Reflection of Artificial Intelligence Applications in Enhancing Career Maturity: An Analytical Study at the Iraqi General Retirement Authority/Baghdad

Reflection of Artificial Intelligence Applications in Enhancing Career Maturity: An Analytical Study at the Iraqi General Retirement Authority/Baghdad

*Maysoon ali Hussein¹, Marwan sabah hasan², Khalidiya Mostafa atta³

¹human resource Management/organizational behavior University of aliraqia Business management department

²Theory of organization and organizational behavior University of aliraqia Business management department

³Strategic management University of aliraqia Business management department

DOI: https://dx.doi.org/10.47772/IJRISS.2025.905000356

Received: 06 May 2025; Accepted: 13 May 2025; Published: 17 June 2025

ABSTRACT

Understanding career maturity is vital as it enhances the impact of artificial intelligence (AI). Career maturity represents a planned effort directed towards improving and increasing the effectiveness and quality of performance through systematic and programmed interventions via organizational learning. Career maturity aligns individual and organizational goals to create cohesive and effective groups within the organization. Organizations striving to extract value from AI have shown that they often face organizational challenges rather than technical ones. Organizations capable of deriving value from their AI activities exhibit distinctive organizational behaviour, in contrast to many organizations that perceive AI merely as a technological tool. Those organizations that approach AI from a functional and organizational perspective are more likely to extract greater value from their AI investments and utilize it optimally. This research aims to identify the key AI applications that have contributed to achieving career maturity within the General Retirement Authority/Baghdad, given the importance of this institution which serves a broad segment of Iraqi society. A questionnaire was randomly distributed among 100 employees of the Authority. The descriptive-analytical method was employed. The key findings highlight the necessity of adopting AI applications to process retirement transactions, eliminate bureaucracy and monotony, and achieve career maturity.

Keywords: Artificial Intelligence (AI); Career Maturity; Organizational Learning; Public Sector Efficiency

INTRODUCTION

In the contemporary landscape of organizational development, the integration of Artificial Intelligence (AI) within various industries has led to transformative changes in operational strategies, decision-making processes, and overall performance. The rapid advancements in AI technologies have garnered significant attention, particularly in the realms of sustainability, organizational maturity, and career development. AI’s potential to optimize organizational performance has prompted researchers and practitioners to explore its impacts on corporate structures, with a focus on its ability to mitigate risks, enhance productivity, and foster adaptive organizational behavior. This study aims to investigate the intersection of AI adoption and organizational maturity, exploring the implications of AI in improving the agility and long-term sustainability of firms.

The significance of AI in shaping organizational trajectories is further underscored by its ability to assist firms in navigating an increasingly complex business environment. As companies face mounting pressures to innovate, maintain competitive advantages, and respond to shifting market dynamics, AI has emerged as a critical tool in enhancing both organizational and career development processes. The study by Tanveer, Hassan, and Bhaumik (2020) highlights the role of AI in academic policy regarding sustainability, emphasizing AI’s potential to guide decision-making that aligns with environmental and economic objectives. Moreover, AI’s influence is particularly notable in the financial sector, as demonstrated in Moilanen’s (2020) exploration of AI’s impact on the financial labor market, where perceptions of AI’s ability to streamline operations and reduce risks are central to organizational success.

A deeper exploration of AI’s role in organizational contexts is presented by Ho et al. (2022), who argue that AI’s capabilities extend beyond operational efficiency, serving as a safeguard against financial risks. The connection between AI and organizational maturity models, such as those proposed by Attafar, Barzoki, and Radmehr (2013), further illustrates the critical relationship between AI-driven strategies and organizational agility. As organizations seek to enhance their competitive edge, the maturity of their internal processes becomes crucial, with AI serving as a catalyst for the evolution of organizational structures and performance metrics. Furthermore, the development of AI-enhanced learning systems, as explored by Jia and Tu (2024), highlights the importance of integrating AI with organizational learning processes, enabling employees to adapt and innovate in response to new challenges.

This study also considers the broader implications of AI in career development, particularly in relation to career maturity and organizational performance. Research by Savickas et al. (2002) and Salimi (2008) underscores the importance of career maturity in the professional development of individuals, especially as they interact with increasingly sophisticated AI systems. AI’s ability to influence career trajectories is pivotal not only for individuals but also for organizations seeking to foster a culture of continuous learning and development.

The present research aims to contribute to the ongoing dialogue surrounding AI’s impact on organizational maturity by developing a comprehensive model that links AI capabilities, organizational performance, and career development. Drawing upon established models such as PMI’s Organizational Project Management Maturity Model (2008) and Langston and Ghanbaripour’s (2016) framework for assessing project-based organizational performance, this study will explore how AI can enhance both individual and organizational maturity. By integrating AI with career guidance and development frameworks, the study will provide valuable insights into how organizations can leverage technological advancements to promote sustainable growth, enhance employee competencies, and adapt to the evolving demands of the modern workforce.

