Keywords

Artificial Intelligence, Human Resource Management, optimization, SEM, UAE.

Introduction

The development of artificial intelligence (AI) technology expands the boundary of business practice, inducing the emergence and application of business intelligence (BI) that has promoted the transformation of information techniques to optimize business decision and operation. The concept of "intelligence + "emerged in China where the application of internet technology has been expanded to a much larger extent in social and business practices, as a trend of promoting deep integration of internet, big data, artificial intelligence and other new IT and techniques to stimulate business growth in the real economy (Y. Chen and Z. Lin 2021). HRM practices have a significant positive impact on employee performance in the public sector context of Malaysia. Similarly, a study found that HRM practices such as training and development, performance appraisal, and compensation and benefits have a significant positive impact on employee performance in the public sector context of Pakistan (A. M. Hubais et al. 2023). To encounter this new age challenges, practitioners are trying hard to shatter the constraints and work edge-to-edge to achieve higher performances. It’s an evidence that organizations desire to exploit maximum of their injected resources, but often fail to reap their actual potential. Developing resource-based capabilities stands out to be the most concerned aspect for the firms in recent times, and the same is studied by the previous scholars. Organizations try to build sustainable performance via proper blending of organizational capabilities and resources to maintain the equilibrium between operational and economic performance; this involves sustaining and expanding economic growth show the positive relationship between big data analytics and superior organizational performance (S. Gupta et al. 2020). Traditional HRM systems are unable to examine data linkages or estimate future developments using data that has already been recorded in the system. In order to broaden the HRM system's range of applications and experimental simulation have been done (P. William et al. 2023). HRM as a critical function that directs the work force, has been entrusted with the responsibility to drive initiatives that align with sustainability goals. The integration of AI tools into HRM processes presents an exceptional scope to achieve this target, underlining the tenets of Green HRM. Despite a varied array of AI applications centered on HRM employed within organizations, some countries still under problem. United Arab Emirate (UAE) is one of the 19 Middle East and North Africa (MENA) countries clustered into regions which previous researchers highlighted that lack of law enforcement, lack of stakeholder communication, lack of management commitment, lack of interests, corruption, and financial debts are some of the barriers vividly mentioned by many industries. Gaining an understanding of the intricacy of enterprise resource planning (ERP), is essential for enabling project managers and general managers to work together on everyday tasks (A. E. Sarker and M. H. Rahman 2020). The national government leadership of the UAE has considered a policy known as "Emiratization," which aims to increase the employment rate of Emirati nationals. The exceptionally low incidence of employment of Emirati nationals in the private sector compounds the policy conundrum facing the UAE government. This policy impasse resulted from the nation's distinct social and economic circumstances. Finding and keeping the appropriate people is becoming the competitive advantage required for businesses to perform and expand in an increasingly diverse and competitive labour market. It is also growing more difficult to locate the proper position. The UAE faces a shortage of skilled workers in certain industries, which can hinder growth and innovation. Scams, Frauds, and Unsolicited Offers in Retail, hospitality, healthcare, manufacturing industry has got different sections in its umbrella that covers the manufacturing of: metals, building materials, petrochemicals and chemicals, pharmaceuticals, food, beverage and consumer goods, aerospace and defense equipment, industrial equipment. real estate & construction: Through HRM operations including hiring, performance reviews, on boarding and off boarding procedures, employee engagement programs, talent development and training, workforce planning, and HR Chabot’s and virtual assistants, AI will help businesses become more efficient in UAE Robotics, 3D printing, transportation, tourism and travel, real estate, health and hospitality, hotel chains and restaurants, cleaning services, security services, and FinTech industries in the country. Through identifying new opportunities, highlighting potential hazards, disclosing additional industry insights, and fortifying decision-making frameworks, BI could significantly contribute to maximizing organizational effectiveness. Major business organizational benefits; Facilities on demand, Data and network information, Source pooling, Efficient cost, vital to building good consumer relationships, seeks to fulfill employees unsatisfied desire, etc. Therefore this research aims to synergizing the AI-HRM with organizational performance metrics.

