AI-Driven Innovation and Collaboration in Public Services

A Review and Taxonomy

Abstract

Recently, governments and organizations have expanded their interest and efforts toward acquiring Artificial Intelligence-powered systems to support the delivery of public services. However, there is insufficient information in the literature to help us understand how different types of public services are supported by AI, particularly regarding aspects such as collaboration support and the innovation achieved within organizations. To address these literature gaps, we conducted a rapid review to identify types of AI applications, problem solved, government functions supported, their impacts on innovation in public organizations, and the collaborative arrangements they have fostered among various stakeholders. Furthermore, in this same scope, we also designed a taxonomy as an analytical framework to answer the literature review questions. The review found that AI in the public sector is mainly used for prediction, data visualization, predictive analysis, and automating repetitive processes, particularly in economic affairs, health, and public order. Governments develop AI solutions primarily for administrative and repetitive tasks. In this sense, there is potential growth for new AI adoption forms and public management innovations, benefiting public service delivery and society. These contributions aim to help researchers, public managers, and tech companies better understand and analyze the complex domain of AI in public services, enabling them to develop and responsibly implement new AI solutions in the future.