The aging water infrastructure in Italy, exacerbated by historical underinvestment, demands strategic asset management to ensure sustainable water resources. Leveraging the Piano Nazionale di Ripresa e Resilienza (PNRR), this study examines a comprehensive methodology integrating physical inspections and AI-enhanced data analysis for ETRA SpA, a water utility in Northern Italy managing 5,400 km of network infrastructure. By combining diverse datasets with digital transformation tools, this approach improves risk assessment and investment prioritization.

Key components include network digitalization, enriched GIS and hydraulic models, soil characterization, high-frequency pressure monitoring, water quality analysis, and pipe inspections. These data sources inform AI-driven Likelihood of Failure (LoF) models, incorporating leak history, structural assessments, and soil-related vulnerabilities. Consequence of Failure (CoF) is assessed through participatory workshops and hydraulic criticality analysis, aligning risk assessments with ETRA’s operational context.

Preliminary results demonstrate enhanced resilience, optimized resource allocation, and improved decision-making for aging infrastructure. Integrating inspection data with AI refines risk models, targeting priority worksites and maximizing investment impact under budget constraints. While scalable, challenges such as economic models and stakeholder engagement persist. This methodology offers a replicable solution for modernizing asset management in water utilities worldwide.