A NEW ERA FOR AUTOMATION IN CUSTOMER SERVICE WITH CONVERSATIONAL AI
Agent assist is a strategy that uses an artificial intelligence bot to help human agents efficiently resolve customer ques...
Agent handover is the process by which an agent-assist tool hands off a conversation from a bot to a human agent. Typicall...
Amazon Connect is a cloud-based contact center service that was launched in 2017 as an Amazon Web Services (AWS) product. ...
AudioCodes is a global company that specializes in voice technologies. AudioCodes operates in over 100 countries and is a ...
Avaya is a global company that specializes in communication technologies, specifically contact centers, unified communicat...
Average handle time (AHT) is a metric that service centers use to measure the average amount of time agents spend on each ...
Agent assist is a strategy that uses an artificial intelligence bot to help human agents efficiently resolve customer questions and concerns. Agent assist is easy to integrate with an existing customer service support system; when properly utilized, agent assist can result in significant cost savings, increased agent productivity, and increased customer satisfaction.
Agent assist, also known as agent support, provides agents with the information they need to resolve customer requests quickly and consistently. When a customer begins a live chat with an agent, the agent assist bot can monitor the conversation, recognize customer questions, and suggest answers to common questions from a specified template or information base. These features enable many common service requests such as account management and order tracking to be almost completely automated, which allows agents to process requests more quickly and focus on more complex issues that require personalized assistance.
Agent assist is also invaluable for training agents. The tool helps agents get familiar with new products and services quickly, and it ensures that routine questions are accurately answered. Agent assist helps businesses seamlessly transition between agents and ensures that customer satisfaction is not disrupted in the process. Streamlined agent training, efficient use of resources, and increased customer satisfaction make agent assist a powerful tool to increase business profitability and enable scalability.
Agent handover is the process by which an agent-assist tool hands off a conversation from a bot to a human agent. Typically, the agent handover process is designed to ensure that conversations are handed off in certain scenarios related to user preference, user feedback, and issue complexity/criticality.
User preference and feedback are crucial variables to consider in order to maintain customer satisfaction. If a user asks for a human agent or expresses frustration, the agent handover process should be initiated. Similarly, if the bot is unable to resolve an issue or is faced with a high-stakes issue, the issue should be handed off.
For the agent handover process to be effective, the bot must be able to recognize its limitations and be intelligent enough to identify situations that require handoff. One way of achieving this is to train the bot to recognize key words, phrases, or patterns that should trigger a handoff. In addition to training the bot, it is also common to include a user-driven handoff option after each message (e.g. a “chat with agent” button).
Amazon Connect is a cloud-based contact center service that was launched in 2017 as an Amazon Web Services (AWS) product. Amazon Connect is designed for omnichannel use cases and is based on the same technologies that Amazon uses for its customer service. The service uses a graphical user interface, which enables non-technical users to easily set up and manage it.
Amazon Connect works for organizations of any size and can easily scale up or down to meet short-term business demands. Amazon Connect can support up to tens of thousands of agents and uses a pay-as-you-go model. It enables businesses to set up contact centers with just a few clicks and allows third party applications such as Cognigy.AI to be seamlessly integrated via its comprehensive API features.
AudioCodes is a global company that specializes in voice technologies. AudioCodes operates in over 100 countries and is a vendor for 50/100 Fortune 100 companies. Products and services offered by AudioCodes include IP phones, media gateways, routing applications, session border controllers, and more. AudioCodes technologies are used in many popular applications such as Microsoft Teams and Skype for Business.
In 2018, AudioCodes released Voice.AI Gateway, which utilizes the company’s speech recognition technology, call recording, and artificial intelligence. Its cognitive voice-based applications can integrate with private and/or public voice networks and services.
Cognigy and AudioCodes have partnered to offer Voice.AI gateway as a conversation management solution to facilitate intelligent voice conversations, handle service transactions and provide detailed analytics to streamline business processes.
