Artificial intelligence adoption for pharmaceutical salesforce performance: a case study in Kuwait Open Access

De Carolis (2003) states that pharmaceutical industry companies utilize advanced technologies to boost sales and enhance their operational processes. Such beliefs can include AI shifts in sales and marketing, such as customer relationships, analytics, and tailored marketing communications (Bag et al., 2022). AI integrates into many aspects of life by optimizing market analysis, customer engagement, and resource management, including pharmaceutical salesforce performance (Nunavath and Nagappan, 2024). AI analyzes market trends and customer data to refine marketing strategies and improve product positioning. AI-powered chatbots and virtual assistants provide personalized customer service (Abdallah et al., 2023). Pharmaceutical industry companies can use AI tools to identify potential key opinion leaders. Managers can utilize AI tools to assess client engagement levels among prescribers and identify opportunities by analyzing prescribing patterns. AI-guided plans can transform sales teams from high levels of unproductive engagement, such as client calls or visits, to more productive outcomes, including increased sales, improved engagement, or greater efficiency. AI cannot work alone, and it must integrate with humans to simplify reporting systems, ensure plan adherence, and automatically generate a plan of action (Al Wael et al., 2023). Nunavath and Nagappan (2024) maintain that adopting AI in the pharmaceutical industry, from raw material selection to final product manufacturing, improves the quality and safety of pharmaceutical products and reduces time and cost. Pharmaceutical companies that have adopted AI offer training programs tailored to individual sales needs and AI-powered virtual assistants to enhance the productivity of their sales force (Blanchard and Thacker, 2023), and improve sales forecasting. AI technology analyzes large datasets to make accurate predictions and real-time dynamic adjustments (Reddy et al., 2023). Integrating AI into the pharmaceutical salesforce offers significant benefits regarding efficiency, personalization, and compliance, while presenting challenges related to data quality, system integration, and ethical considerations.

However, AI integration within the pharmaceutical sales force should be more balanced, indicating that its adoption must align with the organization’s and its employees’ needs and capabilities. In addition, AI determinants, meaning critical factors influencing the successful adoption of AI in the pharmaceutical salesforce, should be defined. The factors that affect the introduction of pharmaceutical AI continue to be unspecified. This research utilizes AI to investigate the factors influencing the pharmaceutical sector in Kuwait. Like many other industries, the Kuwaiti pharmaceutical sector is under competitive pressure and enduring technological changes. Understanding these factors will help bridge the gap between AI capabilities and their practical applications, enabling enterprises to optimize their profits and enhance their competitive edge.

The current study aims to bridge this gap by investigating the factors influencing AI adoption in the pharmaceutical industry, specifically focusing on its impact on salesforce performance. To the authors’ knowledge, this study is the first of its kind in Kuwait. Thus, the research is significant because it can offer insights into the critical factors that impact AI adoption to improve the pharmaceutical sales force in Kuwait. In the context of a highly competitive and technologically advanced pharmaceutical sector, it is crucial to comprehend the factors that influence the integration of AI (Nunavath and Nagappan, 2024). This study offers pharmaceutical industry companies great practical significance for industry practitioners, policymakers, and vendors of AI applications. Such insights will enable them to formulate a strategy that responds to the problems and needs of the pharmaceutical salesforce by adding to the scant research on AI adoption in the Middle East, specifically Kuwait.

As of 2024, the global pharmaceutical industry companies are valued at about USD 1.5 trillion, expected to reach $1.9 trillion by 2027, growing at a compound annual growth rate (CAGR) of 5.9% (Bhardwaj, 2024). The increasing prevalence of chronic diseases, an aging population, and rising healthcare spending drive this growth. Significant growth areas include obesity, neurological conditions, and cancer treatments, with immunomodulators, oncology, and central nervous system therapies expanding the fastest.

Regionally, North America and Europe dominate the market. They have the largest multinational companies in the world. Their strong economies, considerable investments in R&D, advanced technology, and political influence are the primary factors behind this dominance. However, due to population growth and improved healthcare access, the highest growth volume is expected in Asia, Latin America, and Africa (Bhardwaj, 2024).