The research problem at the General Retirement Authority lies in the lack of necessary expertise and the weak adoption of modern applications and automation in processing retirement transactions. This deficiency causes delays and widespread dissatisfaction among retirees due to the multiple visits and requirements needed to obtain their pension. Thus, greater attention must be given to workforce planning, ensuring the right person is placed in the right position, training employees on the best technologies to guarantee speed and accuracy, and empowering them to make effective decisions. The main research question is:

Does the application of artificial intelligence play a role in achieving career maturity at the General Retirement Authority?

The research developed based on two main hypotheses:

H1: There is a statistically significant correlation between AI applications and career maturity at the General Retirement Authority.

H2: There is a statistically significant impact of AI applications on the dimensions of career maturity at the General Retirement Authority.

Figure 1 Conceptual Framework

The Applications of Artificial Intelligence

The Historical Development of the Concept of Artificial Intelligence

Advanced nations are keen to leverage the tools of artificial intelligence across various domains of governance and administration, within the broader framework of what is known as “e-government.” This approach has led to the emergence of “smart administration” in the public sector, now considered essential for enhancing the management of public services. It promotes the use of algorithmic management over human intervention to achieve fairness, eliminate bias, and prevent administrative corruption. The value of artificial intelligence is particularly evident in the delivery of public services, including telecommunications, and in the transformation of traditional administration into smart management through digitization. Digitization enables the completion of tasks within minutes—tasks that previously required hours or even days—and the electronic storage of documents and files ensures faster responsiveness and creates an impression of a professional and innovative administration.

Applications of artificial intelligence emerged in the 1950s, pioneered by the renowned logician and mathematician Alan Turing. Turing raised a fundamental question in his famous article “Computing Machinery and Intelligence”: can machines “think”? Turing proposed an experiment that became the benchmark for testing machine intelligence, known as the “Turing Test.” In this test, two humans and a machine (computer) interact within a closed environment where the identities of the participants are concealed. If the interrogator, through written or spoken communication, cannot distinguish between the machine and the human, the machine is considered intelligent.

In 1956, the field of artificial intelligence became more defined with the first conference held at Dartmouth College in the United States. The conference was attended by leading researchers in artificial intelligence applications, including Marvin Minsky, Herbert Simon, John McCarthy, and Allen Newell. John McCarthy proposed the term “Artificial Intelligence (AI)” “to describe computers capable of performing functions associated with human cognition” (Al-Sayyid and Karima, 2020, p. 13).

Between 1961 and 1970, Marvin Minsky and Seymour Papert developed several simple neural networks. Concurrently, Allen Colmer contributed to the development of the programming language “Prolog,” and Ted Shortliffe pioneered rule-based systems for knowledge representation and reasoning in medical diagnosis and treatment, often referred to as “expert systems.” This era also witnessed the creation of the first remotely controlled robot via computer systems.
However, in 1974, artificial intelligence research faced significant challenges and criticisms, prompting both the U.S. and British governments to cut funding for exploratory research in the field. This marked the first major setback for AI research (“Hayda and Salima, 2020, p. 9”).

AI applications experienced a resurgence in the early 1980s, driven by the commercial success of expert systems, a type of AI program that simulates the knowledge of human experts. By 1985, AI research had generated over one billion dollars in market revenue, reigniting government interest and investment in the field.

Nevertheless, in 1987, “AI research suffered another, more prolonged decline following the collapse of the Lisp machine market, a significant programming platform for AI at the time” (Abdul Azim, 2023, p. 357). In the early 1990s and into the 21st century, AI applications witnessed a transformative leap with the advent of modern technologies that fueled a broader technological revolution. Innovations such as deep learning—featuring capabilities surpassing human intelligence—and the immense power of modern computing played critical roles. There was also a heightened focus on solving specific problems and creating new interconnections. Consequently, “AI research became highly specialized and fragmented into multiple independent subfields” (Abdul Malik, 2021, p. 16).

The field of artificial intelligence has traversed phases of advancement and decline, often due to the complexity of research problems that either exceeded expectations or appeared insurmountable with the available technology. The field remained highly specialized until the late 1990s, when a significant shift—referred to as the “Great Leap”—occurred. This leap involved progress in three key areas: the explosion of available data, continuously increasing computational power, and the development of new algorithms, particularly those involving “neural networks” and “deep learning.” Also, “these advances collectively generated a significant technological wave” (Tanveer et al., 2020, p. 2).

At the start of the 21st century, artificial intelligence began to play a decisive role in shaping and advancing modern understanding of the AI domain, achieving capabilities far beyond what early 20th-century imaginations had conceived. Today, AI is a “tangible reality that continues to challenge expectations with its innovative ideas, projects, and concepts as the decades progress: (Moilanen, 2020, p. 9).