Literature Review

According to the frontiers of business practice are being pushed by the advancement of AI technology, which has led to the rise and use of business intelligence (BI) and the transformation of information techniques for the purpose of optimizing corporate operations and decision-making. In contemporary businesses, managing human resources is essential to retaining and developing high-calibre employees. For sales, marketing, and HR purposes, it is preferable to create a prediction model of the yearly hourly cost per employee in large maintenance organizations to optimize the business performance as well as the employee engagement and satisfaction. The deployed annual hourly cost per employee model is fed by past data and does not need any critical prior knowledge or assumption on the current year(F. Abbracciavento et al. 2020). Mechanical, analytical, empathetic, and intuitive AI dimensions make this technology a valuable tool to support organizations by providing a vast amount of data and automating many steps of business processes to speed up and optimize all phases of HRM(P. Li 2023). HR Metrics are also a quantitative algorithms applied to job data that aid in decision making in areas such as recruitment, selection, transferability, promotion, training, and development. It’s a sophisticated, user-friendly approach to analysing job data that have the potential to meet the needs of human resource professionals in today's dynamic workplace table 1 (McEntire et al. 2006).

Table 1 Human Resource Metrics in Different Levels
1st level is efficiency metrics	2nd level human capital metrics	3rd level cost benefit metrics	4th level impact or strategic
Performs basic administrative tasks, productive and cost.	resources allocation	Intended effect on the people or talent pools, measure the value of human capital	financial,customer, process, and people HR's impact on business outcomes.
Cost per hire, Employer-sponsored health plan cost per employee, HR expense per employee, and Yield ratios	Average time to hire= (1st candidate time to hire in days + 2nd candidate time to hire + nth candidate time to hire)/ Total number of jobs	Expense factor: (Operating Expense / Total Full-time Equivalent (FTE) Profit/employee:(Revenue−Operating Expense/Total FTE) Labor cost factor:(Compensation + Benefit Costs)/FTE Human capital value added revenue: (Operating Expenses−(Compensation + Benefit Costs) / FTE	Firm salary/competitor salary ratio, Number and quality of cross- functional teams, Progression of employees through development plans, and Percentage of total salary at risk.

Performance metrics such as Operational, Financial and Relational are compatible with the firm’s strategic orientation i.e. Defender or Prospector, the synergy between the metrics and strategy pursued will enhance organizational performance. Further, the synergy will enhance organizational performance on those indicators Financial or Customer-focused table 2 (Kathuria & Lucianetti 2024).

Table 2 HR Decision Making Framework and Structural Equation Modeling Steps
MANAGEMENT DECISION MAKING LEVELS				PLS-SEM ANALYSIS STEPS
	Benefits enrolled	Recruitment efficiency	HR Supply analysis	Convergent validity
STRUCTURED
Degree of problem	Applicant screening	Monitoring succession plan	Strategic & workforce planning	Discriminate Validity
UNSTRUCTURED
	Managing sudden absenteeism	Implementing HRIS	Mergers and acquis ions	Inner model

