  Public data ecosystems (PDEs) represent complex socio-technical systems
crucial for optimizing data use in the public sector and outside it.
Recognizing their multifaceted nature, previous research pro-posed a
six-generation Evolutionary Model of Public Data Ecosystems (EMPDE). Designed
as a result of a systematic literature review on the topic spanning three
decade, this model, while theoretically robust, necessitates empirical
validation to enhance its practical applicability. This study addresses this
gap by validating the theoretical model through a real-life examination in five
European countries - Latvia, Serbia, Czech Republic, Spain, and Poland. This
empirical validation provides insights into PDEs dynamics and variations of
implementations across contexts, particularly focusing on the 6th generation of
forward-looking PDE generation named "Intelligent Public Data Generation" that
represents a paradigm shift driven by emerging technologies such as cloud
computing, Artificial Intelligence, Natural Language Processing tools,
Generative AI, and Large Language Models (LLM) with potential to contribute to
both automation and augmentation of business processes within these ecosystems.
By transcending their traditional status as a mere component, evolving into
both an actor and a stakeholder simultaneously, these technologies catalyze
innovation and progress, enhancing PDE management strategies to align with
societal, regulatory, and technical imperatives in the digital era.
