Artificial intelligence will have an impact on human resources (Jia et al., 2018; Johansson and Herranen, 2019; Chowdhury et al., 2023). When the human element of human resources is integrated with the intelligence of technology, organizations will be able to offer better working circumstances to candidates and workers (Hashimoto et al., 2018). In addition, AI will help in completing the crucial task faster and better (Kim, 2020). AI-based HR solutions increase worker productivity (Oyetunde et al., 2022). It can analyze, forecast, diagnose, and develop into a more capable asset by focusing on employee requirements and outcomes (Toh et al., 2019). Businesses should implement AI technologies that align with their needs and corporate culture (Wamba et al., 2017). They should also create the necessary digital maps. Human resource management has evolved significantly as it has grown more contemporarily (Wijayati et al., 2022). This is due to the fact that digitization has spread to time-consuming activities within its many businesses (Baldegger et al., 2020). HRM has worked out how to employ previous technologies, such as the computer and the internet, to enhance market competition, cost-effectiveness, and the output (Agarwal et al., 2022). The McKinsey analysis estimates that AI will have a $13 trillion impact on the global economy (Brynjolfsson and Mcafee, 2017). Everyone believes that machine learning will affect all corporate functions, including human resources (Sahoo et al., 2022). Experts in human resources will employ technology to improve how individuals collaborate (Vrontis et al., 2022). By utilizing tested data, the industry can be enhanced and a more rational work atmosphere can be created (Chen, 2019; Jain et al., 2022; Goswami et al., 2023). Such a conducive environment will help in creating a positive workforce for the Indian pharmaceutical sector, which is the need of the hour after the COVID 19 outburst. Therefore, the following hypothesis can be formulated:

H1

Artificial intelligence adoption has a significant impact on effective HRM in the Indian pharmaceutical sector.

Organizational preparedness comprises organizational structures, processes, and other resources that promote the use of technology (Goodhue and Thompson, 1995; Yang et al., 2015; Wang and Siau, 2019; Jöhnk et al., 2021). According to the previous study, the characteristics such as a company’s size (Kaufman, 2015), the level of formalization, resource availability, and managerial support, all influence its readiness to adopt new technologies (Patel and Cardon, 2010). According to the resource-based view (RBV), an organization’s financial resources impede the adoption of AI in this manner (Tarannum and Farheen, 2020). Few studies on human resource systems suggest that the amount of knowledge in the HR department effects the adoption of new technologies (Stone et al., 2015).

AI use in the workplace is still in its early stages, and larger enterprises currently have more resources than smaller firms; thus, we believe that larger companies will employ more HRM-related AI solutions. AI can give businesses a competitive advantage, compelling them to use it (Benbya et al., 2020). An organization’s preparedness is an important factor in how new IT innovations are adopted and implemented (Alam et al., 2016; Hangl et al., 2022). Organizations that foster an open environment for innovation, demonstrate support for it, and provide adequate resources are more likely to embrace new IT innovations or engage in adoptive behaviors than their competitors (Wang et al., 2010; Wang and Siau, 2019), keeping in mind top management’s strategic direction, how they perceive and understand new AI HRM solutions, and how they feel about AI adoption in HRM. This is due to the fact that AI adoption significantly alters the traditional function of HRM (Chen, 2019).

Organizational preparedness to adopt AI must be tailored to the unique characteristics of each company, such as its industry or organizational structure (Jöhnk et al., 2021; Hradecky et al., 2022). Furthermore, because of how organizations evaluate themselves, the results of readiness models are frequently skewed (Fischer et al., 2020). However, before successfully adopting new technologies, organizations must be prepared and have the necessary capabilities (Daradkeh, 2019). An organization’s preparedness to adopt AI determines how well an organization is prepared to fully realize the promise of an innovation (Awa et al., 2016). Thus, the following can be hypothesized:

H2

Organizational preparedness has a significant impact on artificial intelligence adoption in the Indian pharmaceutical sector.