The orchestration of talent acquisition arises in the turbulent expanse of the modern business ecosystem as a pivot that profoundly determines the course of corporate success, not just as a routine task (J. H. Dulebohn and R. D. Johnson 2013). The ability to attract, assess, and secure elite personnel becomes increasingly crucial when industries experience seismic transformations brought on by the relentless march of technological advancement and escalating competitive currents. Industry changes are becoming more intense as a result of the relentless march of technology. Moreover, the image of personalized experiences akin to tailored processes resonates deeply (M. A. Kassem 2022). It suggests that AI’s competency in curating individualized interactions may usher in a paradigm where candidate engagement flourishes (P. N. Tinguely et al.2023; K. Tongkachok et al. 2023). This personal touch encapsulates AI’s role in fostering engagement, painting a picture of a recruitment journey where candidates feel distinctly valued. The collective discourse thus weaves a tapestry that champions the transformative prowess of Cognitive AI and ML in redefining the very landscape of recruitment. This transposition, characterized by heightened accuracy, streamlined efficiency, precision, and immersive engagement, heralds a new dawn for talent acquisition (J. Filipe et al. 2023).Because AI is a significant by product of the advancement of science and technology, which has been actively supported in many different sectors of the economy and society. It has produced positive application outcomes and offers a wide range of future development possibilities. In the full application of enterprise human resource management work, AI reflects a great advantage, realizing the optimization of organizational structure and HRM innovation, improving the overall work efficiency, etc. It can provide personalized training content for employees by analysing their learning needs and interests and an adaptive training experience through intelligent learning platforms and tools. While the challenges in the application of AI in enterprise HRM are Data Security/Privacy issues, limitation of technological algorithm, unemployment and skills retraining, moral and ethical issues, human-computer collaboration and change of management modes etc. Since the solution should explore new models compatibly along with ethical regulations and easy to users mode (L. Han 2024).

Algorithm-based technology is thought to produce more productive and profitable outcomes, as well as reducing conventional biases. AI has been widely deployed in the company's HR management processes, including the hiring process, interviews, coaching, advancement, remuneration, and staff effectiveness appraisals. It is now increasingly employed in HRM tasks like coaching, skill building, assessment, the hiring, and the reward system (K. Islami and D. Sopiah 2022; J. Meijerink et al. 2022). AI proponents have envisioned a future in which intelligent computers would take over routine activities from humans, freeing people up to pursue creative endeavours. The impact of technology on the global economy, businesses and societies is exponential and has enabled unprecedented advancement, leading experts to predict that the upcoming decade will witness tremendous changes in the nature of work owing to AI. Further, while AI-systems facilitate problem-solving, it is critical to account for the national cultural context for business decision making. AI-based products and solutions are primarily data-driven and data is highly contextual. As there are cultural differences across countries, this may be interesting to investigate in more depth( A. Jaiswal et al.).

Green Recruitment centers its efforts on implementing a paperless recruitment process to minimize its environmental footprint by adopting eco-friendly practices aimed at mitigating environmental. Organizations are using Internet facilities to drive major green practices, which include online interviews, company websites, job portals, video conferencing methods,etc. (E. J. Janso 2024; S. K. Singh et al.2020). AI based Design platform consists of four modules including data preprocessor, data analyzer, optimizer and modeler. For importing the data types, it can be a real-time data through API and/or the big-data from the private, Social Networking Services (SNS) and government. An optimization based on competence and MILP to place the right applicant with the right company. This application's goal is to provide all firms with qualified job seekers depending on their preferred skill set and potential working location. As a decision support tool, AID works to increase the proportion of candidates who are matched with employers (D. S. Lee and C. K. Ahn 2020).

Through efficient hiring and selection procedures, onboarding, career and development, performance tracking and management, learning facilitation, and talent management, AI is streamlining remote work. Following the pandemic, AI-driven technologies such as data mining, predictive analytics, big data analytics, NLP, intelligent robots, ML, VR and AR, etc. have made it possible to manage HRM practices efficiently, which has improved employee well-being, automation, and cost savings(A. Mer and A. S. Virdi 2023; R. Pillai and K. B. L. Srivastava 2024). Any organization that uses BI and decision-making must overcome obstacles like poor planning, a lack of resources, incapacity to take risks, and a lack of readiness. In order to improve organizational intelligence and decision-making analysis, scholars suggest a paradigm for Optimized Data Management utilizing Big Data Analytics (ODM-BDA). In this context, a backtracking technique is developed to improve plan failure and risk-taking abilities. The ODM-BDA framework uses the steep optimized technique to enhance the training program and handle finances(Y. Niu et al. 2021).

