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Conversational AI V

What is Conversational AI?

Conversational AI is an umbrella term used to describe various methods of enabling computers to carry on a conversation with a human. This technology ranges from fairly simple natural language processing (NLP) to more sophisticated machine learning (ML) models that can interpret a much wider range of inputs and carry on more complex conversations.

One of the most common applications of conversational AI is in chatbots, which use NLP to interpret user inputs and carry on a conversation. Other applications include virtual assistants, customer service chatbots, and voice assistants.

Savvy consumers expect to communicate via mobile app, web, interactive voice response (IVR), chat, or messaging channels. They look for a consistent and enjoyable experience that’s fast, easy, and personalized. 

For businesses, the key to meeting and exceeding these expectations across channels and at scale is intelligent automation. Conversational artificial intelligence (AI) powers interactions that are near human, improving CX, boosting satisfaction, driving loyalty, and increasing customer lifetime value (LTV).

Components of Conversational AI

Conversational AI can be broken down into five core components. These five core components work together to enable a computer to understand and respond to human conversation:

1. Natural language processing

NLP is the ability of a computer to understand human language and respond in a way that is natural for humans. This involves understanding the meaning of words and the structure of sentences, as well as being able to handle idiomatic expressions and slang.

NLP is made possible by machine learning, which is used to train computers to understand language. NLP algorithms use large data sets to learn how words are related to each other, and how they are used in different contexts.

2. Machine learning

Machine learning is a field of artificial intelligence that enables computers to learn from data without being explicitly programmed. Machine learning algorithms can automatically improve their performance as they are exposed to more data.

Machine learning is used to train computers to understand language, as well as to recognize patterns in data. It is also used to create models of how different things work, including the human brain.

3. Text analysis

Text analysis is the process of extracting information from text data. This involves identifying the different parts of a sentence, such as the subject, verb, and object. It also includes identifying the different types of words in a sentence, such as nouns, verbs, and adjectives.

Text analysis is used to understand the meaning of a sentence, as well as the relationships between different words. It is also used to identify the topic of a text, as well as the sentiment (positive or negative) of the text.

4. Computer vision

Computer vision is the ability of a computer to interpret and understand digital images. This involves identifying the different objects in an image, as well as the location and orientation of those objects.

Computer vision is used to identify the contents of an image, as well as the relationships between different objects in the image. It is also used to interpret the emotions of people in photos, and to understand the context of a photo.

5. Speech recognition

Speech recognition is the ability of a computer to understand human speech. This involves recognizing the different sounds in a spoken sentence, as well as the grammar and syntax of the sentence.

 

Speech recognition is used to convert spoken words into text, and to understand the meaning of the words. It is also used to interpret the emotions of people speaking in a video, and to understand the context of a conversation.

How Does Conversational AI Work?

Driven by underlying machine learning and deep neural networks (DNN), a typical conversational AI flow includes:

An interface that allows the user to input text into the system or Automatic Speech Recognition (ASR), a user interface that converts speech into text. 

Natural language processing (NLP) to extract the user's intent from the text or audio input, and translate the text into structured data.

Natural Language Understanding (NLU) to process the data based on grammar, meaning, and context; to comprehend intent and entity; and to act as a dialogue management unit for building appropriate responses.

An AI model that predicts the best response for the user based on the user's intent and the AI model's training data. Natural Language Generation (NLG) infers from the above processes, and forms an appropriate response to interact with humans.

In many cases, the user interface, NLP, and AI model are all provided by the same provider, often a conversational AI platform provider. However, it's is also possible to use different providers for each of these components. 
  
How to create Conversational AI?

There is no one-size-fits-all answer to this question, as the best way to create conversational AI depends on the specific needs and use cases of your organization. However, some tips on how to create conversational AI include:
1. Start by understanding your use cases and requirements.

The first step in creating conversational AI is understanding your organization’s specific needs and use cases. What are you trying to achieve with your chatbot? What type of conversations do you want it to be able to have? What data do you need to collect and track? Defining these requirements will help you determine the best approach to creating your chatbot.

2. Choose the right platform and toolkit.

There are a number of different platforms and toolkits that you can use to create conversational AI. Each platform has its own strengths and weaknesses, so you need to choose the platform that best suits your needs. Some popular platforms include [24]7.ai Conversations, Microsoft Bot Framework, Amazon Lex, Google Dialogflow, and IBM Watson.

 

3. Build a prototype.

Once you have defined your requirements and chosen a platform, it’s time to start building your prototype. Building a prototype will help you test your chatbot and iron out any kinks before deploying it to your users.

