APPLICATION OF ARTIFICIAL INTELLIGENCE AND DATA ANALYTICS IN ENGINEERING PROJECT MANAGEMENT: IMPROVING COST, TIME, AND QUALITY PERFORMANCES IN PHILIPPINE PROJECTS
Keywords:
Artificial Intelligence, Data Analytics, Engineering Project Management, Project Performance, Cost Overruns, Construction Delays, Philippines InfrastructureAbstract
Major engineering and infrastructure projects in the Philippines suffer from systemic cost overruns, chronic schedule delays, and inconsistent quality compliance, as traditional construction management methodologies struggle to adapt to dynamic risk factors, climate vulnerabilities, complex supply chain disruptions, and bureaucratic bottlenecks in public works administration. To address these persistent delivery challenges, this study evaluates the empirical impact of Artificial Intelligence (AI) and Data Analytics (DA) adoption—disaggregated into Predictive Risk Analytics (PRA), Automated Progress Monitoring (APM), Building Information Modeling with AI (BAI), and Prescriptive Decision Support (PDS)—on the triple-constraint project outcomes (Cost, Time, and Quality Performance) in Philippine engineering projects, while evaluating the moderating role of Organizational Digital Readiness (ODR). Utilizing an explanatory cross-sectional quantitative design, empirical data were gathered from a sample of 248 certified project management professionals, Department of Public Works and Highways (DPWH) project engineers, and executive engineers from top-tier Philippine contractors across major economic hubs (Metro Manila, Central Luzon, Calabarzon, and Davao Region). Psychometric scales were validated using Confirmatory Factor Analysis (CFA), and hypotheses were tested using Multiple Linear Regression and Moderated Structural Regression. Empirical findings indicate that AI and DA dimensions significantly explain variance across Cost Performance (R-squared = 0.548, p < 0.001), Time Performance (R-squared = 0.612, p < 0.001), and Quality Performance (R-squared = 0.521, p < 0.001). Predictive Risk Analytics emerged as the primary predictor of cost-overrun reduction (β = 0.382, p < 0.001), Automated Progress Monitoring drove schedule adherence (β = 0.415, p < 0.001), and BIM-AI Integration led to quality enhancement (β = 0.442, p < 0.001). Organizational Digital Readiness significantly moderated these relationships (delta R-squared = 0.042, p = 0.001), amplifying performance gains in digitally mature firms. Ultimately, transitioning from conventional reactive management to predictive, AI-augmented decision support systems fundamentally mitigates the risks of triple-constraint failure in tropical, emerging-economy project environments, providing a strategic framework to guide engineering firms and state infrastructure agencies in allocating digital investments, modernizing procurement frameworks, and mitigating delay drivers in public works.
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