Human-machine workforce redesign remains one of the most urgent challenges to sustainable organizational development, institutional integrity, and societal progress in the 21st century. It disrupts traditional roles, damages trust in management, and weakens organizations’ ability to fulfill their mandates effectively. Its influence spans all sectors, from manufacturing to services, and most concerningly, the workplace. According to the World Economic Forum (2023), AI integration is the augmentation of human tasks with machine intelligence, while the International Labour Organization (ILO, 2024) broadens this to include any act that undermines human autonomy, transparency, and accountability in both human and machine affairs. These definitions reflect a growing global concern that AI is not solely about technological advancement but also a systemic issue that undermines values, institutional efficiency, and equitable development. Despite increased efforts to promote good governance and AI ethics reforms, unethical practices continue to thrive in both obvious and hidden forms (Brynjolfsson & McAfee, 2024), especially in sectors like work where ethics and human dignity are expected to be fundamental. 

In the workforce sector, AI integration manifests through task automation, algorithmic surveillance, job displacement, and procedural manipulation. This has far-reaching consequences for the quality of work, equity in career access, and the credibility of employment relationships. Institutions like the ILO and World Economic Forum warn that AI in the workforce not only displaces vital human roles but also undermines employee morale, promotes unfairness in performance assessments, and weakens the integrity of career paths and promotions. Reports from the United States, China, and Europe have uncovered incidents of AI bias in hiring, excessive surveillance, fabricated performance metrics, and opaque algorithmic decision-making (Acemoglu & Restrepo, 2023; Kellogg et al., 2023). These issues erode the core mission of organizations, diminish public and employee confidence, and hinder the development of competent and adaptable professionals. Ultimately, poor human-machine redesign disrupts the merit system, widens inequality, and weakens the human capital necessary for national development. 

In sub-Saharan Africa, AI adoption remains a systemic challenge rooted in weak institutions, limited transparency, and poor enforcement of accountability measures. The African Union (2024) estimates that the continent risks losing millions of jobs annually to unchecked automation, with the workforce sector being significantly impacted. In African organizations, AI challenges include algorithmic bias in promotions, surveillance without consent, misuse of performance data, and procurement fraud in AI tools (ILO, 2024). These practices not only promote inequality but also harm the reputation of organizations and employees’ professional skills. Nigeria is particularly heavily affected. According to the Nigerian Bureau of Statistics and AI adoption reports (2023), Nigeria ranks low in ethical AI readiness, with widespread issues ranging from opaque algorithmic monitoring by managers and fear of job loss to biased performance scoring and lack of employee involvement in redesign processes. These behaviors have become normalized in many institutions, undermining psychological safety and devaluing employee well-being. While organizations continue to claim they develop workers “in competence and character,” there is a clear lack of deliberate and structured human-machine redesign education, making reforms in management practices and institutional accountability more urgent than ever. 

This normalization of AI-related challenges is not just institutional but deeply cultural, as it gradually conditions employees to accept low psychological safety as part of the work experience, thus reinforcing the very disengagement that organizations claim to oppose. According to Edmondson (2019) and Kellogg et al. (2023), when employees witness or experience practices such as unconsulted task automation, constant surveillance, or opaque performance algorithms, they are slowly socialized into a culture where fear and distrust become normal. Such behavior not only hampers individual well-being but also influences peer conduct, organizational norms, and broader societal expectations. In Nigerian organizations, additional examples of AI-related issues include excessive monitoring in exchange for perceived productivity, ghost tasks created by algorithms, and managers using forged AI-generated reports (Brynjolfsson & McAfee, 2024; Adebayo, 2023). These systemic malpractices undermine trust, weaken organizational credibility, and produce workers who may lack both the skills and emotional foundation necessary for national development. Therefore, tackling AI challenges in the workforce requires a deliberate and comprehensive reform strategy focused on promoting transparency, enforcing accountability, and embedding psychological safety in job redesign and managerial practices. 

Human-machine workforce redesign serves various strategic purposes, including optimizing task allocation between humans and machines, securing employee buy-in or placement, bypassing outdated requirements, and enhancing organizational efficiency through ethical means such as co-design workshops or transparent AI tools (Kellogg et al., 2023; Acemoglu & Restrepo, 2023). The forms it takes are diverse: technical redesign includes algorithmic task allocation and automation; administrative redesign involves data governance and procurement; relational redesign includes surveillance ethics and abuse of authority; while credential redesign involves AI-assisted performance tracking and manipulation of metrics (ILO, 2024). 

