Kjellmark, G; Hjelseth, E; Peñaloza, G A and Riemer-Sørensen, S (2026) Toward construction 5.0: Bridging AI and people through continuous learning. Journal of Construction Engineering and Management, 152(1): 04025227, ISSN 0733-9364
Abstract
The construction industry is experiencing a rapid digital transformation with the integration of advanced technologies, including artificial intelligence (AI). However, the successful adoption of AI remains a challenge due to barriers in balancing technology with human and process factors. Under emerging Construction 5.0 principles, which emphasize human-machine collaboration and continuous learning, there is a critical need to better understand how AI-driven decision support can be effectively implemented in construction management. This study investigates key drivers, enablers, barriers, and opportunities in AI adoption within mass transportation logistics and pollution pattern analysis at a road construction site. The research builds on experience from this case to adapt a generic framework that supports a balanced integration of AI, organizational processes, and human insights. Using the integrated design and delivery solutions (IDDS) framework, this study employs an exploratory, abductive research approach, combining document reviews, stakeholder interviews, and operational observations to explore technological and organizational challenges. Key findings include: (1) AI adoption requires a balanced approach. Technological performance is crucial, but organizational learning, workforce engagement, and leadership support are equally critical; (2) lack of structured knowledge-sharing and training hinders AI adoption; further, some workers have limited awareness of AI's potential, and leadership lacks clarity to communicate complex goals of digitalization efforts, thus hindering motivation and trust; and (3) adoption of AI in construction is fragmented. Without industry-wide collaboration and clear digitalization strategies, AI implementation is inconsistent, and interoperability challenges slow integration. This study contributes to the body of knowledge by providing a structured framework for AI operational deployment in construction that integrates organizational learning, human-machine collaboration, and process adaptation. The findings offer practical recommendations for improving AI-driven decision support through better leadership strategies, structured workforce training, and industry-wide collaboration to enhance interoperability and digital integration.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; construction 5.0; construction machinery management; continuous learning; data driven; human interaction; integrated design and delivery solutions; organizational change |
| Index terms: | strategy, motivation, interview, organizational learning, organizational change, decision support, digitalization, body of knowledge, collaboration, integration, adaptation, transformation, pollution, implementation, transportation logistic, interaction, construction industry, artificial intelligence, interoperability, road construction |
| Subjects: | artificial intelligence, data collection methods, business, industry analysis, environmental health, systems and processes, organizational analysis, decision analysis, professional development, management, civil engineering, knowledge management, contractual arrangements, construction operations, psychology, digital technology, user focus, behavioral psychology |
| Topics: | Business Strategy, Research Practice, Information Management, Design Practice, Digital Applications, Human Resources, Site Management, Organizational Design, Engineering Principles, Risk Management, Procurement, Sustainability |
| Descriptive scope: | 4 PCTE |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here