Hospitality and human instructiveness are essential in connecting with consumers. Few would rather talk to a robot caller to handle their issue than a genuine human being. Additionally, customers typically dislike waiting for a representative for extended periods. And personnel sorting through enormous volumes of calls might produce a massive workload. Conversational AI can be used in a variety of organizations to attend to the needs of specific customers to lessen this issue and increase efficiency.

Today's customer expects the ability to engage with businesses through various communication channels like email, SMS, Push notifications, and in-app notifications when they have a question or need a problem resolved. SMS is one of the fastest growing communication channels, and we've seen that customers enjoy the ease and speed of texting for help versus traditional call channels. However, building an SMS system at scale to address millions of inquiries can be challenging for even the most advanced IT departments. Research also shows that customers prefer a personalized experience over a generic one, but using agents or employees to personalize millions of messages on a case-by-case basis is not practical. To solve this problem, we can use Amazon Pinpoint, AWS' multichannel communication service, to interact in personalized 2-way SMS messages with customers.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Phone banking is starting to get a dramatic personality shift, thanks in no small part to artificial intelligence (AI) and conversational AI. The first generation of phone banking was largely driven by interactive voice response (IVR) technology. That's the touch tone-driven technology that provides the monotonous voice tone telling you to "push 3 for your bank balance." IVR is a technology that was never particularly loved by anyone but it has done the job for many banks around the world for decades, albeit in a suboptimal approach.

Customer satisfaction is a potent metric that directly influences the profitability of an organization. Establishing highly efficient contact centers requires significant automation, the ability to scale, and a mechanism of active learning through customer feedback. There is a challenge at every point in the contact center customer journey--from long hold times at the beginning to operational costs associated with long average handle times. In traditional contact centers, one solution for long hold times is enabling self-service options for customers using an Interactive Voice Response system (IVR). An IVR uses a set of automated menu options to help reduce agent call volumes by addressing common frequently asked requests without involving a live agent.

Let's say you have identified a use case in your organization that you would like to handle via a chatbot. You familiarized yourself with Amazon Lex, built a prototype, and did a few trial interactions with the bot. You liked the overall experience and now want to deploy the bot in your production environment, but aren't sure about best practices for Amazon Lex. In this post, we review the best practices for developing and deploying Amazon Lex bots, enabling you to streamline the end-to-end bot lifecycle and optimize your operations. We have covered the planning, design, and configuration phases in previous blog posts.

Conversational AI can deliver powerful, automated, interactive experiences through voice and text. Amazon Lex is a service that combines automatic speech recognition and natural language understanding technologies, so you can build these sophisticated conversational experiences. A common application of conversational AI is found in contact centers: self-service virtual agents. We're excited to announce that you can now use Amazon Chime SDK Public Switched Telephone Network (PSTN) audio to enable conversational self-service applications to reduce call resolution times and automate informational responses. The Amazon Chime SDK is a set of real-time communications components that developers can use to add audio, messaging, video, and screen-sharing to your web and mobile applications.

In parts one and two of our guide to conversation design with Amazon Lex, we discussed how to gather requirements for your conversational AI application and draft conversational flows. In this post, we help you bring all the pieces together. You'll learn how draft an interaction model to deliver natural conversational experiences, and how to test and tune your application. In the second post of this series, you identified some use cases that you wanted to automate and wrote sample interactions between a user and your application. In this post, we use these use cases to build an Amazon Lex framework, called an interaction model, but first, let's review some important definitions.

As air travel starts to pick up in many parts of the world, digitization continues to transform the aviation industry. Airlines are working to reduce the number of touchpoints at the airport. Best practices have been implemented to minimize the number of physical interactions between employees and travelers. As a result, customer service is undergoing an accelerated transformation as airlines strive to provide a smooth and seamless experience. Airlines want to deliver a customer-centric experience that gives passengers a choice on how they engage to ensure high customer satisfaction.

Chatbots have become an increasingly important channel for businesses to service their customers. Chatbots provide 24/7 availability and can help customers interact with brands anywhere, anytime and on any device. To effectively utilize chatbots, they must be built with good design, development, test, and deployment practices. This post provides you with a framework that helps you automate the testing processes and reduce the overall bot development cycle for Amazon Lex bots. Amazon Lex is a service for building conversational interfaces into any application using voice and text.

Understanding the direction and context of an ever-evolving conversation is beneficial to building natural, human-like conversational interfaces. Being able to classify utterances as the conversation develops requires managing context across multiple turns. Consider a caller who asks their financial planner for insights regarding their monthly expenses: "What were my expenses this year?" They may also ask for more granular information, such as "How about for last month?" As the conversation progresses, the bot needs to understand if the context is changing and adjust its responses accordingly.