2004, ‘Building agents to serve customers

■ AI agents combining natural language interaction, task planning, and business ontologies can help companies provide better-quality and more costeffective customer service. Our customer-service agents use natural language to interact with customers, enabling customers to state their intentions directly instead of searching for the places on the Web site that may address their concern. We use planning methods to search systematically for the solution to the customer’s problem, ensuring that a resolution satisfactory for both the customer and the company is found, if one exists. Our agents converse with customers, guaranteeing that needed information is acquired from customers and that relevant information is provided to them in order for both parties to make the right decision. The net effect is a more frictionless interaction process that improves the customer experience and makes businesses more competitive on the service front. As companies optimize their production and supply-chain processes, more people use the quality of customer service to differentiate between alternative vendors or service providers. Customer service is currently a manual process supported by costly call-center infrastructures. Its lack of flexibility in adapting to fluctuations in demand and product change, together with the staffing and training difficulties caused by massive personnel turnovers, often results in long telephone queues and frustrated customers. This is a major cause for concern, as it generally costs five times more to acquire a new customer than to keep an existing one. How can AI help in addressing this problem? For several years we have built a domain-independent AI platform for creating conversational customer-service agents that use a variety of natural language understanding and reasoning methods to interact with customers and resolve their problems. We have applied this platform to customer-service applications such as technical diagnosis of wireless-service delivery problems, product recommendation, order management, quality complaint management, and sales recovery, among others. The resulting solutions and the lessons learned in the process are the subject of this article