4. Deploy and test your chatbot.

Once your prototype is finished, it’s time to deploy and test your chatbot. Make sure to test it with a small group of users first to get feedback and make any necessary adjustments.

5. Optimize and improve your chatbot.

The final step is to continually optimize and improve your chatbot. You can do this by tweaking the algorithms, adding new features, and collecting user feedback.
 


Implementing Conversational AI

There are a number of ways to implement conversational AI. The most common way is to use natural language processing (NLP) to convert text into machine-readable data. This data can then be used to power a chatbot or other conversational AI system.

NLP, as noted earlier, is a process of understanding human language and using that understanding to convert text into a format that a computer can understand. This process can be used to interpret questions and commands from users, as well as to analyze and respond to user feedback.

There are a number of different approaches to NLP. Some systems use machine learning to train a computer to understand natural language. Others use a rules-based approach, where a human editor creates a set of rules that define how the computer should interpret and respond to user input.

Once the computer has been trained or has been given a set of rules, it can then use this information to power a chatbot or other conversational AI system. This system can be used to handle customer support inquiries, answer questions, and carry out other tasks that would traditionally require human interaction.

What is Conversation Design and Why Does Conversational AI Need It?

Many tools are now available for building chatbots and speech bots that deliver automated conversation development, however, conversation design is not straightforward and remains a human-led discipline. 

In customer service, the ability to resolve requests at a high rate and satisfaction level is critical. Successful resolution depends on intent determination and intent handling. To understand intent better, machine learning (ML) models are trained on actual conversations. That conversational data is tagged by human analysts and contact center agents, and augmented with signals including behavioral (for example, prior web pages viewed), enterprise (order status), and external (local weather/events). This makes for smarter intent prediction and faster resolution.

Unsupervised ML techniques are also used to mine customer-agent conversations to determine common dialogue flow patterns. The sample set of conversational data used for model training is chosen from top-notch agents, as determined by resolution rates and customer satisfaction ratings. Identified flows then give conversation designers a much better starting point for writing dialogues.

Conversations often contain more than one intent. To fully automate an interaction, conversation designers must incorporate intent sequences into their bot design. If the bot is unable to handle the second and subsequent intents, the customer will have to escalate to a human agent—which increases the cost of the interaction. And if a human agent isn’t available, the customer is left with a partially complete interaction—which is probably worse than no interaction at all.

Conversational AI technologies depend on an intent-driven conversation design to deliver solutions for specific use cases such as customer support, IT service desk, marketing, and sales support. Conversational AI also offers integration with chat interfaces in SMS, web-based chat, and other messaging platforms.

Explore how to design conversational AI chatbots and remember, thoughtful conversation design is a key component for success and the ability to turn visitors into engaged customers.

Learn why conversational AI is essential for your business.

Video: Learn More About Conversational AI

Conversational AI and Chatbot Differences

When we speak about automated human-computer digital interactions, the line between chatbots and conversational AIcan start to blur. Oftentimes, the terminologies have been used interchangeably.

Is conversational AI different from a chatbot?

To begin with, let’s look at how they’re connected at a fundamental level:

Conversational AI encompasses a set of foundational technologies for developing chatbots. In other words, an intelligent chatbot is an application that’s built based on a conversational AI platform.

Nevertheless, not all chatbots are based on conversational AI technologies. In fact, a large proportion of chatbots are human scripted and/or rule-based—and not conversational at all. These bots can only produce one-time responses, aren’t interactive, and perform barely one step above an old-school IVR system.

Chatbots, virtual personal assistants, automated messaging systems, agent-assisting bots, and AI-powered FAQ bots, are all types of applications built on conversational AI platforms.

Conversational AI brings together a range of advanced capabilities for an omnichannel UI, contextual awareness, language processing, response generation, intent management, exception/escalation management, advanced analytics, and integration.

A chatbot, on the other hand, is a computer application that simulates human conversation through voice commands, text input, or both. Chatbots make it easy for users to find the information they need in real time, automate responses to user queries, and can complete tasks without the need for human intervention.

Chatbots support a range of digital (for example, messaging apps, mobile apps, website) and voice channels (IVR, smart speakers) to offer both customers and employees a conversational, self-serve experience at scale.

The name chatbot, short for chatterbot, is also often used interchangeably with bot, virtual assistant, AI chatbot, conversational agent, and talkbot.

Want to deep dive into the different type of chatbots?

Start here: All About AI-Powered Chatbots.

Conversational AI contains components that allow it to capture user inputs; break down, process, and understand them; and generate a meaningful response in a natural way—all within microseconds. This is possible because conversational AI combines NLP with machine learning (ML) to continuously improve the AI algorithms.

