Front. Built Environ. Frontiers in Built Environment Front. Built Environ. 2297-3362 Frontiers Media S.A. 1758283 10.3389/fbuil.2026.1758283 Original Research Strategic approaches to AI adoption in the construction industry of Ghana Aboagye et al. 10.3389/fbuil.2026.1758283 Aboagye Rexford Henaku 1 * Writing - review and editing Software Writing - original draft Project administration Visualization Aigbavboa Clinton 2 * Supervision Conceptualization Writing - review and editing Ametepey Simon Ofori 1 2 3 Writing - review and editing Methodology Supervision Addy Hutton 1 Software Formal Analysis Writing - review and editing 1 Centre for Sustainable Development (CenSuD), Koforidua Technical University , Koforidua , Ghana 2 Department of Construction Management and Quantity Surveying, Faculty of Engineering and Built Environment, University of Johannesburg , Johannesburg , South Africa 3 Department of Building Technology, Faculty of Built and Natural Environment, Koforidua Technical University , Koforidua , Ghana * Correspondence: Rexford Henaku Aboagye, harex97@gmail.com ; Clinton Aigbavboa, caigbavboa@uj.ac.za 11 03 2026 2026 12 1758283 01 12 2025 05 02 2026 20 02 2026 Copyright © 2026 Aboagye, Aigbavboa, Ametepey and Addy. 2026 Aboagye, Aigbavboa, Ametepey and Addy https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Introduction The integration of artificial intelligence (AI) in the construction industry holds immense potential to enhance productivity, improve decision-making, and increase operational efficiency. However, in many resource-constrained environments, the adoption of AI technologies remains slow owing to infrastructural limitations, skills deficits, regulatory uncertainties, and institutional challenges. This study investigates strategic approaches to AI adoption within the construction industry of resource-constrained environments, using Ghana as a representative case. Methods A quantitative research design was employed, involving 239 responses from construction professionals. The data was analysed using exploratory factor analysis (EFA) to identify and validate the strategic approaches. Results The analysis revealed a three-cluster structure comprising strategic collaboration and governance, operational integration and safety, and workforce development and technical infrastructure. These clusters offer a context-specific approach that captures the multifaceted requirements for AI adoption in resource-constrained construction environments. Discussion Rather than the application of EFA, the contribution of this study lies in the empirically validated framework it produces which clarifies how AI adoption strategies interrelate in the construction industry of Ghana. The study contributes to the growing literature on digital transformation in construction by offering practical, evidence-based guidance to industry leaders, policymakers, and researchers seeking to drive AI adoption in resource-constrained environments. AI adoption strategies artificial intelligence (AI) capacity building construction industry developing countries digital infrastructure The author(s) declared that financial support was not received for this work and/or its publication. section-at-acceptance Construction Management 1 Introduction The rapid development and deployment of artificial intelligence (AI) have brought transformative potential across industries including the construction industry ( Abioye et al., 2021 ; Faheem et al., 2024 ). The construction industry has historically been characterized by slow adoption of technological advancements ( Ametepey et al., 2024 ; Nik Fatma Arisya et al., 2019 ; Wang et al., 2020 ). In resource-constrained environments where infrastructure development is an important feature of economic progress ( Baporikar, 2016 ; Dang and Pheng, 2015 ; Owusu-Manu et al., 2019 ), the strategic adoption of AI in construction presents a unique opportunity to address the existing inefficiencies, improve productivity, and enhance sustainability within the industry ( Pan and Zhang, 2021 ). AI models and AI-driven technologies such as machine learning, predictive analytics, robotics, and automation offer solutions to persistent challenges in construction, including project delays, cost overruns, labour shortages, safety concerns and resource inefficiencies ( Rane, 2023a ; Regona et al., 2024 ). By leveraging these advancements in artificial intelligence, the construction industry can achieve faster project execution, reduced environmental impact, and enhanced decision-making capabilities ( Kineber et al., 2024 ). However, the adoption of AI in the construction industry within resource-constrained environments is not without challenges. The adoption of AI in the construction industries of resource-constrained environments has been described as slow as it is in the case of the adoption of such disruptive technologies ( Shang et al., 2023 ; Ugural et al., 2024 ). Although widely used AI adoption strategies have been developed primarily in Western contexts, their direct applicability to the construction industry of Ghana is limited due to contextual mismatches. These strategies are often premised on robust digital infrastructure, strong institutional support and highly skilled workforce. These conditions are largely insufficient in Ghana’s construction industry ( Pittri et al., 2025 ). Evidence exists to reinforce the stance that other resource-constrained environments are not able to adopt technological advancements along the models adopted by developed countries ( Ruiz-Villavicencio et al., 2025 ). The lack of advanced technological infrastructure, limited access to financial resources and gaps in skilled labour create significant barriers to the widespread adoption of AI technologies as solutions to the numerous challenges of the construction industry ( Farahani and Ghasemi, 2024 ). Additionally, there are numerous societal and organizational obstacles such as resistance to change, low levels of digital literacy, and concerns regarding the potential displacement of jobs. These challenges necessitate the development of strategic approaches that are tailored to the specific economic, social, and technological realities of developing nations ( Babashahi et al., 2024 ; Dwivedi et al., 2021 ). Addressing these issues requires effective collaborations between governments, private industry, and academic institutions. Such partnerships can drive investment in digital infrastructure, promote capacity building, and support the creation of conducive regulatory frameworks that encourage innovation while addressing ethical and privacy concerns ( Tran and Nguyen, 2024 ). Moreover, targeted initiatives such as workforce training