When artificial intelligence emerged from the labs and vendors started offering it as a component of their software, many contact-center buyers shied away from it. From their point of view, AI and machine learning tools were new, expensive, relatively untested and had an uncertain use case. This stance was understandable, as contact center professionals are traditionally expected to be risk-averse when deploying technology into their operations. Contact centers are, by design, supposed to be hardened, mission-critical sites of high reliability. There has historically been a bias towards avoiding new technology, deploying only when it has been thoroughly vetted across the industry.
Tools for contact center agent management have changed considerably in the past few years. The suite of software applications has grown from those that perform core functions in scheduling and quality control to include more advanced solutions for agent guidance, integrated desktops, and workflow and automation design. One area of intense investment by vendors has been analytics, specifically for assessing customer satisfaction and hearing the “voice of the customer.”
Customer support operations increasingly rely on automation and complex workflow processes to reduce costs and improve experiences. Automation also allows organizations to make their service processes richer, incorporating information and staff from back offices, for example, or embedding conversational tools into contact center processes.
Topics: Customer Experience, embedded analytics, Analytics, Contact Center, natural language processing, AI and Machine Learning, agent management, Customer Experience Management, Field Service, Process Mining, Streaming Analytics, customer service and support