Avaya is a global company that specializes in communication technologies, specifically contact centers, unified communications, and related services. Avaya is the global leader for these services; more than 90% of the largest US companies are Avaya customers. Avaya strives to take business communications to the next level through technologies that are built to connect organizations to their employees, customers, and communities.
One of Avaya’s well-known products in the Avaya Oceana Solution. Oceana is a contact center that enables organizations to interact with customers across all types of channels, including but not limited to email, mobile, web, social media, voice, and video. Oceana includes an analytics framework, browser-based desktop client, and features that enable users to build specialized clients and visual process workflows.
Cognigy.AI seamlessly integrates with the Avaya technology stack and enables contact center automation through deploying powerful virtual agents based on conversational AI.
Average handle time (AHT) is a metric that service centers use to measure the average amount of time agents spend on each transaction. AHT is calculated for a given time period by adding the total talk time and total post-call tasks and dividing the sum by the total number of calls. AHT may also be used as a metric for other service activities, such as emails or chat support (but may be calculated slightly differently depending on the task).
AHT is one of the most important performance indicators for a service center. While a low AHT is desirable, it is important for businesses to focus on the right variables to lower AHT. If a goal is set to minimize AHT in general, it often results in agent behavior that causes decreases in customer satisfaction, such as rushing callers or providing mediocre solutions that result in repeat calls. Instead, more specific goals should be set around improving agent knowledge and performance, which organically results in decreased AHT. For example, organizations should prioritize agent training, creation of shared knowledge bases, and investment in tools that can streamline support. Conversational AI can be a key component to reduce AHT without sacrificing customer satisfaction.
Automated speech recognition (ASR) is the process by which machines recognize spoken human language. The process involves using algorithms to translate human speech into a sequence of text that the machine can understand. High performing ASR is a key feature for any technology that aims to enable voice-based communication between humans and machines.
Automated speech recognition has a wide range of applications that span across various industries; many people utilize ASR daily. Voice prompted customer support lines, voice command systems in cars, voice activated smart home devices are among the most familiar technologies that rely on ASR. However, ASR also has many lesser-known applications including automatic language translation, automatic subtitle generation for the hearing impaired, and others.
Business process management (BPM) is the method by which organizations create, maintain, and update their processes. The goal of BPM is to output efficient processes that can evolve to meet business needs and market demands.
BPM consists of several cyclical phases. First, a process must be designed and modeled; the process should be broken into discrete tasks and put into a visual framework that identifies required data and how the tasks relate to each other (e.g. a flowchart). The process should then be implemented, preferably on a small scale at first to work out any process issues. Once a process has been fully rolled out, it should be monitored for performance by using metrics to measure quality, efficiency, bottlenecks, etc. Gathered metrics can then be used to further optimize the process. Optimization may involve incorporating tools or process automation, often powered by conversational AI.
Benefits of BPM include cost optimization, process efficiency and scalability, and increased productivity. It is an ideal management strategy for agile companies who want to constantly improve their processes and products.
A chatbot is a software application that enables machines to communicate with humans in written natural language. A well-d...
Cloud-native is a broadly used term describing applications optimized for cloud environments and the software development ...
A contact center is an office designed to receive and transmit various types of communication such as calls, emails, socia...
A chatbot is a software application that enables machines to communicate with humans in written natural language. A well-designed chatbot “understands” human communication and can respond appropriately. Machine learning can be used to make bots handle more complex applications that require the chatbot to understand the nuances of human conversation.
Studies have shown that consumers increasingly prefer to communicate via messaging applications, and many expect to be able to communicate with businesses on a messaging platform. Chatbots are a crucial component of such platforms.
Businesses have much to gain from using chatbots. Chatbots allow businesses to engage with multiple customers simultaneously without requiring valuable human resources, which results in cost savings, increased efficiency, and scalability. Chatbots also have the potential to improve customer experience and satisfaction by quickly resolving issues and streamlining communication with the business.