Human-machine workforce redesign severely damages the core goals of the organizational system, especially regarding employee growth. As the main recipients and participants in work processes, employees are greatly impacted by unethical AI practices like unconsulted automation, surveillance for grades/performance, falsification of metrics, and favoritism. These actions not only weaken psychological safety and ethical standards among employees but also reduce the rigor necessary for real learning and skill development. Lee and Kim (2022) state that regular exposure to such practices leads to moral disengagement among employees, making distrust seem acceptable or even necessary for survival not only in competitive work settings but also in other areas of life. This ethical disconnection ultimately results in poor professional skills, as employees who fear AI often lack the critical thinking and collaborative abilities needed in their careers. Adewale and Akinyemi (2023) also warn that workers who internalize low safety during AI transitions are likely to repeat these behaviors in their careers, fueling a cycle of disengagement in public service and the job market. 

At the institutional level, the effects of poor redesign are equally devastating. Organizations face severe damage to their reputation, internal governance, and ability to attract and retain both qualified staff and talent. Ochefu and Agabi (2022) contend that administrative AI issues, such as misappropriation of automation budgets, procurement fraud, and nepotism in AI tool selection, erode institutional standards and excellence. Accreditation and compliance risks also become imminent, particularly when regulatory bodies discover that work programmes are not meeting established standards due to compromised evaluation systems. Moreover, poor AI redesign in management leads to resource misallocation, infrastructural decay, and a decline in staff morale. These issues are not isolated; they ripple across the entire system, weakening policy implementation and producing poor performance outcomes. Ede and Imhonopi (2023) note that many competent professionals and innovators are compelled to leave organizations with poor redesign, fueling a brain drain and reducing local capacity for innovation, research, and development. This exodus further weakens the organizational ecosystem and perpetuates inequality in access to quality work environments. 

On a broader societal level, the consequences of poor human-machine redesign extend beyond organizational boundaries. When institutions produce workers with compromised safety and insufficient competence, these individuals eventually become part of the country’s leadership, civil service, and corporate management, carrying with them the same flawed values learned at work. Thus, further deepening the system’s failures. Okolie (2024) asserts that the presence of such individuals in strategic positions weakens public institutions, erodes democratic governance, and hampers socio-economic development. Furthermore, public trust in organizations as a tool for social mobility and national transformation declines when citizens perceive that success is based not on merit but on algorithmic manipulation and distrust. This breakdown in trust widens social inequality, fosters cynicism among the youth, and impedes national development goals. The cumulative impact of these issues highlights the urgent need for systemic reforms in the workforce sector, with a particular focus on human-machine redesign strategies, institutional accountability, and the enforcement of psychological safety frameworks in organizations. 

Given the far-reaching consequences of poor AI integration in the workforce, particularly its role in producing employees with low psychological safety who later occupy leadership and decision-making positions, it becomes increasingly urgent to address the human failures at their root. This is where human-machine workforce redesign emerges as a strategic intervention. Human-machine workforce redesign refers to intentional management strategies that aim to balance human and machine roles to instill psychological safety, adaptability, and ethical values in workers through both formal job redesign and informal learning experiences. At the organizational level, managers are not only task allocators but also role models responsible for cultivating responsible collaboration among employees. Through team interactions, the incorporation of ethics-related content in all processes, active dialogue, reflective learning, and participatory methodologies such as co-design workshops and service learning, managers can help employees internalize values such as safety, accountability, fairness, and respect (Edmondson, 2019). Additionally, organization-wide initiatives, such as employee-led safety forums, mentorship programmes, codes of conduct, and institutional ceremonies that celebrate ethical AI use are platforms through which human-machine redesign can be institutionalized (Lickona, 2021). Managers are central to this effort, as their redesign choices, personal conduct, and engagement with employees significantly influence how workers perceive and practice safe behavior (Narvaez & Bock, 2022). A redesign-driven approach thus holds promise as a transformative tool to reverse the culture of fear and rebuild trust. 

However, the reality in many Nigerian organizations stands in sharp contrast to these ideals. The absence of a well-articulated and enforced human-machine redesign framework means that safety development is often sidelined in favour of automation speed and productivity-oriented practices. Most managers are not trained in ethical AI redesign and may themselves model unethical behaviour through excessive surveillance, exploitation of data, or manipulation of performance outcomes. As Adewale and Akinyemi (2023) observed, the normalization of AI-related issues within work spaces creates an environment where neither managers nor employees are held accountable for safety failings. Moreover, institutional structures often prioritize rankings, funding, or political patronage over ethical leadership, leaving minimal room for psychological safety to thrive (Okolie, 2024). Even when organizational mottos claim to promote innovation and well-being, there is little evidence of structured programming to develop employees’ emotional capacities. This disconnection between rhetoric and practice highlights the urgent need for institutional reforms and staff reorientation toward redesign-driven work. Without deliberate strategies to embed safety reasoning and ethical awareness into the work culture, organizations risk producing individuals who perpetuate the very disengagement the system seeks to combat. This reality, if left unaddressed, threatens to institutionalize fear and undermine national development goals at their very foundation. It is against this backdrop that this study investigated the relationship between human-machine workforce redesign (managers’ AI job redesign methods and managers’ attitudes and level of engagement) and employees’ psychological safety in organizations in the AI era. 