Current disruptive technologies include AI, Robotics, IoT, Autonomous Vehicles, 3D Printing, Nanotechnology, Biotechnology, Materials Science, Energy Storage and Quantum Computing (K. G. Priyashantha et al. 2024). AI can reform the structure of HRM and be an integrated system that delivers excellent results with the aim of enhancing performancemanagement, workforce planning, people analytics, virtual assistants for self-service or HR service delivery, career patching, leadership, and coaching(E. H. A. L. Qahtani and M. A. K. Alsmairat 2023).

Businesses are depending more and more on the abilities, energy, and inventiveness of humans while simultaneously trying to fully embrace digitization as a crucial component of their operations. There is a need for more theory based and empirical explanations of how HR and IT create synergistic effects to improve firm performance like digital HR strategy. A digital HR strategy refers to a set of HRM practices that cover the full employee lifecycle and where technologies are integrated. It involves managing its people to optimal effect by selecting, using, leveraging, and merging digital technologies with the firm’s HR practices. Drawing on the sociotechnical and IS business value perspective along with previous research on digital business strategy (i.e., business-IT fusion), we propose that the fusion of technologies and HR practices generates better results than those of the isolated elements on their own and therefore that a firm’s digital HR strategy enables companies to improve e firm performance (L. Ruiz et al. 2024).Therefore, the future versions of generative AI able to perform non-routine, highly cognitively complex tasks autonomously once the technology surpasses human abilities in terms of creativity, synthesis, and sense making. In order to optimize the corporate HR information system, IoT first-off technology is used, together with the system demand phase, with edge control in mind. Initially, the hardware and software system are set up, and only then the communication models between the edge layer of the system and other parties are created edge layer, and the business type-driven connection selection method (Y. Saleh Ibrahim et al.2022). To solve the issue of reducing the processing delay of tiny jobs in IoT situations, a centralized multitasking scheduling approach is suggested that uses a single edge node advanced technologies, AI and robotics, and their sub-themes allows us to understand how HRM is progressively shifting from eHRM towards an HRM defined by intelligent automation(D. Vrontis et al. 2022).

Structural Equation Modeling

SEM allows researchers to account for measurement error in observed variables as well as unobservable ones evaluated indirectly through indicator variables. The partial least squares path modeling or partial least squares structural equation modeling (PLS-PM, PLS- SEM) is a method for structural equation modeling that allows an estimation of complex cause–effect relationships in path models with latent variables. The five most frequently mentioned reasons using PLS-SEM are: small sample size, nonnormally data, theory development and exploratory research, high model complexity, predictive study focus, and formative measures (M. Sarstedt et al. 2022).

SEM has the ability to model theoretical concepts, to consider various forms of measurement errors, and to model relations between theoretical concepts ensure that this method is frequently and widely applied. It is composed of two parts structural model and theoretical concepts (T. Schamberger). Various disciplines of management science like marketing, strategic management, psychology, etc., have deployed Structural Equation Modeling (SEM) for data analysis. There are two types of SEM techniques: one is Covariance-Based (CB) SEM and the other is Partial Least Squares (PLS)-based SEM. When the relationship between dependent and independent variables is exploratory in nature, PLS- SEM is more suitable as opposed to CB-SEM for confirmatory studies. Data fabric, which is a useful way of organizing data, affects decision-making and risk assessment through Structural Equation Modeling (SEM) using IBM AMOS soft-ware. Researchers make sure their measurement model met the requirements for construct reliability, discriminant validity, convergent validity, and one-dimensionality before creating a structural model. The conceptual SEM analysis ensures business success by establishing the relationship between decision-making, risk management, and the data fabric (A. Abu Rumman and L. Al-Abbadi 2023). Achieving a balanced organizational workforce requires human resource planners to adjust the supply of optimization (M. Amorim-Lopes et al.2021)

This revealed that green human resource management significantly and positively affects perceived green organizational support and organizational sustainability (M. Y. Yusliza and D. W. S. Renwick). Human resource network model to describe the relationship among persons, the structure of organization positions and the matching between employees and positions table 3.It is worth mentioning that the heterogeneous network is used to construct the model of the human resource adjustment problem (D. Zhao et al. 2018).