For convenience, let’s bundle scripted and rule-based chatbots together and call them “traditional chatbots”. So, there are traditional chatbots and AI-powered chatbots. Here’s a side-by-side comparison of the two:

Traditional chatbots

AI-powered chatbots


Low complexity

Basic answer and response machines

Allow for simple integration

Based on limited scope

Need explicit training for every scenario (not “intelligent”)

Require low back-end effort
 


Focused, transactional

Can manage complex dialogues

Integrate with multiple legacy/back-end systems

Based on larger scope

Specialize in completing tasks interacting with multiple systems

Require high back-end effort
 


Complex, contextual

Goes beyond conversations

Contextually aware and intelligent

Can self-learn and improve over time

Can anticipate user needs

Require massive back-end effort
 

For more on this topic, check out the blog: Conversational AI and Chatbots: How We Got Here.

[24]7.ai Conversational AI Differentiators

Conversational AI Challenges

Conversational AI’s maturity has steadily increased over the past few years to the point where it can now deliver excellent business value and outcomes for companies. Nevertheless, challenges abound since this is also a fast-evolving conversational commerce category where very few vendors are constantly innovating and bringing new technologies to the market. Challenges include:

Developing natural language processing (NLP) capabilities that can understand and interpret human interactions. This is a complex task that requires significant effort and investment in research and development. 

Understanding the context of a conversation in order to provide accurate responses. This can be particularly challenging in conversations that involve multiple people or multiple topics.

The need for sophisticated design and development efforts to create a customer experience that engages users and keeps them engaged in the conversation.  

Deploying and integrating a Conversational AI solution into an existing business or application can be a significant challenge. Proper planning and execution are essential to ensure a successful deployment. 

Ensuring the security and privacy of data exchanged via multiple conversational AI-powered channels—this applies to any CX-related information exchange. Compliance with standards such as GDPR, CCCP, and other country-specific regulations is also critical.

As conversational AI permeates global CX platforms, local language support becomes a high priority. Leading brands operating worldwide can’t rely on availability in just one language to meet local needs at scale. Building a robust conversational AI platform to operate in regional languages, dialects, slang, noisy environments, with crosstalk, etc., is a huge challenge.

Dialogue management and conversation design are non-trivial parts of conversational AI. Annotating the intelligence gathered from real agent conversations and building the right model-training data requires ongoing human-in-the-loop expertise.

Building a conversational AI-based application that takes into consideration intent, entity extraction, sentiment analysis, and empathy is challenging and very few vendors offer solutions with these features.

Explainable AI—not all conversational AI platforms use this data science tool, which eliminates algorithmic black boxes and helps answer the “why” within the model’s functionality. Explainable AI also improves trust in the platform’s ability to produce accurate, fair, and transparent results.

Keeping automated conversations relevant can also be a real challenge, with customer needs and preferences changing faster than ever before. As a result, you may need people with coding skills, multiple-persona models, or IT input, making the solution more expensive. Conversational AI platforms that have no-code/low-code self-serve capabilities can enable business users to build and deploy voice and digital bots and context-aware conversational flows in just a few days.

Improve CX with Conversational Commerce

State-of-the-Art Conversational AI

Technology behind conversational bot experiences is based on the latest advances in artificial intelligence, NLP, sentiment analysis, deep learning, and intent prediction. Together, these features encourage engagement, improve customer experience and agent satisfaction, accelerate time to resolution, and grow business value.

Natural Language Processing (NLP)

Most conversational AI uses NLU to intelligently process user inputs against multiple models, enabling a bot to respond in a more human-like way to non-transactional journeys. The core technology understands slang, local nuances, colloquial speech, and can be trained to emulate different tones by using AI-powered speech synthesis.

Sentiment Analysis

This leading conversational AI technology layer abstracts pre-built sentiment and social models to prioritize and seamlessly escalate to an agent when it detects that a customer needs expert advice. Sentiment detection will recognize, for example, an upset customer and immediately route them to an agent. You can also prioritize unhappy customers in the system, placing them in special queues or offering exceptional services.

Deep Learning

This machine learning technique is inspired by the human brain or ‘neural network’ and allows AI to learn by association, just like a child. The more data AI is exposed to, the better it gets—and the more accurately it can respond over time. AI models trained with many years of contact center data from various voice and digital channels result in smarter and more accurate responses to human inquiries. Response accuracy can be further improved over time by learning from interactions between customers, chatbots, and human agents, and optimizing intent models using AI-powered speech synthesis.

Intent Prediction

Using behavioral analysis and tagging activities, conversational AI technologies can understand the true meaning behind each consumer’s request. Knowing intent allows companies to deliver the right response at the right moment through an automated bot or human agent.