Ledley et al. (2020) maintain that from 2000 to 2018, 35 large pharmaceutical industry companies reported cumulative revenue of $11.5 trillion, gross profit of $8.6 trillion, earnings before interest, taxes, depreciation, and amortization (EBITDA) of $3.7 trillion, and net income of $1.9 trillion. The global active pharmaceutical ingredients (APIs) market was valued at USD 191.3 billion in 2022 and is projected to reach USD 306.2 billion by 2030, with a compound annual growth rate (CAGR) of 4.7%.

In 2020, the United States spent around USD 348.4 billion on prescription drugs, equating to USD 1,126 per capita. Germany, Europe’s largest pharmaceutical market, saw revenues reach EUR 46.4 billion in 2019. Prescription medicines account for significant sales (Stacciarini, 2023). Germany’s largest healthcare market in Europe saw health expenditures hit EUR 390.6 billion in 2018, growing by 4.4% annually (Schulz, 2021).

Emerging economies present a positive outlook for the pharmaceutical sector due to rising healthcare demand and better market access. Key growth drivers include biotech innovation, generic drug production, and regional partnerships (Arden et al., 2021).

Five essential factors are interrelated, influencing individual and organizational performance. These factors include: selling skills such as trust building, addressing customers’ needs, and competent communication. 2: Recognition and commission, which motivate employees. 3: Career development enables sales representatives to acquire the necessary skills and knowledge to adapt to changing market conditions. 4: role perceptions comprising job scope and understanding, and 5: supervisory assistance (Churchill et al., 1979). Together, these factors provide a helpful structure for enhancing performance within the pharmaceutical sales industry.

Role perceptions are arrived at with imperfect understanding, but they define our actions and are visible in everything (Raha and Hajdini, 2023). In most cases, only a gradual change in role perception is probable, but some people may change suddenly, and others do not understand the concept of the big picture, including managers (Silva et al., 2023). The most significant barrier associated with role perception is that individuals dislike adapting. Change becomes familiar as a means of enhanced resistance, with uncertainties and a lack of vision being key contributing factors (Raha and Hajdini, 2023). People usually perceive change favorably if they grasp the change and the new information (Silva et al., 2023).

Selling skills are the core and most important part of the job description of the sales force, as indicated by the Churchill et al. (1979) model. Churchill et al. (1985) argue that selling skills have three distinct components. Firstly, interpersonal skills include knowing how to overcome and resolve conflicts. Secondly, salesmanship skills include closing a deal and making effective presentations. Thirdly, technical skills or product knowledge include selling points, features, and benefits. The integration framework of selling skills and behaviors will reflect the sales force’s performance.

The supervisor has the appropriate experience and know-how, leveraging their expertise when managing or directing their teams. A competent supervisor fosters the same qualities in their team members. A skilled supervisor encourages the formation of teams willing to cooperate and share experiences (Ramseook-Munhurrun et al., 2025). In his view, Russ (2011) argues that supervisors can be categorized into two types. Managers who fall under the Theory X classification tend to view themselves as giving instructions, overseeing operations, and being motivated primarily by financial rewards. The managers who follow Theory Y encourage independence and shareholder responsibility, viewing challenging tasks as motivating.

Training of the sales force keeps them qualified enough to represent the company in the best way (Blanchard and Thacker, 2023). However, training is not a fast and simple step, as the impact of training may take time to show results (Kadić-Maglajlić et al., 2021). Training the sales force may require many stages to reach the organization’s goals, and managers should know the sales force’s needs and conduct customized training. Selective training and training of the sales force with different tenures is highly effective and helps to spill over to the untrained team (Blanchard and Thacker, 2023).

Amalia et al. (2024) argue that sales managers believe that a merit-based promotion system drives high performance. Non-financial rewards can be as powerful, if not more powerful, motivators than money. Recognition builds the sales force’s loyalty to the firm. A company’s pay system is one of the most important motivators for its sales force. In addition to meeting the fundamental needs of employees, it also meets their aspirations. In addition to meeting their immediate needs, a good salary and income package contribute to their sense of long-term security (Johnston and Marshall, 2020).

Yurt and Kasarci (2024) believe that adopting AI can be understood through expectancy-value, task-value, and utility-value. These components emphasize the interplay between individuals’ beliefs about their capabilities and the value they assign to AI-related tasks.