Cloud-native is a broadly used term describing applications optimized for cloud environments and the software development approach by which those applications are designed. The defining feature of cloud-native applications is how they are created and deployed. Cloud-based applications are typically created using a microservices approach and deployed in containers using open source software stacks. The microservices approach results in applications that are comprised of small, independent, loosely coupled services.
Cloud-native applications have a significant edge over traditional applications because they are flexible, scalable, and designed to work within an agile framework. Developers can easily update cloud-native applications based on changing business needs and market demands. System downtime is minimized, and product time-to-market is optimized, resulting in an improved user experience.
Software that is designed cloud-native is not necessarily restricted to cloud / SaaS offerings. Cloud-native applications can also be operated on-premises or in private cloud environments providing similar advantages in up-time, scalability and other metrics.
A contact center is an office designed to receive and transmit various types of communication such as calls, emails, social media, letters, live web-based chat, etc. Contact center operations may be inbound, outbound, or both; inbound contact centers typically handle customer service issues, while outbound contact centers typically handle marketing and data collection.
A contact center is a crucial piece of infrastructure for any large company that routinely handles customer service requests. Having a centralized, designated office to manage customer interactions streamlines customer service efforts and often results in improved customer outreach and quicker resolution of customer concerns. Technology for Contact Center Automation and deployment of voice bots can increase contact center efficiency and help providing customers a frictionless service experience.
In recent years, technology has allowed the creation of virtual, cloud-based contact centers. In this model, a business opts to pay a vendor to host the equipment instead of having a centralized office; agents connect to the equipment remotely. Virtual contact centers allow employees to work remotely, which can result in cost savings for the business and greater staffing flexibility.
Conversational AI is a branch of artificial intelligence that utilizes software and technologies such as natural language processing, machine learning, and automatic speech recognition to facilitate communication between a human and a machine. The goal of conversational AI is to mimic human conversation; to effectively do this, the AI must sound natural and be capable of responding rapidly and intelligently. A high-quality conversational AI should be able to offer responses that are indistinguishable from human responses.
Conversational AIs come is many different forms such as chatbots, messaging apps, and digital assistants. Most people interact with conversational AIs in their daily lives; Google Home, Amazon Alexa, and customer service applications are all types of conversational AI.
Many businesses have recognized the potential for conversational AI to revolutionize the way they interact with their customers. A well-designed conversational AI can provide a personalized user experience and result in significant cost savings for a business over time. Airline carriers, retailers, healthcare providers, and financial institutions are just a few examples of sectors that use conversational AI to help resolve consumer problems and automate customer support.
Many studies predict that conversational AI will become increasingly important in upcoming years. Conversational AI platforms are often seen as easier and faster than in-person communication and phone calls. Younger generations seem to favor conversational AI, and many consumers now expect to be able to communicate with businesses via chat platforms and their preferred messaging apps such as WhatsApp or Facebook Messenger.
Deep learning is a form of machine learning that utilizes artificial neural networks. Deep learning algorithms have one or more intermediate layers of neurons inspired by signal processing patterns in biological brains. For example, a well-known application of machine/deep learning is image recognition. Here, a typical deep neural network would learn to recognize basic patterns such as edges, shapes or shades in lower levels of the network from unstructured raw image data. Higher layers subsequently capture increasingly complex patterns in order to allow the network to label complex features such as a human face or physical objects in an image successfully. A traditional machine learning model would rely on human-labeled images to learn.
Deep learning has many applications, including but not limited to self-driving technology, identification of security threats, medical research, object detection, damage prediction in oil and gas operations, and industrial automation. The potential uses of deep learning are endless, and as such it has become a hot topic in recent years.
First contact resolution (FCR) is a metric used by customer service centers that tracks how well agents can resolve customer queries in a single interaction. FCR can be measured across all channels of communication; examples of FCR include an email ticket that is resolved with a single reply, a call that is resolved with a single conversation, and a web chat that is resolved in a single session. Resolution may be provided by a human agent or applications that utilize artificial intelligence.
