Talent Management Strategies and AI Adoption in Saudi Tech Startups: Empirical Evidence on the Mediating Role of Knowledge Sharing

Talent Management Strategies and AI Adoption in Saudi Tech Startups: Empirical Evidence on the Mediating Role of Knowledge Sharing

Mohammad Saad Abuhaimed., Abdoulrahman Aljounaidi., Alharath ateik

Faculty of Finance and Administrative Science, Al Madinah International University, Taman Desa Petaling, 57100 Kuala Lumpur, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000318

Received: 12 August 2025; Accepted: 18 August 2025; Published: 09 September 2025

ABSTRACT

This study looks at how personnel management practices affect the use of artificial intelligence (AI) in new digital businesses in Saudi Arabia, with a focus on how sharing expertise might help.  The study uses Partial Least Squares (PLS) to look at how five key talent management areas attracting and selecting talents, developing talents, empowering talents, retaining talents, and career succession affect the outcomes of AI adoption. The data comes from 300 employees and managers at different Saudi tech startups.  The results show that talent management practices make it much easier for people to share expertise, which directly and indirectly leads to more AI adoption.  Knowledge sharing is an important way to connect the development of human capital with technology innovation, which makes it easier for digital transformation to happen.  The study adds to the expanding body of knowledge about how to manage people in technology-driven environments. It also gives startup executives useful information on how to improve their workforce management strategies to make AI integration easier.  The results also show that there are some things that are unique to Saudi Arabia’s growing tech ecosystem. These are things that policymakers and practitioners may use to speed up digital innovation by managing talent strategically.

Keywords: Talent Management, Artificial Intelligence Adoption, Knowledge Sharing, Tech Startups, Saudi Arabia, Partial Least Squares (PLS)

INTRODUCTION

The Fourth Industrial Revolution has begun a time when digital technologies, especially artificial intelligence (AI), are changing the way businesses work, how they are organized, and how people work together.  As the world changes, more and more people see new tech firms as drivers of innovation and digital disruption.  The official Vision 2030 strategy in Saudi Arabia has made digital transformation and innovation key foundations for economic diversification. This has greatly increased the importance of AI in the private sector, especially among high-growth firms (Alzahrani & Seth, 2021).  But investing in technology alone won’t make AI adoption successful. It also needs strong human resource systems that encourage the growth and use of digital skills.  Talent management techniques have gotten a lot of attention because they could help create a workforce that can drive and support digital innovation.

Talent management is the organized way of finding, developing, keeping, and promoting people with high potential in line with the aims of the organization (Rana & Abbasi, 2022).  This includes giving staff the digital skills they need, encouraging a culture of learning, and encouraging behaviors that support technological integration, all of which are important for AI adoption.  Knowledge sharing has become an important link between talent management and innovation results. It makes it easier for people to share their knowledge, solve problems, and learn together, which are all important for using AI effectively (Lee et al., 2020).  Even though this is known, there isn’t much empirical study that looks at how certain talent management methods affect AI adoption through knowledge sharing, especially in the socio-cultural and economic environment of Saudi Arabian businesses.

This study tries to fill this gap by looking at the link between talent management methods and AI adoption in a real-world setting, with knowledge exchange as a middleman.  The study looks at five aspects of talent management using data from new software businesses in Saudi Arabia: attracting and choosing talents, developing skills, empowering talents, keeping talents, and career succession.  The study uses a Partial Least Squares (PLS) technique to try to find evidence-based ways that human resource policies help AI-driven change.  In this way, it adds to both theory and practice by making clear how sharing knowledge may help businesses that are changing digitally to innovate with technology.

