Exploring the Readiness of Organisations to Adopt Artificial Intelligence

Abstract

Front-end planning (FEP) is the first step in identifying a problem and analysing a project’s goals and the business case for management to decide whether to proceed with the project. Despite its crucial significance, projects are still underperforming and failing to achieve their objectives. Current research suggests that the emergence of AI promises significant advantages to organisations, particularly for FEP. The purpose of this paper was to explore the readiness of organisations to use AI in the FEP phase to enhance project outcomes. The technology–organisation–environment (TOE) framework was used to evaluate factors influencing the readiness to adopt AI in construction projects in Saudi Arabia. Thirty interviews were conducted with public and private stakeholders in the sector. The knowledge and insight gained from the viewpoints of key decision makers and practitioners allowed for an examination of the main factors impacting the adoption of AI, and any challenges and barriers to it. Findings showed that the support of the government and senior management, and the attitudes and behaviour of employees, were the top three factors in the framework that facilitate the readiness of organisations to adopt AI. Government support influences external support and enhances competitive pressure between organisations; senior management support influences the absorptive capacity and maturity of an organisation; and employees’ attitudes and behaviours are the main contributors to organisational readiness. The proposed framework will assist policymakers in using these factors to overcome the challenges of AI adoption. Additionally, creating strategies aligned with Vision 2030 focuses not only on choosing the best technology to implement but also on how employees can benefit from it.

1. Introduction

In 2016, the Kingdom of Saudi Arabia (KSA) announced its Vision 2030 plan, the main objective of which is to minimise the dependency of the country on oil and encourage other non-oil-based economic initiatives. One of the key future growth ambitions of this vision is to develop the public sector, which is vital for economic resilience, for improving the quality of life of its citizens, and for ensuring the effective implementation of infrastructure projects. As a result, numerous projects have been planned, including the NEOM global hub, the Red Sea Project, and the Qiddiya Projects [1]. The ambitious Vision 2030 plan requires public organisations to map their strategy with the KSA long-term plan. By aligning and reflecting on those long-term plans, organisations are confronting challenging obstacles with unique levels of complexity, such as project failure [2]. Failure describes a project that faces time and cost overruns and does not meet the required quality and project goals [3]. Previous research has pointed out that the origins of issues arising in the later phases of a project can be traced back to earlier phases, known as the front-end planning (FEP) phase. This first phase of a project is where problems are identified and a business case is formulated, with the outcome being whether to proceed. Front-end planning has many advantages, such as improved project performance and increased chances of success [4,5,6,7]. Over recent years, the construction industry has come a long way in integrating digital technologies, such as AI, machine learning (ML), and deep learning (DL), into its processes and practices to improve project performance [8]. Construction organisations can benefit from this by predicting risks, increasing the speed of decision-making, and improving work performance [8,9].

To fulfil the KSA Vision 2030 goals, it is necessary to promote adequate FEP in organisations, as it significantly impacts project performance. Nevertheless, the construction industry continues to face significant challenges related to FEP and the potential of adopting AI in FEP could significantly overcome these challenges. These challenges are particularly significant in KSA due to the country’s current state of infrastructure and economic transformation, as well as the absence of specialised studies and practical evaluation of FEP practices.

This study aims to offer valuable insights into organisations’ readiness to embrace AI in FEP in the KSA’s construction industry. This study adapts the technology–organisation–environment (TOE) framework to develop a specific, contextualised TOE framework for this. The following question is the basis of this research: “What are the key technological, organisational, and environmental factors that influence AI adoption at the front-end planning phase in Saudi Arabia’s construction industry?”

This study presents a novel framework that has not been previously investigated to identify the factors influencing an organisation’s readiness to utilise AI in the FEP phase within the KSA construction industry.

2. Literature Review

Front-end planning is an essential stage in the construction process. The implementation of efficient FEP is critical to achieving project objectives, as it establishes the foundation for project implementation, mitigates ambiguities and supports decision-makers, which ultimately results in enhanced project outcomes. The integration of AI into FEP in construction projects to enhance project efficiency enables the use of data-driven analyses, predictive insights, improved decision-making, and enhanced construction performance. In the context of KSA, the application of the TOE framework offers a structured approach to understanding the factors influencing organisations’ readiness to adopt and implement AI.

2.1. Front-End Planning

Front-end planning is the first phase of a project, which involves pre-design and feasibility analysis, scope definition [10], pre-planning, and front-end loading [11]. The front-end phase is where decisions to invest are made at a time when the degree of accessible knowledge is low [12]. Front-end planning can be defined as a systematic process to maximise project success by establishing the strategy, developing strategic knowledge, generating adequate information to mitigate project risks, and assisting in decisions on whether to proceed with the project [10,13]. Researchers have indicated a direct, clear link between the effort spent on FEP and overall project success [4,6,14]. Additionally, the benefits of front-end planning for projects include meeting business goals [15], mitigating risk and reducing cost during execution [16], and improving project performance [4,5,15,17]. Other researchers [14,18] have pointed out the relationship between proper front-end planning and the accurate prediction and overall quality of the project. Additionally, the greater the level of project definition (front-end loading) before authorisation, the better the project outcome [19]. Effective FEP practices can substantially improve project performance if implemented consistently and correctly [4].

2.2. Artificial Intelligence

Artificial intelligence is a powerful technology that provides multiple possibilities to access a huge amount of information, process it, analyse it, and provide a range of solutions [20,21]. As construction projects are exposed to risks and uncertainty, there is increased demand to integrate AI into construction to improve communication between stakeholders and the diffusion of information throughout project phases [22].

