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. 1-7 Issue-3 Volume-10 Denish Sonani