Performance Indicators Development for Public Services using an AI-based clustering approach

Despite the hype for AI-based generative applications, the original pitfalls in implementing AI-based technologies remain: data quality, readiness, and availability. AI results are tied to the quality of the data provided to the underlying algorithms. Inevitably, bad data produces bad results. The same happens for data readiness and availability: without correct preparation and access, even high-quality data cannot be used in AI-based solutions. To help decision-makers in the public sector, performance indicators applied to processes and, therefore, to public services can be used as tools to guide public project selection, allocation of resources, monitoring of results, etc. In this way, performance indicators can be used to standardize the evaluation of publicpolicies, helping citizens and city administrators make more informed decisions regarding the environment they live in. In the present work, we propose a performance indicator development process (PIDP) based on AI clustering techniques, which can be used to gather key performance indicators (KPI) among available data from open data sources and consumed and/or produced by the core processes that implement a public service.