Currently, the adoption and implementation of AI is growing rapidly. This presents a variety of opportunities for construction projects, such as virtual reality and augmented reality during the project lifecycle [22], the use of drones for effective management and safer working sites [23,24,25], and AI at the planning phase, to ensure the success of a project [26].

Many organisations have recognised these benefits, including cost savings and improved productivity [27], and have started to invest in AI [28] to automate processes, mitigate risk, enhance efficiency [9,28], and improve decision-making and solutions throughout the full project lifecycle [9]. In addition, AI boosts productivity time, and cost-savings [26].

However, persistent challenges may hinder the implementation and adoption of AI, such as the industry’s resistance to change and a lack of understanding of the technology’s procedures and workflows [29]. Additional challenges relate to security, ethics and governance, high costs, unrealistic expectations, case-related issues, organisational restrictions, lack of key AI experts, and technological issues [30,31]. Despite all the powerful benefits of implementing AI, obstacles and challenges to its adoption remain. Organisations are still struggling to develop a clear roadmap for its implementation and adoption [30,32].

Projects are based on inadequate manual techniques that can increase human error [33]; however, AI is capable of learning from the past to make informed decisions [34]. There is a growing amount of data generated during the project lifecycle, along with an increase in the capacity of AI applications.

2.3. Previous Work and Research Gaps

Systematic reviews of previous research have revealed significant contributions of FEP to project outcomes. However, existing studies share a fundamental challenge in FEP within the construction industry, primarily due to the managerial and systemic complexities involved in extracting crucial insights and lessons learned from past experiences. For instance, a survey was conducted to better understand the importance of FEP and its impact on project outcomes in Singapore [17]. The findings revealed that 44% of companies applied FEP in their projects. Similarly, professionals in the construction industry in South Africa were surveyed to identify the challenges of adapting the front-end loading method during the early stages of projects [6]. The findings revealed that the significant challenges included an inability to identify process importance, conflicting information, inadequate time to complete the process, a lack of knowledge by the client, and the absence of a structured project team. Additionally, a study of Brazilian companies involved in large engineering and construction projects seeking to understand the application and effectiveness of FEP methods [11], found that 56% of respondents actively applied FEP in their projects, 67% reported that projects using FEP exceeded their budgets, and 43% believed that goals were rarely or never achieved. This study concluded that although Brazilian corporations recognise and use the FEP approach in managing megaprojects, there are substantial obstacles to achieving its theoretical advantages. A further study [5] aimed to gain an in-depth understanding of the front-end phase in order to improve hospital project planning, using a mixed methods approach that combined in-depth semi-structured interviews and a questionnaire survey. The findings identified 62 front-end performance challenges, categorising them into four groups: structure and tools, context and frame factors, management, and relational factors and properties. The results indicated that project performance could be considerably enhanced through the early detection and management of these performance challenges at the FEP phase, as this enables timely interventions to address potential issues. The study also showed how important it is to use both formal assessments and process-based methods to effectively manage the complexities of the front-end phase of hospital projects.

These challenges suggest a need for efficient FEP practices. The potential of adopting AI in FEP promises to overcome issues such as improving practices, leading to precise predictions and accurate decision-making, and boosting project performance. Recent research on generative AI (GenAI) in knowledge management (KM) systems indicates that generative AI can enhance the process of creating novel insights and lessons, as well as facilitate decision-making and problem-solving by improving the codification, documentation, capturing, dissemination, and management of knowledge. AI, in essence, assists in transforming raw data and information into valuable insights and lessons for FEP [33].

Therefore, using AI in FEP can significantly enhance the way we manage projects. Using lessons learned, we can establish project success criteria and validate predictions, forecasts, and estimates with confidence. This has the potential to enhance the time allocated to this stage and increase the quality of the decisions made not only during FEP but throughout the whole project. This signifies the importance of utilising the TOE framework to address the research gap regarding the factors influencing the adoption of AI in FEP. KSA’s Vision 2030 provides a suitable context for empirical research in the construction industry.

2.4. Technology–Organisation–Environment Framework

Numerous theoretical frameworks have been established and introduced to understand the adoption of new technology, addressing both individual and organisational contexts. Individual behavioural theories, such as the technology acceptance model (TAM) and the theory of planned behaviour (TPB), are commonly employed to predict intention and actual behaviour among individuals. In contrast, numerous frameworks and theories have been developed when examining the adoption of innovations within organisations, such as the diffusion of innovation theory (DOI) [35], the TOE framework [36] and the TAM [37]. The TOE framework is one of the most frequently employed models, which enables the modification and enhancement of variables in accordance with a study’s specific context [37,38]. It allows for a multidimensional understanding of an organisation’s readiness to adopt AI. The framework offers a comprehensive understanding to assess the three dimensions, technological, organisational, and environmental, making it essential to formulate specific strategies to facilitate the successful adoption of technology. Despite its flexibility, the TOE framework is subject to limitations, such as a lack of specificity, potential overlapping constructs relating to the technological, organisational, and environmental factors, context-specific variability, and complexity in implementation, despite its comprehensive scope and adaptability. Therefore, this study adapts the TOE framework, as it is recognised for its capacity to assess the degree of organisational readiness and its ability to adapt to different contexts [39,40].

Previous research in many industries has used the TOE framework; for instance, to examine the adoption of e-procurement [41], e-commerce [42], web service [43], electronic customer relationship management e-CRM [44], and cloud computing [45]. In construction industry research, several studies have used the TOE framework to investigate building information modelling (BIM) implementation [46,47] and project complexity assessment [48]. As stated previously, the TOE framework is built on aspects that may influence the readiness of organisations to adopt AI. In the technological context, internal and external technologies, which combine established and new systems, are essential to the organisation. Numerous variables have been identified in previous research in the technological context, with relative advantage, compatibility, complexity, and technology preparedness being the most predominant [47,49,50,51]. In terms of the organisational context, the most common variables were senior management support, firm size, organisational readiness, and prior IT experience, all of which have a significant impact on innovation adoption [46,47,49,50,52]. In terms of environmental factors, it is suggested that the most common factors to consider are competitive pressure from trading partners, client requirements, and government and external support, which apply pressure on organisations to adopt IT applications [46,47,49,52].