The widespread adoption of AI can be explained with the help of expectancy-value theory, which focuses on the relationship between self-efficacy and the perceived relevance of AI-related activities. Expectancy beliefs are subjective assessments of one’s ability to implement AI, which is conditioned, among others, by experience and context (Wang et al., 2023). A favorable context enhances the availability of resources and fosters expectancy beliefs, as well as access to institutional support. This enhances AI usage in the degree of occupational tasks (Yurt and Kasarci, 2024).

In expectancy-value theory, the value placed on a task, or task-value, shows the intrinsic, attainment, and utility value a person places on activities that relate to AI. In this case, value is innate, and individuals enjoy AI. Still, the value associated with attainment is that the individual seeks to master AI as part of self-identity and competence (Yurt and Kasarci, 2024).

Utility-value depicts attainment efficacy in achieving higher-level needs, goals, and effectiveness in resolving issues through AI (Yurt and Kasarci, 2024). For instance, those impressed by how AI works in resolving challenging issues or improving efficiency are likely to embrace new technologies with AI (Sankaran et al., 2023). The utility-value of AI consumption, characterized by its usage in organizational spheres, implies that immediate gratification may be a potential motivating factor. Accordingly, professionals are ready to embrace AI when they envision what it will do: reduce processes, enhance the quality of the end product, or increase job opportunities (Yurt and Kasarci, 2024).

To develop one effective drug, pharmaceutical companies require approximately $ 2.5 billion over 10–15 years. Current estimates suggest that only 12% of clinical trials are successful, making the pharmaceutical industry’s drug development cycle quite expensive and lengthy. Implementing AI can be one way to optimize drug and business process development (Kulkov, 2021). AI can quickly scan and evaluate medical marketing data, thus increasing its relevance and speed of decision-making, providing optimal solutions to pharmaceutical marketers’ issues (Verma et al., 2024). The utility of AI for pharmaceutical industry companies is broader than anticipated, as it enables improved efficiency, reduced costs, and more informed decision-making. However, AI’s sufficiency and deployment differ depending on the business size (Kulkov, 2021). As a sales team, there are some specifications in place for pharmaceutical salesforces. Thus, deploying AI for them is no different from what is done in other sectors. Similarly, pharmaceutical salesforces can utilize AI in many areas, such as marketing, sales management, training, CRM, and stock management (Baviskar et al., 2023).

AI applications analyze market trends and customer data to optimize marketing strategies and product positioning, and can analyze and understand customers (Verma et al., 2024). According to Shinde et al. (2021) AI improves customer service efficiency, marketing concepts, and interactive communication, ensuring precise dissemination of medical facts. Thus, this study proposes the following hypothesis:

According to Parmenter (2015), most enterprises identify acceptable performance benchmarks called key performance indicators (KPIs), enabling their employees to know what is expected. These performance benchmarks are often incorporated into promotion and compensation systems to motivate employees to achieve their targets. One suggestion to improve the sales force’s performance would be to incorporate AI into this process by entering KPIs and requesting that it recommend strategies to meet these targets (Parmenter, 2015).

AI can also help Med Reps stick to their KPIs. It will track their daily KPIs and provide different reports on what has been done and what needs to be focused on (Luo et al., 2021). Following its analysis of the sales team’s actions and salespeople’s productivity, AI can recommend best practice strategies tailored to specific representatives. AI can also actively endorse motivating and informative tracks and instructional videos showcasing the best practices (Kim et al., 2021).

In addition, sharing peers’ success stories through AI allows the sales force to build a culture of success internally. This inspires individuals and provides a concrete strategy for achieving such results, thereby improving the entire team’s performance (Pelau et al., 2021). Therefore,

Management can utilize AI coaches to train the sales force and improve their selling skills (Singh et al., 2019). AI can record medical representatives’ calls and analyze the reports to detect the skills that must be empowered (Luo et al., 2021). Therefore, integrating management input and AI can suggest the most critical skills the sales representative must consider, considering the rep’s performance and experience. AI can also recommend the optimum course for the missing skills (Luo et al., 2021). Thus, this study proposes:

One of the most important aspects is the organization’s support of supervisors who assist employees during the process. Effective supervisors provide clear direction and encouragement to their juniors, making them feel confident to work with AI technologies (Eckhardt et al., 2009). Chatterjee et al. (2021) explain that the presence of supervisors helps alleviate the fear of change by demonstrating the usefulness and the reasons for its utilization in a particular area. Moreover, it is the supervisors who are the first to use AI technologies. This facilitates the employees in comprehending the practicality of the tools, making them accept the AI technologies faster (Venkatesh and Davis, 2000). Active, supportive managers motivate employees to see the process differently as an asset that, once adopted, can enhance worker commitment and ensure the success of execution within the organization. Thus,

