Assessing the Impact of Artificial Intelligence on Organizational Performance: A Comparative Study of Public and Private Enterprises

 Corresponding author: Abdullahi Umar Nasiru; ORCID ID: 0009-0004-6010-911X Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Assessing the Impact of Artificial Intelligence on Organizational Performance: A Comparative Study of Public and Private Enterprises Abdullahi Umar Nasiru 1, *, Olalekan Olawale 2, Kingsley Senior Sarpong Abeyie 3, Isiaka Oshobugie Ibrahim 4, Ismail Abiodun Jamiu 5 and Mariam Abdullahi 6 1 Department of Economics, Faculty of Social and Management Science, Yobe State University, Damaturu, Yobe State, Nigeria. 2 Department of Public Administration, Faculty of Administration, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria. 3 Department of Accounting and Finance, KNUST School of Business, Kwame Nkrumah University of Science and Technology, Ghana. 4 Department of Engineering Management, Faculty of Science and Engineering Technology, University of Houston Clear Lake, USA. 5 Department of Science Education (Mathematics), Faculty of Education, Ekiti State University, Ado-Ekiti, Ekiti State, Nigeria. 6 Department of Human Resource Automation / process Improvement, Banking / Oil and Gas / Fintech. World Journal of Advanced Research and Reviews, 2025, 28(02), 990-1002 Publication history: Received on 01 October 2025; revised on 08 November 2025; accepted on 10 November 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.28.2.3802 Abstract This study investigates the impact of Artificial Intelligence (AI) on organizational performance, comparing public and private enterprises. The research aims to assess how AI adoption influences key performance indicators such as operational efficiency, decision-making, innovation, employee productivity, and customer satisfaction. Using a descriptive and comparative research design, data were collected from 120 respondents 60 from public enterprises and 60 from private enterprises through structured questionnaires. Descriptive and inferential statistical analyses, including correlation and regression, were employed to evaluate relationships between AI adoption and performance outcomes. The findings reveal that private enterprises demonstrate a significantly higher AI adoption rate (65%) compared to public enterprises (30%). Mean performance scores indicate that private enterprises (4.4) outperform public enterprises (3.5) across all performance dimensions. Correlation analysis shows a strong positive relationship between AI adoption and organizational performance in both sectors (r = 0.81 for private enterprises; r = 0.62 for public enterprises). Regression results further confirm that AI adoption explains 65% of performance variations in private enterprises and 48% in public enterprises. The study concludes that AI serves as a strategic catalyst for enhancing efficiency, innovation, and decision-making, with private enterprises reaping greater benefits due to their flexible structures and innovation-oriented management culture. It recommends increased investment in digital infrastructure, employee training, and supportive policy frameworks to strengthen AI readiness, particularly in public enterprises. Keywords: Artificial Intelligence; Organizational Performance; Public Enterprises; Private Enterprises; Innovation; Efficiency World Journal of Advanced Research and Reviews, 2025, 28(02), 990-1002 991 1. Introduction Artificial Intelligence (AI) has become a disruptive innovation in the management of organizations and has transformed how decisions are made, operations are conducted, and strategic performance in any industry. Over the past few years, both the state and commercial companies began to rely on AI to make their operations more productive and effective and provide services to customers (Makridakis, 2017; Dwivedi et al., 2021). The digital transformation pace, which is growing more advanced all over the world, especially following the COVID-19 pandemic, has only intensified the adoption of AI in the organisational processes to enhance resilience, competitiveness, and efficiency (Brynjolfsson and McAfee, 2017; Agrawal, Gans and Goldfarb, 2019). The broad definition of organisational performance is efficiency, effectiveness, profitability, innovation, and service quality (Venkatraman and Ramanujam, 1986). The use of AI in the business world has been driven by profit maximisation, automation of processes and interaction with customers in many cases (Chui et al., 2018). On the contrary, in state-owned enterprises, the main focus of AI implementation is improving the efficiency and transparency of the administration, responsibility, and satisfaction among the citizens (Wirtz, Weyerer and Geyer, 2019). Although these are common objectives, the structural flexibility and resource allocation and innovation culture between the public and