programmes, public awareness campaigns and pilot projects can help build trust in AI technologies and demonstrate their value in improving various activities in the construction process ( Rane et al., 2024 ). This study aims to explore strategic approaches to AI adoption in the construction industry of resource-constrained environments. By identifying enabling strategies, the study seeks to provide a comprehensive roadmap for stakeholders, including policymakers, construction firms, and technology providers. This approach emphasizes the importance of localized solutions while highlighting how AI can be integrated in ways that maximize benefits while minimizing risks. The findings of this research contribute to bridging the gap between the technological potential of AI and its practical application in the construction industry as the industry is critical to the economic and social development of emerging economies. Doing so provides actionable insights to harness the transformative power of AI to accelerate progress and sustainability of the construction industry. 2 Overview of AI adoption in the construction industry The integration of AI within the construction industry marks a transformative departure from conventional operational and managerial frameworks, introducing paradigmatic advancements in efficiency, safety, and project delivery outcomes ( Piras et al., 2024 ). Historically characterized by systemic inefficiencies and persistent safety hazards, the sector now stands to benefit from AI’s capacity to optimize project planning, execution, and resource allocation algorithmically. Advanced AI applications, particularly those involving predictive analytics, have demonstrated considerable utility in forecasting schedule deviations and cost overruns, thereby reinforcing adherence to predefined project constraints ( Nagireddy, 2023 ). Moreover, AI significantly enhances occupational safety management through the deployment of unmanned aerial vehicles (UAVs) for site surveillance and autonomous systems for real-time hazard detection. These innovations not only increase the precision and responsiveness of safety protocols but also substantially reduce human error, contributing to safer and more resilient construction environments ( Rabbi and Jeelani, 2024 ). Such technological augmentations are critical to enhancing both on-site productivity and worker protection. Despite these transformative potentials, the sector’s adoption of AI remains encumbered by several critical challenges. Foremost among these are the high capital costs associated with AI infrastructure, entrenched resistance to change rooted in traditional construction practices, and a persistent deficit in the requisite technical competencies among the work force. These impediments are further exacerbated by prevailing scepticism regarding the return on investment in AI technologies, which has tempered enthusiasm and slowed diffusion across the industries ( Acheampong et al., 2025 ). Nonetheless, the long-term prospects for AI integration in construction remain optimistic. As stakeholders increasingly recognize the substantial benefits, ranging from lifecycle cost efficiencies and enhanced site safety to improved project precision, the momentum for AI adoption is poised to accelerate ( Kapoor et al., 2024 ). This evolving landscape underscores the imperative for sustained scholarly inquiry aimed at refining AI applications, reducing adoption barriers, and generating cost-effective, scalable solutions tailored to the nuanced exigencies of the construction domain. Continued investment in AI-centric research, accompanied by policy innovation and industry-wide capacity building, will be essential for fully leveraging the transformative potential of AI. These strategic efforts will not only facilitate the seamless integration of advanced technologies but will also ensure that AI adoption aligns with the sector’s operational complexities and dynamic risk environments ( Onatayo et al., 2024 ). 3 Strategies for the adoption of AI in construction The literature emphasizes the need for clear strategic approaches to guide the adoption of AI in construction especially in the context of resource-constrained environments to ensure that it aligns with organisational objectives and reduces uncertainty ( Singh et al., 2023 ; Tjebane et al., 2022 ). It is important to have sustained investment in training and upskilling to equip professionals with the required competencies to manage AI -driven systems ( Abioye et al., 2021 ; Khan, 2023 ; Rane, 2023a ). Several studies advocate the use of pilot projects as a risk mitigation strategy allowing organisations to test AI applications and refine implementation approaches prior too large-scale deployment ( Kenge and Khan, 2020 ; Tunji-Olayeni et al., 2022a ; Allouzi and Aljaafreh, 2024 ). Effective AI adoption in the various industries depended the integration of AI systems with existing technologies and improving data management practices as interoperability and high-quality data are crucial for reliable AI performance ( Chen et al., 2024 ; Santos and Jocson, 2024 ; Alotaibi and Alshemmari, 2025 ; Parekh and Mitchell, 2024 ). Collaboration among key stakeholders has further been identified as a critical enabler of coordinated implementation and knowledge sharing ( Khan et al., 2024 ). It is evident that the importance of explainable AI and ethical frameworks in enhancing transparency, trust and responsible AI adoption is growing rapidy. There is also the need for a supportive regulatory environment to guide AI adoption in the construction industry ( Na et al., 2023 ; Love et al., 2023 ; Liang et al., 2024 ; Rasheed et al., 2024 ; Rinchen et al., 2024 ; Ujaimi et al., 2024 ; Yang C. et al., 2024 ). Investment in research and development has been consistently cited as a driver of innovation and contextual adoption of AI technologies ( Tunji-Olayeni et al., 2022b ; Regona et al., 2023 ). The literature underscores the role of AI in enhancing construction safety through advanced risk detection and monitoring while emphasizing the importance of supportive organisational culture for reducing resistance to technological change ( Santos and Jocson, 2024 ; Khan, 2023 ; Chong et al., 2025 ). Addressing interoperability challenges and building a skille workforce have been identified as prerequisites for sustainable AI adoption across industries ( Tjebane et al., 2022 ; Khan et al., 2024 ). Again, ensuring access to relevant datasets, fostering industrial partnerships, promoting public education and engagement, establishing continuous monitoring and feedback mechanisms are crucial for sustaining the adoption of AI in the construction industry ( Rafsanjani and