Chatbots were simple in the beginning, often limited to web browser pop-ups that could only send straightforward greetings like “Hi, how are you?” to website visitors.
Because of AI and cloud technology, chatbots are no longer just a nice-to-have greeting tool, but a key element in fostering a more engaging customer experience for businesses across every industry. By tapping into deep learning technologies, these tools learn, converse, and understand the world similarly to the way humans do, making customer service simpler and more efficient.
Consumers are encountering these technologies more frequently — according to Gartner, by 2018, 30 percent of our interactions with new technologies will be through “conversations” with smart machines. Chatbots can be found everywhere, helping customers with nearly any task, from online shopping to planning a wedding. Chatbot popularity and capabilities have evolved in just a few short years from automated phone calls to basic customer service bots to AI-enabled conversational agents that are able to effectively interact with customers in natural language. An increasing number of enterprises are tapping into the flexibility of cloud infrastructure to easily leverage cognitive technology to power chatbots for a variety of these tasks.
Today, many of the world’s leading brands are turning to AI and cloud platforms to create chatbots that can assist customers with a wide range of tasks, at a higher level of emotional intelligence and understanding than ever before.
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A task for every bot
The casual consumer may see chatbots as simple technologies that are meant only for processing basic questions and answers. Many of today’s popular voice-activated devices are just that — shallow bots centered on simple voice recognition features. However, advanced bots are built on deep forms of conversational engagement, powered by deep learning and many other underlying AI and cloud technologies. These technologies allow bots to better understand and process natural language, including tone and sentiment; analyze external data; and much more. This means businesses and developers can build bots to fit their specific needs, rather than stick to a one-size-fits-all model.
For example, while one company might rely on a chatbot to help consumers with simple questions or product orders, another might employ a chatbot to assist its employees with research, using natural language to communicate. The range of technologies available through AI platforms allows businesses the flexibility to determine how to design a chatbot that will best meet the needs of its workforce, partners, and customers.
A partnership between man and machine
Many shallow online chatbots focus solely on command and response-like interactions, which can be sufficient in instances where only basic interaction is required. For example, a customer might ask a bank’s chatbot a question like “What is my account balance?” and get an accurate response. AI-enabled conversational agents can go beyond that type of interaction. They’re smart enough to know that just providing the customer with their account balance doesn’t address the full picture — the customer might be asking that question because they are preparing to make a big purchase or save for a child’s college education. It’s this understanding of intent that makes cognitive agents distinct from more shallow offerings.
While the consumer may not have expressed their motivation upfront, the key for the chatbot is to understand what that person wants to get done. Through deep conversational technology, a virtual agent can pick up on a user’s situation through natural language cues and knowledge from prior interactions, and can then provide assistance in a more holistic way by offering new insights and solutions.
However, chatbots aren’t just for customers. Increasingly we’re seeing how human customer service agents are tapping these virtual assistants to provide more personalized guidance. Incorporating a chatbot into a customer service program is an opportunity for a partnership between man and machine — for example, Bradesco, one of Brazil’s largest banks, created a virtual customer service solution that enables employees to help customers get answers to questions faster. After a few months of training with bank representatives, the bot can answer questions with 80 percent more accuracy than when training started, leading to more satisfied customers.
This partnership between technology and customer service representatives helps customers quickly and simply with questions and problems, while keeping waiting times to a minimum. And since AI technology gets smarter with each interaction, the chatbot can help the bank improve customer experiences and gain new and greater insights into customer behaviors to inform future offerings.
Emotional intelligence: the next frontier
Advances in AI will continue to allow businesses to layer more emotional intelligence into their bots, with cognitive computing capabilities that can analyze a consumer’s tone or intention through language. The goal of every conversation with an AI-enabled chatbot should be to ensure the user feels satisfied and understood, even in sensitive situations.
Many companies are already leveraging AI technologies that can detect user tone. 1-800-Flowers, the floral gift retailer, launched GWYN (Gifts When You Need) recently. It’s an AI-powered gift concierge. Using conversational AI capabilities, GWYN can interact with online customers using natural language. GWYN is designed to interpret customers’ questions and then ask several qualifying questions about the occasion, sentiment, and who the gift is for to ensure it shares an appropriate, tailored gift suggestion with each customer. Through this process, GWYN understands the human intention behind the purchase, rather merely acting as an automated tool.
As cloud and AI technology progresses, chatbots will continue to become more skilled in data analysis, natural communication, and emotional intelligence through deep conversational technology. But this is just the beginning — by using AI and the cloud, companies will continue to easily build even more personalized and engaging business solutions. Ultimately, cloud and cognitive technology will fuel the customer service interaction of the future by understanding human intention, learning, and reasoning at scale, and augmenting human thinking to help consumers make better purchases and smarter decisions across every aspect of the customer experience.
Rob High is an IBM Fellow, vice president, and chief technology officer for IBM Watson.