2.4.1. Technological Context

The technological context refers to the capacity of a business to adopt new technology. It involves both existing and new significant technologies in the business. One study [35] defined relative advantage as the extent to which an innovation is perceived to be superior to the concept it replaces. In contrast, compatibility refers to integration across applications to support business activities with technology. Data availability refers to aspects such as the availability, privacy, security, and accuracy of data. Relative advantage, compatibility, and data availability will be considered in this research.

2.4.2. Organisational Context

The organisational context refers to measures relating to the organisation, such as scope, size, and resource availability. Senior management support refers to the level of support received from higher management to adopt innovative technology and overcome any internal resistance [52]. Management support helps organisations to overcome these barriers. Process refers to the set of activities an organisation executes to attain its goals. Resources refer to organisational assets that help an organisation to achieve its goals, including human and financial resources. Having sufficient resources is an important factor [51], and financial readiness is considered an influencing factor [53]. This research will consider senior management support, processes, and resources.

2.4.3. Environmental Context

In the context of the environment, competitive pressure can be defined as the external forces and market conditions that drive organisations to adopt new technologies in order to remain competitive in their industry or market [54]. Government support can be defined by the rules, regulations, policies, and legal frameworks established by the government to develop an innovation [54].

Prior research has employed the TOE framework in Saudi Arabia across many fields, such as healthcare [55], education [56], social commerce [57], and e-commerce [58]. Additionally, research has employed the TOE framework in Saudi Arabia for digital technologies such as BIM adoption [59,60], AI adoption for small and medium enterprises (SMEs) [61], ICT adoption [62], cloud adoption [63], and automation and robotics adoption [64].

Therefore, this study will try to fill the gap in understanding the current state of AI adoption in the KSA construction industry based on the TOE framework. It will investigate the key drivers or enablers that impact and assist its effective adoption of AI and the possibility of using it at the FEP phase. A proposed framework (see Figure 1) comprising a number of constructs aims to offer improved explanations of AI preparedness for adoption and project performance in the public sector of the construction industry in the KSA.

3. Materials and Methods

The aim of this study was to investigate in-depth information and opinions about the current state of organisational readiness to adopt AI. A qualitative approach was used, as it is an appropriate method for understanding social science and exploring human emotions, attitudes, and beliefs to understand a phenomenon [65] and for generating hypotheses and theories from the data [66]. Semi-structured interviews require experienced interviewers and careful analysis, but they also provide flexibility and in-depth information. They offer comprehensive and detailed information; however, they are time-consuming and may introduce interviewer bias. As a result, semi-structured interviews were selected as the most appropriate method for investigation. The semi-structured interviews were conducted to acquire an in-depth understanding of interviewees’ opinions [67] regarding organisational readiness for AI, employees’ perceptions, and how AI can improve project performance based on the TOE framework in the construction industry in the KSA.

This study employed purposive sampling to identify key stakeholders in the construction industry with the necessary expertise to provide extensive insights into the readiness of AI for implementation in construction projects. The unit of analysis is what the researcher uses to describe the phenomena studied and may be at the individual, group, or organisation level [68]. In this study, units of analysis at the individual level (clients, consultants, and contractors) were used, as shown in

Table 1. Interviewee characteristics.

Participants	Code	Age Group	Gender	Years of Experience	Organisation Size
Clients	CL_01	Below 35	Male	Below 10 years	100–499
	CL_02	Below 35	Female	Below 10 years	100–499
	CL_03	Above 35	Male	Above 10 years	100–499
	CL_04	Above 35	Male	Above 10 years	100–499
	CL_05	Above 35	Male	Above 10 years	100–499
	CL_06	Above 35	Male	Above 10 years	100–499
	CL_07	Below 35	Male	Above 10 years	100–499
	CL_08	Above 35	Male	Above 10 years	500+
	CL_09	Above 35	Male	Above 10 years	500+
	CL_10	Above 35	Male	Above 10 years	500+
	CL_11	Above 35	Male	Above 10 years	500+
	CL_12	Above 35	Male	Above 10 years	500+
	CL_13	Above 35	Male	Above 10 years	500+
	CL_14	Above 35	Female	Above 10 years	500+
	CL_15	Above 35	Male	Above 10 years	500+
Consultants	CONS_01	Above 35	Male	Above 10 years	500+
	CONS_02	Below 35	Male	Above 10 years	100–499
	CONS_03	Below 35	Male	Below 10 years	100–499
	CONS_04	Above 35	Male	Above 10 years	500+
	CONS_05	Below 35	Female	Below 10 years	500+
	CONS_06	Above 35	Male	Above 10 years	500+
	CONS_07	Above 35	Male	Above 10 years	500+
	CONS_08	Above 35	Male	Above 10 years	500+
	CONS_09	Above 35	Male	Above 10 years	500+
Contractors	CONT_01	Above 35	Male	Below 10 years	100–499
	CONT_02	Above 35	Male	Above 10 years	10–99
	CONT_03	Above 35	Male	Below 10 years	10–99
	CONT_04	Above 35	Male	Above 10 years	100–499
	CONT_05	Below 35	Male	Below 10 years	100–499
	CONT_06	Below 35	Male	Below 10 years	100–499

. The participants were clients, contractor consultants, professional engineers, and decision-makers who had vast expertise in construction projects. Additionally, these roles were selected due to their critical involvement in decision-making and implementation processes and their status as critical stakeholders in construction projects. This diverse group of participants, with extensive experience and expertise in construction projects, ensured an comprehensive perspective from many different points of view.