private sectors could affect the scale and scope of the AI effect on performance (Zhang et al., 2020). Moreover, research indicates that organisations that successfully implement AI in their management operations can attain a strategic advantage by making decisions and predicting outcomes based on data (Davenport and Ronanki, 2018; Shrestha, Ben-Menahem and Krogh, 2019). Nonetheless, such issues as a shortage of skills, ethical issues, confidentiality of data, and high implementation prices remain to persist especially in developing countries (Nkwo, 2022; Akinwale and Adebayo, 2023). The realisation of the variability of these challenges and advantages of public versus private enterprises offers a useful contribution to the enhancement of AI utilisation to the benefit of organisations. The interest in AI-based transformation has increased in both industries in Nigeria and other developing economies as a result of the necessity to become efficient and competitive (Olayemi, 2022). However, there is still a lack of empirical studies on the effects of AI on the performance of organisations in both public and private businesses. This paper thus aims at filling this gap by evaluating the effects of AI adoption on performance in the two sectors. 1.1. Statement of the Problem Although the field of AI promises tremendous opportunities to enhance organisational performance, numerous organisations, especially those associated with the state, have difficulties in integrating technology in their operations because of inadequate infrastructure, low digitisation rates, and change reluctance (Almeida et al., 2020; Wirtz and Muller, 2019). Conversely, individual companies can be more adaptable and have greater ability to innovate but can be focused on the profitability of AI implementation, overlooking the issue of ethics and governance (Bughin et al., 2018). This difference poses worrying questions concerning the impact of AI on organisational performance in the two industries differently. Although research on the use of AI and innovation has been abundant, comparative research on how AI has dissimilar effects on government and privately governed organisations, particularly in the emergent economy like Nigeria, is lacking. The gap in knowledge thus proposed by this study is to explore how much AI can help in performance outcomes in these unique organisational environments. 1.2. Research Objectives The main point of the research is to evaluate the influence of Artificial Intelligence on the performance of organisations in the government and privately owned businesses. The objectives to be achieved are: • Interview the rate of AI implementation within both public and private businesses. • Assess the correlation between the adoption of AI and the performance of organisations within both industries. • Compare the effects of AI implementation on efficiency, innovation, and service delivery among the public and the private enterprise. • Specify the obstacles and opportunities relating to the introduction of AI in the two industries. 1.3. Research Questions • What is the adoption rate of AI in both public and private organizations? • What is the impact of the adoption of AI on organisational performance in the two spheres? World Journal of Advanced Research and Reviews, 2025, 28(02), 990-1002 992 • How do inefficiencies, innovation, and service delivery by AI vary between public and non-public businesses? • What are the threats and opportunities facing the implementation of AI in the two industries? 2. Literature review 2.1. Conceptual Review Artificial Intelligence (AI) is a concept in which computers and robotics are employed to handle the majority of tasks currently performed by humans (2.1.2). Artificial Intelligence (AI) is a type of computer systems that could make decisions traditionally based on human intelligence, including reasoning, learning, perception, and solving problems (Russell and Norvig, 2021). AI has a number of subfields, such as machine learning, deep learning, natural language processing and robotics (Agrawal, Gans and Goldfarb, 2019). AI technologies can be applied in organisations to automate and support decision-making based on data and forecasts which improves the performance outcomes (Davenport and Ronanki, 2018; Dwivedi et al., 2021). The AI usage is permeated throughout industries, such as automated customer services and strategic prediction and human resources (Makridakis, 2017). AI facilitates the e-government, citizen interaction, as well as administrative transparency in the public sector; innovation, competitiveness, and cost-cutting in the private sector (Wirtz, Weyerer and Geyer, 2019). 