H3

Organizational preparedness has a significant impact on effective HRM in the Indian pharmaceutical sector.

Perceived benefits are views about the benefits of a behavior in response to a real or perceived threat (Fischer et al., 2020). The perceived benefits of using AI are an important component of the research. People believe a technique is superior to other approaches because of its perceived benefits (Kambur and Akar, 2022). It has been discovered that AI’s perceived benefits diminish resistance to its adoption (Hradecky et al., 2022). HR managers are less likely to adopt AI if they are not sufficiently informed about its benefits and effectiveness (Guenole and Feinzig, 2018). Another important factor influencing attitudes is the expense of implementing AI (Jöhnk et al., 2021). The perceived benefits of adopting AI can be assessed based on its functionality, cost, and predicted benefits (Alam et al., 2016). As a result of the COVID pandemic, businesses have realized that automation and AI can help make work less personal (Gaur et al., 2021). As a result, how people perceive AI is determined by how it works, how much it costs, and what features it includes (Pillai and Sivathanu, 2020). All of these elements have an impact on how the decision-maker views the benefits of utilizing AI when more benefits are visible (Baig et al., 2019). Drawing on the aforementioned discussion, the following relations are hypothesized:

H4

Perceived benefits have a significant impact on artificial intelligence adoption in the Indian pharmaceutical sector.

H5

Perceived benefits have a significant impact on effective HRM in the Indian pharmaceutical sector.

AI adoption is primarily concerned with the qualities of the current technology and the ease with which an organization might embrace new technology (London, 2019; Sharma et al., 2022a). Technological readiness includes all of a company’s internal and external technologies, as well as their use in the business (Bhardwaj et al., 2020). The level of technological readiness and awareness of an organization’s employees are also major variables in the adoption of artificial intelligence. This is due to the fact that these factors influence the decision-making process and AI application. The HR personnel should also be aware of how AI fits into the overall business strategy (Pillai et al., 2022; Rahman et al., 2022). Technical experience makes it easier to embrace AI and integrate it with the existing HR systems; a company will not adopt a technology unless it has the essential knowledge and ability (Obeidat, 2016; Wang and Siau, 2019). Additionally, the successful use of AI has resulted in the reduction of numerous expenses, the effective and efficient use of resources, and the attainment of goals (Hedt and Pagano, 2011; Muduli and Trivedi, 2020). AI requires adaptive networks, software, hardware, and data deployments from IT (Wamba et al., 2017). According to research, organizations that have a strong technology infrastructure and the capacity to keep it current are better prepared for AI adoption (Jarrahi, 2018; Goswami, 2021). Furthermore, it has been suggested that a company’s technological preparedness is determined by factors other than its physical infrastructure. Drawing on the aforementioned discussion, the following relations are hypothesized:

H6

Technological readiness has a significant impact on artificial intelligence adoption in the Indian pharmaceutical sector.

H7

Technological readiness has a significant impact on effective HRM in the Indian pharmaceutical sector.

The pressure that a company believes its competitors are putting on it is known as competitive pressure (van Esch et al., 2021). According to economic studies and surveys, as the global economy shifts toward a knowledge-based, free market economy, there is a greater competitive strain. Firms may feel compelled to adopt new IT innovations in order to remain competitive as a result of this pressure. Rizk (2020) asserted that due to intense competition, AI adoption may become a strategic necessity. Competitive pressure has a significant impact on the rate of AI adoption. Hossain et al. (2017) and Malik et al. (2021) discovered that pressure from trade partners and pressure from competitors are significant determinants of whether or not a corporation will use AI adoption. AI is still in its infancy in HRM; its apps are now affordable enough for use and have few technological issues. Drawing on the aforementioned discussion, the following relations are hypothesized:

H8

Competitive pressure has a significant impact on artificial intelligence adoption in the Indian pharmaceutical sector.