The Concept of Artificial Intelligence

Artificial intelligence is considered one of the branches of computer science and serves as the fundamental pillar in the contemporary technology industry. The term consists of two words: intelligence and artificial. According to Webster’s Dictionary, intelligence is defined as “the ability to comprehend new or changing circumstances,” meaning the capacity to perceive, understand, and learn from new conditions. Thus, the key components of intelligence are perception, understanding, and learning. As for the term “artificial intelligence,” it is linked to the act of “making” or “manufacturing” and refers to all entities created through human activity, differing from those naturally occurring without human intervention. Based on this, artificial intelligence generally means “the intelligence manufactured or created by humans in machines or computers” (Khawalid, 2019, p. 12).

Artificial intelligence is a technology programmed to mimic human judgment and cognitive skills. It can be designed to interpret environmental signals, and based on these signals, AI systems can assess risks to make decisions, predictions, or take actions. Unlike traditional software programs, AI systems “learn” from data and can evolve autonomously over time due to exposure to new data, without being explicitly reprogrammed by humans (Al-Awad, 2020, p. 19).

The science of artificial intelligence is a mode of thinking—specifically algorithms—that enables computers to solve problems according to a particular language. Consequently, AI programs and systems designed for software development provide facilitation tools for programmers, whereby the programmer inputs data or represents it, and the language system undertakes the search process. The most prominent of these languages are Prolog and Lisp. Artificial intelligence “is regarded as one of the modern sciences that has recently seen widespread adoption, entering numerous industrial and research fields, notably robotics and intelligent corporate services” (Rizq, 2020, p. 25).

Despite the immense importance of artificial intelligence and the significant attention it has garnered from researchers, providing a precise definition remains challenging even for field experts. This difficulty stems from two main reasons: firstly, the continuous evolution inherent in artificial intelligence, and secondly, the diversity of its application areas. Consequently, multiple definitions have emerged, with John McCarthy’s definition considered the most comprehensive: “artificial intelligence is the science concerned with intelligent computer programs, or it is a branch of computer science aiming to achieve goals across all fields” (Al-Rawi, 2020, p. 193). It is a science based on mathematical principles, devices, and software systems assembled within computers, performing numerous functions and operations that emulate human intelligence—albeit with superior speed and precision in solving complex problems.

Artificial intelligence “is distinguished by the ability of electronic devices to perform many tasks similar to those undertaken by humans, such as driving cars, recognizing images, distinguishing sounds, and operating speaking robots” (Hayda and Salima, 2020, p. 9). Furthermore, it “is a field that relies on computer sciences and the utilization of datasets to enhance problem-solving and decision-making processes” (Ho, Linh Tu, et al., 2022).

Characteristics of Artificial Intelligence Applications

One of the key objectives of artificial intelligence (AI) is to understand human intelligence and its nature by developing computer programs capable of simulating intelligent and high-quality human behaviors. This represents a radical transformation that transcends the traditional concept of information technology. It refers to the ability of a computer program to make decisions or solve specific problems in given situations. The immense speed of computers is among the primary reasons for their use. Accordingly, AI applications “represent behaviors with distinct characteristics that enable computer programs to simulate human work patterns, behaviors, and cognitive abilities, including the capacity for reasoning, learning, and interaction” (Hassan, 2020: 224).

Cording to Al-Anzi (2022: 51), “AI is characterized by several features, including the ability to handle complex and difficult cases, ease of application and knowledge acquisition, and the ability to operate effectively in ambiguous situations with incomplete information”. Al-Khawalid further identifies additional characteristics of AI, including:

Knowledge Representation Capability

Unlike statistical programs, AI applications are designed to represent information through a special structure that describes knowledge. This structure includes facts, relationships between these facts, rules connecting these relationships, and other elements that collectively form the knowledge base. This base provides the maximum possible information to solve the targeted problem.

Handling Incomplete Information

Another advantage of AI applications is their ability to find solutions even when information is not fully available at the required time. While incomplete information may lead to less realistic or less optimal conclusions, it can still result in valid inferences.

Learning Capability:

A key feature of intelligent behavior is the ability to learn from previous experiences and practices, and to improve performance by considering past errors. This ability is linked to the capacity for generalizing information, selectively inferring similar cases, and ignoring unnecessary information.

Inference Capability

This refers to the ability to devise possible solutions to specific problems based on known data and past experiences, particularly for problems where traditional methods cannot be applied. In computers, this is achieved by storing all possible solutions and employing inference strategies and logical rules (Al-Khawalid, 2019: 14).

According to Abdul-Mawla (2023: 28), AI is distinguished by

Being a technology that significantly enhances productivity, serving as a vital strategic tool to achieve higher efficiency and bolster beneficiary loyalty. Furthermore, AI is rapidly becoming a competitive advantage for many organizations.

Providing intelligent training platforms for remote learning. Coupled with the widespread use of mobile technologies, this development represents a significant transformation, offering exciting opportunities for employees and their administrations.

Types of Artificial Intelligence Applications

The concept of AI capabilities refers to the ability of AI systems to perform tasks that typically require human intelligence. “These capabilities focus on the organizational and societal impacts of AI, emphasizing human-centered approaches within the Fourth Industrial Revolution to achieve sustainable development goals” (Jia, et al., 2024: 4).