2.1.1. Organizational Performance Concept. Organisational performance is the capacity of an organisation to accomplish effective and efficient objectives. Venkatraman and Ramanujam (1986) define performance as a combination of financial (profitability, increase in revenues), and non-financial performances (innovation, customer satisfaction, employee engagement). One of the most recent developments in contemporary businesses is an association of performance with technological potential and flexibility (Brynjolfsson, McAfee, 2017). It has been demonstrated that AI is able to boost performance through the improvement of the quality of decisions, the decrease in operational costs, and higher productivity (Chui, Manyika and Miremadi, 2018). Nonetheless, the results of performance between a public and a private organisation are considerably different because they differ in terms of their structure, resource distribution, and motivation (Zhang, Zhao and Kumar, 2020). 2.1.2. Artificial Intelligence in Governmental businesses. The common AI uses in public enterprises are aimed at enhancing the efficiency of the administration, data processing, and service provision (Wirtz and Muller, 2019). The application of AI in governments is used to make policy, egovernment, and predictive analytics in government in the context of public health, education, and security (Mikalef et al., 2021). Nonetheless, the barriers to large-scale adoption of AI include limited funding, bureaucratic inflexibility, and ethical limitations, which affect the functioning of public organisations (Nkwo, 2022). United Nations (2022) emphasizes that AI-based governance improves transparency and accountability yet poses the problem of privacy, inclusiveness, and bias in the algorithm. 2.1.3. Artificial Intelligence in the Private Enterprise. The main aim of companies using AI in the sphere of the private enterprise is to improve its competitiveness and profitability. The AI systems streamline the business activity, marketing policies and relationship management with customers (Bughin et al., 2018). Companies use AI to predict consumer behaviour, trends, and supply chains (Davenport, Guha, Grewal and Bressgott, 2020). The McKinsey Global Institute (2021) states that organisations that have incorporated AI in their business models record up to 2025 per cent efficiency. However, the implementation of AI must be successful on leadership commitment, organizational culture, and digital readiness (Mikalef et al., 2020). 2.1.4. Comparative Perspective: Public vs. Private Enterprises Public and private enterprises differ fundamentally in their objectives and operational contexts. Private firms focus on profitability and innovation, while public institutions prioritize service quality and accountability (Hodgkinson et al., World Journal of Advanced Research and Reviews, 2025, 28(02), 990-1002 993 2017). Consequently, AI’s impact on performance varies private enterprises exhibit faster adoption rates and higher returns on investment, whereas public enterprises demonstrate gradual but socially oriented improvements (Wirtz et al., 2019). Comparative studies (e.g., Zhang et al., 2020) reveal that while both sectors experience positive outcomes from AI, private enterprises often outperform public ones in efficiency and adaptability due to fewer regulatory constraints. 2.2. Theoretical Review 2.2.1. Resource-Based View (RBV) Theory The Resource-Based View (RBV) suggested by Barney (1991) argues that the firm attains sustainable competitive advantage when it practices a strategic exploitation of its resources that are valuable, rare, inimitable, and nonsubstitutable (VRIN). The example of AI capabilities is such strategic resources, where Grant (2019) shows how they improve the process of decision-making and lead to innovation. In both the public and private businesses, the concept of AI is envisioned as an intangible commodity that increases the efficiency of operation and knowledge management. Nevertheless, the success of AI implementation depends on the presence of the corresponding resources, such as human resources, electronic infrastructure, and the presence of an organizational culture that stimulates learning (Mikalef et al., 2020). As a result, the RBV claims that organizations that are able to combine AI with human experience achieve high performance outcomes. 2.2.2. Framework Technology-Organization-Environment (TOE) Framework. Technology-Organization-Environment (TOE) framework proposed by Tornatzky and Fleischer (1990) describes the mediation process in adoption of technological innovations by three contexts interlinking with each other, namely, technological readiness, organizational structure, and environmental pressures. The regulatory requirements and resource limitations in the case of public enterprises often hinder the use of AI, but in the case of a private enterprise, which is forced to compete with others, it is more likely to adopt innovative technologies (Baker, 2019). Therefore, the TOE framework provides a conceptual framework of comparing the preparedness of levels and external factors that precondition the development of AI adoption and the related performance outcomes in the two industries. 