The interview protocol followed a semi-structured format, with a set of predetermined questions and additional flexibility for iterative questions stemming from the conversation.

Table 2. Sample of interview questions.

Sample of Interviews Questions
Does your organisation have a defined FEP process that is consistently followed?
Please could you describe your organisation’s FEP processes/practices?
How is the FEP managed, and how are the responsibilities divided?
In your opinion, what is your organisation’s most significant challenge to achieving a successful FEP?
What are the biggest challenges in the current front-end planning process?
Do your organization adopt any AI technology to manage projects?
From your experience, where AI may best be implemented within FEP to improve project delivery?
In your opinion, do you think adopting AI and innovative technologies can reduce failure and improve the front-end planning process? and why?
Do you think applying technologies is a challenge or problem in public organisations? What are those challenges?
In your opinion, do your organisation ready to adopt AI? If yes, do you have the necessary expertise, data, and technology to comprehend and implement AI opportunities? Do you have sort of data management in the organization?
If not, what does it need and require preparing the organisation to make it ready? Such as Human, enterprise and technology resources.
In your opinion, to which extent organisations are ready to adopt artificial intelligence (AI) technologies in KSA? If not ready, what would it take to prepare organisations to readiness towards the adoption of AI technologies?
In your opinion, do you think applying new technologies is considered as challenges or problems in public sector?
Recommendations for Implementation of AI in organisations?

presents samples of the interview questions used. Thirty interviews were conducted with Saudi Arabian and international professionals between March and May 2023. Depending on the participants’ preferences, interviews were conducted in person or via an online platform (Microsoft Teams). Audio or video-recorded interviews were chosen as the method of data collection. Each interview lasted between 60 and 90 min and was recorded digitally, supplemented by handwritten notes. After collecting the data, the researcher transcribed all of the interviews. Following data transcription, the researcher imported the text into the NVivo software, a qualitative analysis tool used to improve the data analysis process by identifying recurring themes and ideas, categorising and evaluating themes, and retrieving data throughout the analysis of the interview data [69]. Codes and sub-nodes were created to maintain the validity of the participants’ language and words, as they captured a crucial aspects of the stated phenomenon. After developing the concepts, themes, and sub-nodes, the researcher evaluated them and returned to the original transcripts to ensure that the findings were present in the data. The analysis identified critical factors explaining how organisations can vary in managing their readiness process for AI adoption.

4. Analysis

This study identified the organisation’s readiness to adopt AI at the FEP phase in the KSA construction sector. The data were analysed according to the TOE framework. Existing research on the phenomenon employs organisational frameworks such as TOE to shed light on the factors influencing AI adoption and how it shapes and assesses readiness [50]. It presents a framework that includes factors based on theoretical explanations.

After the data was transcribed, it was reviewed multiple times, and each transcript was validated against the audio recording and saved as a Word document. After reviewing the transcripts, the researcher highlighted critical quotations to develop the codes. The coded data were grouped into codes according to the initial TOE framework (Figure 1). Figure 2 presents the frequency of terms used in the interviews in a word cloud, which facilitates the identification of emergent codes and themes.

However, some themes did not have an adequate referent in the TOE literature, such as mindset and attitude, training and expertise, and knowledge and capabilities. This lack of coverage allowed us to integrate these new concepts into the initial TOE Framework (see

Table 3. Proposed and emerged themes and sub-themes.

Context	Proposed Themes	Emerged Themes	Final Themes	Final Sub-Themes
Technological	Relative advantage	-	Relative advantage	-
	Compatibility	Infrastructure Data Compatibility	Technology readiness	Infrastructure Data Compatibility
	Data availability	-	-	-
Organisational	Senior management support	Change management Leadership	Senior management support	Leadership and change management
	Resources	Resources Process Organisational culture	Maturity	Resources Process Organisational culture
	Process	Communication and collaboration Knowledge and capabilities Mindset and attitude Training and expertise	Absorptive capacity	Communication and collaboration Knowledge and capabilities Mindset and attitude Training and expertise
Environmental	Government support	-	Government support	-
	Competitive pressure	-	Competitive pressure	-
	-	-	External support	-

).

This newly adapted framework is more applicable in KSA contexts by incorporating factors such as organisational culture, maturity, and absorptive capacity. This framework enabled us to resolve certain constraints, including context-specific variability and adaptability concerns. For example, this revised framework emphasises the importance of individual perspectives, attitudes, and organisational behaviour in facilitating the adoption of AI. Figure 3 shows the extended framework for AI adoption.

5. Findings

This section is structured based on the adapted TOE framework. It reflects the main dimensions, technology, organisation and the environment, as well as the subcategories that emerged from the interviews (see Figure 3).

5.1. Technological Context

In regard to the technological context, two factors were investigated: relative advantage and technology readiness. Relative advantage refers to the perceived benefits of adopting AI that organisations can receive. Understanding the value and benefits of AI is one of the key drivers of readiness. Technology readiness in organisations encompasses technology infrastructure and expert resources; therefore, sufficient technology readiness within an organisation helps to mitigate obstacles and accelerates adoption.