Nabizadeh, 2023 ; Pillai and Matus, 2020 ; Al Samman, 2024 ; Santos and Jocson, 2024 ; Rinchen et al., 2024 ; Khan et al., 2024 ; Yang C. et al., 2024 ). Table 1 below presents the summary of strategies for the adoption of AI in the construction Industry. TABLE 1 Strategies for the adoption of artificial intelligence in construction. No. Strategy Source 1 Developing a clear roadmap for AI adoption Singh et al. (2023) Tjebane et al. (2022) 2 Investing in training and upskilling Khan (2023) ; Rane (2023b) ; Abioye et al. (2021) 3 Implementing pilot projects Allouzi and Aljaafreh (2024) ; Tunji-Olayeni et al. (2022a) ; Kenge and Khan (2020) 4 Integrating with other existing technologies Chen et al. (2024) ; Santos and Jocson (2024) 5 Improving data management Alotaibi and Alshemmari (2025) ; Parekh and Mitchell (2024) 6 Collaborating among stakeholders Khan et al. (2024) 7 Adopting explainable artificial intelligence Yang L. et al. (2024) ; Na et al. (2023) ; Love et al. (2023) 8 Establishing ethical frameworks Liang et al. (2024) ; Rasheed et al. (2024) ; Rinchen et al. (2024) 9 Establishing regulations Ujaimi et al. (2024) 10 Investing in research and development Regona et al. (2023) ; Tunji-Olayeni et al. (2022a) 11 Adopting enhanced safety protocols Chong et al. (2025) ; Santos and Jocson (2024) 12 Having a supportive organizational culture Khan (2023) 13 Addressing interoperability issues Tjebane et al. (2022) 14 Building a skilled workforce Khan et al. (2024) 15 Forming industrial partnerships Rinchen et al. (2024) 16 Making data sets accessible Al Samman (2024) ; Santos and Jocson (2024) 17 Promiting public education and engagement Rafsanjani and Nabizadeh (2023) ; Pillai and Matus (2020) 18 Ensuring continuous monitoring and feedback Khan et al. (2024) ; Yang L. et al. (2024) 4 Materials and methodology In order to achieve the objectives of the study, a questionnaire survey was deployed. The survey assessed the perceptions of construction professionals in Ghana in relation to the possible ways through which AI can be successfully deployed in the Ghanaian construction industry. The study was conducted through the following steps, namely, a literature review, design of data collection instrument (questionnaire), administration of questionnaire, data collection, data analysis and interpretation as depicted in Figure 1 : FIGURE 1 Research steps. Flowchart showing sequential research process steps: review of related literature, development of survey instrument, data collection, data analysis, and discussion and interpretation of results, each step connected by downward arrows. This research identified the strategic approaches through a structured literature review of relevant academic sources rather than a formal Systematic Literature Review. The search focused on studies addressing AI adoption and digital transformation in the construction industry. The recurring and conceptually similar strategies were synthesized and consolidated to eliminate redundancy. This resulted in eighteen strategies for further analysis of the study The study employed a quantitative methodological framework to assess strategic approaches for the adoption of AI empirically in the construction industry of Ghana. The quantitative approach was selected to enable systematic measurement of variables and to support generalizable inferences through statistical analysis ( Given, 2008 ; Muijs, 2010 ). This design facilitated the efficient collection of data from a broad spectrum of construction professionals within a constrained timeframe, enabling the indirect capture of perceptions and experiential insights related to AI integration. Prior to the main survey, a pilot study involving 15 industry professionals was conducted to assess clarity, wording, and construct validity. Feedback from the pilot informed iterative refinements to ensure validity and alignment with study objectives. The study population consisted of construction practitioners operating across multiple project types in Ghana, encompassing roles such as site supervisors, architects, quantity surveyors, project managers, civil and structural engineers, technicians, and interior designers. A purposive sampling strategy was employed to ensure the inclusion of professionals with relevant expertise in technology and AI applications within the built environment. Given the nascent nature of AI adoption in the Ghanaian construction industry, such expertise is not uniformly distributed across all industry participants which makes probability-based sampling impractical. This approach is consistent with prior quantitative studies in the context of emerging technologies where informed judgement from knowledgeable respondents is crucial in generating meaningful and reliable data ( Re Moxley et al., 2022 ). The sampling focused on Accra, Kumasi and Koforidua where construction innovations are more prevalent. A total of 270 questionnaires were distributed, yielding 239 valid responses and an 88% response rate. This was done to optimize statistical reliability while maintaining operational feasibility. Primary data were collected using a structured questionnaire informed by scholarly literature, governmental publications, and empirical reports ( Roopa and Rani, 2012 ). Distribution was conducted digitally via Google Forms, accompanied by detailed response instructions. Responses were analysed through mean item score (MIS) metrics to facilitate quantitative interpretation. To examine latent structures among the measured constructs, EFA was applied using principal axis factoring and varimax rotation in SPSS Version 26. Given the fact that AI adoption in Ghana’s construction industry is still in the early stage, EFA was used to uncover the underlying structure among the identified adoption strategies. This approach therefore provides an empirical foundation that can be refined or confirmed in future studies. Criteria for factor retention included eigenvalues exceeding 1.0 and factor loadings above 0.50. Instrument reliability was evaluated using Cronbach’s alpha, with coefficients ≥0.70 considered acceptable for internal consistency. Given the ordinal nature of the data, group differences were assessed using non-parametric statistical tests, particularly the Mann-Whitney U test, with statistical significance established at the 0.05 level. Content validity was enhanced through expert evaluation and triangulation with existing research. The questionnaire was iteratively refined based on pilot testing and statistical diagnostics to ensure clarity, precision, and construct relevance. The study adhered to rigorous ethical standards. All data sources were duly acknowledged, and participant confidentiality and anonymity were strictly maintained. Informed consent was obtained prior to data collection, and participants retained the right to withdraw at any point. Data were securely stored and exclusively used for academic purposes. 