Table 3 Literature Analysis Summary of Previous Studies
Method	References
Statistical analysis, relative important index method, and probability impact matrix analysis were carried out to classify and rank the risk factors.	Abu Rumman , & Al-Abbadi, (2023)
multivariate analysis of variance (MANOVA) & ANOVA test	Gupta, et al.(2020)
Myers-Briggs Type Indicator (MBTI)	Pillai, & Srivastava, (2024).
An explorative qualitative analysis	Alsharari, (2022).
Navy job analysis, O⁎NET	Amorim-Lopes et al.(2021)
algorithmic HRM	Niu, et al.(2021)
grounded theory approach)	Tinguely, et al.(2023)
mediation model	Zhao, (2018)

In this case validation of the new model is going to be better checking the simulation through system dynamics simulation software (M. B. Alem 2024).

Methodology

Study Design

Exploratory study along with mixed research approaches conducted, 756 research samples were used to address the data gathered from the 42 selected literatures, drastically in the 23 research factor checklists. Senior HR managers from IT, UAE business owners, consulting, services and E-commerce companies through a semi structured and Likert scale questionnaires. In order to prioritize the factors Relative Importance Index (RII) used, Structural Equation Modeling (SEM) and System dynamics (Venism) simulation software were also applied for analysis. This study conducted on 756 experts online but successfully collected only 536 responses and analysed. The group consists of: various ages, different exposure and experiences, separated regional location, multi professionalism and work levels figure 1.

Retail, hospitality, healthcare, manufacturing industry, Marketing media, Real Estate & Construction, Transportation, Tourism and travel, Cleaning services, Security services, robotics and FinTech are the majority business types over UAE. To determine the necessary survey size for an unknown population size with a 90% confidence level, 50% standard of deviation, a 3% margin of error a scientific standard. For 90% confidence, use the z-score would be 1.65. This means that: z = 1.65 e = 0.03 p = 0.5

Practically 756 respondents were sampled and distributed online. However properly responded and collected questions are 536

Background of the Respondents

A total of 756 questionnaire sets were distributed to randomly selected oil and gas companies operating in Yemen; of which 536 were received, and all were deemed acceptable table 4.

Table 4 Demographic Analysis of Respondents
Years of Experience	Age of the Participants	Based in region	Field of expertise	Job Title of Participants
The participants have	About 64% of the	Sub-	Management	In this study, the sample was
more than 5 years of	participants are	Saharan	(25%)	carefully selected to have a
experience	the largest number	(23%)	Engineering	direct relationship with the
HRM in various	of participants	MENA	(10%)	HRM, and the job titles were
industries. Among 536	aged between	(33%)	Health (5%)	distributed to
respondents, (55.50%)	30 and 50 years,	West	Social science	include the administrative
had above 10 years of	and the second	(12%)	(7%)	side, procurement, contracts,
experience, (15.80%)	category, aged	East (10%)	Humanity (5%)	designs, engineers and
had 5 to 10 years of	20–30 years, with	Australia	Technology	supervising
experience, (21.2%)	36%.	(2%)	(46%)	the site. The respondents were
had 20 to 30 years of		Central	Others (2%)	asked to specify their job title
experience, and		Asia		as junior, senior, middle level,
(7.50%) had more than		(20%)		fresh, retrained, unemployed,
30 years of experience				CEO, entrepreneur, jobseeker
				etc.

Results

Table 5 ranks primarily the leadership style 92.8 %, this indicates that organizational as well as top national decision-making bodies. Next to that the mean RII result shows HRM strategic implementation levels for the long run and sustainable development perspectives. All in all the minimal rank (23^rd) even indicates 74.4% which super high range. Therefore, which implies every factors analyzed here in this research article has a close and significant impact on industrial performances in UAE and all the entire world.

