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Exploring the Benefits and Challenges of Artificial Intelligence (AI)

Implementation in Construction Projects

Iman Fahmi Yusof., Siti Sarah Mat Isa

., Wan Norizan Wan Ismail., Norsyazwana Jenuwa., Nur ‘Ain

Ismail

Department of Built Environment Studies and Technology/Faculty of Built Environment/Universiti

Teknologi MARA/Perak Branch/Seri Iskandar Campus/ 32610 Seri Iskandar/Perak/ Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.914MG00216

Received: 27 October 2025; Accepted: 05 November 2025; Published: 21 November 2025

ABSTRACT

In advancing the construction industry toward the green path, it is crucial to embrace the use of advanced

technologies, such as Artificial Intelligence (AI). This shift is strongly supported as a nation strives to achieve

its sustainable development goals. However, several challenges accompany this ambition, including the

demand for a proficient workforce, high initial expenses, and resistance to change. Thus, this study endeavours

to investigate the benefits of AI implementation, including the challenges that impede its adoption in

construction projects within the Malaysian construction industry. The data for this study were obtained from

G7 contractor firms in Selangor registered with the Construction Industry Development Board (CIDB). A

simple random sampling was used to determine the sample size. A total of 156 respondents participated in this

study. The data obtained was analysed using SPSS software. The findings highlighted the main benefits of

implementing AI in the construction industry, revealing a growing use of AI technologies, including AI-based

risk analysis and mitigation strategies to improve project outcomes, minimise potential disruptions and

enhance project quality control through real-time monitoring. Insufficient data quality and availability hinder

the effective utilisation of AI. Thus, this study recommends that the government offer more initiatives,

incentives, and training programmes to construction practitioners to enhance the integration of AI in

construction projects towards achieving environmental sustainability.

Keywords: Artificial Intelligence, contractor, construction project, Malaysian construction industry,

sustainability.

INTRODUCTION

The construction industry encompasses the entire value chain of the industry, including the construction of

buildings, civil engineering, specialised construction activities, manufacturing of construction products,

facilities management, building maintenance and services (Ozorhon et al., 2010; Rani et al., 2021). The

construction industry plays a crucial role in Malaysia’s economic development and has steadily contributed

around 3% to 5% of the country’s GDP annually over the last decade (DoS Malaysia, 2021). Even though the

construction industry contributes a small percentage to the Malaysian economy compared to services,

manufacturing, tourism, and agriculture, it remains the country’s most vital sector due to its spillover effect,

including its backward and forward connections to other industries. Despite its significance, the sector has long

been challenged by inefficiencies such as project delays, budget overruns, labour shortages, safety hazards and

severe impacts to the environment (Bamgbade et al., 2017). Thus, the Malaysian government has implemented

serious measures to enhance the construction sector’s quality, safety, environmental sustainability, and

productivity for successful project completion.

As global trends push industries toward digital transformation, Artificial Intelligence (AI) emerges as a

powerful enabler for addressing these chronic issues. Technologies such as machine learning, computer vision,

real-time monitoring, and robotics are increasingly being integrated into various phases of construction, from

planning and design to site execution and maintenance, bringing potential for improved productivity, enhanced

quality, and reduced risks. In Malaysia, efforts to digitise the construction sector have gained momentum

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through initiatives like the Construction Industry Transformation Programme (CITP) and the Construction 4.0

Strategic Plan led by the Construction Industry Development Board (CIDB). However, the implementation of

IR 4.0 within the Construction Industry is still lacking tremendously despite having access to these

technologies (Alaloul et al., 2020). The AI adoption remains in its infancy, with uptake concentrated in large

firms and urban centers. Key barriers include high implementation costs, a lack of skilled professionals,

limited data infrastructure, and ethical concerns surrounding surveillance and job displacement. This study

aims to explore the impacts of AI implementation in Malaysian construction projects, identifying the benefits

and challenges that shape its future integration. By doing so, it seeks to provide actionable insights for

policymakers, industry leaders, and researchers to navigate the digital transformation of the construction

industry in Malaysia.

LITERATURE REVIEWS

AI in the Construction Industry

The construction industry is traditionally labour-intensive and slow in adopting digital technologies as

compared to manufacturing and automotive industries, resulting in persistent productivity and safety

challenges (Abioye et al., 2021). Thus, due to digital transformation pressures, sustainability demands, and

productivity challenges, there has been an accelerating interest in AI. The Malaysian construction industry has

made significant strides in positioning AI as a core element in the digital transformation of the construction

industry in redefining construction processes and enabling data-driven decision-making to enhance efficiency

and drive innovation.

