Conversational Artificial Intelligence

Conversational artificial intelligence is one of the important AI terms that has been explained above with a simple question “What is conversational artificial intelligence?” and answer, but we now delve deeper into comprehending this technology. Some may reference the illustrious Turing Test as the pinnacle of human-machine interaction, a standard that AI may aspire to in future years, potentially even transcending human intellectual capacity.

However, for the scope of this discussion, it’s most practical to consider Conversational AI tools in their present context. Their primary objective can be succinctly stated: infusing human-like authenticity, empathy, and cognitive intelligence into the customer experience, or in other terms, rapidly and accurately addressing customer needs while fostering a relationship.

So, the key differentiator of conversational AI is these components enable the technology to understand and process human language, allowing it to interact with users in a way that reflects natural human conversations and dialogue.

When Conversational AI effectively navigates customer and employee issues, leading to successful outcomes, it can be said to have the customer intent and fulfilled its purpose. This takes precedence over convincing an individual that their interaction is with a human.

Regardless of whether individuals discern that a sophisticated chatbot is a “real” person, the resolution of their problems remains paramount. In this respect, Conversational AI technologies are already demonstrating considerable progress.

Can you have an effective conversation with a non-living thing? Yes, thanks to Artificial Intelligence. 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. 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, conversational artificial intelligence 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.

Conversational AI operates through a blend of natural language processing (NLP), understanding (NLU), generation (NLG), and machine learning (ML). The system is trained on copious amounts of data, including text and speech, enabling it to understand, process, and generate human-like dialogue.

NLP converts unstructured data into a structured format, allowing the AI to comprehend and understand human language. The AI continuously learns from these interactions, recognizing speech patterns, improving its responses, and enhancing its efficiency.

NLU, a subset of NLP, discerns the intent behind a user’s query, while NLG facilitates the generation of fitting textual responses. The incorporation of ML ensures that the system constantly evolves and refines its response quality over time.

Natural Language Processing consists of four steps: Input generation, input analysis, output generation, and reinforcement learning. Unstructured data is 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 voice assistants—to carry out interactions between customers and businesses, and to automate activities within businesses.

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 Artificial Intelligence understands the context of dialogue by means of NLP and other supplementary algorithms. These principal components allow it to process, understand, and generate responses in a natural way. Along with large language models (LLMs) and 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 the voice data flow in a constant feedback loop with ML processes to continuously improve and sharpen the AI algorithms. The goal is to comprehend, decipher, and respond appropriately to every interaction.

Here is a slightly more detailed description:

Machine Learning (ML) is a sub-field of artificial intelligence, AI platforms 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 them to make predictions.

Natural language processing is the current method of analyzing language with the help of the machine learning algorithms 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 models will advance the natural language processing capabilities of conversational AI even further.

Automatic Speech Recognition (ASR) is fundamental to conversational automation, enabling spoken language to be identified by the application. If the AI cannot recognize human speech and comprehend and understand the intent of 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 NLP to uncover intent. There are three stages to this:

1. Sound waves created 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 email addresses or even phone calls or numbers difficult, with a high rate of misunderstanding. But progress is ongoing.

The simplest form of Conversational AI is an FAQ bot or conversational ai chatbots, 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 not all chatbots, because they do not use NLP, dialog management, or advanced analytics or machine learning to build their knowledge over time.

Who wouldn’t admire the awesome science and ingenuity that went into conversational artificial intelligence? 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.

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. 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 channels, 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.

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 AI for Customer Service, specifically conversational AI, slashes the OpEx around salaries and training (a particular benefit for SMBs) and this AI Assistant with conversational AI power never loses patience over a difficult issue or a hard-to-please user.

Consistency is another benefit. Since most of human interactions seeking support are repetitive and routine, it becomes simple to program an AI Assistant with conversational AI power 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.

Aisera’s proprietary unsupervised NLP/NLU technology, user behavioral intelligence, and sentiment analytics are protected by several patent-pending applications.

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.

Conversational AI is constantly progressing toward initiating and leading customer interactions, with humans only supporting the conversation flow as needed.

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

Today’s customers demand quick, easy service experiences across the industry spectrum. Aisera’s feature-comprehensive and advanced self-service automation combines conversational AI technology and conversational automation into one SaaS cloud offer for both IT Service Desks and customer service chatbots. See the key benefits here for each business type:

Greater productivity and convenience in financial services & fintech

• Broaden the scope and efficiency of self-serve options by leveraging AI in fintech
• 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

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 very specific tasks only. 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.

The obvious example of conversational AI in our daily lives is using speech-based assistants like Amazon Alexa and Google Home by millions of people

As a result, messaging and speech-based platforms are quickly displacing traditional web and mobile apps to become the new medium for interactive conversations. This overview of conversational AI will detail how this advanced technology works and how it is a driver for digital transformation for businesses.

As users worldwide become more dependent and accustomed to these platforms, it’s no surprise that enterprises are rapidly adopting conversational AI technology to keep up with user interests and demands. While the ultimate goal of deploying these solutions is to revolutionize service experiences for customers and employees, it is important to know what conversational AI and AI chatbots are, how they help brands differentiate themselves within the market, and how best to leverage them.

Conversational AI technology 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 both answer questions and 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 more complex tasks, or unexpected queries. Providing an alternative channel of communication, including a smooth handover to a human representative, 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 using training programs, referrals, and other measures to avert negative public perception.

Today, instant availability and accessibility matter more than ever. Digital businesses are no exception to this. As more and more users now expect, prefer, and demand conversational self-service experiences, it is crucial for businesses to leverage conversational AI to survive and thrive within the market.

Following are expert predictions from Gartner about how AI will transform digital businesses in the next five years:

“By 2022, 70% of white-collar workers will interact with conversational platforms daily (Gartner). $3.9 trillion projected AI-derived business value growth by 2022” (Gartner).

“By 2023, 30% of customer service organizations will deliver proactive customer services by using AI-enabled process orchestration and continuous intelligence” (Gartner).

“By 2024, AI will become the new user interface by redefining user experiences where over 50% of user touches will be augmented by computer vision, speech, natural language, and AR/VR” (IDC Report).

“By 2025, customer service organizations that embed AI in their multichannel customer engagement platform will elevate operational efficiency by 25%” (Gartner).

Aisera offers the most feature-comprehensive and best conversational AI platform in the market, which blends AI virtual assistant technology, cognitive search, and AI workflow automation into one SaaS cloud offer for IT Service Desk and Customer Services. Book a free demo today!

Aisera delivers an AI Service Management (AISM) solution that leverages advanced Conversational AI and 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!