Medical and sales representative training programs must be available for every pharmaceutical business to raise the degree of self-esteem and trust toward the medical team (Blanchard and Thacker, 2023). AI can help with these training programs by producing quizzes to evaluate the medical sales team’s comprehension of the products (Rainsberger, 2022). AI technology delivers customized learning materials tailored to the needs and requests of employees, facilitating the evaluation and improvement of workplace training processes (Na et al., 2022).

Na et al. (2022) maintain that AI can effectively assist by giving practical career progression guidance to employees. It can also effectively assess medical representatives and sales teams by analyzing their profiles and performance, recommending suitable career advancement through portfolio diversification, and determining the best means of achieving these targets. Hence,

AI’s impact on promotion and recognition processes is moderate. However, appreciating sales achievements and the overall work of medical representatives, AI can assist in deciding who deserves promotion and recognition (Gupta et al., 2022). Campbell (2023) argue that AI can determine whether sales targets are realistic, stimulating, or overly challenging for the personnel. It also uses essential parameters such as the size of the market, prior sales results, and percentage of the market held to model demand for the market in the future, and guarantee that incentive strategies are consistent with the business’s practical goals. Therefore,

As shown in Figure 1, the conceptual framework was developed based on the research literature review and theoretical framework. This framework suggests that pharmaceutical salesforces can be promoted by implementing AI technology. In addition, AI adoption can improve the factors that influence the pharmaceutical salesforce, such as role perception, selling skills, supervisory support, career development, recognition, and commission.

The questionnaire of this study was developed from a comprehensive review of relevant literature (Behrman and Perreault Jr, 1984; House and Rizzo, 1972; Yurt and Kasarci, 2024). The questionnaire is divided into three sections and comprises 39 questions. The first section comprises four questions related to demographic characteristics, including gender, age, years of experience, and participant position. In the second section, 23 questions are related to the six organizational performance factors. The factors of organizational performance are role perception (5 items), selling skills (5 items), supervisory support (6 items), career development (4 items), and recognition and commission (3 items). The final section comprises 12 questions regarding the three key factors influencing AI adoption in the pharmaceutical industry. The factors of AI adoption are the expectancy-value (4 items), task-value (4 items), and utility-value (4 items).

The responses are on a 5-point Likert scale, excluding demographic questions. The questionnaire was translated into Arabic using the translation methodology (Abdallah et al., 2021). The English and Arabic versions of the questionnaire are available upon request from the corresponding author.

A judgment sampling approach was employed to select participants for the research, targeting specialized individuals (Jaya et al., 2024). Since this study analyzed data using SmartPLS 4, researchers must first establish the requisite statistical power to ascertain the appropriate sample size for PLS-SEM. A statistical power of no less than 0.8 at a significance level of 0.05 is deemed a sufficient threshold in business studies (Abdallah et al., 2019). Since pharmaceutical firms are a huge sector with many salesforce professionals, a sample size of 150 participants was determined. The Google Forms platform was used to design the questionnaire, which was then sent to the selected pharmaceutical industry companies via WhatsApp. The WhatsApp contacts utilized to share the questionnaire were obtained from the HR divisions of the participating firms. As a requirement of their internal communication procedures, these companies have accurate telephone records of their salesforce personnel.

The current study’s target population comprises the entire sales force of pharmaceutical industry companies in Kuwait. To guarantee participants had relevant experience and the ability to provide valuable insights, the participation criteria required them to be active sales professionals with at least six months of experience in the pharmaceutical industry. Those who did not meet these criteria were excluded from participation. This population provides a representative sample used to collect the required data through questionnaires, which are used to answer the research questions. Data collection occurred over two months, specifically from November to December 2024. The research ethics committee of Box Hill College provides approval to conduct this study, reference number REC/2024/063. All participants received and completed a confidentiality form explaining the study’s purpose and ensuring their names would be kept anonymous.