H9

Competitive pressure has a significant impact on effective HRM in the Indian pharmaceutical sector.

Organizational culture refers to how the workplace influences how employees behave, think, and feel about their jobs (Wilkens, 2020). Despite extensive research on organizational culture, no one can agree on a single definition. Despite the fact that there is no universally accepted definition of an organizational culture, it is commonly defined as “a collection of shared assumptions, values, and beliefs that are reflected in its practices and goals and help its members understand how the organization works”. Some believe that the organizational culture of a company is what keeps it together (Gaur et al., 2021; Wheeler and Buckley, 2021). An organization’s culture is a set of shared principles that distinguishes it from other organizations, which creates effective HRM practices. Something that all members of the group agree on is that the best strategy for maximizing the return on AI investments is to change organizational cultures (Cruz-Jesus et al., 2018). When AI is used correctly, organizations can build their cultures and that strong cultures can help firms use AI more effectively (Vrontis et al., 2022). AI and related technologies, such as machine learning, neural networks, and virtual assistants, have a significant impact on how businesses operate and are structured (Hangl et al., 2022). These excellent findings will undoubtedly persuade many businesses to invest in additional AI solutions. Drawing on the aforementioned discussion, the following relations are hypothesized:

H10

Organizational culture has a significant impact on artificial intelligence adoption in the Indian pharmaceutical sector.

H11

Organizational culture has a significant impact on effective HRM in the Indian pharmaceutical sector.

This study delves into the mediating role of artificial intelligence on both independent and dependent variables. AI is widely recognized as a crucial growth driver that enhances business structures (Wamba et al., 2017; Kshetri, 2020; Kambur and Akar, 2022). Human intelligence involves the capacity to perceive, analyze, learn from past experiences, and devise innovative solutions to intricate problems (Mahmoud, 2021). In the Indian pharmaceutical sector, companies often face a substantial volume of job applications for limited positions. AI-powered chatbots have the potential to streamline and expedite the screening and filtering of applications, resulting in significant savings in time and effort for HR professionals. This enhanced efficiency and cost-effectiveness can lead to a perception of increased efficiency, which can ultimately contribute to more effective human resource management (Guenole and Feinzig, 2018). AI is vital in optimizing recruitment systems as it simplifies and reduces the cost of tasks, like application screening, while simultaneously enhancing the hiring quality (Muhaimin et al., 2020). Adopting AI in human resource operations can lead to greater efficiency and resource optimization (Sharma et al., 2022b). AI is perceived in human resource management as a means of expediting personnel hiring and selection processes while avoiding favoritism or nepotism (Reddy et al., 2019). This approach can facilitate staff development and improve their performance (Vinichenko et al., 2020). Drawing on the aforementioned discussion, the following relations are hypothesized:

H12

Adoption of artificial intelligence mediates the relationship between perceived benefits and effective HRM in the Indian pharmaceutical sector.

H13

Adoption of artificial intelligence mediates the relationship between competitive pressure and effective HRM in the Indian pharmaceutical sector.

H14

Adoption of artificial intelligence mediates the relationship between technological readiness and effective HRM in the Indian pharmaceutical sector.

H15

Adoption of artificial intelligence mediates the relationship between the organizational culture and effective HRM in the Indian pharmaceutical sector.

H16

Adoption of artificial intelligence mediates the relationship between organizational preparedness and effective HRM in the Indian pharmaceutical sector.

The present study aims to address the gaps in the literature by examining the influence of key factors, such as organizational preparedness, perceived benefits, technological readiness, competitive pressure, and organizational culture, on the adoption of artificial intelligence in the Indian pharmaceutical sector. A conceptual framework (Figure 1) has been developed to illustrate the relationships between the aforementioned variables and their impact on AI adoption, which, in turn, leads to effective human resource management. The study will employ partial least squares structural equation modeling (PLS-SEM) to analyze the proposed framework. The adoption of AI is expected to bring significant improvements to HRM in the Indian pharmaceutical sector.