The major types of AI applications can generally be classified as follows (Fadel & Salman, 2019: 43):

Narrow AI Applications

 Also known as weak AI, this type focuses on a single, limited task with a restricted set of capabilities, the “Narrow AI applications are widely encountered in everyday life, such as Google Translate or attendance systems using hand or eye biometrics or smart cards” (Zahra, 2005: 1).

Reactive Machines

 These are the simplest forms of AI applications, lacking the ability to learn from previous experiences to develop future actions. Instead, they interact with current inputs to produce the best possible outcomes. Examples include IBM’s Deep Blue and Google’s AlphaGo systems.

Limited Memory

 Applications with limited memory can store data from past experiences for a limited time. This concept is linked to the aspiration for self-aware AI, where machines would possess self-consciousness and emotions, making them more intelligent than humans. However, this concept is still theoretical and not yet realized. When referring to this type, we point to AI applications that have human-like capabilities. Autonomous vehicles are a prime example, where data such as the last recorded speed of nearby vehicles, distance to other cars, and speed limits are stored for navigation purposes.

Artificial Superintelligence

(Theory of Mind): This type of AI envisions machines that understand human emotions, interact with humans, and communicate effectively. It is important to note that there are currently no practical applications of this form of AI. Nevertheless, it is viewed as the path to the future, where AI could outperform humans across multiple domains.

AI applications can also be categorized based on their functions

Automated Decision-Making

One of the manifestations of information technologies and a crucial administrative tool for executing assigned tasks. It involves precise procedures for decision-making and relies on large databases to facilitate optimal decisions (Al-Sheikh, 1988: 7).

Electronic Documents

These documents maintain confidentiality and privacy, where only the sender and recipient have access. They ensure security and trust by employing advanced preservation techniques, such as encryption systems and digital certification issued by trusted state authorities (Al-Hussain, 2002: 2).

Electronic Signature

A vital component required for issuing electronic administrative decisions.

Personal Accounts

These accounts represent their owners exclusively on social media platforms (Bakhit, 2023: 11).

Occupational Maturity

Researchers unanimously agree on the importance of occupational maturity in ensuring the success of both parties in the organizational process, namely the organization and its employees. This stems from the vital role this organizational concept plays in facilitating the organization’s activities effectively, motivating employees to work, strengthening their loyalty and belonging to the organization, and its connection and influence on other organizational variables.

The Concept of Occupational Maturity

Researchers consider occupational maturity the final link in a series of administrative procedures aimed at adopting sound decisions. It represents employees’ ability to make choices and subsequently direct themselves towards work, coupled with autonomy in decision-making and a strong emphasis on information gathering. Qarqash (2002, p.27) “clarifies that occupational maturity among employees progresses through four essential stages, known as the stages of occupational maturity”, outlined as follows:

First Stage

In this stage, the employee is new to both their work and professional relationships. They are generally incapable of performing the required tasks, and their level of readiness to assume responsibility is quite limited.

Second Stage

Over time, as the employee gains experience and skills from their new role, and through interactions with supervisors and colleagues, they transition to a stage where they can perform assigned tasks, albeit incompletely or at relatively low levels. However, they begin to show some readiness to assume responsibility.

Third Stage

With further passage of time, the employee becomes able to perform their duties effectively and build professional relationships with other employees. Nevertheless, during this phase, they may still experience a lack of self-confidence and a sense of insecurity due to the increasing level of responsibility placed upon them.

Fourth Stage

At this stage, the employee’s capabilities are assumed to have developed to a high level. Their knowledge, skills, and readiness to perform tasks in the best possible manner are complete. They are willing and able to assume responsibility due to enhanced self-confidence and loyalty toward the organization they work for. The key concepts related to occupational maturity can be summarized in the following table:

Table 1 The key concepts related to occupational maturity

The concepts outlined in the table above indicate the following:

Job maturity relies heavily on employees’ psychological maturity. As the level of psychological maturity in a specific domain increase, employees’ self-confidence, willingness to work, and motivation to achieve also rise.

A high level of job maturity is evident in employees’ ability to make appropriate career decisions that align with the current job requirements and expected work behavior. The greater the congruence—up to the point of near-complete alignment—between employee behavior and job expectations, the higher the level of job maturity.

Job maturity is reflected in employees’ commitment to their jobs, coupled with efforts to adapt and adjust to changes in the work environment, ultimately enabling them to capitalize on opportunities.

Based on the above, the researcher concludes that job maturity refers to employees reaching a high level of awareness and intellectual capability, allowing them to make accurate and realistic job choices. This, in turn, results in specific behaviors that are consistent with the individual’s character and the requirements of the job they occupy.

The Importance of Job Maturity

Job maturity holds significant importance, as reflected in the following points (Mubiana, 2010, p. 35; Samuel, 2008, p. 35):

Clarifying the vision regarding work tasks, thereby facilitating the employees’ administrative duties.