2.2.3. Socio-Technical Systems Theory The dynamic interaction between social and technical subsystems of organizations is the theme of the Socio-Technical Systems Theory as presented by Trist and Bamforth (1951). The efficiency of the AI implementation requires a moderate configuration between the technological possibilities and the human factors including skills and competencies, organizational culture, and ethical issues (Dwivedi et al., 2021). This theory gives emphasis on the fact that the enhancing performance of a particular organization cannot be supported by improved efficiency of technology alone except that it is reinforced by strong social systems and flexible organizational culture. 2.3. Empirical Review Numerous empirical studies have been carried out concerning the correlation between AI adoption and organizational performance. According to Makridakis (2017), AI increased productivity and speeds up the decision-making process in manufacturing and service industries by a considerable margin. Likewise, Davenport and Ronanki (2018) determined that AI increases organizational agility via automation of processes and a deeper understanding of customers. The study by Wirtz, Weyerer, and Geyer (2019) involved the evaluation of adoption of AI in European public administrations, and they found significant improvements in the delivery of services to the population and policy implementation with reference to ethical and legal barriers limiting the adoption. Conversely, Bughin et al. (2018) studied 400 samples of private companies and found that there was a strong positive relationship between AI investment and innovation and profitability. In the Nigerian reality, Olayemi (2022) and Akinwale and Adebayo (2023) reported the growing adoption of AI in the government and non-government sector, but the lack of infrastructures and a low level of digital literacy restrict each other. According to their findings, private ventures have stronger performance gains due to their flexibility in operations and accessibility of capital. World Journal of Advanced Research and Reviews, 2025, 28(02), 990-1002 994 More recently, Dwivedi et al. (2023) revealed that the organisations that combine AI and human-focused strategies achieve better productivity and innovation results in comparison to the organisations that solely apply automation. All in all, these empirical studies suggest that although AI contributes to the organizational performance in the sphere of both the process publics and the process privates, the scope of influence is conditioned by the contextual parameters such as the endowment of resources, the organizational structure, and the strategic objectives. 3. Methodology 3.1. Research Design The proposed study assumes the comparative descriptive survey design, which is considered appropriate when considering the patterns of relationships and dissimilar impacts between two separate groups, i.e., public and private enterprises, within the realm of Artificial Intelligence (AI) use and its impact on the organisational performance. The descriptive approach will help to gather the detailed, quantifiable information in a systematic way, and the comparative aspect will allow conducting a rigorous assessment of the differences in the adoption rates and performance outcomes (Creswell and Creswell, 2018). The design facilitates objective evaluation of the effects of AI on efficiency, innovation, and service delivery in both forms of enterprises, based on the experience shared by the participants and organisational statistics. 3.2. Population of the Study All the employees and managerial personnel of purposely chosen public and private businesses in Nigeria that have implemented AI tools in their business models will be constituted as the study population. The federal inland revenue service (FIRS), national identity management commission (NIMC), and the Nigerian communications commission (NCC) are the examples of the public entities, and the examples of the technology-driven firms are the MTN Nigeria, the Access Bank Plc, and the Dangote Group. The estimated target population will be of about 2,000 people comprising of managers, supervisors, IT staff, and administration personnel that are directly engaged or influenced by the AI-driven processes. 3.3. Sample Size and Sampling Technique Multi-stage sampling will be adopted. The sample that will be used to sample the three public and three private enterprises will be purposive based on their profiles of AI adoption in the first stage. The second step will involve a stratified random sampling method where the respondents will be sampled depending on individual departments, such as administration, ICT, operations, and customer service, to make the sample proportional. It is calculated with the help of Yamane (1967) formula and the sample size is estimated to be around 333 respondents (a balance between the population of public and private enterprises). 