Benefits of AI in construction projects

In the construction project, project management and planning are crucial areas where the integration of AI has

made a significant impact. Predictive analytics are now more accessible through software and tools powered

by AI (Bird et al., 2021). This advancement allows construction stakeholders to anticipate potential problems,

create better schedules, and make strategic decision-making supported by data-driven insights (Akinosho et al.,

2020). Consequently, this approach is increasingly seen as essential for enhancing productivity, reducing

delays, and lowering overall project costs in the dynamic landscape of Construction 4.0.

In addition, AI-integrated Building Information Modelling (BIM) have become more prevalent in the

Malaysian construction industry. Studies by Chen et al. (2022) and Xue et al. (2018) emphasise that AI-

integrated BIM can streamline workflow performance and facilitate better decision-making throughout project

lifecycles. AI-BIM integration supports early risk identification, can analyse vast amounts of design data,

detect conflicts more accurately, and recommend corrective actions in real time. This not only minimises

design errors and rework but also strengthens collaboration across multidisciplinary project teams. This

promotes sustainability and cost-effectiveness.

AI plays a crucial role in enhancing safety on construction sites and managing construction projects. By

integrating Internet of Things (IoT) devices with AI-powered sensors, it is now possible to monitor the

condition of a site in real time. These systems can identify potential hazards, track equipment usage, and alert

construction workers if safety protocols are violated (Zhang et al., 2021). This significantly improves

workplace safety and reduces accidents that can cause delays in projects. In the Malaysian construction

industry, the use of AI helps operations run more smoothly and fosters innovative approaches to project

development. Additionally, technologies such as drones, robotics, and predictive analytics are being gradually

adopted by major construction firms to enhance site productivity and improve safety outcomes.

Challenges of AI in construction projects

AI is increasingly recognised as a transformative capability for the construction industry, providing

opportunities to enhance productivity, safety, and improve project delivery. However, the adoption of AI

remains limited, and the construction industry is lagging other industries in digital maturity. The

implementation of AI technologies brings various challenges, including data accessibility, the need for

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technical expertise, financial difficulties, ethical considerations, and organisational culture. Understanding

these challenges is essential to ensuring the successful integration and long-term effectiveness of AI in

construction projects.

One of the primary challenges in implementing AI is the quality and availability of data. AI systems depend

heavily on structured and clean datasets to train predictive models and support real-time decision-making.

However, construction projects often produce fragmented, inconsistent, and unstructured data due to

decentralised project environments and manual documentation practices (Darko et al., 2020; Hilmy et al.,

2024). Inadequate data collection frameworks and limited standardisation hinder model accuracy and prevent

effective AI deployments (Zhong et al., 2022). Mohamed and Mohamad (2021) conducted a survey among

construction firms, identifying poor data quality, lack of interoperability, and high implementation costs as

primary barriers. Without reliable data ecosystems and supportive organisational structures, successfully

implementing AI is difficult. Consequently, AI systems struggle to deliver reliable insights, which limits

organisational trust and can lead to poor implementation outcomes.

Another major barrier is the high initial investment and financial risk associated with AI technologies.

Construction firms, particularly small and medium-sized enterprises (SMEs), often operate on tight project

margins and may view AI adoption as a costly and uncertain investment (Amuda-Yeboah et al., 2023). The

expenses related to hardware, software, licensing, cloud computing, and skilled labour can discourage the

implementation of AI. Furthermore, the return on investment (ROI) may not be immediately visible, creating

reluctance among decision-makers to commit resources to AI transformation (Abioye et al., 2021).

Skills gaps and workforce resistance also present serious obstacles. Successful AI adoption requires data

scientists, software engineers, digital specialists, and project managers who understand AI capabilities and

limitations. However, many construction organisations lack personnel with these competencies (Zhang et al.,

2022). Additionally, workers may resist digital transformation due to concerns about job security, fear of

technology, or lack of digital literacy (Farmer et al., 2021). This resistance can inhibit implementation and

hinder organisational learning.

METHODOLOGY

The unit of analysis of this study is the construction professionals employed by contractor firms registered with

the Construction Industry Development Board (CIDB) under grade G7 in Selangor. This study employed a

quantitative method by administering an online survey using Google Forms to this target population to obtain

their perceptions and responses to the questionnaire. According to CIDB, a total of 25,631 registered

contractor firms are classified from G1 to G7 in Selangor. However, this study specifically focuses on those

G7 contractor firms. Within this category, there are only 3,421 G7 contractors in Selangor. The sample size

was determined following the methodology proposed by Krejcie and Morgan (1970). Hence, to adequately

reflect a specific population, a sample size of 346 was deemed necessary for this investigation. Thus, a total of

600 questionnaires were distributed among the respondents, and 156 usable responses were received from all

sources. This amount indicates a response rate of 26%, which is considered acceptable. According to Nulty

(2008), a 20% response rate is deemed satisfactory.