Enhancing readiness and preparedness, aiding employees’ functional adaptation.

Providing employees with a greater sense of job satisfaction.

Assisting employees in understanding various work patterns and finding solutions to the problems they encounter.

Serving as one of the effective means for developing expertise that enables employees to make informed decisions.

The researcher further notes that job maturity encapsulates the organizational practices aimed at improving processes, thereby offering a continuous performance measurement tool. This is considered one of the primary task’s organizations seek to achieve, providing a starting point for performance evaluation.

Dimensions of Job Maturity

The dimensions of job maturity have received significant attention from researchers in their empirical studies, as they addressed several aspects: “career planning” (Abbas and Ali, 2003, p. 208), career training (Baird et al., 1990, p. 258), the development of work-related attitudes and decision-making ability (Qaryouti, 1997, p. 103), and the combined aspects of career planning, attitude formation, and decision-making ability (Al-Hayali, 2010, p. 62). Given the relevance of these dimensions and their applicability across different environments, including the Iraqi context, they will be applied in the practical aspect of the current study. These dimensions are outlined as follows:

Career Planning

Career planning is regarded as the cornerstone of employees’ job maturity processes. It involves forecasting future needs by identifying employees’ existing skills and qualifications in a manner that enables them to implement their career plans effectively. Career planning is defined as the process by which employees enhance their awareness and understanding of their vocational interests, values, strengths, and weaknesses, facilitated by the organization through information regarding available career opportunities (Abbas & Ali, 2003, p. 208).

Career planning further involves projecting employees’ future career patterns, identifying necessary skills and qualifications, emphasizing methods of acquiring them, and establishing realistic plans for development and improvement (Jones & George, 2003, p. 178). It refers to behaviors exhibited by employees aimed at fostering awareness of their skills, interests, values, opportunities, constraints, and career options. Moreover, this concept encompasses employees’ career goals and the plans they endeavor to achieve. The importance of career planning lies in its ability to assist employees in diagnosing their vocational tendencies, identifying their strengths and weaknesses, and visualizing their career objectives (Bernardin, 2007, p. 228).

Dod and Hooley (2015, p. 3) emphasized that the fundamental intent of career planning is linked to fulfilling employees’ aspirations in a manner that enhances the outcomes of their efforts, ultimately leading to the realization of significant and comprehensive economic and social benefits.”

Figure 2 Dod and Hooley, (2015), The Economic Benefits of Career Guidance, Career England, Promoting social Mobility, achievement and economic Well-being

Job Training

Job training “is a process aimed at enhancing employees’ performance levels, expanding their experience and capabilities, altering their behaviour and attitudes, and boosting their morale to improve their work outcomes and ensure their stability, thereby contributing ultimately to the efficient achievement of the organization’s goals” (Baird et al., 1990:258). Job training is defined “as the process that supports employees in working rigorously, efficiently, and astutely, while appropriately modifying their behaviours and practices” (Amer, 2012:16).
The training process relies on three fundamental stages characterized by coherence and persistence (Salem and Saleh, 2006:135). Job training is considered a critical component of employee growth, aimed at achieving predetermined goals by developing and nurturing employees in a manner consistent with the realization of these objectives.

Shaping Work Attitudes

Attitudes represent one of the key dimensions of employees’ job maturity, contributing to the formulation of implicit anticipated responses and the development of behavioural patterns that vary in intensity and impact within the workplace. Attitudes are an integral part of an employee’s personality and are shaped primarily through social learning, meaning that they are subject to change via processes such as accommodation, elimination, or modification of one or more of their components. Most managers typically prefer to alter one or more employee attitudes to facilitate adaptation to environmental changes within the organization, to enhance performance, or to strengthen harmony and cohesion among employees (Al-Hayali, 2010:6). According to Salem and Saleh (2006:260), “the Attitudes can take various forms due to the multiplicity of contributing sources”, including (Genetic factors, Physiological factors, acquired experience, Work colleagues, social learning)

Decision-Making Ability

Decision-making is considered one of the crucial dimensions of employees’ job maturity, serving as a true indicator of their competencies, expertise, knowledge, and skills. Utilizing such competencies strengthens the entire organizational process, as job maturity reflects the actual performance of administrative activities.