A pilot study was conducted before the actual accomplishment, and a questionnaire was sent to 15 individuals working in pharmaceutical industry companies for their feedback. Some questions were modified to be more precise and straightforward, such as “When I do a good job, I consistently receive recognition from my company,” instead of “When I do a good job, I receive recognition for that.” The data collected from the 15 people working in pharmaceutical industry companies were excluded when further analysis was undertaken.

Excel version 19 was used to analyze the demographic profile of the participants. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the collected data, and SmartPLS4 software was used for calculation purposes.

This study evaluated the data distribution using descriptive analysis, including mean, standard deviation (SD), skewness, and Kurtosis.

The measurement models (reflective and formative models) were analyzed to assess the validity and reliability of the developed questionnaire. In the reflective measurement model, composite reliability, outer loading (which should be greater than 0.6), Cronbach’s alpha (which should be greater than 0.7), and average variance extracted (AVE) (which should be greater than 0.5) were assessed. The Heterotrait-Monotrait ratio (HTMT) was used to evaluate the discriminant validity. In the formative measurement model, outer weight, which should be more than zero and less than one, variance inflation factor (VIF), which should be less than 5, and p-value, which should be less than 0.05, were assessed.

Furthermore, the structural model, using path coefficients, R², and p-values, was evaluated to test the hypotheses. Finally, the standardized root mean square residual (SRMR) was assessed to determine the global model fit. A value of 0 for the SRMR indicates a perfect fit, but a value of 0.085 is considered a good fit (Hair et al., 2019).

A total of 250 survey invitations were sent out, and only 176 questionnaires were completed; after examining them, seven were excluded as incomplete. Therefore, 169 questionnaires were filled out and used for data analysis. According to gender, 53% of the participants are males, whereas 47% are females. Regarding the age distribution of participants, 31% were 33–37 years old, 28% were 38–42 years old, 17% were over 43 years old, 15% were 28–32 years old, and 9% were 20–27 years old. Concerning the participants’ positions, more than 50% were from the managerial and supervisory levels. Of the participants’ experience, 46% have more than 10 years of experience.

Table 1 represents the mean, standard deviations, kurtosis, and skewness values for all pharmaceutical sales force performance variables and AI adoption. The highest mean was found to be 4.335 against the variable “selling skills”; the standard deviation for this variable was relatively low compared to the other items. This means the respondents in the pharmaceutical industry acknowledged the importance of selling skills for performance evaluation. The lowest observed mean of 3.233 was for the variable “expectancy,” indicating the respondents were unsure about knowing and using AI technology. Overall, the mean score of all variables is higher than 3, indicating a positive response from the respondents regarding the pharmaceutical sales force and AI adoption.

All the variables exhibit negative skewness, indicating that the left tail of the probability density function is longer than the right side. This indicates that the distribution is skewed to the left and lacks symmetry. Since most values lie between −1 and −0.5, overall, the distribution is moderately skewed. The kurtosis distribution is approximately normally distributed, as most values fall within the reference range of −3 to +3.

Measurement models consist of two: a reflective measurement model, in which the direction of the arrow points from the construct to the indicator variable, and a formative measurement model, in which the directional arrows point from the indicator variables to the construct (Hair et al., 2019). The results of both models are presented separately below.

The reflective measurement models, including internal consistency reliability, discriminant validity, and convergent validity, were assessed, and the results are outlined below.

Table 2 provides a summary of the measurement model findings. In terms of outer loading, all items are above 0.7 except SS1 and SS3, which are eliminated from the model. However, some loadings (CD4, SPS2, and EXP1) are slightly below the critical value of 0.7. They were included in the measurement model because they displayed acceptable values for composite reliability and the average variance extracted. Cronbach’s alpha should be equal to or higher than 0.7, composite reliability should be higher than 0.6, and the AVE should be higher than 0.5. All the Cronbach alpha values were higher than 0.7, and the composite reliability of constructs was higher than the critical value of 0.6. Also, all the AVEs of constructs were higher than the critical value of 0.5.

The reflective measurement model’s discriminant validity results include the HTMT outlined below.

HTMT results indicate that the correlations between all indicators across variables are below 0.9, as shown in Table 3. This means a high discriminant validity and distinct variables.

The results of the formative measurement model are presented in Table 4. All weights were significant >0, p-value< 0.05, and VIF values were below 3.