The FCR metric is calculated by dividing the number of queries resolved in a single interaction by the total number of queries. To ensure that the metric accurately reflects FRC, it is also important to follow up with customers a few days after processing their issue to confirm that their issue was resolved.
A high FCR is desirable because it indicates business efficiency and customer satisfaction. Research has shown that increases in FCR result in increased customer satisfaction, decreased operating costs, and increased employee satisfaction. Strategies to achieve a high FCR include agent training, incentive programs, and managing customer expectations.
The General Data Protection Regulation (GDPR) is a legal framework that sets guidelines for data protection and privacy in...
The General Data Protection Regulation (GDPR) is a legal framework that sets guidelines for data protection and privacy in the EU. The GDPR was established in May of 2018 and applies across the union; it replaced the Data Protection Directive as the main law outlining how companies must protect personal data of EU citizens.
The GDPR is far more comprehensive and stricter than data protection laws in many other countries, such as the US. The primary goal of the GDPR is to standardize privacy law and provide greater data protection and privacy rights to individuals. The GDPR regulates all aspects of data use, from data collection to data transfer and data destruction. Many consider the GDPR to be the epitome of data protection and privacy guidance; as such, it has become a model for data laws in many other countries such as Japan, Argentina, and South Korea.
Conversational AI applications such as chatbots need to comply with GDPR regulations as they often handle personal end user data. Failure to follow GDPR regulations can result in hefty fines and costs for legal proceedings.
Genesys is a global company that specializes in customer experience and call center technologies both on-premises and in the cloud. Genesys serves over 11,000 companies in over 100 countries and implements solutions that impact marketing, sales, and customer service.
One of Genesys’ most-used products is PureEngage; according to Genesys, it is the only omnichannel and multi-cloud customer experience solution for large businesses. PureEngage facilitates customer and employee engagement across all communication channels using artificial intelligence, real-time contextual journeys, intelligent routing, and machine learning. PureEngage is also highly customizable; it is a powerful, flexible tool for large businesses seeking to optimize their operations.
Cognigy.AI seamlessly integrates with the Genesys technology stack and enables contact center automation through deploying powerful virtual agents based on conversational AI.
Hyperautomation synthesizes multiple technologies such as machine learning (ML), artificial intelligence (AI), and Robotic Process Automation (RPA) to deliver cutting edge automation solutions that rival, exceed, or enhance human abilities. Hyperautomation may also be referred to as “digital process automation” or “intelligent process automation”.
Hyperautomation has the potential to drastically increase business efficiency, reduce business costs, and increase product development rates. Businesses can use hyperautomation to create intelligent digital workers who can learn over time and execute repetitive task work. As a result, an organization can run lean, human resources can be utilized for more complex tasks, and repetitive tasks can be more consistently and quickly executed.
Gartner, a globally recognized research company, named hyperautomation as a top technology trend for 2020. Since then, hyperautomation has been generating a lot of attention. In upcoming years, hyperautomation is likely to become a key component of industry-leading companies.
Interactive voice response (IVR) is a technology that enables machines to interact with humans via voice recognition and/or keypad inputs. IVR systems prompt a user to take a specific action or provide a specific piece of information, such as “how can we help you today?” or “state your date of birth”. The IVR system is typically menu-based and may take a user through multiple steps.
IVR is most notable for the value it brings to customer service. A well designed IVR system can effectively collect information from customers, automate support, prioritize calls, and handle large call volumes. This results in increased business efficiency and cost savings. Additionally, IVR systems enable a business to immediately respond to customer questions and needs, which has a significant positive impact on customer satisfaction. IVR is the ideal technology for businesses seeking to rapidly scale up their customer service operations.
Kofax is a software company that specializes in intelligent, robotic process automation. Kofax serves over 25,000 customers and has over 850 partners. Kofax provides an Intelligent Automation software platform that utilizes robotic process automation and other smart technologies such as cognitive capture, process orchestration, digital messenger, e-signature, and analytics to help take businesses to the next level. Kofax strives to optimize organizations through products that automate repetitive manual tasks, streamline business processes, and improve engagement. Incorporating Kofax software into a business model can reduce process errors and cost, improve customer satisfaction, and help facilitate business growth.