It is important to note that this performance cannot be properly assessed, understood, or enhanced without the existence of job maturity, which inherently requires the adoption of sound decisions across various fields and activities. In the absence of effective decision-making, organizational activities become ambiguous, and levels of job maturity decline, leading to diminished performance. Therefore, emphasizing and instilling the importance of job maturity is essential, as it reveals the vitality and competence of employees—traits that cannot manifest without correct decision-making. Likewise, Al-Hayali (2010:62)An employee who operates machinery accurately and applies their knowledge and expertise in its use and maintenance is typically considered the most capable in decision-making and can be described as having high job maturity. From other hand, According to Sultan (2003:68–69), the significance of decision-making can be assessed through the following factors:

The extent of the decision’s impact on organizational and employee objectives

The parties affected by the decision

The amount of financial resources required for the decision

The frequency of the decision’s recurrence

RESEARCH METHODOLOGY

The research population was identified as the National Retirement Authority – Baghdad, one of the Iraqi governmental institutions affiliated with the Ministry of Finance. It was established in 1921, with its headquarters located in Al-Shaheed Square, Karkh side, Baghdad. Previously, it was known as the General Retirement Directorate, then renamed the Retirement Department, and on December 26, 2005, it was officially designated as the National Retirement Authority. The total population of the authority consisted of 328 employees, from which a complete population frame was considered. A random sample of 100 respondents was selected from this population.

The researchers adopted the descriptive analytical method to achieve the research results and to carry out the analytical and practical components of the study at the National Retirement Authority – Ministry of Finance / Baghdad. Despite the diversity of scientific methodologies, the analytical method is among the most commonly used approaches in research of a social, behavioural, and administrative nature. It is characterised by its comprehensive perspective, as well as its ability to describe and analyse phenomena. Additionally, the nature and objectives of the study played a crucial role in selecting the appropriate methodology and determining the most suitable techniques for obtaining, collecting, organizing, classifying, analysing, and interpreting data in order to draw conclusions, extract key indicators, and understand the nature of the relationships between the research variables.

To collect data, both theoretically and empirically, the researchers relied on the following tools

Theoretical Aspect

This aspect depended on Arabic and foreign references and sources, such as books, theses, dissertations, peer-reviewed scientific journals, and conference proceedings. Moreover, the internet was utilised to access recent sources to enrich the theoretical framework, methodology, and practical dimension of the study.

Applied Aspect

The applied part of the study employed several tools, including

Interviews

These included field visits and conducting interviews to gather the necessary data for identifying the research problem, supporting the findings of the applied section, and obtaining information not captured by the questionnaire. This provided a more comprehensive description of the problem, the research population, the sample, and their responses to the distributed questionnaire.

Questionnaire

The questionnaire served as the primary data collection tool and comprised two main sections:

Section One

Included demographic information of the respondents such as gender, age, educational qualification, years of experience, and job title.

The second section focused on the research variables, as outlined in the (Appendix A).

Practical Application of the Research

Validity, Reliability, and Normal Distribution Tests of Research Variables

Structural Reliability and Validity Test of the Measurement Tool

Reliability and validity tests are among the most important tests that must be adopted to ensure that a specific scale is suitable for use. Reliability means that the scale is dependable and can be relied upon to measure the theoretical constructs it was designed to assess. Cronbach’s Alpha coefficient is one of the most widely used and well-known tools for measuring the reliability of a questionnaire, and it is popular among researchers in various fields of scientific research.

Validity (Sekrana, 2003:206) refers to whether the measurement tool is actually measuring what it is intended to measure. In other words, does the scale measure the phenomenon under investigation and not something else? There are several types of validity, and in this research, the researcher utilized content validity (Cooper & Schindler, 2014:257). Content validity is a judgmental measure that relies on the researcher’s precise identification of the variables of the research topic, which, in turn, depends on the amount of information the researcher has studied regarding the subject matter.

The table below illustrates the reliability coefficients for the research variables and their dimensions.

Table 2 Reliability Coefficients for the Research Variables Dimensions

From Table (2), we can observe that the reliability coefficients for all the research variables (Applications of Artificial Intelligence and Job Maturity) with their respective dimensions fall within the statistically acceptable range, indicating that the scale used for measuring the research items has high reliability. This enables the researcher to rely on the results obtained for making sound decisions.

Test for Normal Distribution of Data

After ensuring the validity of the data collection tool by subjecting it to the aforementioned tests, and since the hypothesis testing in this study relies on parametric statistics, statisticians suggest that when a large sample is used compared to the population, there is no need to worry about the normal distribution of data (Field, 2009: 329). However, in order to ensure the accuracy of the research results, the researcher subjected the data obtained from the questionnaire to one of the most important tests for checking the normal distribution of data, namely the Kolmogorov-Smirnov test. According to this test, if the sample size exceeds 35 observations, the test value can be calculated using the following formula (Copper & Schindler, 2014: 623):

D=1.36nD = \frac {1.36} {\sqrt{n}} D=n​1.36​

Where nnn represents the sample size. Since the sample size in this study is 100 observations, the standard value of DDD will be 0.21. If the Kolmogorov-Smirnov statistic is greater than or close to the standard value of DDD at a significance level of 0.05, the data will follow a normal distribution at this specified level. Therefore, parametric statistical analysis tools can be used, and the results can be trusted. If the data does not conform to a normal distribution, non-parametric statistical tools will be used instead. The table below shows the results of the normality test for the study variables.