In the demographic profile of the 88 respondents of the pharmaceutical sector, out of which only eight were female individuals and the remaining 80 were male individuals, there is a dearth of female workforce in India. The respondents fall in the age bracket of 30–39 years old and have received responses from all parts of the country, namely, Vadodara, Ahmedabad, Baddi, Sikkim, Kolkata, Visakhapatnam, Hyderabad, Bangalore, Chennai, Mumbai, and Pune.

Using the variance inflation factor (VIF) to measure multicollinearity, the researchers found that none of the values in Table 1 were above the threshold of 5, indicating the absence of multicollinearity (Hair et al., 2017a).

The measurement model presents the validity and reliability of the construct. Four measurements are addressed in this study: 1. indicator loading, 2. internal consistency reliability, 3. convergent validity, and 4. discriminant validity (Risher and Hair, 2017).

PLS-SEM results were assessed for indicator loading in this study. Table 2 exhibits the details of loadings crossing the minimum threshold value of >0.70 ranging from 0.76 to 0.91, indicating that the construct has explained more than 70% of indicator variance, thus ensuring construct reliability (Hair et al., 2019).

Internal consistency should be presented through Cronbach's alpha and composite reliability (Hair et al., 2019). The value of Cronbach alpha ranges from 0.87 to 0.93, and composite reliability values are 0.91–0.94; both values are quite above the minimum threshold value of >0.70 (J. Hair and Alamer, 2022), thus establishing the internal consistency.

Convergent validity assessed the relationship among the similar and same construct, and the average variance extracted (AVE) to be reported; the minimum threshold value is 0.50 or higher (Hair et al., 2019). In this study, AVE values range from 0.67 to 0.77, which is quite above the acceptable limit, as shown in Table 1, thus establishing convergent validity.

Discriminant validity explains the distinction among the constructs (Hair and Alamer, 2022). By applying Fornell–Larcker’s criterion, the AVE score of the construct should be less than the shared variance for the entire model construct; this study also depicts the same situation, as shown in Table 2. Thus, discriminant validity is established as per Fornell–Larcker’s criterion.

Furthermore, the heterotrait–monotrait ratio (HTMT), shown in Table 3, also confirms discriminant validity, where the values are less than 0.90 (Roemer et al., 2021; Hair and Alamer, 2022) of all the reflective constructs, indicating that there is no correlation among the constructs.

As per the techniques provided for structure model assessment (Risher and Hair, 2017), collinearity must be evaluated using VIF values of all sets of predictors. As shown in Table 4, all the values of the VIF are below the minimum acceptable value of 5 (Hair et al., 2019). Thus, collinearity is not a concern in this study.

After establishing reliability and validity through the measurement model using PLS-SEM, the structural model is examined, which indicates the relationship between endogenous and exogenous variables. The path coefficients were studied by applying the bootstrapping process (two-tailed, at 5,000 samples, and 95% confidence level) (Hair et al., 2019).