Cognigy.AI seamlessly integrates with the Kofax technology stack and enables simplifying processes through conversational automation and deployment of powerful virtual agents.
Low-code is a software development approach that utilizes graphical interfaces to produce and configure applications. The low-code approach does not require extensive hand-coding or computer programming knowledge. It empowers non-technical business users and domain experts to handle complex tasks that traditionally require a programmer.
Low-code is a valuable approach for organizations because it enables faster software development and allows developers and experts of various experience levels to contribute to the software. Low-code frees up valuable resources and allows users to easily iterate software within an agile framework. This decreases product time-to-market, enables product scalability, and increases business flexibility.
Machine learning is a branch of artificial intelligence that enables machines to process data and improve without explicit...
Machine learning is a branch of artificial intelligence that enables machines to process data and improve without explicit programming. Via machine learning algorithms, machines learn how to recognize data patterns and make decisions based upon the data they receive.
Machine learning algorithms are typically divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, labeled data (i.e. example inputs and outputs) are used to train the machine. In unsupervised learning, unlabeled data are used to train the machine to find and generate structure within the data; instead of using examples to map inputs to outputs, the machine is free to learn about patterns in the data based on predefined criteria and objectives such as finding the most important topics within a book. In reinforcement learning, the machine is provided with a goal and receives feedback (i.e. rewards and errors) from the system that help it learn how to maximize performance.
Machine learning has revolutionized many industries in recent years and has become an integral technology in day-to-day life. Search engines, recommendation platforms, and social media all rely on machine learning algorithms. In the context of conversational AI supervised learning is used to continuously improve conversation quality and reduce frictions. By monitoring user inputs and mapping them to predefined intents, virtual agents learn to deal with a broader variety of utterances and paraphrases that occur in human language.
Machine learning will be increasingly relevant in upcoming years due to our increasingly data-based culture. Big data is more prevalent than ever, and organizations need a way to effectively process it. Machine learning enables organizations to quickly analyze large and complex data sets to make better decisions.
Microsoft launched the Language Understanding Intelligent Service (LUIS) in 2017. LUIS is a cloud service that enables developers to build applications that process human language and recognize user intents. It can understand nuances of natural communication in more than 10 languages and respond appropriately. LUIS has pre-built models for natural language understanding, but it is also highly customizable.
LUIS can be used with any application that communicates with a user to execute a task (chat bots, voice-based applications etc.). LUIS can also be used as a stand-alone NLU to be plugged into any conversational AI platform offering a third party NLU adaptor such as Cognigy.AI.
Natural language processing (NLP) is branch of technology concerned with interaction between human natural languages and m...
Natural language processing (NLP) is branch of technology concerned with interaction between human natural languages and machines. NLP utilizes computer science, artificial intelligence, and linguistics to help machines recognize speech and text and respond in a meaningful way. NLP is considered a challenging technology due to the nuances and subtleties of human language, such as sarcasm.
NLP has been around since the 1950's, but with limited ability; it historically relied on extensive hand coding and was far less effective than it is today. With advances in machine learning and increases in computing power and data availability, NLP has become widely used in recent years.
Most people benefit from NLP every day; it is used to filter junk email, convert voicemail to text, and power voice-based assistants. NLP also has uses across many industries such as healthcare, finance, and retail. NLP technology continues to develop quickly, and it will likely be a key component in many complex future applications.
Natural language understanding (NLU) is a subfield of natural language processing that enables machines to understand human language and intent. NLU goes a step beyond speech recognition technology and uses machine learning to understand nuances such as context, sentiment, and syntax. NLU is designed to be able to understand untrained users; it can understand the intent behind speech including mispronunciations, slang, and colloquialisms.