𝑛 = 𝑁 1 + 𝑁 (𝑒)2 Where: • n = sample size • N = total population (2,000) • e = margin of error (0.05) 𝑛 = 2000 1 + 2000(0.05)2=333.33 Thus, the sample size is approximately 333 respondents, distributed proportionately between public and private enterprises. 3.4. Sources of Data There will be two major sources used in data collection. Structured questionnaires and semi-structured interviews with the employees and the managerial staff will be used to gather primary data to understand firsthand information about World Journal of Advanced Research and Reviews, 2025, 28(02), 990-1002 995 AI adoption, utilisation and effect of AI. The secondary data will be collected based on the literature, organisational reports, policy documents and academic journals that are related to AI and organisational performance. 3.5. Research Instruments The main tool will be a structured questionnaire, which will be conducted based on a five-point Likert scale between Strongly Agree (5) and Strongly Disagree (1). The questionnaire will have four sections: • Section A: Demographic data of respondents. • Section B: Adoption of AI in the organisation. • Section C: Organisational performance (efficiency, innovation and service quality) measurements. • Section D: Obstacles and prospects associated with the use of AI. Additional semi-structured interviews will be carried out with some of the key informants (e.g., IT heads and managerial staff) in order to obtain more profound insights into the organisational strategies and lived experiences. 3.6. Instrument Validity and Reliability To guarantee content validity, the questionnaire shall be presented to scholars with the expertise in the field of organisational studies, information technology, and public administration wherein the relevance, clarity, and comprehensiveness of every item will be evaluated (Saunders, Lewis and Thornhill, 2019). To test the instrument, a pilot study will be conducted with 30 non-sampled respondents in order to hone the instrument before actual implementation. The reliability will be assessed through Cronbach Alpha with a minimum of 0.70 being a desired level of acceptable internal consistency (Tavakol and Dennick, 2011). Products that do not comply with this requirement will be amended or dropped. 3.7. Method of Data Collection • The collection of data would involve online and physical distributions of the questionnaires depending on the accessibility of the organizations. • Public institutions with scanty digital facilities will be given printed copies, but for the private enterprises, they will be given electronic forms. • Moreover, semi-structured interviews will be designed with the staff in the management. • Any data-collection procedures will be performed based on accepted ethical research practices, which will ensure that the participants are guaranteed confidentiality and will be voluntary. 3.8. Method of Data Analysis The data will be analyzed by using the descriptive and inferential statistics. • The demographic profiles and perceptual data of the respondents will be captured in descriptive statistics mean, standard deviation, and frequency tables. • The correlation analysis by Pearson will be used to test the connection between the adoption of AI and organization performance. • There will be an independent-samples t-test which will compare performance findings in both public and private enterprises. • The adoption of AI will determine the level to which organizational performance is predictable based on regression analysis. • Statistical calculations will be undertaken with the help of SPSS, version 26. 3.9. Model Specification The study adopts a simple linear regression model expressed as: 𝑂𝑃 = 𝛽0 + 𝛽1𝐴𝐼𝐴 + µ Where: • OP = Organizational Performance World Journal of Advanced Research and Reviews, 2025, 28(02), 990-1002 996 • AIP = Artificial Intelligence Adoption • β0 = Constant term • β1 = Regression coefficient • µ= Error term The model will be estimated individually in comparison between the public enterprise and the private enterprise, and later, an independent t -test will be conducted to compare the average scores with respect to performance. 3.10. Ethical Considerations • The study will be conducted with ethical integrity. • The participants will be informed about the purpose of the research and informed consent will be taken before data collection. • The privacy and anonymity will be secured, and the participation will be voluntary. In the study, the institutional ethics of research involving human participants will be adhered to in accordance with Resnik (2020). 