Increasing customer service efficiency through artificial intelligence chatbot
Keywords:Artificial intelligence, Chatbot, Digital transformation, IBM Watson, Service efficiency, Technological innovation, Virtual assistant
Purpose – This study investigated the contribution of artificial intelligence (AI) to the efficiency of customer
service. This study contributes to services technological innovation in process management, a field not yet
settled in the literature.
Design/methodology/approach – AI is a multidisciplinary field of research that has stood out for the
technological dynamism provided to organizational products and processes. The study was carried out at an
Analytical Intelligence Unit (AIU) of a Brazilian commercial bank that applies AI integrated with IBM’s Watson
system. The study used data content analysis, structured and supported by Atlas.ti software.
Findings – The notorious AI cognitive maturity evolution allowed 181 million interactions and 7.6 million
attendances in 2020, improving services efficiency, with gains in agility, availability, accessibility, resoluteness,
predictability and transshipment capacity. The chatbot service reduced the queues of call centers and
relationship centers, allowing the human attendant to perform more complex attendances.
Research limitations/implications – The main limitations of this study relate to the research cutout and its
borders, such as the choice of participants and their areas of activity, and the choice of the unit of analysis.
Practical implications – The results indicated that attendance through the virtual assistant increased by
more than a 1,000% from 2019 to 2020, demonstrating the bank was technologically ready to face the Covid-19
Originality/value – In line with the evolutionary theory of innovation, the authors concluded that
technological scaling in AI allows exponential gains in customer service efficiency and business process
management. They also conclude that the strategy for creating AIUs is successful, once it allows centralizing,
structuring and coordinating AI projects in R&D cooperation, cognitive computing and analytics.