5 Findings 5.1 Socio-professional background of respondents The socio-professional data presented in Table 2 reveals a diverse respondent pool encompassing a range of age groupings, occupational categories, professional experience, and academic qualifications. With respect to age distribution, the largest segment (34%) comprises individuals aged 36-40 years, followed closely by those over 40 years (27%), suggesting a workforce predominated by seasoned practitioners. In contrast, younger professionals aged 20-30 years collectively constitute a smaller fraction (19%), indicating a demographic skew toward mid-to-late career stages while still accommodating emergent entrants into the sector. Occupationally, site supervisors represent the most prevalent professional group at 16%, followed by architects (11%). Electrical engineers, contractors, and construction managers each account for 10% of the sample, reflecting a substantial representation of both supervisory and technical personnel. Additional roles, including quantity surveyors, project managers, and mechanical engineers, comprise a combined 9%, whereas civil and structural engineers are among the least represented, underscoring the predominance of managerial and engineering leadership roles within the respondent base. TABLE 2 Demographic information of respondents. Category Description Percentage Age groups 20-25 years 6% ​ 26-30 years 13% ​ 31-35 years 19% ​ 36-40 years 34% ​ Older than 40 years 27% ​ Site supervisors 16% Professions Architects 11% ​ Electrical engineers 10% ​ Contractors 10% ​ Construction managers 10% ​ Quantity surveyors, project managers, mechanical engineers 9% ​ Construction technicians 8% ​ Civil engineers 7% ​ Structural engineers 2% ​ Interior designers 1% Working experience Less than 1 year 4% ​ 1-5 years 19% ​ 6-10 years 31% ​ 11-15 years 17% ​ 16-20 years 14% ​ More than 20 years 15% In terms of industry experience, the majority of respondents (31%) report six to 10 years of professional practice, with 19% having one to 5 years of experience—indicative of a balanced distribution between early-career and moderately experienced professionals. Additionally, 17% possess 11-15 years of experience, and 15% report more than two decades in the field, confirming the presence of senior-level expertise. A minimal share (4%) have less than 1 year of experience, highlighting a modest influx of new professionals. Regarding educational attainment, a significant proportion of respondents (41%) hold a bachelor’s degree, followed by 22% with a higher national diploma and 20% with a master’s degree, illustrating a workforce anchored in robust undergraduate training with a notable contingent possessing postgraduate qualifications. A smaller subset (7%) report holding a doctorate, reflecting the presence—albeit limited—of highly specialized academic expertise within the sample. 5.2 Descriptive statistics and non-parametric test of the strategies for the adoption of artificial intelligence in the construction industry As illustrated in Table 3 , the strategy deemed most critical for advancing AI adoption in the construction industry was ‘Investing in research and development’, which achieved the highest mean score (M = 4.19), accompanied by a Mann-Whitney U statistic of 166.000 and an asymptotic significance (p-value) of 0.437. This was closely followed by ‘Adopting enhanced safety protocols’ (M = 4.15, U = 162.000, p = 0.375), and ‘Implementing pilot projects’ ranked third (M = 4.07, U = 174.000, p = 0.585). In fourth position, ‘Addressing interoperability issues received a mean score of 4.06 (U = 192.000, p = 0.106), while ‘Investing in training and upskilling’ was ranked fifth (M = 3.99, U = 169.500), with a marginally significant p-value of 0.051. ‘Developing a clear roadmap for AI adoption’ followed in sixth place (M = 3.98, U = 150.000, p = 0.185). ‘Having a supportive organizational culture’ and ‘Improving data management’ occupied the seventh and eighth positions, scoring means of 3.97 and 3.96, respectively. These were associated with U values of 196.500 and 162.000, and corresponding significance levels of 0.006 and 0.345. TABLE 3 Strategies for the adoption of artificial intelligence in the construction industry. Strategies Mean Mann-Whitney U Asymp. Sig. (2-tailed) Rank Investing in research and development 4.19 166.000 0.437 1 Adopting enhanced safety protocols 4.15 162.000 0.375 2 Implementing pilot projects 4.07 174.000 0.585 3 Addressing interoperability issues 4.06 192.000 0.106 4 Investing in training and upskilling 3.99 169.500 0.051 5 Developing a clear roadmap for AI adoption 3.98 150.000 0.185 6 Having a supportive organizational culture 3.97 196.500 0.006 7 Improving data management 3.96 162.000 0.345 8 Continuous monitoring and feedback 3.96 130.500 0.085 9 Forming industrial partnerships 3.93 138.500 0.017 10 Integrating with other existing technologies 3.92 165.000 0.436 11 Establishing ethical frameworks 3.91 129.000 0.055 12 Building a skilled workforce 3.89 174.000 0.556 13 Establishing regulations 3.88 125.000 0.042 14 Adopting explainable artificial intelligence 3.86 182.000 0.781 15 Making data sets accessible 3.85 187.000 0.866 16 Collaborating among stakeholders 3.77 165.500 0.456 17 Promoting public education and engagement 3.70 144.000 0.125 18 Ranked ninth, ‘Ensuring continuous monitoring and feedback’ had a mean of 3.96 (U = 130.500, p = 0.085), whereas ‘Forming industrial partnerships’ was placed 10th (M = 3.93, U = 138.500, p = 0.017). In the 11th and 12th ranks were ‘Integrating with other existing technologies’ (M = 3.92, U = 165.000, p = 0.436) and ‘Establishing ethical frameworks’ (M = 3.91, U = 129.000, p = 0.055), respectively. ‘Building a skilled workforce’ ranked 13th (M = 3.89, U = 174.000, p = 0.556), followed by ‘Establishing regulations’ in the 14th position (M = 3.88, U = 125.000, p = 0.042). ‘Adopting explainable AI’ was placed 15th with a mean score of 3.86 (U = 182.000, p = 0.781), followed by ‘Making data sets accessible’ (16th; M = 3.85, U = 187.000, p = 0.866). ‘Collaborating among stakeholders ranked 17th (M = 3.77, U = 165.500, p = 0.456), while the lowest-ranked strategy was ‘Promoting public education and engagement’ in 18th position (M = 3.70, U = 144.000, p = 0.125). 