Integrating Artificial Intelligence and Human Decision-Making to Enhance Risk Management Effectiveness in Construction Projects

Figure Ilustration AI

FORMOSA NEWS - Jakarta - Combining AI Algorithms and Human Expertise Enhances Construction Project Risk Management Performance, Study Finds. Integrating artificial intelligence (AI) with human decision-making significantly improves risk management effectiveness in complex construction projects, according to a survey study authored by Amrin Farikhi from Universitas Dian Nusantara and published in August 2026. The research addresses the persistent challenge of project uncertainty, delays, and cost overruns by demonstrating that while machine learning algorithms excel at early risk prediction, experienced human professionals remain the single most vital factor in executing successful mitigation strategies.

Background and Relevance

Modern construction projects operate in increasingly volatile environments characterized by complex supply chains, tight budgets, strict deadlines, and unpredictable site conditions. Traditional risk management techniques rely heavily on manual evaluations and individual manager experience, which often prove inadequate for rapid, data-driven forecasting. While digital tools powered by Industry 4.0—such as machine learning, natural language processing, and predictive analytics—can process massive datasets to identify potential safety hazards or budget deviations early, technological tools alone struggle to account for non-technical real-world factors. This study fills a critical gap by examining how human judgment and algorithmic risk predictions function together within a socio-technical framework to safeguard major building and infrastructure endeavors.

Research Methodology
The study employed an empirical, quantitative survey design to investigate the interactive relationships between AI adoption, professional judgment, and project outcomes.

• Participant Sample: Data was collected from 100 active construction practitioners, including project managers (18%), construction managers (20%), site engineers (32%), supervisors (20%), and quantity surveyors (10%) across building, road, bridge, and industrial projects.
  
• Measurement Scale: Participants evaluated operational indicators using a 5-point Likert scale measuring AI Integration (X1), Human Decision-Making Capability (X2), and Risk Management Effectiveness (Y).
  
• Analytical Approach: The study utilized multiple linear regression, partial t-tests, simultaneous F-tests, and coefficient of determination ($R^2$) calculations via IBM SPSS Statistics to test the research hypotheses.
  

Key Findings
The statistical evaluation demonstrated that both technological tools and professional human competence positively and significantly boost risk management success.

• Primary Impact of Human Judgment: Human Decision-Making Capability emerged as the strongest individual predictor of risk management effectiveness, with a regression coefficient of $\beta = 0.421$ ($p < 0.05$). Professional experience and contextual evaluation remain the dominant factors in successful risk control.
  
• Value of Artificial Intelligence: AI Integration yielded a positive and statistically significant impact on risk control performance, with a regression coefficient of $\beta = 0.356$ ($p < 0.05$). Using AI for pattern recognition and delay forecasting measurably strengthens proactive risk mitigation.
  
• Combined Power ($R^2$ Variance): When deployed simultaneously, AI tools and human expertise accounted for 50.1% of the total variance in risk management effectiveness ($F = 48.621$, $p < 001$, $R^2 = 0.501$).
  

Practical Implications and Real-World Impact
The findings highlight that digital transformation in the construction sector should focus on enhancing, rather than replacing, human expertise. Construction companies, project developers, and engineering firms can achieve optimal risk reduction by pairing automated predictive software with targeted workforce training. By viewing AI as a decision support framework, project directors can interpret machine-generated risk alerts through the lens of local regulatory environments, site conditions, and stakeholder relationships. Educational institutions and industry policymakers can leverage these insights to design technical training programs that promote digital literacy, data-driven decision-making, and socio-technical management strategies.

Author Profile
Amrin Farikhi, M.T. Universitas Dian Nusantara. Expertise: Construction Management, Civil Engineering, Risk Management, and Technology Integration in Infrastructure.

Source
Amrin Farikhi. Integrating Artificial Intelligence and Human Decision-Making to Enhance Risk Management Effectiveness in Construction Projects. Formosa Journal of Sustainable Research (FJSR). Vol. 5, No. 8, Halaman 649–662
DOI : https://doi.org/10.55927/fjsr.v5i8.59
URL: https://journalfjsr.my.id/index.php/fjsr