Hyperautomation and Generative Artificial Intelligence as Foundations for Sustainable and Human-Centric Smart Cities

Abstract

The rapid evolution of smart cities represents one of the most complex socio-technical transformations of the twenty-first century. Driven by accelerating urbanization, climate change, public health challenges, and digital innovation, cities are increasingly adopting advanced computational systems to improve efficiency, sustainability, resilience, and quality of life. Within this transformation, hyperautomation, artificial intelligence, generative artificial intelligence, vector symbolic architectures, Internet of Things ecosystems, and digital twins are emerging as foundational enablers rather than isolated technologies. This research article develops an integrated and theoretically grounded examination of how hyperautomation and generative artificial intelligence collectively shape the next generation of smart cities. Drawing strictly and exclusively on the provided references, the article synthesizes insights from machine learning theory, urban studies, smart city governance, health, mobility, sustainability, and business model innovation.

The study positions hyperautomation as an evolutionary progression beyond traditional automation and robotic process automation, characterized by the orchestration of artificial intelligence, process mining, generative models, Internet of Behaviours, blockchain smart contracts, and intelligent maintenance systems. In parallel, generative artificial intelligence is examined as a paradigm shift in urban intelligence, enabling autonomous data generation, scenario simulation, policy experimentation, digital twin development, and participatory urban design. Special emphasis is placed on unsupervised learning through vector symbolic architectures, particularly Hyperseed, as a mechanism for scalable, explainable, and adaptive urban cognition.

Methodologically, the article employs a structured, interpretive, and theory-driven synthesis approach, integrating insights from smart city standards, sustainability frameworks, health and mobility research, and emerging AI architectures. The results reveal that the convergence of hyperautomation and generative AI fundamentally redefines urban governance, shifting cities from reactive and efficiency-driven systems toward anticipatory, human-centric, and resilient ecosystems. The discussion critically evaluates ethical, organizational, and infrastructural constraints while identifying future research directions, including open-source AI frameworks, autonomous urban digital twins, and adaptive business models.

The article concludes that sustainable smart cities cannot be realized through fragmented technological adoption. Instead, they require deeply integrated hyperautomation frameworks augmented by generative artificial intelligence, grounded in urban context, social values, and long-term resilience. This work contributes a comprehensive conceptual foundation for researchers, policymakers, and practitioners seeking to design, govern, and evaluate intelligent urban systems in an era of unprecedented complexity.