5.3 Exploratory factor analysis of the strategies for the adoption of artificial intelligence in the construction industry To evaluate the underlying structure of the dataset, an EFA was conducted. Prior to extraction, data suitability was assessed through preliminary diagnostics. Inspection of the correlation matrix confirmed that inter-item correlations exceeded the minimum threshold of 0.30, indicating adequate relationships among variables for factor analysis. As presented in Table 4 , the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy yielded a value of 0.873, substantially surpassing the minimum acceptable benchmark of 0.60 and thus validating the sample’s adequacy for factor extraction. Furthermore, Bartlett’s test of sphericity demonstrated statistical significance (p < 0.001), confirming that the correlation matrix is not an identity matrix and that the data are factorable. These diagnostics collectively support the appropriateness of proceeding with EFA. TABLE 4 KMO measure and Bartlett’s test. Kaiser-Meyer-Olkin measure of sampling adequacy 0.873 Bartlett’s test of sphericity Approx. Chi-square 1903.818 ​ df 153 ​ Sig 0.001 Table 5 presents the communalities associated with each variable following extraction, all of which exceed the minimum threshold of 0.300. This suggests that the variables are well-represented by the extracted factors and exhibit sufficient shared variance. The absence of low communalities affirms that no variable is poorly explained by the factor solution, thereby supporting the internal coherence of the factor structure. Consequently, the factor grouping is considered robust, as all variables demonstrate adequate levels of communality, reinforcing the validity of the component extraction. TABLE 5 Communalities. Strategies Initial Extraction Developing a clear roadmap for AI adoption 1 0.531 Investing in training and upskilling 1 0.576 Implementing pilot projects 1 0.312 Integrating with other existing technologies 1 0.355 Improving data management 1 0.592 Collaborating among stakeholders 1 0.666 Adopting explainable artificial intelligence 1 0.595 Establishing ethical frameworks 1 0.713 Establishing regulations 1 0.618 Investing in research and development 1 0.593 Adopting enhanced safety protocols 1 0.643 Shaving a supportive organizational culture 1 0.658 Addressing interoperability issues 1 0.732 Building a skilled workforce 1 0.643 Forming industrial partnerships 1 0.532 Making data sets accessible 1 0.637 Promoting public education and engagement 1 0.524 Ensuring continuous monitoring and feedback 1 0.583 Table 6 presents the total variance explained by the extracted components, based on eigenvalues derived using Kaiser’s criterion. According to this criterion, components with eigenvalues greater than 1.0 are deemed significant and retained for interpretation. In this analysis, the first two components satisfied this threshold, thereby qualifying for retention in the initial eigenvalue solution. Collectively, these two components account for a cumulative variance of 58.356%, indicating a substantial proportion of the total variance in the dataset and underscoring the explanatory power of the extracted factor structure. TABLE 6 Total variance explained. Component Initial eigenvalues Cumulative % Extraction sums of squared loadings Rotation sums of squared loadings Total % of variance Total % of variance Cumulative % Total 1 7.612 42.289 42.289 7.612 42.289 42.289 6.235 2 1.694 9.413 51.701 1.694 9.413 51.701 2.745 3 1.198 6.655 58.356 1.198 6.655 58.356 5.500 4 0.979 5.437 63.793 ​ ​ ​ ​ 5 0.914 5.078 68.871 ​ ​ ​ ​ 6 0.857 4.763 73.634 ​ ​ ​ ​ 7 0.681 3.783 77.416 ​ ​ ​ ​ 8 0.638 3.542 80.959 ​ ​ ​ ​ 9 0.568 3.154 84.112 ​ ​ ​ ​ 10 0.524 2.911 87.023 ​ ​ ​ ​ 11 0.426 2.368 89.392 ​ ​ ​ ​ 12 0.403 2.237 91.628 ​ ​ ​ ​ 13 0.368 2.042 93.67 ​ ​ ​ ​ 14 0.301 1.673 95.343 ​ ​ ​ ​ 15 0.265 1.472 96.815 ​ ​ ​ ​ 16 0.218 1.212 98.027 ​ ​ ​ ​ 17 0.195 1.084 99.111 ​ ​ ​ ​ 18 0.16 0.889 100 ​ ​ ​ ​ Extraction method: Principal component analysis. The cumulative variance explained (58.356%) indicates that the extracted factors collectively account for a substantial proportion of the total variability in the data. Figure 2 illustrates the scree plot generated from the factor analysis, indicating the distribution of eigenvalues across the extracted components. The plot identifies the factors with eigenvalues exceeding 1.0 on the steep descending slope, indicating their statistical significance according to Kaiser’s criterion. Conversely, components with eigenvalues below this threshold are situated along the more gradual slope, suggesting limited explanatory value. FIGURE 2 Scree plot. Screen plot line graph with component number on the x-axis and eigenvalue on the y-axis, showing a steep drop from the first to second component, then a gradual decline across subsequent components. 5.4 Factor cluster report Table 7 shows the pattern matrix with 16 variables that were identified from the study. Principal component analysis revealed the presence of three (3) factors with eigenvalues above 1, as shown in Table 5 . Two variables ‘Integrating with other existing technologies’ and ‘Having a supportive organizational culture’ were excluded from the final factor interpretation owing to their factor loadings falling below the accepted threshold of 0.30, indicating insufficient association with any single component. Based on the examination of the inherent relationships among the variables under each factor, the following interpretations were made: As shown in Table 7 , six variables loaded into the first cluster: ‘Collaborating among stakeholders’ (80.3%), ‘Establishing ethical frameworks’ (88.6%), ‘Establishing regulations’ (81.6%), ‘Adopting explainable artificial intelligence’ (65.1%), ‘Promoting public education and engagement’ (67.3%), and ‘Continuous monitoring and feedback’ (61.2%). This cluster is termed Strategic Collaboration and Governance. The variables in this cluster focus on collaborative efforts, ethical considerations, and stakeholder engagement, which are essential for the successful adoption and governance of AI in the construction industry. This cluster accounted for a significant portion of the total variance. Cluster 2 accounted for another substantial portion of the total variance and included five variables: ‘Improving data management’ (49.7%), ‘Investing in research and development’ (51.5%), ‘Adopting enhanced safety protocols’ (71.8%), ‘Addressing interoperability issues’ (23.8%), and ‘Having a supportive organizational culture’ (25.8%). These variables are focused on integrating AI into existing operations, enhancing safety, and fostering a supportive environment for AI adoption. Therefore, this cluster is termed Operational Integration and Safety. Five variables were loaded into Cluster 3: ‘Building a skilled workforce’ (88.4%), ‘Forming industrial partnerships’ (45.6%), ‘Making data sets accessible’ (51.3%), ‘Developing a clear roadmap for AI adoption’ (59.7%), and ‘Investing in training and upskilling’ (63.4%). This cluster encompasses factors related to developing the necessary workforce, building partnerships, and establishing the technical infrastructure required for AI adoption. Consequently, this cluster is labelled Workforce Development and Technical Infrastructure. TABLE 7 Pattern matrix. Strategies 1 2 3 Collaborating among stakeholders 0.803 ​ ​ Establishing ethical frameworks 0.886 ​ ​ Establishing regulations 0.816 ​ ​ Adopting explainable artificial intelligence 0.651 ​ ​ Promoting public education and engagement 0.673 ​ ​ Continuous monitoring and feedback 0.612 ​ ​ Improving data management ​ 0.497 ​ Investing in research and development ​ 0.515 ​ Adopting enhanced safety protocols ​ 0.718 ​ Addressing interoperability issues ​ 0.238 ​ Having a supportive organizational culture ​ 0.258 ​ Building a skilled workforce ​ ​ 0.884 Forming industrial partnerships ​ ​ 0.456 Making data sets accessible ​ ​ 0.513 Developing a clear roadmap for AI adoption ​ ​ 0.597 Investing in training and upskilling ​ ​ 0.634 Table 8 shows the component correlation matrix with both cluster having values above 0.300. This indicates that there is a strong relationship between both clusters: TABLE 8 Reliability of clusters. Clusters Cronbach’s alpha coefficient Strategic collaboration and ethical governance 0.944 Operational integration and safety 0.921 Workforce development and technical infrastructure 0.873 6 Discussion 6.1 Cluster one - strategic collaboration and governance The first cluster identified through EFA, encompassing six core variables, namely, Collaborating among stakeholders, establishing ethical frameworks, establishing regulations, adopting explainable artificial intelligence, promoting public education and engagement, and Continuous monitoring and feedback, represents the strategic and governance-oriented dimensions underpinning AI adoption in the construction industry. These variables collectively highlight that successful implementation of AI technologies extends beyond technical readiness and necessitates deliberate institutional, ethical, and collaborative foundations. The inclusion of regulatory and ethical elements within this cluster is particularly significant. Recent empirical studies have underscored that the absence of standardized regulatory frameworks remains one of the most persistent barriers to AI integration in construction, particularly in developing economies where institutional infrastructures are still evolving ( Tjebane et al., 2022 ). The role of ethical frameworks in guiding the responsible use of AI is further emphasized by Regona et al. (2024) , who advocate for transparent governance models that align AI deployment with the broader goals of sustainable development. Their systematic literature review found that ethical guidance not only supports risk mitigation but also enhances legitimacy and public trust in technology-driven construction projects. Explainable AI (XAI), which also features in this cluster, serves as a critical bridge between technical decision-making and human interpretability. Love et al. (2023) contend that XAI is essential for enhancing trust and accountability in construction processes, where opaque algorithms can lead to serious consequences in safety and cost estimation. The relevance of XAI is particularly salient in contexts where public safety and regulatory compliance are paramount, such as structural design, project scheduling, and risk assessment. Furthermore, collaboration among stakeholders emerges as a cornerstone of this factor, reinforcing the idea that multi-actor participation is indispensable for AI implementation. Rane et al. (2024) emphasized that fragmented stakeholder alignment and lack of inter-organizational communication are among the chief impediments to AI-driven innovation in construction. Effective collaboration ensures shared ownership of AI initiatives, promotes interoperability among systems, and facilitates collective problem-solving across organizational boundaries. Public education and engagement further enrich the cluster by signaling that awareness and inclusivity are necessary precursors to AI acceptance. This is supported by the findings of Rosengren and Kvarnmarker (2024) , who argue that skepticism and cultural resistance to AI can be mitigated through sustained educational outreach and participatory governance models. Finally, the inclusion of continuous monitoring and feedback mechanisms reinforces the need for iterative learning and adaptive management. Such systems allow construction firms to track performance, evaluate outcomes, and recalibrate strategies in real-time, thereby ensuring that AI applications remain responsive to evolving project requirements and ethical standards. 6.2 Cluster two - operational integration and safety Cluster 2 encapsulates critical enablers for operationalizing artificial intelligence in the construction industry, focusing on integration, data efficiency, safety, and organizational readiness. These factors, namely, Improving data management, investing in research and development, Adopting enhanced safety protocols, addressing interoperability issues, and having a supportive organizational culture, collectively signal the infrastructural and institutional prerequisites for scalable AI implementation. A foundational pillar in this cluster is improved data management, which plays a pivotal role in enabling AI-driven analytics, predictive modelling, and automation. Effective data governance ensures accuracy, interoperability, and accessibility across project lifecycles, which is essential for technologies such as building information modelling (BIM) and digital twins ( Abioye et al., 2021 ). Investment in research and development (R&D) further propels AI innovation by fostering context-specific applications and mitigating risks associated with premature or misaligned technology deployment. As observed by Regona et al. (2024) , continuous R&D bridges the gap between theoretical AI capabilities and practical industry needs, particularly in rapidly evolving construction environments. Safety remains a perennial concern in construction, and adopting enhanced safety protocols through AI reflects a strategic move from reactive to proactive risk management. AI-powered systems, including real-time hazard detection, wearable sensors, and predictive analytics, have demonstrated effectiveness in reducing on-site accidents and ensuring regulatory compliance ( Acheampong et al., 2025 ; Chong et al., 2025 ). Addressing interoperability issues is critical given the fragmented nature of construction workflows. Disparate digital systems across contractors, subcontractors, and consultants often hinder seamless AI adoption. Studies highlight that resolving interoperability through standardized data formats, APIs, and integration frameworks enhances information flow and aligns with the goals of Construction 4.0 ( Wang et al., 2020 ). Lastly, a supportive organizational culture acts as an enabler for all other operational strategies. Organizational readiness marked by leadership commitment, cross-functional collaboration, and willingness to embrace change is a recurrent theme in empirical findings on AI adoption ( Tjebane et al., 2022 ; Yang L. et al., 2024 ). Without cultural alignment, even the most sophisticated AI tools may face internal resistance, undermining their transformative potential. 