The future roadmap for conversational AI platforms includes support for multiple use cases, multi-domain, and multiple vertical needs, along with explainable AI.

The Conversational AI Vendor Market

According to Gartner™, over 1500 conversational AI providers now offer various levels of capability, language support, use-cases, and business models.

Sophistication swings widely depending on what’s supported, such as:

Number of integrations with back-end systems such as CRM

Number and type of channels (voice, text-based chatbots, messaging, etc.).

Customization of chatbots and virtual assistants for vertical specific use cases and applications for faster adoption into production

Number of languages, slang, dialects, local lingo, shorthand, phonetic spelling, grammatical structures, intents, entity, etc.

Horizontal solutions are the most flexible and controllable but take longer to implement, while vertical specific ones come with pre-built capabilities that are a better fit for a specialized use cases in a target domain. Vendors that offer vertical solutions built on an established horizontal platform give companies full flexibility in customizing to meet their precise needs.

According to Gartner, the conversational AI platform market is predicted to grow 75% year-over-year from about $2.5 billion in 2020. Platform vendors now provide differentiated value to businesses with advanced functionality that supports automated intent and entity detection, smaller training datasets, human-in-the loop tools for annotation and conversation design, and a low-code/no-code paradigm for non-technical people to build smart chatbots and virtual assistants.

How to pick the right Conversational AI Solution?

When it comes to selecting a conversational AI solution, there are a few key factors to consider.

First, consider the needs of your business. What questions or tasks do your customers commonly ask or need help with? What areas of your business could benefit from automation?

Next, evaluate the capabilities of different conversational AI solutions. Some platforms are better suited for specific tasks or industries, while others are more versatile.

Finally, consider the cost and complexity of implementing different solutions. Some platforms are more expensive or require more technical expertise to set up and use.

Once you have a better understanding of your business needs and the capabilities of different conversational AI solutions, you can begin to narrow down your options and select the right platform for your business.

The [24]7.ai Conversational AI Difference

[24]7 AIVA™ Conversational AI is a technology layer that combines the world’s most advanced NLP technology with an intent-driven engagement platform to enable ‘near-human’ conversations in digital and voice channels. AIVA understands slang, local nuances, and colloquial speech, and can be trained to emulate different tones by using AI-powered speech synthesis.

Consistent brand experiences: omnichannel orchestration: Consumers today use more devices and channels than ever. With AIVA, you can let them choose when, where, and how to connect with your brand and ensure a familiar experience wherever they are—including digital, voice, and messaging channels. With AIVA at the core, the experience is seamless and consistent at every touch point.

AI with emotional intelligence: sentiment analysis and social detection: Unlike most AI platforms, AIVA can detect when a customer is upset and prioritize service by escalating them to a live agent. This emotional intelligence (EQ) paired with advanced NLP helps you understand what your customers mean, not just what they say. Plus, social models give chatbots or virtual assistants personality—because automated interactions that feel more human make a big difference to customer satisfaction.

Continuous improvement: deep learning technology: AIVA combines deep learning technologies with our unique collaborative tagging to self-learn and evolve, bringing ever smarter and more accurate automation into the equation. We start with AI models trained on two decades of contact center expertise and add technology that enables the AI to learn by association. AIVA uses interactions between customers, bots, and agents to learn and improve continuously.

Smart to collaborate with a human agent: Handing off a conversation to a human agent is just the beginning. AIVA conversational AI uniquely enables virtual assistants that can retrieve one piece of data from a human agent while the customer waits briefly. Once AIVA gets what it needs, it continues the dialogue. The customer gets served and the increased ability boosts containment by leveraging the best of human agents and machine intelligence.

Support for asynchronous messaging: Messaging has transformed the way people keep in touch with each other in their day-to-day lives, and now it’s changing the way they interact with brands as well. The ability to carry on conversations asynchronously is a game changer for companies and consumers alike. AIVA powers conversational AI across messaging channels to meet your customers where they are, boosting satisfaction and loyalty. Powering every interaction with AIVA enables conversational AI clients to deliver superior experiences, control operational costs, and elevate outcomes. It’s better for customers, better for agents, and better for business.

Transforming customer service with conversational AI 

Control operational costs by automating conversations 

Anticipate customer intent and accelerate resolution 

Drive digital transformation and self-service containment 

Boost customer satisfaction by delivering superior experiences 

Increase agent productivity and reduce average handle time (AHT) 

Reach key global markets with support for multiple language
To find out how [24]7.ai’s leading conversational AI technology can change the game for your automated customer conversations, contact us today.

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