This study used a 5-point Likert scale to measure each construct in the survey. The scale ranges from 1 to 5,

where 1 indicates ‘strongly disagree’, 2 indicates ‘disagree’, 3 indicates ‘neutral’, 4 indicates ‘agree’, and 5

indicates ‘strongly agree’ regarding the benefits and challenges of AI implementation in construction projects.

The questionnaire consisted of three (3) sections. Section A: Demographic background, Section B: The

benefits of AI implementation in construction projects, and Section C: The challenges of AI implementation in

construction projects. The collected data were analysed using descriptive analysis using SPSS software. The

Mean Score (MS) analysis was employed to calculate the mean score for each variable and to rank the

variables based on the level of agreement among the respondents. The mean score ranking technique has been

widely used in the field of built environment studies to assess the relative significance or importance of

specific factors (Shi et al., 2013). The Cronbach’s Alpha coefficient for the collected data is 0.942. These

figures were above the limit of .70, as suggested by Nunnally (1978, cited by Cheng et al., 2014, p.85). This

finding provides evidence of the consistency and reliability of the gathered data.

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FINDINGS AND DISCUSSIONS

Demographic background

Table 1 displays the distribution of respondents based on their position within their respective firms.

According to the findings, all respondents are from the middle management level and hold a variety of

positions in the firms, including project manager, project engineer, construction manager, site manager, and

site supervisor, as well as quantity surveyor. This data shows that the information acquired is reliable, as the

respondents are knowledgeable and actively involved in the operation and management of construction

projects.

Table I Respondents Working Positions

Working Position

Frequency

Percentage (%)

Project Manager

20.5

Project Engineer

14.7

Construction Manager

16.7

Site Manager / Site Supervisor

24.4

Quantity Surveyors

23.7

Total

156

100

Meanwhile, Table II shows the distribution of respondents based on their working experience in the

construction industry. Among the 156 respondents, the majority (33.3%) had 6–10 years of experience,

indicating a substantial representation of middle-level professionals actively engaged in construction

operations. This is followed by 11–20 years (32%) and more than 21 years (27.6%), indicating well-

experienced contractors with significant industry exposure. Meanwhile, only 7.1% of respondents had less than

5 years’ experience, reflecting the participation of younger contractor professionals.

Table II Respondents Working Experience

Working Experience

Frequency

Percentage (%)

1 – 5 years

7.1

6 – 10 years

33.3

11 – 20 years

32.0

More than 21 years

27.6

Total

156

100

Benefits of AI implementation in construction projects

Based on the findings indicate that respondents highly value AI-based risk analysis and mitigation strategies,

with a mean score of 4.40. This suggests a strong consensus on their effectiveness in improving project

outcomes and minimising potential disruptions. Baryannis et al. (2018) support this view, noting that AI

techniques, such as machine learning and big data analytics, are successfully used to identify, assess, and

respond to supply chain risks, which ultimately enhances decision-making and adaptability. Additionally, real-

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time monitoring and AI-driven data insights, which improve the quality of project control and assurance,

received a mean score of 4.36. The use of AI technologies to reduce human error and enhance project safety is

highly regarded, with a mean score of 4.35. This finding is aligned with Bigham et al. (2018), who mention

that the risk of accidents on the construction site can be significantly decreased by an AI platform’s ability,

which can identify hazards before and during the construction operation and recommend appropriate safety

standards. Furthermore, the construction industry’s productivity and project timelines are dramatically

improved by AI applications, with a mean score of 4.32. In general, respondents perceive the implementation

of AI in construction projects as highly beneficial, as they acknowledge its ability to enhance various aspects

of project management and execution.

Table I Benefits of AI Implementation

Benefits of Artificial Intelligence (AI) Implementation

Mean

Standard Deviation

Rank

AI provide risk analysis and mitigation strategies to improve project

outcomes and minimise potential disruptions.

4.40

0.721

AI-driven data insights and real-time monitoring enhance project quality

control and assurance.

4.36

0.733

AI technologies reduce human errors and enhance overall project safety

in construction.

