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Assessing Artificial Intelligence Adoption Readiness: A Framework for Digital Transition in The DPWH Nueva Ecija 1st District Engineering Office
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Modern infrastructure project management increasingly relies on Artificial Intelligence (AI) and integrated digital tools to enhance efficiency, project transparency, and schedule compliance. However, a significant gap often exists between high-level central office digitization mandates and local field implementation capabilities. This study assessed the baseline AI adoption readiness of technical personnel and the institutional framework within the Department of Public Works and Highways (DPWH) Nueva Ecija 1st District Engineering Office in Talavera, Nueva Ecija, serving as the empirical foundation for a localized AI Implementation Plan. Utilizing a descriptive-quantitative research design, a complete enumeration sample of twenty-five (N = 25) technical personnel was surveyed, representing equal quotas (n = 5) across five core technical designations: Project Engineers, Resident Engineers, Project Inspectors, Materials Engineers, and Monitoring Personnel. Quantitative data was evaluated using weighted means, standard deviations, and a standardized five-point Likert scale continuum, while open-ended qualitative feedback was treated using thematic analysis. The empirical findings revealed that personnel possess a Moderately Aware level of general AI concepts (x = 3.38, SD = 1.10) and a Moderate Knowledge baseline regarding predictive data dashboards (x = 2.98, SD = 1.20). However, a sharp "proficiency gap" exists in specialized engineering software, with AI knowledge in Building Information Modeling (BIM) and Generative Design scoring lowest (x = 2.64). In terms of experience, while 100% of personnel utilize general AI tools for administrative drafting and reporting, only 12% to 16% engage with specialized civil engineering AI applications. Overall Organizational Readiness was evaluated as Partially Ready (x = 2.96, SD = 1.07), with solid data organization (x = 3.24) offset by severe structural bottlenecks in local software budget allocation likelihood (x = 2.64), internet infrastructure (x = 2.76), and official training provision (x = 2.76). The study concludes that frontline engineering staff do not reject AI technology but require clear central office Department Orders, internet infrastructure upgrades, centralized enterprise software licensing, and practical, civil engineering-specific prompt engineering workshops. These diagnostic gaps directly justify the strategic AI Implementation Plan formulated to guide the office through a smooth digital transition.
How to cite this paper
@article{1722236,
author = {Nilo, Mary Joy V., Ladignon, Christopher, Florencondia, Noel T.},
title = {Assessing Artificial Intelligence Adoption Readiness: A Framework for Digital Transition in The DPWH Nueva Ecija 1st District Engineering Office},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {4159-4179},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722236.pdf},
abstract = {Modern infrastructure project management increasingly relies on Artificial Intelligence (AI) and integrated digital tools to enhance efficiency, project transparency, and schedule compliance. However, a significant gap often exists between high-level central office digitization mandates and local field implementation capabilities. This study assessed the baseline AI adoption readiness of technical personnel and the institutional framework within the Department of Public Works and Highways (DPWH) Nueva Ecija 1st District Engineering Office in Talavera, Nueva Ecija, serving as the empirical foundation for a localized AI Implementation Plan. Utilizing a descriptive-quantitative research design, a complete enumeration sample of twenty-five (N = 25) technical personnel was surveyed, representing equal quotas (n = 5) across five core technical designations: Project Engineers, Resident Engineers, Project Inspectors, Materials Engineers, and Monitoring Personnel. Quantitative data was evaluated using weighted means, standard deviations, and a standardized five-point Likert scale continuum, while open-ended qualitative feedback was treated using thematic analysis. The empirical findings revealed that personnel possess a Moderately Aware level of general AI concepts (x = 3.38, SD = 1.10) and a Moderate Knowledge baseline regarding predictive data dashboards (x = 2.98, SD = 1.20). However, a sharp "proficiency gap" exists in specialized engineering software, with AI knowledge in Building Information Modeling (BIM) and Generative Design scoring lowest (x = 2.64). In terms of experience, while 100% of personnel utilize general AI tools for administrative drafting and reporting, only 12% to 16% engage with specialized civil engineering AI applications. Overall Organizational Readiness was evaluated as Partially Ready (x = 2.96, SD = 1.07), with solid data organization (x = 3.24) offset by severe structural bottlenecks in local software budget allocation likelihood (x = 2.64), internet infrastructure (x = 2.76), and official training provision (x = 2.76). The study concludes that frontline engineering staff do not reject AI technology but require clear central office Department Orders, internet infrastructure upgrades, centralized enterprise software licensing, and practical, civil engineering-specific prompt engineering workshops. These diagnostic gaps directly justify the strategic AI Implementation Plan formulated to guide the office through a smooth digital transition.},
month = {July},
}