Statement of the Problem 

Employees in ideal organizational settings are expected to uphold values such as psychological safety, adaptability, trust, and accountability. These moral virtues form the bedrock of a responsible workforce and are crucial for national development. Particularly in Nigerian organizations, employees should demonstrate safe attitudes that reflect strong internalized values acquired through formal and informal work experiences. This expectation is further reinforced by the long-standing traditional claim that organizational success is based on “competence and character,” emphasizing the importance of both technical skills and emotional uprightness. However, there is a growing probability that many employees in these institutions are straying from these ideals, with increasing reports of unethical behaviours such as resistance to AI, fear of surveillance, low engagement, cult-like avoidance of feedback, and blatant disregard for collaborative processes. This disturbing shift signals a potential erosion of the very safety foundations upon which the workforce system was built, and it raises serious concerns about the effectiveness of human-machine redesign in shaping employees’ well-being. 

While human-machine workforce redesign is designed to instill safety reasoning, adaptability, and socially responsible decision-making, the disconnect between redesign intentions and employee behaviour is becoming alarmingly evident. Ideally, through participatory redesign strategies, reflective team discussions, and real-life AI case studies, redesign should equip employees to embrace AI and contribute meaningfully to organizational success. However, in reality, the probability is high that the implementation of such redesign is either weak, inconsistent, or merely symbolic. Despite the recurring assertion that Nigerian organizations develop workers with both competence and character, there is limited evidence to show that organizations systematically and deliberately integrate safety into job redesign and assessment practices. As a result, the transformative potential of human-machine redesign in developing resilient workers is increasingly being undermined. 

Even more troubling is the growing influence of a “hidden curriculum” (a set of informal, often unethical AI practices embedded within organizational culture) which appears to contradict directly and even undermine formal redesign and instruction. Mounting evidence suggests that some managers demand acceptance of opaque algorithms, surveillance tools, or performance metrics (often unwritten). At the same time, certain administrators are involved in acts of data nepotism, favoritism, and outright manipulation. In some cases, promotion into critical or high-demand roles is allegedly influenced by algorithmic bias. Employees without influential networks or the emotional resilience to navigate such systems are frequently sidelined. The question arises: What kind of safety foundation is being laid when organizations meant to build trust actively reward distrustful conduct? These unspoken yet widely practiced norms subtly, but powerfully, condition employees to believe that low psychological safety is not only permissible but profitable. Such practices send conflicting safety signals, thereby increasing the probability that employees internalize a culture of fear, exploitation, and manipulation. In this context, human-machine redesign is stripped of its legitimacy and moral authority, and the long-standing institutional claim of producing workers grounded in both competence and character becomes deeply questionable. These alarming realities make it imperative to empirically investigate the relationship between human-machine workforce redesign and employees’ psychological safety in organizations in the AI era. 

Purpose of the Study 

The purpose of this study was to assess the relationship between human-machine workforce redesign and employees’ psychological safety in organizations in the AI era. Specifically, the study sought to: 

1. Examine the relationship between managers’ AI job redesign methods and employees’ psychological safety in organizations in the AI era. 
2. Assess the relationship between managers’ attitude and level of engagement and employees’ psychological safety in organizations in the AI era. 

Research Questions 

The following research questions guided the study: 

1. What is the relationship between managers’ AI job redesign methods and employees’ psychological safety in organizations in the AI era? 
2. What is the relationship between managers’ attitudes and level of engagement and employees’ psychological safety in organizations in the AI era? 

Hypotheses 

The following null hypotheses were formulated and tested at the 0.05 level of significance: 

1. There is no significant relationship between managers’ AI job redesign methods and employees’ psychological safety in organizations in the AI era. 
2. There is no significant relationship between managers’ attitudes and level of engagement and employees’ psychological safety in organizations in the AI era. 

Research Methodology 

The study, conducted in organizations in Nigeria, used a correlational survey research design. The population included 72,621 employees in mid- and senior-level positions across 12 selected organizations. These employees were specifically chosen because, at this stage in their careers, they would have had enough interaction with managers to evaluate human-machine workforce redesign effectively. A sample size of 382 employees was selected based on both Krejcie and Morgan’s (1970) sample size table and Cochran’s formula, which states that for a finite population of 72,621, the appropriate sample size is about 382. This confirms that the chosen sample is statistically adequate to represent the target population at a 95% confidence level with a 5% margin of error. A multistage sampling procedure (including purposive, proportionate stratified, and simple random sampling techniques) was used. 