4.35

0.748

AI applications significantly enhance productivity and accelerate project

timelines

4.32

0.480

AI-enabled automation streamlines repetitive tasks, allowing for better

focus on complex project requirements.

4.28

0.748

AI integration fosters innovation and competitiveness within the

Malaysian construction industry.

4.12

0.703

AI-driven tools and systems enable the reduction of costs and the

optimisation of budgets in construction projects.

4.04

0.813

AI-powered analytics and predictive modelling enhance decision-

making processes in construction project management.

3.99

0.810

The integration of AI improves project efficiency by optimising the

allocation of resources and scheduling.

3.72

0.494

Challenges of AI implementation in construction projects

Most respondents agree that the effective use of AI technologies is hindered by insufficient data quality and

availability, as reflected in the highest mean score of 4.50. This finding aligns with Whang et al. (2023), who

stated that unreliable AI models and poor decision-making outcomes stem from poor data quality, which

includes issues such as incompleteness, errors, and biases. As supported by Abioye et al. (2021) and Omar et

al. (2022), most of the construction information is stored in various formats like PDFs, CAD files, and

spreadsheets across different systems, often lacking consistency and control. Data collection is often viewed as

an administrative burden rather than a core project activity, and without clear incentives or training for field

staff, the accuracy and consistency of site-level data remain poor (Omar et al., 2022). Moreover, the lack of

collaboration and partnerships between construction firms and AI solution providers closely follows this

concern, with a mean score of 4.48. Data security and privacy are major concerns when adopting AI

applications, with a mean score of 4.46. This finding is consistent with Murdoch (2021), who revealed that AI

technologies pose significant threats to data security, heightening the risk of reidentification and serious

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privacy violations. Another significant challenge in Malaysia is the current infrastructure, which requires more

support for the effective implementation of AI in construction projects, reflected by a mean score of 4.33.

Additionally, the seamless integration of AI applications was hindered by financial constraints and regulatory

complexities, which received an average score of 4.22. Several studies have emphasised that financial

constraint is not merely limited to the purchase of AI tools but stems from a combination of structural,

technical, and organisational factors (Abioye et al., 2021; Alaloul et al., 2020; Chen et al., 2022). Furthermore,

the lack of skilled professionals compounds challenges. The shortage of data engineers and AI specialists

means that many firms depend on external consultants for implementation, inflating project budgets (Omar et

al., 2022). Accordingly, this perception can impede the growth of technology adoption in construction projects

within the Malaysian construction industry, leading many practitioners to prefer the traditional practices they

are accustomed to.

Table I Challenges of AI Implementation

Challenges of Artificial Intelligence (AI) Implementation

Mean

Standard Deviation

Rank

Insufficient data quality and availability hinder the effective utilisation

of AI technologies.

4.50

0.527

Lack of collaborations and partnerships between construction firms and

AI solution providers

4.48

0.501

Concerns regarding the security and privacy of data in adopting AI

applications.

4.46

0.551

The current infrastructure in Malaysia lacks support to implement AI

successfully in construction projects.

4.33

0.761

Financial constraints in AI implementation.

4.22

0.699

Regulatory complexities and unclear guidelines hinder the seamless

integration of AI applications.

4.22

0.863

There is a lack of skilled personnel knowledgeable about AI

technologies.

4.05

0.796

Resistance to change and a lack of willingness to embrace new

technologies.

3.98

0.793

There is a fear of job displacement among the existing workforce due to

AI adoption.

3.66

0.577

CONCLUSIONS

AI offers remarkable potential to tackle inefficiencies in the construction industry, improve safety, and

facilitate meeting environmental sustainability targets. Additionally, AI can significantly transform

construction operations by increasing efficiency, reducing risks, and supporting sustainable project delivery.

While the adoption of AI technologies requires investment in skills, infrastructure, and digital maturity, the

competitive advantages and operational improvements they offer are substantial. Thus, despite the challenges

that come with the implementation, the Malaysian construction industry is expected to invest more in AI-

driven digital transformation. Although challenges in adoption persist, strategic investments in digital talent,

robust data systems, and regulatory support can accelerate AI integration and foster Construction 4.0

transformation. The government, including other relevant agencies, must implement a vigorous range of

initiatives, incentives, and comprehensive training programs for construction practitioners to enhance the

integration of AI in the industry to effectively promote environmental sustainability and drive significant

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progress toward a more sustainable future. Thus, future research should focus on developing AI

implementation frameworks, assessing the construction industry’s readiness, and evaluating a comprehensive

strategy to overcome these challenges, allowing the full potential of AI to be used in construction projects.

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