6.3 Cluster three - Workforce development and technical infrastructure The third cluster captures five pivotal variables: Building a skilled workforce, forming industrial partnerships, ensuring data accessibility, conducting strategic road mapping, and investing in training and upskilling. Collectively, these components form the foundational backbone required to sustain and scale artificial intelligence (AI) integration in the construction industry. Foremost among the identified factors is the development of a skilled workforce, which registered the highest loading (88.4%) in this cluster. The successful deployment of AI technologies demands not only technical expertise but also interdisciplinary knowledge in construction processes and digital innovation. A lack of qualified personnel has consistently emerged as a critical barrier to AI diffusion in the construction industry, particularly in resource-constrained environments ( Abioye et al., 2021 ; Tjebane et al., 2022 ). Without targeted educational programmes and professional training, organizations may find themselves unable to leverage AI tools, perpetuating inefficiencies and technological stagnation. Supporting this view, the systematic review by Rosengren and Kvarnmarker (2024) underscores that human capital is a decisive enabler of AI maturity in large-scale enterprises, especially where operational complexity is high. The role of industrial partnerships is equally important, with a factor loading of 45.6%. Collaborative engagements between construction firms, technology developers, academic institutions, and government agencies can accelerate innovation transfer and reduce the cost of AI implementation. Such partnerships help bridge the knowledge-practice gap, facilitating co-development of context-specific AI tools and shared learning opportunities. Evidence from Ugural et al. (2024) and Wang et al. (2020) indicates that public-private partnerships are instrumental in operationalizing AI-enabled solutions, particularly in resource-constrained environments where economies of scale and technical support are essential. Closely tied to these factors is the accessibility of data, which registered a 51.3% loading. AI models rely heavily on large, high-quality datasets to train, validate, and optimize performance. In many construction environments, however, data is siloed, unstructured, or unavailable, hampering the efficacy of predictive analytics, automated scheduling, and quality assurance systems ( Shang et al., 2023 ). Consequently, creating infrastructures for centralized data repositories, open-access platforms, and standardized data schemas is crucial for unlocking the full potential of AI. This aligns with findings by Liang et al. (2024) , who argue that data democratization is a precondition for implementing explainable and trustworthy AI in construction. The importance of a strategic roadmap for AI adoption, loading at 59.7%, reflects the need for long-term planning and structured implementation frameworks. Many construction firms lack a clear vision or implementation strategy for AI, leading to fragmented efforts and sunk investments ( Na et al., 2023 ). A well-formulated roadmap not only aligns AI projects with business goals but also clarifies roles, timelines, and performance metrics, thereby reducing organizational resistance and enhancing accountability ( Rinchen et al., 2024 ). Finally, investment in training and upskilling, with a loading of 63.4%, highlights the need for continuous professional development to ensure that existing personnel can adapt to new technological paradigms. The dynamic nature of AI tools necessitates ongoing reskilling, not just for engineers and data scientists, but also for managers, safety officers, and field personnel ( Onatayo et al., 2024 ). Training investments should be aligned with strategic objectives and incorporate both technical competencies and digital literacy, fostering a workforce that is both capable and confident in AI usage ( Khan et al., 2024 ). A non-parametric test using the Mann-Whitney U test revealed the rankings of various strategies for adopting artificial intelligence (AI) in the construction industry. ‘Investing in research and development’ ranked highest with a mean value of 4.19, followed by ‘Adopting enhanced safety protocols’ (mean of 4.15) and ‘Implementing pilot projects’ (mean of 4.07). Other notable strategies include ‘Addressing interoperability issues’ (mean of 4.06) and ‘Investing in training and upskilling’ (mean of 3.99). The lower-ranked strategies included ‘Promoting public education and engagement’ with a mean value of 3.70, indicating varying levels of perceived importance among the respondents regarding the adoption strategies. 7 Implications for research and practice A critical implication of this study lies in its contribution to addressing a significant geographical and contextual gap in the existing body of literature on AI adoption in the construction industry. While numerous studies have examined the implementation and benefits of AI in construction, these are predominantly situated within the context of advanced economies where digital infrastructure, regulatory maturity, and institutional capacity are relatively well developed. As such, the strategic frameworks and adoption pathways discussed in these works are often not directly applicable to the unique socio-economic and institutional conditions prevailing in resource-constrained environments. This study responds to that gap by offering a comprehensive, empirically grounded analysis of AI adoption strategies tailored to the realities of the construction industry in the context of a resource-constrained environment. Through the identification of three key clusters, namely, Strategic Collaboration and Governance, Operational Integration and Safety, and Workforce Development and Technical Infrastructure, the research advances a nuanced understanding of