AI Virtual Assistants: Chatbots vs Conversational AI

Ask a fairly computer-literate individual the question, “What is conversational AI?” And they may answer with something like: “An artificial intelligence technology that combines Natural Language Processing (NLP) with chatbot software to generate conversational responses and cues that approximate human interaction.” They might mention the vaunted Turing Test as the ultimate measure of human-machine interaction, though it will probably be years before AI can aspire to that gold standard—let alone “Artificial Superintelligence that actually surpasses human intellectual reasoning.

For our purposes, it’s best to examine Conversational AI tools pragmatically, in terms of their current goal, which is simple to state: delivering human authenticity, empathy, and cognitive intelligence to the Customer Experience. Or, to put it another way, addressing customer needs quickly and accurately while building rapport.

Conversational AI has achieved its purpose when it can drive successful outcomes for customer and employee issues. And that takes precedence over convincing somebody that they are actually speaking with a human. After all, even if people are sure that a clever chatbot is a “real” person, they still need their problems solved. And Conversational AI is already succeeding in that by leaps and bounds.

Humans are not the only creatures to converse, of course. Not only do animals converse in ways whose sophistication we are only now realizing, but apparently even plants converse, with a huge impact on the earth itself. So there are as many answers to “what is a conversation” as there are living things conversing.

But can you have a true conversation with a non-living thing? Yes, thanks to Artificial Intelligence; we call it Conversational AI. For our purposes, the conversation is a function of an entity taking part in an interaction. What enables that interaction to have meaning is language—the most complex and intricate function of the human brain.

Conversational AI faced a major gestational challenge in confronting the complexities of the human brain as it manufactured language. And language could only be generated when computers grew powerful enough to handle the countless subtle processes that the brain uses to turn thoughts into words.

When computer science created ways to inject context, personalization, and relevance into human-computer interaction, then Conversational AI could make its debut at last. Conversational design, which creates flows that ‘sound’ natural to the human brain, was also vital to developing Conversational AI.

Before Conversational AI can emote like a human, it must recognize speech and text and ‘understand’ intent, as well as decipher various languages and dialects that people use to convey their thoughts and requests. Spoken, written, and sign languages include these dialects, as well as accents, sarcasm, vernacular, jargon, and slang. So there are countless details and permutations that affect communication between a human and a machine. Further, Conversational AI must be able to successfully function on all current channels or modalities used by humans: text, voice, web chat, SMS, phone, and so on.

Having solved all these linguistic challenges and arrived at the gist of interaction, the AI application must then search for the most appropriate, correct, and relevant response. When it delivers its answer, either by vocalization or text, the solution needs to not only mimic human communication—but convince the conversational partner that their issue has been comprehended and understood.

The simplest form of Conversational AI is an FAQ bot, which most people recognize by now. These are basic answer and response engines, also called chatbots. You type the exact keyword and duly receive a relevant response. Chatbots are so basic that it’s arguable they are even Conversational AI at all. This is because they do not use NLP, dialog management, or machine learning to build their knowledge over time.

This leads us into the next chapter…

Venturing into the nuts and bolts of Conversational AI involves deciphering a number of acronyms that define the structure and underpinnings of the technology.

Conversational AI understands the context of dialogue by means of NLP and other supplementary algorithms. These principal components allow it to process, understand, and generate response in a natural way. Along with NLP, the technology is founded on Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Advanced Dialog Management (ADM), and Machine Learning (ML)—as well as deeper technologies. NLP processes flow in a constant feedback loop with machine learning processes to continuously improve and sharpen the AI algorithms. The goal is to comprehend, decipher, and respond to every interaction.

Here is a slightly more detailed description:

Machine Learning (ML) is a sub-field of artificial intelligence, made up of a set of algorithms, features, and data sets that continually improve themselves with experience. As the input grows, the AI platform machine gets better at recognizing patterns and uses it to make predictions.

Natural language processing is the current method of analyzing language with the help of machine learning used in conversational AI. Before machine learning, the evolution of language processing methodologies went from linguistics to computational linguistics to statistical natural language processing. In the future, deep learning will advance the natural language processing capabilities of conversational AI even further.

Automatic Speech Recognition (ASR) is fundamental to Conversational AI, enabling spoken language to be identified by the application. If the AI cannot recognize and comprehend what a speaker is saying, then an appropriate response becomes just an aspirational pipe dream.

ASR’s acoustic models analyze phonemes (which are perceptually distinct units of sound, for example p, b, d, and t in English) to decode words used by Natural Language Processing (NLP) to uncover intent. There are three stages to this:

1. Sound wave creation by the speaker using a device
2. Volume normalization to minimize background noise
3. Breakdown of sound waves into phonemes, which are connected via analytical models to interpret the spoken words and give meaning to the input.

Alphanumerical characters present a challenge, as they can “sound” similar and make spelling out pf email addresses or phone numbers difficult, with a high rate of misunderstanding. But progress is ongoing.