5.1.1. Relative Advantage

All participants acknowledged that their organisations recognise the value of AI and how it can enhance, modify, and accelerate processes, producing better work. Therefore, sufficient technology readiness within an organisation helps to mitigate obstacles and accelerate adoption. The findings revealed that understanding the benefits of AI is a fundamental prerequisite for the successful adoption of AI within public organisations in the KSA. These benefits can be categorised into three main categories: work administration, which includes cost reduction, the quality of the outcome, and faster and more efficient working; decision-making support, which involves accurate decisions based on accurate data; and customer satisfaction, which includes reduced costs that offer value to the customer and an enhanced experience.

5.1.2. Technology Readiness

Technology readiness includes the following sub-categories: data availability, infrastructure, and compatibility of AI with current procedures and processes. The term refers to technology infrastructure and expert resources [54]. Our study defined technology readiness as the availability of the necessary infrastructure and IT experts, the compatibility of AI with current procedures and processes, and having data that includes data availability, infrastructure, and compatibility of AI with current procedures and processes.

Technology infrastructure refers to the availability of IT systems and an established platform for implementing and integrating AI technologies. The technical knowledge required includes developing and operating applications while ensuring data privacy, safety, and security. CL_14 stated, “As part of the organisation’s strategy and vision, they are hiring cyber security and data analysts”. Some organisations have taken the initiative and attempted to improve their IT infrastructure, providing essential requirements as a proactive action to ensure successful implementation and integration between technologies and IT platforms in the future. Organisational technological solutions should not only be embedded in routine daily tasks but also in the organisation’s culture as well as the procedure itself. In addition, organisations need to understand the technological solutions and the required skills, resources, and knowledge to establish their own data and AI platforms. However, CONT_02 pointed out that “KSA digitisation plan has a standard implementation guideline, then integration is the exchange of different data between different systems”, therefore, this can help organisations to understand their needs and develop the necessary infrastructure to connect technologies within the organisation and ensure the safety and security of the data produced.

Compatibility refers to integrating across applications to support business activities with technology. However, integrating new technologies in organisations is not that easy; misalignment between current operations and the integration of AI technologies can create complications, impede the business, and create conflicts at work. Additionally, the limited integration between AI systems, legacy construction systems, and processes is what creates complications. CONS_07 noted that “It’s not about system it’s about culture first and business process adoption”. Besides, organisations need to understand their working culture to re-engineer processes and make periodic updates or modifications when shifting from manual to system-based approaches, which is considered to be more important than the systems themselves. Furthermore, when integrating new systems, they need to be tested properly to have tangible effects. CONS_09 added “We do awareness to all departments, and digital transformation cannot be done only from our side but also by the contributions of other departments to make it happen”. One of the important things organisations are doing is integrating systems with daily tasks and providing more support to employees to help them adapt to the new integrated systems and keep the data stored to build and establish platforms to share and use it.

Data refers to the processes of collecting and organising information to ensure the integrity of the data, including aspects such as availability, accuracy, privacy, and security. A common concern in any organisation is data. The findings revealed that data availability and sharing is a common aspect of all organisations and one that they are working to improve and implement. CONS_07 stated “Another major issue is the data availability; some of the initiatives stopped because of data availability”. Organisations are working to capitalise on their data by having a transparent and integrated approach that can enhance data collection. However, what is more challenging is the type of data stored and used, its accessibility, maturity, and security, and data sharing within and between organisations. CL_09 highlighted “Creating an ecosystem where people are comfortable with data sharing across the industry will be big; it will be a huge challenge” and CL_03 added “Most organisations are still not mature from the data side”.

The findings revealed that data sharing presents a key challenge for organisational readiness to adopt AI in the KSA. Trust plays a crucial role in data sharing between organisations. This can be explained by the fact that some governance and regulations have not yet been established by the government. A significant point is that process and data are two connected aspects of the same coin; if you improve one, the other will also improve. Therefore, fixing the process and interlinking it will help to enhance the way data is collected.

For this reason, the KSA has established a data hub as one of its strategic priorities. The importance of open data is to enhance transparency between the public and private sectors and support decision-makers [70].

5.2. Organisational Context

In the organisational context, the factors identified are dependent on an organisation’s internal processes, playing a crucial role in the rapid adoption of AI. The most influential organisational theme and its associated sub-themes are presented in three main categories. The first category is support from senior management, with leadership and change management as critical sub-themes. The second category is organisational maturity, which encompasses the sub-themes of culture, availability of processes, and human and financial resources. The final category is absorptive capacity, with communication and collaboration, knowledge and capabilities, mindset and attitude, and training and expertise as its sub-themes.

5.2.1. Senior Management Support

Senior management support is a major factor in the successful adoption of AI. The findings indicate that decision-makers are pushing and encouraging the adoption of technology and digital transformation. Moreover, support from senior management enables transparency in the organisation, which in turn enables trust, creates a culture of change, and facilitates monitoring, adaptation, and measurement. This adds responsibility and enhances the process of adopting AI. CONS_02 indicated that “Any change can be easy to implement with top management support; all the barriers will be removed”. Also, it empowers innovation in the organisation, which helps to change the mindset of organisations. The entire workforce has transformed this by targeting young, talented leaders and females. CL_14 confirmed that “They are trying to change the culture by employing new young generations that can create balance and help change the mindset of the people”.

Change management is about understanding the concerns, anxieties, or apprehensions of individuals affected by the change to anticipate and resolve resistance, creating a positive and supportive environment, which is essential for organisations. Before implementing anything, management must also present the benefits and recognise the significance of change management. Before gathering the requirements, it is important to include change management in the scope, ensure dissemination of awareness, and conduct workshops.

