Chatbots have become a staple for enterprises in every industry. Here is a list of trends we identified in enterprise conversational automation.
Chatbots have come a long way since MIT professor Joseph Weizenbaum introduced ELIZA in 1966. ELIZA used substitution methodology and pattern matching to simulate conversation. Over the ensuing decades, chatbot technology has dramatically evolved. Chatbots and virtual assistants have become a staple for enterprises basically in every industry. And now, conversational AI is powering these ten trends in enterprise chatbot use-cases.
Artificial intelligence (AI) has become all but ubiquitous, and it’s powering a new generation of chatbots. A traditional chatbot is scripted and bi-directional, with limited ability to recognize intent. They work because IT teams create keyword questions with predetermined answers. The answers are automated, and interactions that don’t “fit the script” usually require human intervention. Interactions with these chatbots can often be frustrating for users.
Thanks to natural language processing (NLP) and automatic semantic understanding (ASU), conversational AI allows for more nuanced interactions that aren’t limited to a script. A conversational AI bot differs from a regular chatbot because it can understand a user’s intent regardless of how the communication is worded. And the more it interacts with users, the better it gets at discerning intent and interacting more naturally. With time, a conversational AI chatbot delivers a progressively better user experience.
Chatbots and virtual assistants are continuing to improve, becoming more personalized and human-like. They remember past dialogues to provide better future conversations. They now analyze each unique experience and deploy user preferences. Some virtual assistants are even learning the tone of voice and moods of users and responding accordingly. Conversational AI allows for this kind of personalization. These intelligent tools can be used to up-sell products or services to consumers by providing a personalized buying experience.
Employees want to work smarter, not harder. Many enterprises have implemented the use of private digital assistants for handling time-consuming, repetitive tasks and they have seen tremendous results with DRUID even up to 97% reduction in time needed to finish a process. Any business unit can map its processes and use conversational automation to save time. We have seen major improvements with our customers in HR, Finance, IT, Customer Support, and more.
Chatbots aren’t just for conversations anymore. They can integrate with ERP, CRM, or BI systems and act as a secure gateway to your core data. This means that employees can access the data they need from anywhere. In the context of the Covid-19 pandemic, this capability is especially important--and it’s likely that the need for remote work is here to stay. Even at the office, however, these integrations increase efficiency and productivity. For example, managers can seamlessly manage expense requests, budgets, appointments, and more. The sales team can update the CRM in real time, via voice calls.
Chatbots become even more helpful when we can just speak without having to type. Hands-free has become an essential aspect for many tools across all industries. Gartner predicts that by the end of the year, virtual employee assistants will make up 25% of the entire digital workforce. Chatbots and virtual assistants are starting to implement voice assistance to make them more efficient and accessible for users. Products like Amazon Echo, Alexa, and Google Assistant already use voice assistants for scheduling appointments, setting alarms, or reading emails out loud.
To get a better user experience, chatbots should offer a user’s native language. Enterprises must acknowledge that not all customers will speak English. It is important that chatbots have the ability to speak multiple languages. Especially if an enterprise has global customers, then customer service should accommodate international users. Natural language processing (NLP) allows the chatbot to focus on the user's intent to provide context that is based on their behavior and preferences. This is a vital tool to have if a company serves clients and customers globally.
To increase user engagement, implementing personality chatbots is at the top of the list. Not only does it help increase an enterprise's revenue, but using a chatbot is more fun when it has a personality. To build a lasting relationship between your brand and a customer, it is vital to make conversing with a chatbot worthwhile and enjoyable. Chatbots are there to help humans, not other bots, so they should have their own attitudes and a sense of voice. This creates a human-like interaction, making it more comfortable for the user.
Without the manual input, machine to machine (M2M) conversations are advancing to share information and diagnosis across multiple network devices. Conversational AI bots can be used to interact between man-made machines. Chatbots can decipher information, collect it, and make recommendations based on IoT devices. For example, chatbots in the banking industry can help keep finances under control by making suggestions on how to save money based on transaction history. M2M conversation trends are becoming more advanced and used widely in IoT devices.
Augmented reality (AR) is implemented into chatbots to provide a better, more realistic shopping experience. Both mobile and websites are beginning to use AR. Gartner defines augmented reality as “the real-time use of information in the form of text, graphics, audio, and other enhancements integrated with real world objects.” Right now, you do not have to leave your house to tour homes for sale. AR allows a user to step into a virtual reality where all buying, shopping, and selling can be done from a smart device. In the future, AR may become more realistic; for example, you might be able to touch or smell products. AR has a long way to go, but it is continuously advancing in today's market.
AI governance is the legal process of ensuring machine learning (ML) technologies are constructed and developed so that enterprises use AI systems fairly. AI governance is spread across all industries, including business, transportation, education, healthcare, public safety, and economics. AI and its algorithms are supposed to be evaluated and monitored for risk, effectiveness, bias, and ROI. With continuous advancements in technology, AI governance was introduced to close the gap between accountability and ethics.
Rich communication services (RCS) have steadily been replacing conventional SMS channels. For example, Google messenger RCS messaging features typing indicators, reading receipts, and sharing rich content (images, videos, pdfs, etc.). RCS would be similar to WhatsApp or iMessage; however, it would work over a cell phone network, not the internet. Companies are moving from the old way of messaging (SMS) and advancing to RCS for a more robust user experience.
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