Conversational AI
Shaping the way we do business and the future of customer engagement – today
What is Conversational AI?
Conversational AI is a set of technologies that enable computers to understand, process, and respond to voice or text inputs in natural ways, and is typically used in conjunction with bots or intelligent virtual agents (IVAs). Done well, it helps people interact with complex systems in faster and easier ways, and helps businesses deliver personalized engagements and support at scale.
Why Conversational AI?
Devices are getting smaller, and menus, systems, and apps are growing increasingly complex. As a result, people often don’t know how to find or use feature X or feature Y, but they know what they want to do, and they know how to chat and text. By replacing traditional UIs with human-like dialogs, companies can make customer experiences simpler and more intuitive, and make employee workflows faster and more efficient.
Recent advances in language technologies have also made possible more complex methods of linguistic decision making beyond linear scripts and crude yes/no trees. Because of this, bots and IVAs have matured into solutions that enterprises across many industries are taking seriously.
How Does Conversational AI Work?
Conversational AI makes use of a combination of natural language processing (NLP), machine learning (ML), speech recognition, natural language understanding (NLU), and other language technologies to process and contextualize the spoken or written word as well as figure out the best way to handle and respond to a user input.
Natural Language Processing
Conversational AI works by breaking sentences down to their root level, by handling the many quirks of human language, and by acknowledging that there is information or a command to be parsed. The process by which a computer can understand human language is known as NLP. It does so by pulling out intents and entities, by looking for statistically significant patterns that it has been trained to identify, and by considering factors such as synonyms, canonical word forms, grammar, slang, and more.
Intent refers to what the user is trying to accomplish. This can be a single verb and noun combination, or a complex series of patterns that cover a large number of possibilities in a single phrase.
The system’s goal then is known as intent recognition, or matching a user’s goal to a predefined task or question. For example, the intent of the user here is to search for a specific product.
Entities refer to the elements that define and shape what is needed to complete the task or find the right answer, such as dates, times, locations, numbers, and more. For example, the entities here are black shoes and husband.
Entity recognition, then, refers to the ability for the system to extract all the relevant information that is needed to accurately fulfill the user’s intent.
Training Models
Machine learning and other forms of training models allow computers to recognize the combinations of words that typically indicate an intent, as well as learn and improve from experience without being explicitly programmed by a human. Most platforms and frameworks provide only one of these types of training engines.
Within the world of machine learning, there are two main types of learning methods. Supervised ML refers to analyzing a training dataset and using some form of learning algorithm to make predictions, compare its output with the correct, intended outputs, and identify errors. This is then used to modify the model accordingly – making it more accurate over time. Unsupervised ML, on the other hand, refers to analyzing a set of data that isn’t explicitly classified or labeled, and it is typically used after the bot has been deployed for internal testing or to the field. For chatbots, unsupervised ML usually involves automatically expanding a bot’s language model by adding all successfully identified utterances to its model.
Fundamental Meaning is a deterministic process, meaning input information will always produce the same output information, that uses semantic rules, such as grammar, word match, word coverage, word position, and sentence structure, as well as language context, to match the user utterance to an intent.
Knowledge Graph is another training model that enables you to create an ontological structure, that is a method of grouping according to similarities and differences, of key domain terms. The model then associates them with context-specific questions and their alternatives, synonyms, and ML-enabled classes.
Task Execution
Conversational AI exists, in part, to help users do things faster and more efficiently. Bots and IVAs can collect, modify, and post information to systems of record, request reports from backend systems, retrieve pertinent information, alert humans, and more. Thanks to the prevalence of cloud computing and the availability of API libraries, they can now also unify disparate systems by enabling interplay between underlying technologies.
Omnichannel Experiences
Omnichannel refers to a multi-channel approach to marketing, selling, and serving customers in a way that creates a consistent and cohesive experience regardless of how or where they reach out from. While the majority of businesses today are multi-channel, most lack a way to effectively integrate all these channels while providing consistent messaging at every point of contact. Conversational AI allows customers to start a conversation in one channel and complete the same thread in another – without losing conversation context or continuity.
Conversational Intelligence
Human language consists of many complex rules and nuances, including multiple word meanings, interruptions, slang, clarifications, and more – all of which can fundamentally change the meaning and importance of a user utterance. To account for this, conversational AI provides a number of capabilities that allow you to go beyond predefined and linear resolution paths:
Dialog turn management
Conversations often consist of many twists and turns. Conversational AI allows developers to create complex, fluid, and dynamic dialogs. Examples include pausing, starting, and resuming tasks, processing multiple intents in a single utterance, and amending entities at any point in a conversation.
Context management
Conversations between people can differ greatly based on relationship and past knowledge. Conversational AI allows bots to remember key details from past dialogs, user information, preferences, and more, so you can personalize your bot’s message and sales pitch and apply rules and standards whenever needed.