Another essential factor is leadership. Although the findings indicate that senior management support is vital to success, there could be a bottleneck and delay. CONT_01 from the private sector and CONS_08 from the public sector emphasised an attitude and mindset of senior management that sometimes obstructs innovation—“People in leading positions sometimes consider old mentality, which means they sometimes hold the adoption behind”—that is “Decision Makers’ attitude”. Having senior management support includes having the right mindset, leadership style (consistent growth), mentality, and management style. CL_08 from the public sector highlighted the importance of leadership and the leader’s competency, “It comes down to competency. Both sides, contractor and the client, and the leadership”.

5.2.2. Organisational Maturity

Organisational maturity is another important factor. Maturity refers to the organisation’s processes, policies, and procedures being explicitly and consistently defined, managed, measured, controlled, and used effectively, besides organisational culture, process, and resources.

Organisational culture refers to a collective framework of shared values, beliefs, assumptions, and behaviours that is a fundamental element in shaping the identity and operations of an organisation. This includes the work environment, employee engagement, decision-making processes, and overall performance. Understanding the significant influence of organisational culture is imperative to comprehend its impact on the success or failure of an organisation. As mentioned above, change in organisational culture is essential. CONS_08 stated that senior management is targeting and attracting younger talents to create a culture of creativity. CONS_07 confirmed this, “Transparency created a culture of change, easy to adopt, monitor, and mature to add responsibility”. It is essential to notice that although diversity is important, it could lead to delays and slow the organisation’s readiness. CL_05 mentioned that “diversity in the organisational culture creates a stubborn and challenging environment, and that creates conflicts between stakeholders and departments”.

Processes play a crucial role in the maturity of an organisation; these refer to the set of activities carried out to attain its goals. CL_02 argued that “we have a very great clear, detailed process” and also “We have a process, but they always enforce some changes”. CL_10 highlighted that “Different organisations follow different processes”. Significantly, process and data are two connected aspects of the same concept; if you improve one, the other will improve. Therefore, fixing the process and interlinking it will help to obtain the correct data, which in turn will repair the process. CONS_08 highlighted the importance of accountability of the process: “What is more important is to define the accountability of everyone in the process, clear role, and responsibility for the processes, and what makes any process successful is the escalation mechanism”, and in addition, “Having an escalation mechanism shows transparency and trust, will help to know where the issue is, will hold everyone accountable for their role and help to make the process successful”. Overall, participants confirmed that having a process is an essential factor. Also, they confirmed that their organisations changed some of the old procedures or workflows to be compatible with organisations adopting AI.

Resources are a vital factor for adoption and readiness within an organisation; this includes the availability of financial and human resources, including expertise. CL_15 declared that all obstacles and failures are associated with financial resource limitations. Individuals are the most fundamental and indispensable aspect of an organisation’s adoption capability. The availability of specialised knowledge can enhance the acceptance of innovation, facilitate readiness, and provide expertise for a skilled team that then leads to success. CONT_04 noted that “talent and diversity in expertise among team members are essential to projects, having the same mindset regarding efficiency, quality, operation, and safety for a successful, smooth project”. CL_12 argued that having expertise in some cases can be a challenge because people are merely focused on their delivery and do not listen to innovation or take any risks, “You hire people that come from a traditional delivery model. Everything they will do is operate according to that traditional model”.

Overall, it is important to have processes and sufficient resources (financial and individual) to execute the work, along with appropriate expertise to help implement new technologies, facilitate readiness, and accelerate AI adoption.

5.2.3. Absorptive Capacity

Absorptive capacity is a critical factor in an organisation’s ability to recognise, assimilate, and apply new knowledge or external information to enhance its performance and technological innovation [71,72]. Absorptive capacity plays a vital role in adopting and implementing technology in the organisation, as it measures the capacity for learning and innovation [73]. The subcategories identified here on absorptive capacity were derived from the literature and insights emerged from the data, which refer to communication and collaboration, knowledge and capabilities, mindset and attitude, and training and expertise [74,75].

Communication and collaboration among stakeholders across different departments are crucial factors in the adoption of AI. CONT_02 elaborated “Big waste that there are no alignment and analogy between divisions in the same organisations; they are all working individually and create a different type of data among the same organisation”. Furthermore, CL_05 mentioned that “diversity in the organisational culture creates a stubborn, challenging culture, and that creates conflicts between stakeholders and departments, which leads to failure of projects”. Employee resistance to change is unavoidable and can be mitigated by enhancing communication and collaboration among employees. CL_06 remarked that the organisation is “keen on having the best environment for their employees”. It is vital to foresee and overcome potential resistance to change in order to evaluate an organisation’s readiness for AI adoption. Organisations need to acknowledge the possibility of resistance so that they can proactively develop strategies to educate and engage employees, thereby facilitating this transition.

Employee knowledge and capabilities are critical factors that contribute to the success of an organisation. Many interviewees emphasised that team members’ capabilities are essential. Some participants confirmed the significance of training and the efforts their organisation was making to improve employee skills, reduce technical issues, and foster a culture of learning. CL_06 stated “Our organisation cares about training resources to enhance and raise their capabilities … our performance bonuses and promotion are linked not only to how we perform but also how we will learn and train”.

Therefore, organisations need to periodically check their knowledge base for continuous improvement in learning, sharing, and creating an environment that cares about their teams, provides knowledge and awareness, and fosters trust. Individual employees’ knowledge and capabilities can be enhanced by training and education, which can be facilitated by a culture and environment that supports the sharing and exchange of knowledge.

Employees’ attitudes and mindsets present one of the challenges faced by the organisation; most interviewees emphasised that people are the most significant challenge relate to the readiness of an organisation to adopt AI. CL_01 noted “Employees’ behaviour and perception plays an important role in the organisation”. Any change, whether positive or negative, will face resistance since people are afraid to lose their jobs due to the impacts of technology. On the other hand, CONS_03 and CONS_06 cited fear as a critical factor, including fear of change, of technology, of learning something new, and of failing to be an expert. This illustrates that people are the biggest challenge, and the mindset of those working in the organisation can be an obstacle to adoption.