Table 5 Relative Importance Index Analysis of Gathered 23 Questioners
No	Mediating Factors	Relative Importance Index			Mean	Rank
		HR Metrics	AI	Modern HRM
1	Wage Protection System (WPS)	0.869	0.828	0.94925373	0.882	17th
2	Lack of professional employees	0.932	0.824	0.89440299	0.883	16th
3	Payment delays and issue	0.806	0.856	0.85858209	0.84	15th
4	Value-added tax (VAT) system	0.78	0.658	0.84962687	0.763	22nd
5	Social and Cultural Challenges	0.931	0.828	0.9761194	0.912	6th
6	Linguistic Challenges	0.958	0.836	0.92313433	0.906	8th
7	Recruiting system	0.974	0.801	0.95634328	0.91	7th
8	Social-cultural factors	0.934	0.834	0.94776119	0.905	9th
9	Qualitative composition	0.95	0.804	0.94179104	0.899	12th
10	Personnel assessment	0.91	0.781	0.95074627	0.881	18th
11	Staff training and development	0.66	0.725	0.94477612	0.777	21st
12	Young professionals and youth	0.932	0.799	0.95447761	0.895	14th
13	Personnel administration	0.953	0.831	0.96567164	0.917	3rd
14	Staffing	0.978	0.8	0.92350746	0.901	10th
15	Personnel reserve	0.79	0.734	0.90149254	0.808	20th
16	Appointment of candidates	0.993	0.804	0.90186567	0.9	11th
17	Governmental interference	0.782	0.576	0.87425373	0.744	23rd
18	Odds with contemporary HRM imperatives	0.996	0.8	0.89626866	0.897	13th
19	Inappropriate leadership styles,	0.912	0.888	0.98395522	0.928	1st
20	Confrontational labor relations,	0.958	0.798	0.98470149	0.914	5th
21	Lack of Internet infrastructure	0.765	0.813	0.89962687	0.826	19th
22	Skills, knowledge, and competencies of the workforce	0.996	0.804	0.94402985	0.915	4th
23	Strategic levels that HR occupies	0.995	0.804	0.97052239	0.923	2nd

The common factor Structural equation model of the above 23 mediating factors in the , X=t+e equation (3)

y- observed variables (X₁, X₂,…..... ,X₂₃)

t-true score

e-error or performance gap in the model of figure 2 vividly presented 6 main parts. Firstly, shared circles represented the three synergetic approaches in the designed scenario, second HRM true score loop (existing performance of organizations) in terms of measured variables. Third efficiency quantitatively, fourth 23 mediating variables as in the IRR check lists table shown. Fifth one also error (e) = (100-true score) and the last one feedback loop once the error technically identified, it should easily monitor by triple combination aspects.

Then, in order to validate the structural equation modeling Venism PLE*64 inputs were; initial value 0

final value 100 and normal step

Dmnl (fraction) units

The software analysis outcome in figure 2 shows that when the implementation of three scenarios are realized the performance of each individual per month improvement demarcation, it could be directly proportional. Because the software by itself is industrial- strength simulation for improving the performance of real systems.