As shown in Table 5, the relationship between pharmaceutical salesforce performance and AI adoption is significant, with a path coefficient of 0.647, a p-value of less than 0.000, a t-value of 9.548, and an R² of 0.584. Also, the relationship between all variables of pharmaceutical salesforce performance (role perception, selling skills, supervisory support, career development, and recognition) was significant. It was observed that the strongest relationship was between supervisory support and AI adoption, with a path coefficient of 0.434 and a p-value of 0.000. The second strongest relationship was between career development and AI adoption, with a path coefficient of 0.429 and a p-value of 0.000. The weakest relationship was between role perception and AI adoption, with a path coefficient of 0.119 and a p-value of 0.025.

The SRMR with a value of zero indicates a perfect fit, but a value < 0.085 is considered a good fit (Hair et al., 2019). The SRMR score, with a value of 0.068, showed a good global fit of the model.

This research focuses on understanding the determinants of AI technology integration in pharmaceutical companies, emphasizing its impact on the salesforce, as the industry is highly competitive.

The research uncovered several imperative relationships between key factors and AI adoption in pharmaceutical sales teams. The results indicate a significant relationship between pharmaceutical salesforce performance and AI adoption (a path coefficient of 0.647, p-value <0.001, a t-value of 9.548, and an R² of 0.684). The relationship between pharmaceutical salesforce role perception and AI adoption was significant (a path coefficient of 0.119, p-value <0.025, and a t-value of 3.54). In addition, the relationship between pharmaceutical salesforce role perception and AI adoption was significant (a path coefficient of 0.119, p-value <0.025, and a t-value of 3.541). Pharmaceutical salesforce selling skills were another determinant positively correlated with AI adoption (a path coefficient of 0.166, p-value significant <0.011, and a t-value of 3.761). Additionally, the relationship between supervisory support and AI adoption is highly significant and robust (a path coefficient of 0.434, p-value of less than 0.000, and a t-value of 8.794). The relationship between career development and AI adoption is significant, ranking second in terms of the highest path coefficient 0.429, a p-value <0.000, and a t-value of 8.200. Finally, a significant relationship was found between recognition and AI adoption (a path coefficient of 0.182, p-value <0.004, and a t-value of 2.857). In summary, these findings demonstrate that performance, role perception, selling skills, supervisory support, career development, and recognition are all crucial factors influencing the adoption of AI in pharmaceutical sales teams.

Our findings confirm that pharmaceutical salesforces’ performance significantly impacts AI technology adoption (H1). This strong relationship (β = 0.647, p < 0.000, t = 9.548, R² = 0.684) suggests that pharmaceutical industry companies that adopt AI technology will likely achieve improved results, operational efficiency, and greater customer satisfaction. This result should encourage business decision-makers to invest in AI technologies, enabling pharmaceutical industry companies to grow and innovate in the global marketplace characterized by stiff competition. This aligns with Roy (2022) arguments that AI can help marketers segment, target, and provide proper and customized marketing by deeply understanding healthcare provider profiles and attitudes. Pharmaceutical marketers may be able to leverage AI to evaluate their transition from personalization to hyper-customization.

For pharmaceutical companies, this finding implies that fostering a high-performance sales culture may naturally accelerate AI adoption. Companies should consider showcasing AI-driven success stories and demonstrating how AI tools can transition sales approaches from personalization to hyper-customization.

Our analysis confirmed that the role perception of pharmaceutical sales representatives significantly influences the extent to which AI technology is accepted for use (H2). This means that by providing personalized perspectives, AI enables salesforces to approach clients based on their strengths and preferences. Moreover, the perception of an employee’s role within pharmaceutical industry companies can be socially constructed and integrated with corporate ESG, facilitating the salesforce’s sense of identity. The finding supports Luo et al. (2021) assertion that AI can enhance role clarity by tracking activities, reminding representatives of their KPIs, and displaying progress toward goals. Similarly, it aligns with Pelau et al. (2021) observation that AI can contribute to a thriving salesforce culture by disseminating peers’ achievements, thereby reinforcing positive role models. Future implementations may benefit from involving sales representatives in AI design processes to ensure that new tools complement existing role perceptions.