Participants have confirmed the significance of employee training and expertise, and the efforts made by their organisation to improve their skills, reduce technical issues, and foster a culture of learning. As mentioned above, senior management support helps to improve the maturity of organisations, for example, using a proactive approach to enhance processes, governance, and procedures. Senior management support influences organisational maturity, which influences the absorptive capacity of organisations. It helps to facilitate organisational learning and knowledge sharing among employees and raises accountability among them.

5.3. Environmental Context

The findings from the interviews identified government support, competitive pressure, and external support from vendors and trading partners as critical factors for readiness.

5.3.1. Government Support

Government support plays an important role in the adoption of technology. There is a significant relationship between the adoption of technology and government pressure to promote the use of technology. All participants confirmed that the government played a key role in establishing a government regulatory plan. CL_03 stated “There has been a huge change in the past three years, especially in investing in technology” and CONS_07 added, “The support from the government is what causes organisations to follow this”. As mentioned above, the important role of government support for AI adoption causes increased competitive pressure among organisations, which positively influences AI adoption.

5.3.2. Competitive Pressure

The market is driven by the needs and demands of the different sectors, which are, in turn, driven by government demand. Some interviewees highlighted the importance of having enthusiastic support from external stakeholders (contractors, vendors, and consulting companies). CL_15 stated, “They need to have the knowledge and the know-how of the country (KSA Context) and the availability of products and materials”. This served to increase market competition among vendors and suppliers and increased supply chain demand for AI products and solutions. CL_12 said, “There is much competition in that industry”, with CL_06 adding “Organisations are making big improvements toward digitalisation and using AI”.

5.3.3. External Support

External support beyond the firm’s control mainly stems from contractors, vendors, and consulting companies. This support is a crucial factor for AI adoption and can influence its success or failure. Also, the growing demand for AI products and services has created competition among vendors and supply chains, as described by CONT_06 from the public sector, “There is a huge development in the industry in the KSA in the digital transformation; policymakers and people responsible in the industry are supporting the adoption and pushing and helping organisations to be part of this. They have created transparency and trust in the industry between contractors and clients by the organisation of the industry more and setting clear policies to follow”.

Together, these results provide important insights into the data. The findings indicate that although senior management and government support can facilitate the readiness for and adoption of AI, people are the main contributors to the success and failure of these processes. It was found that employees’ behaviour and attitude are the most significant determinants of readiness.

6. Discussion

The TOE framework was developed for the KSA to better understand organisational readiness to adopt AI and assess the possibility of implementing it at the earlier FEP stages of the project. Overall, the results indicated that government support is a huge influence on all three aspects of the TOE framework’s technological, organisational, and environmental contexts, which, in turn, helps influence the organisation’s readiness to adopt AI. In the KSA, government support is expressed by the rules, regulations, policies, and legal frameworks established by the government to develop an innovation. This is exemplified by the government’s investments to enrich and accelerate digital transformation by launching the National Transformation Programme (NTP), which creates the necessary infrastructure and environment for the public, private, and nonprofit sectors to achieve Vision 2030 plans and objectives [76]. This includes many initiatives and programs, including construction and digital transformation [76].

Participants confirmed that the government played a crucial role in establishing a regulatory plan. These conditions are likely to occur when the government develops and implements digital transformation plans and roadmaps, supports and helps organisations to implement them, and influences and promotes competitive advantage among organisations and vendors. It translates into numerous recent government initiatives guided by Vision 2030, operationalised by the NTP and the Digital Government Authority (DGA); this underscores the government’s commitment to creating a digitally empowered society.

Previous research [77,78,79] revealed that competitive pressure has no significant relation to the adoption of cloud computing innovations. As mentioned above, government support played a significant role and directly influenced organisational and environmental contexts. The government and decision-makers encourage the adoption of technology and digital transformation. This support from the government has been reflected in senior management support, which plays a critical role in facilitating organisational readiness, driving organisational change, and influencing behaviours. Previous researchers have confirmed [41,46,47,49,50,52,53,80] that senior management influences and motivates the whole organisation to participate in the adoption process. Senior management support also enables organisational transparency, trust, and a culture of change, helping to monitor, adapt, and measure, which adds responsibility and support for automation and AI adoption. Moreover, it facilitates the promotion of creativity within organisations, which contributes to a change in mindset and improves the organisation’s maturity [41,81]. Organisational maturity has a direct relationship with absorptive capacity, the greater the maturity of the organisation, the greater absorptive capacity. This confirms findings that a higher level of organisational maturity can enhance a company’s absorptive capacity, leading to improved learning and innovation [82].

Another finding is that organisational culture is a critical driver of success and can impact AI adoption and readiness either positively or negatively. Organisations with homogeneous cultures can bring various perspectives, ideas, and solutions, which means that they can be more adaptable to change [83]. Diversity can foster innovation and creativity; it can also establish balanced employee coordination, excellent variety, and increased learning [84]. A heterogeneous culture can hinder and slow adoption, as it can be challenging to establish common goals and strategies when there are significant cultural differences. If not effectively managed, it can lead to misalignment and confusion, causing tension, miscommunication, and frustration among employees. There is evidence that employees in heterogeneous cultures are slower to recognise the need for change than those with diverse cultures [85].