NLP consists of four steps: Input generation, input analysis, output generation, and reinforcement learning. Unstructured data transformed into a format that can be read by a computer, which is then analyzed to generate an appropriate response. Underlying ML algorithms improve response quality over time as it learns. These four NLP steps can be broken down further below:

1. Input Generation is the means by which the user delivers their communication through either voice or text.
2. Input Analysis, which engages (if text-based) by means of natural language understanding (NLU), which is one element of Natural Language Processing (NLP). This derives meaning and intent from the words. When the input is spoken, automatic speech recognition (ASR) is applied to make sense of the spoken words and convert them to language tokens for analysis.
3. Dialogue Management is the response technology that allows natural language generation to answer a user’s query.
4. Reinforcement Learning is responsible for learning and improving the application over time. This function analyzes user inputs to sharpen and reinforce the accuracy of the interaction and response.

Conversational AI applications can be programmed to reflect different levels of complexity. This allows for variegated end products—such as personal assistants—to carry out interactions between customers and businesses, and to automate activities within businesses.

Data-driven and predictive, Conversational AI chatbots are also known as virtual assistants, virtual support agents, voice assistants, or digital assistants (digital workers). Apple’s Siri and Amazon’s Alexa are examples of consumer-oriented, data-driven, predictive AI chatbots. Of course, they are a dimension up the chain of evolution—far more advanced, capable, personalized, and sophisticated than the simple task-oriented chatbot. Their ability to elevate the customer experience is vast.

A conversational virtual assistant is a contextually aware virtual chatbot. This sophisticated chatbot uses NLU, NLP, and ML to actually acquire new knowledge even as it interacts. They also offer predictive intelligence and analytical capabilities to personalize conversational flows; they can respond based on user profiles or on other information made available to them. They may even ‘recall’ a user’s previous preferences, and then offer appropriate solutions and recommendations—or even guess at future needs, as well as initiate conversations.

Another sophisticated function is to connect single-purpose chatbots under one umbrella. Then the virtual assistant can pull information from each chatbot and aggregate that to answer a question or carry out a task, all the time maintaining appropriate contact with the human user.

Virtual customer assistants use advanced Conversational AI to serve a specific purpose; they are therefore more specialized in dialog management. You may have interacted with one, since they are increasingly popular as customer service resources. Not only do they scale effortlessly, but they also carry context from one interaction to the next to enhance the user experience.

Just as advanced as virtual customer assistants are virtual employee assistants. These can be purpose-built for specific needs and lines of business. They are engineered to automate common business processes—using Robotic Process Automation (RBA). They are extremely valuable in streamlining and smoothing out enterprise operations. Companies integrate them into back office systems to meet the needs of both customers and employees, depending on the functions they address.

Who wouldn’t admire the awesome science and ingenuity that went into Conversational AI? But the most powerful motivator of progress has been the pragmatic, bread-and-butter benefits of technology. Investing in Conversational AI pays off tremendous cost efficiency, enterprise-wide as it delivers rapid responses to busy, impatient users, and also educates via helpful prompts and insightful questions.

Well-engineered, fully implemented, and personalized, Conversational AI spreads satisfaction at every level, from the budget-conscious CFO all the way through HR, IT, and other departments rescued from the drudgery of mundane, repetitive tasks.

Customer service can be a money pit. For example, availability to address issues outside regular office hours in a global landscape sets up a tough choice between paying overtime or potentially losing a customer or employee. But Conversational AI slashes the OpEx around salaries and training (a particular benefit for SMBs). And Conversational AI never loses patience over a difficult issue or a hard-to-please user.

Consistency is another benefit. Since most interactions seeking support are repetitive and routine, it becomes simple to program conversational AI to handle popular use cases. This availability and continuity are fuel for the vaunted Customer Experience. Meanwhile, professional agents are free to participate in more complex queries and help build out their resumes and careers.

Thanks to mobile devices, businesses can increasingly provide real-time responses to end users around the clock, ending the chronic annoyance of long call center wait times. And while a human worker can spot and offer to upsell and cross-sell opportunities, so can a properly trained virtual assistant—improving conversion rate from lead to purchase.

By asking tested, tailored questions, can pique customer interest and support sales team efforts through the funnel. And simply satisfying a mundane customer request often manifests in loyalty and referrals.

Being so scalable, cheap, and fast, Conversational AI relieves the costly hiring and onboarding of new employees. Quickly and infinitely scalable, an application can expand to accommodate spikes in holiday demand, respond to new markets, address competitive messaging, or take on other challenges.

Conversational AI also helps triage and divert customer service inquiries so human agents can apply their training to more complex concerns.

Limited or “Weak AI,” also known as narrow AI is the commonest type: customer support, omnichannel deployment, and even Alexa, Siri, and Watson are considered limited because they focus on a very confined span of tasks. Strong AI, on the other hand, attempts to utilize human-like consciousness to address a broad range of problems.

Even with comparatively rudimentary problem-solving skills, Conversational AI can save time and cost, improve cost efficiency and solve most customer support interactions. Someday, however, Artificial Super Intelligence (ASI), with self-aware consciousness, will surpass what we can do today. And that doesn’t mean a sci-fi scenario out of Westworld!