The research confirms that pharmaceutical sales professionals with higher selling skills have a significant impact on the integration of AI solutions in Kuwait (H3) (β = 0.166, p < 0.011, t = 3.761). This highlights the importance of incorporating AI technology into pharmaceutical sales processes. By embracing AI, firms will improve the capabilities of their salesforce to interact with customers, maneuver around intricate sales environments, and achieve superior business results. This relationship suggests that representatives with well-developed sales capabilities are more likely to adopt AI technologies to further enhance their effectiveness. This is verified by Luo et al. (2021) assertion that AI can enhance Salesforce’s selling abilities, record medical representatives’ calls, and review the reports to see the skills requiring focus. Therefore, integrating management support and AI can recommend the skills the sales representative should utilize when evaluating their outcomes. This relationship suggests that pharmaceutical companies may benefit from creating specialized roles that combine advanced sales skills with AI expertise, potentially establishing maximum effectiveness in increasingly complex healthcare markets.

The findings revealed that supervisory support is crucial in AI adoption in pharmaceutical industry companies (H4) (β = 0.434, p < 0.000, t = 8.794). This strong relationship highlights supervisors’ pivotal influence in shaping technological change within pharmaceutical sales teams. Pharmaceutical industry companies must allocate resources towards leadership initiatives for digital transformation that will enable managers to stay abreast of developments in AI. This finding is supported by the argument of Eckhardt et al. (2009) that one of the most important aspects is the support of supervisors who assist employees throughout the process. Effective supervisors provide clear direction and encouragement to their juniors, making them feel confident to work with AI technologies. Moreover, the reasoning of supervisors is further complicated by the argument presented by Chatterjee et al. (2021) regarding their roles in enhancing understanding and acceptance of change within a given context. This finding suggests that investing in leadership development for digital transformation should be a priority for pharmaceutical companies.

The pharmaceutical salesforce in Kuwait perceives career growth and development as a catalyst toward adopting AI (H5), representing the second strongest relationship in our model (β = 0.429, p < 0.000, t = 8.200). This robust connection suggests that when sales representatives perceive AI proficiency as beneficial to their career advancement, they become substantially more motivated to adopt these technologies. Companies in the pharmaceutical industry can develop career frameworks that prioritize the strategic importance of AI skills for managerial and senior positions. Rainsberger (2022) supports this by stating that AI can assist in creating quizzes to test the sales teams’ understanding of the product, ensuring they are well-prepared. Na (2023) argument also supports that AI technology provides innovative assisting tools for training within the workplace. This aligns with the opinion of Na et al. (2022), who suggest that AI can provide employees with precise advice on career development and training opportunities tailored to their specific needs.

Our finding confirms that recognition has a significant impact on pharmaceutical sales representatives’ willingness to adopt AI solutions (H6) (β = 0.182, p < 0.004, t = 2.857). While this relationship is not as strong as some other factors, it nonetheless demonstrates that acknowledging and rewarding the utilization of AI plays a meaningful role in technological integration. This result emphasizes that recognition is a motivational factor and a strategic facilitator of AI adoption in pharmaceutical industry companies. Companies in the pharmaceutical industry can enhance AI integration, improve their organizational culture, and gain a competitive edge by recognizing and valuing their employees’ contributions. This also corresponds with Campbell (2023) view, which contends that AI can help evaluate whether the set sales targets are feasible, reasonable, or overly ambitious for the team.

Our current study has limitations, such as not including other variables that might impact pharmaceutical salesforce performance using AI. Also, the target sample focused only on Kuwait’s context. Therefore, future research is recommended to have a larger sample size, encompass different countries, and expand the variables used in the investigation. A qualitative approach should also be employed to better understand sales forces’ attitudes toward AI adoption in pharmaceutical companies.

Our study demonstrated a significant relationship between pharmaceutical salesforce performance and AI adoption. Supervisory support and career development emerged as notably the most essential variables, which may be utilized to enhance the training of sales representatives by customizing content and identifying skills deficiencies, and help mitigate employees’ resistance to change by providing brief direction and clear guidelines. It is recommended that employees’ progress be combined with AI-driven educational activities conducted at any time to improve motivation and prepare for work in AI-enhanced conditions. By coupling AI adoption with career progression synergies, pharmaceutical companies can achieve more effective and sustained operational excellence and workforce development advancements.

We are grateful to Alaa Radwan for his essential contribution to this study. His skill and experience in translating the questionnaire ensured its correctness and cultural relevance, which contributed to the success of this research.