The findings indicated that the reasons why some organisations are struggling to adopt AI are due to the attitudes, behaviour, and mindsets of employees; CL_10 said, “People are the more important determiner of success and predictor of failure”. Individuals’ behaviour is influenced by their attitudes; in other words, attitudes determine our behaviours [86]. Some participants indicated that employees fear technology due to anxiety about learning new concepts and the potential of failure. In contrast, others misunderstand the advantages of AI and fear that it will replace them. Several factors could explain this, including fear of learning and failing, fear of authority, lack of understanding of AI’s benefits, trust in technology, and employees’ capabilities. This confirms research findings that emotions can significantly influence employees’ attitudes towards technology adoption and the intentions behind it [87,88]. Adopting technology involves understanding the technology and mastering sophisticated skills, which can lead to a certain degree of hesitance among employees when it comes to embracing such technology.

Table 4. Summary of key findings.

Key Findings
Government support greatly influences all aspects of the technological, organisational, and environmental contexts.
Support from the government has been reflected in senior management support, which has a critical role in facilitating organisational readiness, driving organisational change, and influencing behaviours.
Senior management support enables organisational transparency, trust, and a culture of change, helping to monitor, adapt, and measure, which adds responsibility and support for automation and AI adoption.
Organisational culture is a critical driver of success and can impact AI adoption and readiness positively or negatively.
Organisational maturity has a direct relationship with absorptive capacity: the greater the maturity of the organisation, the greater absorptive capacity.
Employees’ attitudes, behaviour, and mindsets are key to an organisation’s readiness to adopt AI. They misunderstand the advantages of AI, fearing that it will take their jobs.

presents the key findings of the study.

Implications and Recommendations

The primary objective of this research was to elucidate the current state of organisational readiness for AI with a focus on its implementation during the FEP phase of construction projects. The findings of this study suggest several important implications for future practice in the KSA construction sector. The results indicate that government support greatly influences all aspects of the technological, organisational, and environmental contexts, which influences the organisation’s readiness to adopt AI. Furthermore, this support from the government has influenced senior management support, which plays a critical role in facilitating organisational readiness, driving organisational change, and influencing behaviours. Senior management influences and motivates the whole organisation to participate in this readiness, therefore, senior managers should invest in change management. Senior management can accelerate the level of readiness by leveraging the benefits of AI, promoting its use, and increasing awareness of its advantages among employees. Employees are found to be key to an organisation’s readiness to adopt AI; however, individuals may misunderstand its advantages, fearing that it will take their jobs. This study confirms that employees, through their attitudes, behaviour and mindset, are the most significant challenge to an organisation’s readiness to adopt AI.

In this case, policymakers should pay attention to employees’ concerns and perceptions about AI technology. This can be achieved through effective change management, focusing on employees’ capabilities, communicating the benefits of AI adoption with employees, and providing training and workshops within organisations to encourage and soothe the process.

7. Conclusions

Front-end planning has been identified as a critical process that maximises the probability of project success. It involves strategic planning, defining initial requirements for projects, and generating adequate information to assist management in making decisions to mitigate potential project risks [89]. Front-end planning can be considerably improved by incorporating AI at this stage, as it provides advanced analytical tools, predictive insights, and efficient resource management [90].

It is imperative to assess organisations’ readiness prior to the implementation and adoption of AI. The TOE framework is widely used for its flexibility in adapting to various contexts and for measuring the level of organisational readiness to adopt innovation [36].

The aim of this study was to answer the research question “What are the key technological, organisational and environmental factors that influence AI adoption at the front-end planning phase in Saudi Arabia’s construction industry?” using the TOE framework and interview professionals.

Several critical insights were disclosed from interviews with industry professionals:

• Government support and commitment are positively linked to organisations’ readiness to adopt AI. However, the research also suggests that although government support plays a vital role in the transformation and adoption process, it impacts all three aspects of the TOE framework. Surprisingly, although the support comes from the government and senior management, it was found that there are different levels of capabilities between entities, which can indicate different levels of readiness; there are entities that are known to be capable of achieving what is required, and there are entities that are struggling to meet the aspirations that bring the improvements needed.
• Senior management support is a significant factor in readiness. On the other hand, although findings indicate that senior management support is crucial to success, it can also become a bottleneck and delay factor. Lack of senior management support, management interference, micromanagement, and leadership attitudes are the main hindrances to an organisation’s readiness.
• People are a crucial influence on an organisation’s readiness to adopt AI, especially in terms of employees’ attitudes, behaviours, and mindsets. Established attitudes, knowledge, and experiences with technology significantly influence perceptions of technology. People are often unwilling to change since change generates anxiety, uncertainty, and discomfort, which can negatively impact employees’ performance and affect organisational outcomes. This may explain why change is driven by the younger generation in KSA. The government has transformed the workforce by targeting young, talented leaders and females to establish a diverse culture with a wide range of skills, which can enhance innovation and increase employee involvement and commitment.

The following strategies are recommended to resolve these challenges and improve the FEP process through the adoption of AI:

• Comprehensive change management: Implement effective change management programmes that address employees’ concerns and their resistance and promote a culture of continuous learning and adaptation.
• Ongoing training and development: Offer ongoing training to improve employees’ skills and confidence in using AI technologies, thereby ensuring that they feel competent and appreciated.
• Transparent communication: Encourage the development of open and transparent communication channels to ensure that employees are kept informed about the benefits and progress of AI adoption, thereby reducing uncertainty and fostering trust.
• Standardisation and collaboration: Promote collaboration between government entities and industry actors to standardise best practices and share resources, thereby facilitating more seamless transitions throughout the sector.

Incorporating AI into the FEP stage of construction projects can considerably improve the planning processes and the chances of project success. Nevertheless, it is imperative to evaluate organisational readiness using the TOE framework to guarantee its effective adoption. Organisations can leverage AI to achieve superior project outcomes by addressing the human element and fostering a supportive environment, thereby overcoming potential barriers.