The structural model helps in verifying all the projected hypotheses. The results are shown in Table 5, which reveals that artificial intelligence had a strong positive relationship with effective HRM (H₁) (b = 0.40; t = 3.71; p = 0.00); hence, H₁ is supported. However, the relationship between competitive pressure and artificial intelligence (H₂) could not be established in the study (b = 0.08; t = 0.72; p = 0.47); hence, it was found to be insignificant. However, competitive pressure had a strong negative relationship with effective HRM (H₃) (b = −0.20; t = −2.14; p = 0.03), which indicates that with the increase in competitive pressure, the effectiveness of HRM would decrease (Wang and Siau, 2019); hence, H₃ is supported. The statistical analysis also confirms that organizational culture had a strong positive relationship with artificial intelligence (H₄) (b = 0.40; t = 3.24; p = 0.00); thus, H₄ is also supported. However, the relationship between organizational culture and effective HRM (H₅) (b = −0.04; t = 0.34; p = 0.73) could not be established in this study; similarly, organizational preparedness had an impact on artificial intelligence (H₆) (b = 0.12; t = 1.15; p = 0.25), which was found to be insignificant; thus, H₅ and H₆ are not supported. However, organizational preparedness proved to have a strong positive relationship with effective HRM (H₇) (b = 0.26; t = 2.44; p = 0.01). Similarly, the perceived benefit has a strong positive relation with artificial intelligence (H₈) (b = 0.21; t = 2.01; p = 0.04); thus, H₇ and H₈ are supported. However, perceived benefits fail to establish a relationship with effective HRM in the study (H₉) (b = 0.15; t = 1.51; p = 0.13). Similarly, the relationship of technological readiness with artificial intelligence also could not be established (H₁₀) (b = 0.12; t = 0.78; p = 0.44). However, the relationship between technological readiness and effective HRM (H₁₁) (b = 0.39; t = 3.56; p = 0.00) was found to be significant; hence, H₁₁ is supported.

Regarding effect sizes (f²), values of 0.35, 0.15, and 0.02 are considered large, medium, and small for f², as shown in Table 5 and Graph 1; the effect size (f²) of artificial intelligence is found to be an important variable in assessing the effectiveness of HRM in the Indian pharmaceutical sector. Second, organizational culture was close to a medium impact in assessing artificial intelligence adoption in the Indian pharmaceutical sector. Furthermore, technological readiness is also identified in this research as an important variable to assess the effectiveness of HRM in the Indian pharmaceutical sector. Furthermore, it is observed that at places where the hypothesis has insignificant p-values, the effect size f² of that same hypothesis is revealed to be very small, even less than the threshold value of 0.02 (Cohen, 1988; Hair et al., 2019).

As per Hair et al. (2017b)’s recommendations, a mediation analysis was assessed using SmartPLS. The results reveal the effective mediation of artificial intelligence adoption in developing an effective HRM system in pharmaceutical companies. As shown in Table 6, PB ->AI ->EH perceived benefits have a significant relationship with effective HRM through the mediation of AI adoption (H ₁₂ ) (b = 0.15; t = 3.37; p = 0.00), indicating full mediation as the perceived benefit was found to reject the direct relation with effective HRM (H ₉ ) (Boon et al., 2011). Furthermore, CP ->AI ->EH competitive pressure has a significant relationship with effective HRM through the mediation of AI adoption (H ₁₃ ) (b = 0.12; t = 2.24; p = 0.02), indicating partial mediation, as competitive pressure was found to have a direct impact on effective HRM (H ₃ ) (Tan and Nasurdin, 2011). Similarly, TR ->AI ->EH technological readiness is also proved to have a significant relationship with effective HRM via the mediation of AI adoption (H ₁₄ ) (b = 0.10; t = 3.59; p = 0.00), indicating partial mediation, as technological readiness was found to have a direct impact on effective HRM (H ₁₁ ) (Esen and Erdogmus, 2014). OC ->AI ->EH organizational culture also proved to have significant relationship with effective HRM through the mediation of AI adoption (H ₁₅ ) (b = 0.16; t = 2.44; p = 0.01), indicating full mediation, as organizational culture was found to reject the direct relationship with effective HRM (H ₅ ) (Al-Musadieq et al., 2018). Furthermore, this study proves another partial mediation of OP ->AI ->EH AI adoption on the relationship between organizational pressure and effective HRM (H ₁₆ ) (Hmoud, 2021) (b = 0.15; t = 3.98; p = 0.00) as organizational pressure is proven to have a direct impact on effective HRM (H ₇ ) (Agarwala, 2003).