Rather, think of capabilities like sophisticated robotics-enabled surgeries in remote or underserved areas; a dramatic reduction in healthcare costs; with better outcomes: all are imminent possibilities. Think of unlocking unusable documents at scale; autonomous driving; shipping of goods worldwide with minimal human intervention, and other breakthroughs. Think of robo-advisors for financial transactions that take algorithmic trading to levels we cannot currently imagine.

Down-to-earth examples of today’s Conversational AI use cases include:

• Online customer support relieves human agents of onerous tasks as mentioned above, saving money and morale
• Accessibility that reduces entry barriers to assistive technology users, including text-to-speech dictation and language translation
• HR processes that streamline employee training, onboarding, password changes, and updating employee personal information
• Healthcare efficiency improvements that help make services more accessible and affordable while easing and speeding claims processing
• Internet of things (IoT) devices that use automated speech recognition to interact with end users. As we’ve mentioned, we now enjoy Amazon Alexa, Apple Siri and Google Home, but watch for these to sprout new capabilities
• Computer software that we all use and appreciate, such as spellcheck.

Today’s customers demand quick, easy service experiences across the industry spectrum. Aisera’s feature-comprehensive and advanced self-service automation combines blends Conversational AI Technology and Conversational Automation into one SaaS cloud offer for both IT Service Desks and Customer Service. See the benefits for each business type:

Greater productivity and convenience in financial services & fintech

• Broaden the scope and efficiency of self-serve options
• Improve personalization and streamlining of the banking experience
• Speed resolution across the omnichannel

Improved CSAT in telecommunications

• Optimize and improve the customer experience
• Raise per-agent support productivity
• Simplify workflows and escalation procedures
• Enable robust data collection

Dramatic efficiency gains in airlines & travel

• Drastically reduce customer support costs
• Deploy more human agents to non-standard requests
• Mitigate and reduce churn during challenging times
• Save costs by smoothly handling refunds, flight changes, and other requests

Enhanced E-commerce productivity and customer satisfaction

• Improve landing page conversion rates
• Reduce costs of acquisition
• Increase average order size via customer education
• Drive repeat purchases through smart follow-up
• Retain customers and drive loyalty

Reaching new horizons in SaaS growth

• Raises the quality of customer support solutions for greater CX
• Delivers responses effortlessly across the omnichannel in text or speech
• Self-educates through ML and improves the application over time
• Self-corrects and provides increasing quality responses in future interactions

Conversational AI is still a newborn, with widespread business adoption debuting comparatively recently. As with any technological advance, challenges arise, some foreseeable, while others are “unknown unknowns.” Transforming to Conversational AI applications can be a trek to new horizons. Concerns include:

Language mechanics, including dialects, accents, and background noises affect the understanding of raw input. Slang, vernacular, and unscripted language, as well as purposeful or careless sabotage, can generate problems with processing the input. Emotion and tone raise obstacles to conversational AI interpreting user intent and responding accurately.

In addition, the breach or sharing of confidential information is always a worry. Because Conversational AI must aggregate data to answer user queries, it is vulnerable to risks and threats. Developing scrupulous privacy and security standards for apps, as well as monitoring systems vigilantly will build trust among end users apprehensive about sharing personal or sensitive information. Companies can address hesitancies by educating and reassuring audiences, documenting safety standards and regulatory compliance, and reinforcing commitment to a superior customer experience.

Chatbots will inevitably fall short of answering certain complex or unexpected queries. Providing an alternative channel of communication, including a smooth handover to a human, will preempt user frustration.

It’s also important to address potential workforce reductions frankly and to be prepared to support those employees who may be displaced by Conversational AI by means of training programs, referrals, and other measures to avert negative public perception.

Technological frontiers are beckoning, and the dash to reach them is on. Expect Conversational AI to expand and accommodate new functions. And while the contact center remains a human-directed model, the future will only invite further involvement by Conversational AI.

Each and every dissatisfaction with the AI contact center can impact the Customer Experience and eventually the company brand. Yet, transformation to ever more efficient and cost-effective models is inevitable. Meanwhile, it’s important to avoid having AI become only a barrier for users to “game through” in order to reach a human agent quickly.

Make sure that the Conversational AI application is optimized to handle traffic spikes. And that machine learning grows its ability to connect meaningfully, respond to utterances appropriately and empathetically, and offer relevant information.

Conversational AI is constantly progressing toward initiating and leading customer interactions, with humans only supporting the conversation as needed. Even with technology driving the conversation flow, you will find opportunities arise to build positive relationships between the Conversational AI agent and the human being at the other end of the transaction. Watch for and take advantage of these opportunities.

By investing in creating meaningful user experiences, you strengthen loyalty and provide greater value to your brand name.

Request a free demo today!

Aisera delivers an AI Service Management (AISM) solution that leverages advanced Conversational AI & Automation to provide an end-to-end Conversational AI Platform. These advanced AI capabilities automate tasks, actions, and workflows for ITSM, HR, Facilities, Sales, Customer Service, and IT Operations.

Now businesses can deliver greater real-time self-service resolutions through consumer-like service experiences for employees and customers. Digital acceleration and transformation for conversational interfaces are achieved in seconds with Aisera. Book a free conversational ai demo today!