Sentiment analysis
Emotion and tone can significantly change a word’s meaning. Conversational AI allows bots to identify key triggers, including connotation and word placement, that signal the type and intensity of an emotion. This can be used to assess user inputs, trigger custom flows, bring in human agents, and steer conversations.
Multilingual support
Modern business is global. Conversational AI supports nearly every major language, allowing you to more easily expand into new markets and serve more customers. It means you can work with the language of your choice when defining intents, entities, training data, and more.
Putting It All Together
Conversational AI consists of a complex web of language technologies, many of which are not new. What is new is that they’ve finally reached a level of sophistication that allows you to tackle use cases that have a real impact on your bottom line. Thanks to bot platforms, using these technologies is anything but complex.
What Processes Are Relevant to Conversational AI?
Conversational AI enables enterprises to build transformational chat and voice-based solutions for diverse functions and industries. These solutions can handle the majority of existing tasks, workflows, and service interactions with ease.
Automate customer support
A leader in lighting goods was inundated with installation-related service requests, leading to long wait times. With conversational AI, the company was able to deploy a multi-channel bot that guided users through the installation process, reducing problem redressal time from 15 minutes to 80 seconds.
Streamline information retrieval across languages and time zones
A leading remittance company was struggling to reduce high call volume during peak hours. Thanks to conversational AI, the company was able to deploy a bot that answered forex-related inquiries 24/7 – increasing self-service rates by 33%, improving NPS by 17%, and reducing transaction times by 74%.
Leverage conversational IVR to improve call containment
A global telecommunications company was struggling to provide an engaging and effective IVR experience. With conversational IVR, the company was able to automate 34% of existing intents and improve accuracy to include 97% of all targeted user utterances – significantly improving call containment rates and reducing wait times and service costs.
Potential Use Cases
Banking
Insurance
Healthcare
Retail
Improve agent knowledge and agility
A leading healthcare tech company wanted to automate their manual and error-prone agent query process. With conversational AI, the company was able to deploy a bot that answered questions for agents in real-time by pulling data from backend systems – greatly reducing turnaround time and increasing response accuracy.
Increase employee productivity
A telecom giant wanted to consolidate their complex application landscape, as their sales ops team was struggling to use and manage them all. By leveraging conversational AI, the company was able to automate and streamline 110+ tasks – helping to free up 6,000+ man hours and $380k annually.
Potential Use Cases
ITSM
Sales
Accounting
HR
- Consumer Processes
Automate customer support
A leader in lighting goods was inundated with installation-related service requests, leading to long wait times. With conversational AI, the company was able to deploy a multi-channel bot that guided users through the installation process, reducing problem redressal time from 15 minutes to 80 seconds.
Streamline information retrieval across languages and time zones
A leading remittance company was struggling to reduce high call volume during peak hours. Thanks to conversational AI, the company was able to deploy a bot that answered forex-related inquiries 24/7 – increasing self-service rates by 33%, improving NPS by 17%, and reducing transaction times by 74%.
Leverage conversational IVR to improve call containment
A global telecommunications company was struggling to provide an engaging and effective IVR experience. With conversational IVR, the company was able to automate 34% of existing intents and improve accuracy to include 97% of all targeted user utterances – significantly improving call containment rates and reducing wait times and service costs.
Potential Use Cases
Banking
Insurance
Healthcare
Retail
- Employee Processes
Improve agent knowledge and agility
A leading healthcare tech company wanted to automate their manual and error-prone agent query process. With conversational AI, the company was able to deploy a bot that answered questions for agents in real-time by pulling data from backend systems – greatly reducing turnaround time and increasing response accuracy.
Increase employee productivity
A telecom giant wanted to consolidate their complex application landscape, as their sales ops team was struggling to use and manage them all. By leveraging conversational AI, the company was able to automate and streamline 110+ tasks – helping to free up 6,000+ man hours and $380k annually.
Potential Use Cases
ITSM
Sales
Accounting
HR
What Are the Business Benefits of Conversational AI?
Conversational AI-first' will supersede. 'cloud-first, mobile-first' as the most important, high-level imperative for the next 10 years.
8 out of 10 businesses have already implemented or are planning to adopt AI as a customer service solution by 2020
By 2020, conversational artificial intelligence (AI) will be a supported user experience (UX) for more than 50% of large, consumer-centric enterprises.
What Does This Mean for Me?
The tech that forms the bedrock of conversational AI – like chat interfaces, voice usage, and NLP – are no longer new, and the space is moving fast. As conversational experiences become more commonplace, your customers will want and expect them and your competitors will try to fill that gap. Enterprises that ignore paradigm shifts in technology and that fail to keep up with evolving customer expectations often get left behind.
Where Do I Start?
Let us answer your questions. We are here to help you get the most from conversational AI.