"Adoption of Artificial Intelligence in the Construction Industry in Saudi Arabia: Challenges and Proposed Solutions"

This paper examines the integration of Artificial Intelligence (AI) within the Saudi Arabian construction industry, addressing key challenges, potential solutions, and its transformative potential. With Saudi Arabia’s Vision 2030 emphasizing technological innovation and economic diversification, the construction sector stands to benefit significantly from AI-driven advancements that improve productivity and enhance project management. Key challenges identified include technical barriers, such as data quality issues, complex system integration, and the limited availability of specialized AI tools. Additionally, regulatory hurdles related to compliance and data privacy, high implementation costs, limited ROI, and social resistance driven by job displacement fears and lack of AI familiarity pose significant obstacles. A survey conducted among industry professionals confirmed these challenges, highlighting difficulties in AI system integration, economic concerns about costs and talent shortages, and regulatory uncertainties. Social resistance to AI adoption also emerged as a critical barrier, underscoring the need for workforce training and change management strategies to foster acceptance. The paper proposes targeted solutions, including improving data integration capabilities, establishing comprehensive regulatory frameworks, providing financial incentives, and offering training programs to build AI-related expertise. Case studies and hypothetical scenarios illustrate AI’s potential benefits, such as optimizing resource allocation, enhancing safety, reducing costs, and streamlining project timelines. Government-backed initiatives like King Salman Energy Park (SPARK) emphasize the importance of infrastructure and state support in facilitating AI adoption across industries. These efforts align with national goals to transform construction practices and drive innovation. The findings demonstrate AI’s potential to revolutionize the construction sector, but overcoming existing challenges requires continuous collaboration between industry stakeholders, policymakers, and technology providers. Further research is necessary to validate these assumptions and explore practical strategies for AI integration, ultimately paving the way for a more efficient, innovative, and sustainable construction industry.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-02
DOI
https://doi.org/10.5281/zenodo.14258725
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

"Adoption of Artificial Intelligence in the Construction Industry in Saudi Arabia: Challenges and Proposed Solutions"

Zenodo (CERN European Organization for Nuclear Research)
BIM and Construction Integration
article

"Adoption of Artificial Intelligence in the Construction Industry in Saudi Arabia: Challenges and Proposed Solutions"

article en

Abstract

This paper examines the integration of Artificial Intelligence (AI) within the Saudi Arabian construction industry, addressing key challenges, potential solutions, and its transformative potential. With Saudi Arabia’s Vision 2030 emphasizing technological innovation and economic diversification, the construction sector stands to benefit significantly from AI-driven advancements that improve productivity and enhance project management. Key challenges identified include technical barriers, such as data quality issues, complex system integration, and the limited availability of specialized AI tools. Additionally, regulatory hurdles related to compliance and data privacy, high implementation costs, limited ROI, and social resistance driven by job displacement fears and lack of AI familiarity pose significant obstacles. A survey conducted among industry professionals confirmed these challenges, highlighting difficulties in AI system integration, economic concerns about costs and talent shortages, and regulatory uncertainties. Social resistance to AI adoption also emerged as a critical barrier, underscoring the need for workforce training and change management strategies to foster acceptance. The paper proposes targeted solutions, including improving data integration capabilities, establishing comprehensive regulatory frameworks, providing financial incentives, and offering training programs to build AI-related expertise. Case studies and hypothetical scenarios illustrate AI’s potential benefits, such as optimizing resource allocation, enhancing safety, reducing costs, and streamlining project timelines. Government-backed initiatives like King Salman Energy Park (SPARK) emphasize the importance of infrastructure and state support in facilitating AI adoption across industries. These efforts align with national goals to transform construction practices and drive innovation. The findings demonstrate AI’s potential to revolutionize the construction sector, but overcoming existing challenges requires continuous collaboration between industry stakeholders, policymakers, and technology providers. Further research is necessary to validate these assumptions and explore practical strategies for AI integration, ultimately paving the way for a more efficient, innovative, and sustainable construction industry.

Zenodo (CERN European Organization for Nuclear Research)
Sulaiman Al Rajhi Colleges (SA)
Industry, innovation and infrastructure
Openalex Percentile: Top 66%
BIM and Construction Integration
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.