Table 3 Kolmogorov-Smirnov Test for Normal Distribution of Variables’ Data

Source: SPSS V.27 Outputs

It is evident from Table (3) that the data for the variables (Artificial Intelligence Applications and Job Maturity), both at the sub-level and the overall level, follow a normal distribution. This makes them suitable for parametric analysis tools.

Describing and Diagnosing the Research Variables

This section aims to present, analyze, and interpret the results of the responses from the research sample regarding the items included in the questionnaire. The study determined the level of responses based on the mean values by categorizing them into specific groups. Since the questionnaire uses a five-point Likert scale (Strongly Agree – Strongly Disagree), this aspect will be addressed through the following sections:

Presenting, Analyzing, and Interpreting the Responses of the Research Sample Regarding Artificial Intelligence Applications

The items of this variable will be discussed through the weighted mean values, response intensity values, standard deviations, and calculated coefficients of variation, whether at the partial or total level, as shown below:

Table 4 Descriptive Statistics for Artificial Intelligence Applications (n=100)

Source: SPSS V.27 Outputs

It is clear from the data in Table (4) that the variable “Artificial Intelligence Applications” achieved a weighted mean of (3.82), meaning it falls within the “High” category. The response intensity value reached (76%), while the standard deviation was (0.91), and the coefficient of variation was approximately (23.93%). Based on this, it is evident that the organization is utilizing artificial intelligence applications in line with global technological advancements, benefiting from positive impacts that enhance the accuracy and speed of processing transactions.

Presentation, Analysis, and Interpretation of the Responses of the Sample Individuals Regarding Job Maturity

This section presents and analyzes the responses of the sample individuals regarding the variable “Job Maturity,” utilizing weighted means, response intensity values, standard deviations, and calculated coefficients of variation, both at the partial and total levels, as shown below:

Table 5 Descriptive Statistics for the Job Maturity Variable (n=100)

Source: SPSS V.27 Outputs

From the data presented in Table (5), it is evident that the “Job Planning” dimension achieved a weighted mean of (3.86), meaning it falls within the “High” category. The response intensity value was (77%), with a standard deviation of (0.84), and the coefficient of variation was approximately (21.94%). Based on these results, it is clear that the management plans for both the current and potential future human resources.

Regarding “Job Training,” the dimension achieved a weighted mean of (4.06), placing it in the “High” category. The response intensity was (81%), with a standard deviation of (0.84), and the coefficient of variation was (20.74%). This suggests that the organization follows a strategic approach to staffing within its management.

The “Shaping Work-Related Attitudes” dimension achieved a weighted mean of (3.74), which also falls within the “High” category. The response intensity was (75%), with a standard deviation of (0.82), and the coefficient of variation was (21.97%). From this, we can infer that the organization strives to create harmony and collaboration among employees to improve work performance.

Finally, the “Decision-Making Ability” dimension achieved a weighted mean of (3.94), indicating a “High” category. The response intensity was (78%), with a standard deviation of (0.98), and the coefficient of variation was (25.05%). These results indicate that management allows employees to participate in decision-making in an objective and accurate manner, and that the decisions made are innovative and well-executed.

Testing Hypotheses of Correlation and Effect Among the Research Variables

The Pearson correlation method will be adopted to test the main hypotheses related to the correlation relationships between the main variables and their sub-dimensions, as outlined below:

First:Testing the First Main Hypothesis Regarding the Correlation Between AI Applications and Job Maturity

(There is a statistically significant correlation between AI applications and job maturity)

Table ( 6) presents the correlation matrix that tested the first main hypothesis and its sub-hypotheses. The results indicate strong positive correlations with statistical significance at the (1%) level between AI applications and the job maturity variable. The correlation value was (0.719) at the (5%) significance level, which is considered a strong correlation according to Cohen’s guidelines.

The significance value (P-Value) was (0.000), indicating statistical significance. Based on this, the hypothesis stating that “there is a statistically significant correlation between AI applications and job maturity” is accepted.

Table 6 Analysis of Correlations Between the Investigated Variables

*.Correlation is significant at the 0.05 level (2-tailed).

This indicates the strong relationship between the use of artificial intelligence applications by the public administration across all its functions and job maturity, particularly in its four dimensions. The relationship between artificial intelligence applications and job maturity is both complementary and reciprocal, with each feeding into the other. Artificial intelligence is an important tool for enhancing job maturity, while job maturity is a prerequisite for the success and sustainability of artificial intelligence applications. Below are the aspects of their interconnection:

Enhancing job competencies

The use of artificial intelligence applications (such as predictive analytics tools or chatbots) enables employees to improve their performance and increase productivity, thereby accelerating their professional maturity.

Developing analytical and decision-making skills

Artificial intelligence provides workers with accurate and quickly analyzable data, helping them make more mature decisions based on facts.

Reshaping roles and responsibilities

Traditional roles may change or be redefined due to task automation, requiring employees to adapt and acquire new skills, thus enhancing job maturity.

Promoting continuous learning

Artificial intelligence facilitates access to smart learning systems and adaptive training, which contributes to the professional and functional development of employees.