NLU is a component of many business applications such as chatbots, virtual assistants, and voice bots. NLU helps businesses quickly and easily capture user data and intent and route them to appropriate resources.
Open Data Protocol (OData) is a protocol for data queries and updates. OData analytics is a category of services that use OData to create reports and queries for data of interest. Some of the most popular OData analytics services are Azure DevOps Analytics (including Power BI), Google Analytics, and Adobe Analytics.
Analytics services automatically populate with available data; for example, if using Azure DevOps Analytics, all available DevOps data will be populated, and the service will self-update when data changes occur. Analytics services can be used in conjunction with OData queries, which allows users to directly generate queries across an entire organization or multiple projects of interest.
Robotic process automation (RPA) is a technology that utilizes robots to automatically execute business processes. Robot workers are configured using a low-code approach which makes RPA an easy, low technical barrier solution for many businesses. RPA can mimic most human-computer interactions and is most often used to automate repetitive, labor-intensive tasks. RPA is used across most business sectors for tasks including but not limited to inventory management, data migration, invoicing, and updating CRM data.
RPA has many benefits. Unlike traditional automation, RPA does not require integration across existing applications and does not change the underlying system, which eliminates the need for complex development efforts. RPA also enables repetitive, high-volume tasks to be completed 24/7 with higher accuracy than a human worker could achieve. It frees up valuable human resources to focus on more complex and engaging tasks, resulting in increased employee satisfaction. Investing in RPA typically results in a high ROI because it maximizes an organization’s ability to complete routine work and leverage employee talent.
By combing Conversational AI and RPA organizations can offer services through channels such as voice and chat, along with various social platforms (FB, Slack, etc.) and make automation more intuitive and accessible to their employees and customers.
Sentiment analysis, also referred to as opinion mining, is a method that uses natural language processing and data analytics algorithms to extract subjective information from text, such as satisfaction and emotion. Sentiment analysis is often used on customer reviews, social media posts, and other online feedback to measure the public opinion of a product, company, or issue.
Sentiment analysis categorizes text into buckets, commonly “positive”, “neutral”, and “negative”. These buckets can be customized depending on how granular of a result is desired. Buckets can also represent emotional states, such as “happy”, “frustrated”, or “angry”.
Sentiment analysis techniques range from simple and rule-based to complex and driven by machine learning. Advanced techniques are capable of real-time sentiment analysis and more nuanced interpretation of text.
Sentiment analysis has a wide range of applications, including but not limited to tracking trends, monitoring competition, and determining urgency. In conversational AI applications, sentiment analysis can help to optimize interaction between humans and virtual agents to provide better services and retain customers.
Twilio is a cloud-based platform that allows developers to add communication capabilities such as video, voice, and messaging to applications. Twilio can support worldwide communications via a software layer that connects global communication networks.
Twilio is used by over one million developers and can be used with almost any software application. In addition to enabling communication in apps, Twilio can be used for tasks such as user authentication and call routing. Twilio enables companies across all industries to revolutionize the way they connect with their customers.
Cognigy and Twilio have partnered to provide powerful conversational AI solutions that cover a broad range of channels and touchpoints.
UiPath is a global company that specializes in software for robotic process automation (RPA). A 2005 start-up with 10 people, UiPath has grown to approx. 3000 employees, making it the most rapidly growing enterprise software company in history.
UiPath is best known for their industry-leading RPA platform, which utilizes artificial intelligence, machine learning, process mining, and analytics to provide powerful hyperautomation capabilities. The UiPath RPA platform enables organizations to identify automation opportunities, build bots of varying complexity, manage and deploy bots, run tests, communicate with bots, and measure bot performance. UiPath is also known for UiPath Academy, an online platform that offers hundreds of hours of free RPA courses.
Cognigy.AI seamlessly integrates with the UiPath technology stack and enables simplifying processes through conversational automation and deployment of powerful virtual agents.
A virtual agent is a computer-generated program that uses artificial intelligence, machine learning, and natural language ...
