Purpose This paper addresses the lack of practice-ready frameworks for integrating AI-driven predictive analytics into K-12 construction governance. It proposes the Proactive Program Intelligence (PPI) Framework specifically for public K-12 capital programs managed by owner’s representatives. Design/methodology/approach This conceptual paper synthesizes eight peer-reviewed studies across decision theory, machine learning, and organizational adoption. It utilizes DECAS decision theory as a theoretical anchor and draws on empirical documentation of K-12 governance deficiencies. Findings The resulting PPI Framework features a three-layer architecture: data governance, predictive modelling, and decision activation. It provides a structured pathway to transition K-12 management from reactive judgment to proactive, data-informed governance. Originality This is the first framework to synthesize AI-driven cost and risk identification specifically for the owner’s representative role in K-12 public construction. Research limitations/implications The framework requires future empirical validation through case studies and K-12-specific datasets. Practical implications The framework offers a staged, capability-first roadmap suitable for the governance constraints of public bond-funded programs.
predictive analytics, AI-driven risk management, construction cost estimation, K-12 construction, owner’s representative, proactive governance, machine learning, DECAS framework, program management, data-driven decision-making, organizational adoption, construction leadership.
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