The coefficient of determination (R ²) values indicate the ability of the exogenous latent variable to predict the endogenous latent variables (Cohen, 1988); R ² is 0.66 for artificial intelligence adoption and 0.73 for the effective HRM endogenous latent variable, indicating that the other five latent variables (like perceived benefits, competitive pressure, technological readiness, organizational culture, and organizational pressure) significantly explain 66% (R ² = 0.66) of variance in artificial intelligence adoption. Similarly, the six other latent variables (like perceived benefits, competitive pressure, technological readiness, organizational culture, organizational pressure, and artificial intelligence) explain 73% (R ² = 0.73) of variance in developing effective HRM.

Artificial intelligence has had a significant impact on HRM practices, but little research has been conducted to determine how well it works within organizations (Wamba et al., 2017). Several HR experts discussed how AI works and why it is important to use it in hiring. Businesses must carefully consider what AI can do for them and why it should be incorporated into their hiring processes.

AI, in my opinion, can improve the accuracy and effectiveness of the hiring process, but it all depends on the organization. Each organization will have unique efficiency goals and AI requirements. If organizations are to fully benefit from using AI in recruitment, they must have access to it and enough time to put it to use. This is because many organizations, whether they realize it or not, believe they want to deploy AI. They may not require this level of efficiency or quality or they may lack the technical expertise to implement it.

Long-term organizational changes should be prioritized, and we should work to develop methods for teaching both humans and machines to recognize employee skills that will benefit the company in the long run. Recruiters used to perform repetitive tasks manually, but AI would make some of these tasks obsolete (Pillai et al., 2022). Recruiters can now delegate these mundane tasks to AI systems, freeing up their time and resources for more strategic issues (Pillai and Sivathanu, 2020).

AI assists recruiters in completing less time-consuming tasks in order to conduct direct applicant interviews. Application screening saves time during recruitment and AI can assist recruiters in identifying top candidates by generating short lists. When artificial intelligence is used in hiring, recruiters can access a larger prospect pool while doing less administrative work (Vardarlier and Zafer, 2020). One of the most amazing aspects of AI-based recruiting is that it allows recruiters to spend more time with the best talent management candidates (van Esch et al., 2021).

This study provides corporate executives in the pharmaceutical sector and HR managers with recommendations for improving the effectiveness of their HR departments. HR leaders and upper management should be aware that integrating AI into the HR system may have benefits that extend beyond the HR department and to the entire company. Before implementing artificial intelligence, organizations must be prepared, understand the benefits, and have the necessary technical skills. HR managers and leaders should conduct research before developing a strategy for integrating AI into the current system. The first step can be to determine which tasks, procedures, and processes need to be modified. People may consider whether tasks can be automated in order to increase their productivity and efficiency at work. Furthermore, the value that people contribute can be quantified, considering how people analytics can help you improve your individual performance, education, and career development, as well as your pay and benefits. However, it is critical to determine whether a major overhaul is required. Once this preliminary research is completed, a strategy for automating AI use can be developed. AI will be used more and more in future HR departments, particularly when communicating with applicants and assisting them in preparing for interviews.

This study provides important and novel information on the driving forces behind the use of AI in the HR department of pharmaceutical companies of India. However, the study has several limitations that must be addressed. First, we only looked at data from major cities. Second, we only considered the pharmaceutical industry as a whole. Third, we regard AI as a high-tech tool. However, AI’s cognitive capabilities differ significantly from those of traditional e-HRM tools. Many people believe that AI is more than just a technology (van Esch et al., 2021; Vrontis et al., 2022). Future research should look into people’s reactions to the idea of artificial intelligence technology replacing humans. AI can be used in a variety of ways by different cultures and nations. Because our study was limited to India, the current findings cannot be applied to other countries. Only 160 human resource managers from Indian pharmaceutical companies were involved. People believe that in the future, researchers should conduct comparable studies in other fields to learn more. Although the questions and ideas used in this study came from the studies conducted in Western countries, the survey was conducted in accordance with the rules to ensure high-quality data. It is suggested that India should develop its own scales for future investigations.