A voice assistant (VA) is an intelligent application that uses natural language processing, voice recognition, and voice s...
Voice automation entails the use of spoken human language to trigger and automate processes in software, hardware, and mac...
A virtual agent is a computer-generated program that uses artificial intelligence, machine learning, and natural language processing to address user questions and concerns. Virtual agents can intelligently respond to customer questions and route customers to additional resources or human agents if necessary.
Virtual agents are sometimes designed to appear as animated characters or given a designated identity representing a human service agent with a name and face. Virtual agents can also act in the background and handle text-based customer interactions posing as a real human agent for some conversations or parts of it. A seamless transition between virtual / human agent and continuous support of the human agents through AI is key for customer satisfaction. Virtual agents can communicate to humans on various digital channels including phone, messengers, webchat and many others.
Virtual agents are a valuable business investment because they can work 24/7, reduce customer wait times, improve support consistency, free up human agents for more complex tasks, and enable business growth.
A voice assistant (VA) is an intelligent application that uses natural language processing, voice recognition, and voice synthesis to communicate with users and execute user requests. Voice assistants are integrated into most of the devices that people use daily – smartphones, computers, speakers, etc. Among the best-known VAs are Apple Siri, Amazon Alexa, Google Home, and Microsoft Cortana.
Voice assistants started to become wildly popular around 2010, when Siri was developed. Other well-known assistants shortly followed, and today more than three billion VAs are in use. While many VAs today are used in a home setting, VAs are also valuable in a business setting. Organizations can use a VA in meetings to take notes and record action items. A VA can also execute simple tasks such as setting up meetings on calendars, creating lists, and finding contact information.
Voice assistants are always improving; they are becoming more intelligent and able to understand more language nuances such as accents and slang. It is expected that VA use will continue to grow in upcoming years as technology continues to improve.
Voice automation entails the use of spoken human language to trigger and automate processes in software, hardware, and machines. Voice automation also relies on artificial intelligence, which is used to create voice systems that can understand human voice commands and execute tasks accordingly.
Voice automation is commonly used for smart home assistants such as Alexa, Siri, and Google Assistant. However, voice automation also has applications in various sectors of business. Voice automation has been used for everything from aiding software development to improving customer service. As consumers increasingly expect to be able to communicate with businesses and execute tasks via voice command, voice automation will become increasingly prevalent in both business and personal life.
Voice bots are similar to chatbots; both use artificial intelligence to enable machines to communicate with humans in natural language. Voice bots and chatbots should be able to understand human conversation and respond appropriately. The main difference between voice bots and chatbots is that voice bots process spoken human language and translate it into text, while chatbots process written human language.
Voice bots can be used to take Interactive Voice Response (IVR) systems to the next level. Instead of having to listen to menu options and prompts, users can interact with a voice bot to resolve their specific needs more quickly. A high performing voice bot is nearly indistinguishable from a human; unlike a traditional IVR system, it can understand customer demands, provide solutions, and multitask.
Voice bots can help businesses improve and quickly scale their customer service operations. A voice bot platform can interact with thousands of customers simultaneously, provide personalized support to each, and free up human agents to focus on more complex service issues.
Watson Assistant is a service that enables software developers to create conversational interfaces for applications across any device or channel. Watson Assistant is cloud-based and has access to Watson AI, which provides machine learning and natural language processing capabilities.
Watson Assistant has three industry-specific solutions included: Watson Assistant for Automotive, Watson Assistant for Hospitality, and Watson Assistant for Industry. Ultimately, all these solutions provide a framework for embedding a virtual assistant that can engage with users and execute tasks such as answering customer questions.
Watson Assistant can be used as a stand-alone NLU as it exposes its functionality via API. This makes it easy for external applications offering third party NLU features such as Cognigy.AI to run their conversation intent mapping from pre-built Watson intents. Watson Assistant is a flexible solution with broad business applications that can be used to streamline operations, provide personalized customer service, and reduce costs.