A Data-Driven Framework for Digital Transformation in Smart Cities: Integrating AI, Dashboards, and IoT Readiness

• URL: http://arxiv.org/abs/2509.22721v1
• Date: Wed, 24 Sep 2025 21:39:40 GMT
• Title: A Data-Driven Framework for Digital Transformation in Smart Cities: Integrating AI, Dashboards, and IoT Readiness
• Authors: Ángel Lloret, Jesús Peral, Antonio Ferrández, María Auladell, Rafael Muñoz,
• Abstract summary: This study proposes an innovative methodology to evaluate the level of digital transformation (DT) in public sector organizations.<n>The proposed approach combines traditional assessment methods with Artificial Intelligence (AI) techniques.<n>Our approach has been applied to a real-world case study involving local public administrations in the Valencian Community (Spain)
• Score: 0.5390835285335905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital transformation (DT) has become a strategic priority for public administrations, particularly due to the need to deliver more efficient and citizen-centered services and respond to societal expectations, ESG (Environmental, Social, and Governance) criteria, and the United Nations Sustainable Development Goals (UN SDGs). In this context, the main objective of this study is to propose an innovative methodology to automatically evaluate the level of digital transformation (DT) in public sector organizations. The proposed approach combines traditional assessment methods with Artificial Intelligence (AI) techniques. The methodology follows a dual approach: on the one hand, surveys are conducted using specialized staff from various public entities; on the other, AI-based models (including neural networks and transformer architectures) are used to estimate the DT level of the organizations automatically. Our approach has been applied to a real-world case study involving local public administrations in the Valencian Community (Spain) and shown effective performance in assessing DT. While the proposed methodology has been validated in a specific local context, its modular structure and dual-source data foundation support its international scalability, acknowledging that administrative, regulatory, and DT maturity factors may condition its broader applicability. The experiments carried out in this work include (i) the creation of a domain-specific corpus derived from the surveys and websites of several organizations, used to train the proposed models; (ii) the use and comparison of diverse AI methods; and (iii) the validation of our approach using real data. The integration of technologies such as the IoT, sensor networks, and AI-based analytics can significantly support resilient, agile urban environments and the transition towards more effective and sustainable Smart City models.

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