Second: Testing the second main hypothesis regarding the impact relationship between artificial intelligence applications and job maturity

(“There is a significant impact relationship of artificial intelligence applications on job maturity.”) To prove the validity of the impact hypotheses, simple linear regression was used to assess the impact between the research variables. The table ( 7) presents the regression results.

Table 8 Regression Test Parameters for the Impact of Artificial Intelligence Applications on Job Maturity – Model Summary

Table 9 Coefficients for Job Maturity

The results from Table (7) show that the standardized impact coefficient of artificial intelligence applications on job maturity is (0.71). This means that a one-unit change in artificial intelligence applications will lead to a 71% change in job maturity. This value is significant, as the t-test value is (2.922), which is statistically significant at the significance level (P-Value) of (0.000).

Additionally, as shown in Table (7), the R-squared value (R²) is (0.52), which means that 52% of the changes in job maturity are due to changes in artificial intelligence applications, while the remaining 48% is attributed to the influence of other variables outside the current analytical model.

The F-test value is (8.838), which is statistically significant at the significance level (P-Value) of (0.000), indicating that the estimated model is significant overall.

Based on the above, the second main hypothesis is accepted, indicating that the use of artificial intelligence applications has a positive effect on the organization. This is beneficial for the administration, as it has used these applications in planning, job training, decision-making, and involving employees in a manner that aligns with the administration’s aspirations.

CONCLUSIONS AND RECOMMENDATIONS

This study concluded that artificial intelligence (AI) plays a vital role in enhancing intellectual capital within organizations by supporting administrative decision-making with accurate data and advanced analytics. It enables the analysis of large datasets and the prediction of future trends, thereby strengthening strategic planning. Moreover, AI contributes significantly to continuous improvement processes by identifying performance gaps and fostering adaptive capabilities that prepare individuals for new or evolving roles. One of the key findings highlights how AI stimulates creativity and innovation by offering specialized, interactive knowledge content that supports professional development. Additionally, automation—particularly through intelligent tools such as robots—has proven effective in enhancing individual productivity, improving operational efficiency, minimizing errors, and increasing work independence.

The findings also revealed a clear and significant role for artificial intelligence applications in promoting job maturity across all its dimensions. Among these, job training emerged as the most emphasized, underscoring the necessity for employees to continuously learn about and integrate new technologies into their work. A strong correlation was observed between the use of AI applications and the development of job maturity. Furthermore, the study confirmed a robust impact of AI across all four dimensions of job maturity, demonstrating the transformative potential of these technologies in shaping a modern, competent workforce.

Based on these insights, the study recommends that organizational leadership prioritize the implementation of AI technologies, particularly in areas like retirement processing, to ensure accuracy and speed. Continuous training programs should be developed and maintained to keep pace with emerging technologies and applications. Additionally, strategic work plans that highlight the importance of job planning should be established. A secure and reliable database infrastructure is also essential to guarantee fast access and efficient data management. Finally, it is imperative to invest in qualified and specialized personnel who possess the expertise necessary to effectively deploy AI solutions and enhance overall organizational efficiency and effectiveness.

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APPENDIX

A Survey Form

Dear respondent, This questionnaire includes a set of indicators related to the topic

“The Reflection of Artificial Intelligence Applications in Enhancing Job Maturity” – An Analytical Study of the Opinions of a Sample of Employees in the General Retirement Authority / Baghdad. This form is a scientific measurement tool used solely for research purposes. Your kind and accurate responses will significantly contribute to achieving the research objectives. Please note that the information provided will be used for academic purposes only, and there is no need to mention your name.

With sincere appreciation

First: Artificial Intelligence Applications

Artificial intelligence is a science based on mathematical rules, devices, and software assembled within computers that perform many functions and operations simulating human intelligence, though differing in speed and accuracy.

Please indicate your level of agreement with the following statements:

Second: Job Maturity

Job maturity represents the administration’s ability to understand the employees’ psychological readiness for a specific job, their awareness of their capabilities, and their ability to plan, gather information about job requirements and the labour market, receive training, and shape work-related attitudes and behaviours. It also reflects how they deal with changes, seize opportunities, and make decisions to progress and excel.

Functional Planning Dimension

Functional planning is a fundamental pillar of job maturity. It involves studying future expectations while identifying the skills, qualifications, and experiences one possesses to effectively implement career plans.

Job Training Dimension

This dimension aims to improve employees’ performance, develop their knowledge and skills, positively change their behaviours and attitudes, and raise morale to enhance performance and achieve organizational goals effectively.

Shaping Work Attitudes Dimension

This involves changes in employees’ behaviour, feelings, and attitudes through social learning, which may be altered by adding, removing, or modifying one or more of its components.

Decision-Making Ability Dimension

This reflects the practical application of employees’ abilities, knowledge, and capabilities, and how well they use them to make sound decisions. Effective use of such abilities is a true indicator of job maturity.