A researcher-constructed questionnaire titled “Human-Machine Workforce Redesign Questionnaire (HMWRQ) and Employees’ Psychological Safety Questionnaire (EPSQ)” was used for data collection. The instrument was divided into three sections: A, B, and C. Section A included items on respondents’ personal data. Section B was split into two parts: Part I (Items 1–5) focused on managers’ AI job redesign methods, while Part II (Items 6–10) addressed managers’ attitudes and engagement levels. Section C consisted of Items 11–20, which examined employees’ psychological safety in organizations in the AI era. The instrument was validated by three experts: two from the Department of Management (specializing in Human Resource Management) and one from the Department of Psychology, all within the Faculty of Management Sciences at the University of Lagos. Subsequently, the instrument was tested for reliability using Cronbach’s Alpha, yielding coefficients of 0.78 and 0.84, both considered sufficiently high for the instrument to be reliable for use in the study. 

The data collected was analyzed using Pearson Product Moment Correlation (PPMC) to answer the research questions. The decision rule was that if the calculated r lies between 0-0.25, it indicates a very weak positive correlation; 0.25-0.50 shows a weak positive correlation; 0.50-0.75 indicates a strong positive correlation; and 0.75-0.99 represents a powerful positive correlation. The value of r must always be between -1 and +1, which is expressed as -1 ≤ r ≤ +1. The range of r values extends from negative 1 to positive 1. Simple linear regression was used to test the hypotheses. The reason for choosing linear regression was to determine whether human-machine workforce redesign is a positive or negative determinant of employees’ psychological safety in Nigerian organizations. The decision rule is that if the p-value is greater than the set alpha level of 0.05, the null hypothesis will not be rejected; if the p-value is less than 0.05, the null hypothesis will be rejected. 

Data Analysis and Interpretation 

The data were analyzed and interpreted in response to the research questions and hypotheses. 

Research Question 1: What is the relationship between managers’ AI job redesign methods and employees’ psychological safety in organizations in the AI era? 

Table 1:  Relationship between managers’ AI job redesign methods and employees’ psychological safety in organizations in the AI era 

Table 1 above illustrates the relationship between managers’ AI job redesign methods and employees’ psychological safety in Nigerian organizations. The data showed that employees’ psychological safety had a mean score of 27.2984 and a standard deviation of 10.46253, while the managers’ AI job redesign methods had a mean score of 13.2906 and a standard deviation of 5.48507. The correlation coefficient was .988, indicating that managers’ AI job redesign methods have a strong positive relationship with employees’ psychological safety in Nigerian organizations. 

Research Question 2: What is the relationship between managers’ attitudes and level of engagement and employees’ psychological safety in organizations in the AI era? 

Table 2: Relationship between managers’ attitudes and level of engagement and employees’ psychological safety in organizations in the AI era 

Table 2 above showed the relationship between managers’ attitudes and levels of engagement and employees’ psychological safety in organizations in the AI era. The data revealed that employees’ psychological safety had a mean score of 27.2984 and a standard deviation of 10.46253, while the managers’ attitudes and levels of engagement had mean scores of 13.3822 and standard deviations of 5.39276. The correlation coefficient was .995, which implies that managers’ attitudes and levels of engagement have a very high positive relationship with employees’ psychological safety in organizations in the AI era. 

Hypothesis 1: There is no significant relationship between managers’ AI job redesign methods and employees’ psychological safety in organizations in the AI era. 

Table 3: Regression analysis of relationship between managers’ AI job redesign methods and employees’ psychological safety in organizations in the AI era

The result from Table 3 indicated a significant relationship between managers’ AI job redesign methods and employees’ psychological safety in organizations in the AI era [R=.988, R²=.976, F (1, 380) =15462.013; p<.05]. The result showed that managers’ AI job redesign methods accounted for 97.6% of the total variance in employees’ psychological safety. Therefore, 2.4% could be attributed to other variables not included in this study. The result also revealed that there is a significant relationship between managers’ AI job redesign methods and employees’ psychological safety in organizations in the AI era (β=.988, t=124.346, p<.05). 

Hypothesis 2: There is no significant relationship between managers’ attitudes and level of engagement and employees’ psychological safety in organizations in the AI era. 

Table 4: Regression analysis of the relationship between managers’ attitudes and level of engagement and employees’ psychological safety in organizations in the AI era