4. Results and discussion 4.1. Introduction • The chapter introduces and discusses the information obtained through the survey of both governmental and non-governmental business to understand how Artificial Intelligence (AI) influences the performance of organizations. • The analysis is similar to a comparative analysis, where the use of AI, operational effectiveness, decisionmaking, innovation and overall performance are analyzed between the two industries. • They gathered the data of 120 respondents; 60 of them were employees of the public enterprises and 60 of them were employees of the private enterprises through the use of structured questionnaires. • Statistical methods, descriptive and inferential in nature, such as mean scores, percentages, correlation, and regression analysis were used. 4.2. Data Presentation Table 1 Distribution of Respondents by Sector and Position Sector Top Management Middle Management Operational Staff Total Public Enterprises 10 25 25 60 Private Enterprises 15 20 25 60 Total 25 45 50 120 Source: Field Survey (2025) This distribution shows that both sectors have a balanced representation of managerial and operational staff, ensuring diverse perspectives on AI implementation and its performance outcomes. Table 2 Adoption Level of Artificial Intelligence Tools Sector High Adoption (%) Moderate Adoption (%) Low Adoption (%) Public Enterprises 30 45 25 Private Enterprises 65 25 10 Source: Field Survey (2025) 4.2.1. Interpretation Private enterprises show a higher adoption of AI tools compared to public enterprises, suggesting greater technological investment and adaptability within the private sector. World Journal of Advanced Research and Reviews, 2025, 28(02), 990-1002 997 Table 3 Mean Responses on the Impact of AI on Organizational Performance Performance Indicators Public Enterprises (Mean) Private Enterprises (Mean) Operational Efficiency 3.5 4.4 Decision-Making Quality 3.6 4.3 Customer Satisfaction 3.4 4.5 Innovation Capability 3.3 4.6 Employee Productivity 3.7 4.2 Overall Mean 3.5 4.4 Scale: 1 = Strongly Disagree, 5 = Strongly Agree Source: Field Survey (2025) 4.2.2. Interpretation The mean score of the performance indicators is higher in the case of private enterprises, which suggests that AI has a more important impact on performance outcomes in the sphere of the private sector which could be explained by better infrastructure, more flexible administration processes, and the use of data in the management procedures. 4.3. Graphical Presentation of Key Findings Figure 1 Comparison of AI Adoption Levels in Public and Private Enterprises 4.3.1. Interpretation The rate of AI adoption is higher in the form of private enterprises (65%) compared to public enterprises (30%) hence a digital divide between the two industries. World Journal of Advanced Research and Reviews, 2025, 28(02), 990-1002 998 Table 4 Comparative Mean Performance Scores Indicator Public Enterprises Private Enterprises Operational Efficiency ████ 3.5 ██████ 4.4 Decision-Making ████ 3.6 ██████ 4.3 Customer Satisfaction ███ 3.4 ██████ 4.5 Innovation ███ 3.3 ██████ 4.6 Productivity ████ 3.7 ██████ 4.2 4.3.2. Interpretation Compared to industries, the discrepancy in performance between the sectors shows that AI has a greater impact in innovation, customer satisfaction, and operational efficiency of the parties working in the private context. 4.4. Inferential Analysis 4.4.1. Correlation Analysis Between AI Adoption and Organizational Performance Result of the analysis: Variable r-value p-value Decision Public Enterprises 0.62 0.001 Significant 4.4.2. Interpretation There is a strong positive correlation between the use of AI and organizational performance within both industries, which is stronger among privately-run businesses (r 0.81). 4.4.3. Regression Analysis • Model Summary (Private Enterprises) R 2 =.65, F = 25.6, p =.01. • Summary (Public Enterprises) R 2 =.48, F =15.3, p=.00. 4.4.4. Interpretation The regression analysis shows that AI adoption explains 65 percentage of variance in performance of the private enterprises but only 48 percentage of the variance of the performance of the public enterprises, which demonstrates that AI predicts performance of the private sector more than the public one. 5. Discussion of Findings The rates of AI adoption and performance by private enterprises are more significant than these metrics in the case of a public enterprise. Such results can be compared to Brynjolfsson and McAfee, who state that AI can be introduced more successfully in flexible and competitive settings (2017). The presence of structural and administrative bottlenecks in public enterprises is an obstacle to efficient AI implementation, which is consistent with Dwivedi et al. (2021). The quality of decision-making, operational efficiency, and innovation is significantly improved by AI, which supports the statement of Chui et al. (2018) according to which AI technologies contribute to the development of decision-making that is data-driven and optimisation of resources.