLG to respond in a comprehensible, conversational—human—way. AI Conversational Assistants continuously learn and grow over time, improving the user experience as they become more exposed to conversations.

The result is that supervised or rule-based chatbots restrict automation and rely on human effort and intervention, which impedes self-service, automation, cost savings, and user satisfaction. Humans must fill in these deficits, which mandates programming and writing scripts. Chatbots are being rapidly left behind.

While rule-based chatbots can still handle limited user traffic and simple inquiries,  Conversational AI Assistants far outstrip them in efficiency, handling vast numbers of user requests. They interpret the intent behind a user’s query even when sentence structure, spelling, or grammar are inconsistent, ambiguous, or informally unclear. They can intuit sentiment from user vocalization and tone. Like humans, they learn from experience and store that insight for future interactions, becoming more intelligent and useful with each conversation, each support ticket, and each knowledge article they access.

Aisera’s Conversational AI Platform: Automating the Enterprise End-to-End

Aisera has taken Unsupervised Natural Language Understanding into a whole new arena with its out-of-the-box Conversational AI Platform, whose unsupervised Conversational Assistants can effortlessly reach across the omnichannel to resolve user requests. This platform is easily tailored to industry verticals, dispensing knowledge that enables comprehensive self-service and automates manual service desk tasks at an unprecedented scale.

Unsupervised AI frees service desk agents from manual processes such as solving individual IT issues as routine as password resets. Instead, employees can simply speak with a virtual assistant and take care of their own issues without even consulting the IT team. The reduction in complexity not only reduces costs but yields astonishing improvements in user satisfaction.

Aisera’s Conversational AI Assistants address the entire spectrum of Artificial Intelligence Service Management (AISM)—employing Conversational AI to resolve Customer Service issues, plus modernizing and speeding workflow automation for the service desk. These advances translate into auto-resolution of 65 percent of support requests; a 90 percent improvement in resolution times; and an 80 percent boost in customer satisfaction.  The proof is coming in daily, and picking up momentum. The sophistication and monetizing of the AI Conversational Platform has virtually no limitations in sight at this point. Even the Covid pandemic has inadvertently turned out to be an engine of growth for this dynamic technology.

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