the enabling factors and barriers to AI adoption in resource-constrained environments. The findings underscore the necessity of context-specific strategies that account for infrastructural limitations, fragmented stakeholder landscapes, and skills deficits. Adopting AI in construction requires a well-structured and strategic approach tailored to the unique challenges of the industry in resource-constrained environments. The strategic approaches identified in this study strategic collaboration and governance, operational integration and safety, and workforce development and technical infrastructure serve as a practical framework for guiding AI implementation efforts. To begin with, organisations must foster strategic collaboration and ethical governance. This includes strengthening partnerships among government agencies, industry players, and academia to create unified AI adoption frameworks. Ethical considerations such as data privacy, algorithmic transparency, and public engagement are essential to building trust and accountability in AI-driven construction processes. Further, the study emphasises the need for operational integration and safety strategies. This involves improving internal data management systems, enhancing safety protocols through AI-powered monitoring tools, and addressing interoperability issues among construction technologies. Construction firms should prioritize investment in research and development to create AI applications that align with local project conditions and infrastructure limitations. Equally important is the development of technical infrastructure and human capital. Building a skilled workforce through targeted training and upskilling programmes is central to ensuring a smooth transition to AI-enhanced construction practices. Organisations must invest in technical readiness by improving access to quality datasets, forming industrial partnerships, and developing clear roadmaps that outline goals, timelines, and responsibilities for AI implementation. 8 Conclusion The adoption of AI in the construction industry remains limited and uneven, particularly within resource-constrained environments where infrastructural, institutional, and human capacity challenges persist. Although the transformative potential of AI has been widely documented in global literature, much of this evidence is drawn from advanced economies. As such, contextual strategies that address the specific needs and constraints of construction firms in developing regions are underexplored. This study contributes to filling this gap by investigating strategic approaches to AI adoption tailored to the realities of resource-constrained environments, with a particular focus on the South African construction industry. Through a quantitative analysis involving 169 construction professionals, the study identified 18 strategic factors influencing AI adoption. Using EFA, these factors were clustered into three core components: Strategic Collaboration and Governance, Operational Integration and Safety, and Workforce Development and Technical Infrastructure. These clusters represent a structured framework for understanding how AI can be effectively integrated within resource-constrained construction environments. The significance of this framework lies in its empirical validation of multidimensional strategies that extend beyond technology deployment to include regulatory structures, organisational culture, capacity-building, and inter-stakeholder collaboration. In particular, the study emphasises the role of ethical governance, safety-driven innovation, and human capital development as foundational pillars for AI readiness. The findings also provide actionable guidance for policymakers, industry stakeholders, and academic institutions aiming to design AI implementation roadmaps that are both practical and locally relevant. Future research should seek to broaden the geographic scope of the current study to other developing economies for comparative analysis. Additionally, expert-based consensus methods could be employed to refine and validate the identified strategic factors. Such efforts will deepen scholarly understanding and support the development of scalable, context-sensitive frameworks for AI adoption in the construction industries of the Global South. Although the objective of this study was achieved, there are limitations to the conclusions derived from the results. The study only covered the opinions of construction professionals in Ghana. Therefore, the findings should be interpreted within the Ghanaian context and may not be fully generalisable to other resource constrained environments with different economic, technological, and institutional conditions. Furthermore, the study employed a purely quantitative approach using structured online questionnaires, which may have limited the depth of insights obtained. The exclusion of qualitative methods means that rich contextual information and stakeholder experiences may not have been considered. Lastly, while EFA was used to extract key components, confirmatory factor analysis (CFA) was not conducted to validate the factor structure, which presents an opportunity for future research. Based on the key findings of the study, the study recommends that construction organizations should develop clear and practical roadmaps for AI adoption that reflect the local construction environment. This will help align AI strategies with project goals, regulatory requirements, and available resources. They should further invest in training programmes and upskilling initiatives to build the technical capacity of their workforce. Enhancing AI knowledge among employees is key to successful implementation and long-term integration. Top management should prioritise collaboration and communication by involving employees in the planning and execution of AI adoption strategies. This inclusive approach will reduce resistance and encourage ownership at all levels of the organisation. Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Ethics statement Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements. Author contributions RA: Writing - review and editing, Software, Writing - original draft, Project administration, Visualization. CA: Supervision, Conceptualization, Writing - review and editing. SA: Writing - review and editing, Methodology, Supervision. HA: Software, Formal Analysis, Writing - review and editing. Conflict of interest The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Generative AI statement The author(s) declared that generative AI was not used in the creation of this manuscript. Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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