Discussions

The proposed methodology has been applied to a case study involving HRM planning in UAE, but its scope far extends HR planning, being especially suited for addressing strategic and policy planning problems that to another world. Mathematical and software programming models have been widely used to assist such planning, but the way uncertainty is usually considered in these models entails methodological and practical issues and often disregards radical yet plausible changes to the future. The methodological approach makes use of foresight and scenario planning concepts to build tailor-made scenarios and scenario fit input parameters, which are then used within mathematical programming models are sensitive to input parameters. The challenges of global change for HR: set goals and objectives in terms of changes in the economy and new trends for business, digital transformation, new technologies and super-technologies, speed and depth of change, endless information flow, new generation of employees and their expectations, new business models, changes in customer experience, cultural changes. All the above mentioned should contribute to actualization of HR functions and development of HR as a new source of company value(A. Aghahosseini et al. 2020; A. Alizadeh 2022; N. Alsharari 2022) . It entails Stages of change in HRM are; Digitalization of HR management system and automation. The analysis of HR problems of evaluating the effectiveness of personnel management services (departments) of JSC “Russian Railways” policy and HR management strategy allowed us to create a set of HR metrics that consider the main directions of the company’s HR processes. Each department has a specific development, which is explained by the production conditions, organization and management features. The RStudio development environment based on k-means clustering was chosen as a tool for analyzing key indicators(S. I. Ashmarina et al.). Understanding how HR concepts and designs which are frequently linked to Western management principles are used in developing nations has grown more interested in the evolution of HRM in sub-Saharan Africa. Organizations have over the years invested a lot in information systems to ensure that their businesses function effectively and efficiently. The point is that organizations are expected to use technology on people management to achieve and sustain competitive edge leading to organizational performance. That is because technology adoption and HRM are highly significant for high performance work systems. Thus, digital technology has reinforced the internal and external competition within the global market (J. Baba et al.). Because of this vast increase in the availability and reach of HR data, human resource professionals have the opportunity to more effectively employ HR metrics to assess HR in terms of its efficiency, effectiveness, and impact and service. Increasingly managers and employees are utilizing decision support systems or business intelligence capabilities available as part of HRIS that include metrics and analysis tools to help solve key HR problems. A metric is an accountability tool that enables the assessment of a function's results. With respect to HR, a primary idea has been that through metrics, HR units could build a business case for their work and this could contribute to an increased partnership between HR and the broader business functions. Scholars and practitioners have defined multiple levels of HR metrics.

Conclusion

This study was conducted with 536 participants of the most commonly available industrial organizations of the world in UAE based as a sampling of populations. The relative importance index (RII) method was applied to prioritize the project risk factors. Leadership styles shows 0.928 in RII mean result which is the 1^st rank. The 23 mediating factors were used to develop a risk map for abundant industries in UAE and identified checklists based on foreign investors complain. The second rank Strategic levels that HR occupies and overall RII analysis shows the HRM process needs an UpToDate kind of systems.

It clearly shows that Social-cultural factors, VAT, Staff training and development, Appointment of candidates and lack of professional employees are were the most categorical factors effects in lack of organizational performances with RII=0.74.4, (Governmental interference) with last twenty third factor and the first factor is 92.8% (Inappropriate Leadership styles), while the (Skills, knowledge, and competencies of the workforce) is the fourth factor percent with RII=91.5. Thus, the ranking of factors is determined according to their relative importance to the success of the project. The difference between all these 23 factors only 18.4 %.

It is not possible to sustain in a singularly situations in this dynamic world as all the factors implication. It will incur an expected huge lose and give them the same attention, time, effort and cost. The new models arranged the appropriate synergies to the priority and the rankings in influencing the success of every organizational goal.

In conclusion, this study demonstrates that Artificial intelligence along with HRM has a significant impact on organizational performances in United Arab Emirates and the entire world. The findings suggest that policy makers, Human resource managers, industry owners or any responsible stakeholders should take it in to account and implement accordingly. However in remarks the research would strongly recommend to you that do not replace humans by AI rather uses as an assistance for the recruiting and handling processes. Therefore it would be an effective strategy to address employee turn overs, deducted performances and frequent complaints of organizations in UAE. Future research should investigate the long-term (longitudinal research effects of AI on organizational HRM and explore potential moderating factors.

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Received: 25-Sep-2024, Manuscript No. AJEE-24-15299; Editor assigned: 26-Sep-2024, PreQC No. AJEE-24-15299(PQ); Reviewed: 01-Oct-2024, QC No. AJEE-24-15299; Revised: 07-Oct-2024, Manuscript No. AJEE-24-15299(R); Published: 16-Oct-2024