Problem Statement

Even though there is a lot of talk about how artificial intelligence (AI) might help companies get ahead of their competitors, many new tech firms in Saudi Arabia are still having trouble using AI technologies in their day-to-day and long-term business plans.  National programs like Vision 2030 have sped up digital transformation across the kingdom, but not all organizations are ready to use AI, especially smaller, more innovative businesses (Alshamrani & Ahmad, 2021).  The lack of technology is not the main problem with integrating AI; it’s the ability of people to promote and maintain digital innovation.  To establish this capacity, talent management tactics including attracting, developing, keeping, and empowering competent people are very important.  But the ways in which these techniques affect the use of AI have not been studied enough in the literature, especially in the context of Saudi Arabian entrepreneurship.

More and more studies imply that sharing knowledge may be a crucial factor in turning talent management efforts into technical results (Ahmed et al., 2020; Pournader et al., 2022).  In digital companies, where quick innovation and flexibility are important, employees sharing and spreading knowledge can make it much easier for the company to learn and use new technologies like AI.  However, there aren’t many real-world studies that look at how sharing information might help with both workforce management and AI adoption, especially in developing countries.  Because there isn’t enough real-world evidence, governments and startup executives can’t make focused human resource interventions that support their digital transformation goals.  Because of this, it is very important to find out through research how talent management techniques affect the use of AI and how much knowledge exchange plays a role in this relationship in the unique social, economic, and technological environment of Saudi Arabia.

Research Framework

The conceptual model talks about how a research idea may proceed. It functions similarly to a map by connecting the research methods and providing clues about every possible step of the investigation. From issue creation to data analysis, this model provides a framework that recognizes, develops, and explains the interrelated activities of research (McDonald, 2015). According to the theoretical framework, the model includes the primary determinants of AI adoption, which include career succession, talent development, talent empowerment, talent retention, and talent attractiveness.

(Davis, 1989; Grant, 1996; Blau, 1964)

Research Hypothesis

First Hypothesis

H1: There is a significant relationship between Talent Management Strategies and AI adoption in Saudi Arabia’s Emerging Tech Startups.

Second Hypothesis:

H2: Knowledge Sharing mediates the relationship between Talent Management Strategies and AI adoption in Saudi Arabia’s Emerging Tech Startups.

Third Hypothesis:

H3: There is a significant direct relationship between Knowledge Sharing and AI adoption in Saudi Arabia’s Emerging Tech Startups.

METHODOLOGY

This study uses a quantitative research methodology and a survey to look into how talent management practices affect the use of AI in new tech firms in Saudi Arabia, with knowledge exchange as a mediating variable.  The target group is made up of managers and technical professionals who work for tech-based businesses that are part of Saudi Arabia’s Vision 2030 innovation ecosystem.  We constructed a questionnaire and tested it using measurement questions that were modified from scales that had already been tested in peer-reviewed literature. This made sure that the content and construct were valid.  We took items about attracting, developing, empowering, keeping, and moving up in a career from Al Ariss et al. (2020). We measured knowledge sharing using the Lee and Ha (2020) scale, and we got AI adoption items from Pournader et al. (2022).  We used purposive sampling to obtain data from 300 people who worked at startups registered with the Monsha’at (General Authority for Small and Medium Enterprises). This made sure that all technological fields were represented.  Before doing a complete examination of the data, a pilot test was done to improve the tool and check its reliability with Cronbach’s alpha.  We used SmartPLS 4.0 to look at the data. We looked at convergent and discriminant validity, path coefficients, and mediation effects to see if the associations we thought were there were indeed there.

Discriminant validity for Measurement Model

By means of SmartPLS and SPSS, pon investigates the goodness of fit of the data, convergent validity, and discriminant validity of the measurement model, so attesting to the validity and reliability of the modified measuring scale applied to evaluate the constructions and their respective items. The study verified the model’s robustness in terms of internal consistency, composite reliability, and average variance extracted (AVE), as well as sufficient discriminant validity, thereby guaranteeing the construction’s distinctiveness from one another. After guaranteeing the validity and reliability of the model, figure 4.1 shows the measurement model created in SmartPLS including the remaining 35 components. With their corresponding indicators, the constructions become operational; the model captures the relationships among the latent variables. The statistical tests verified that the measuring model fits the data rather nicely,therefore laying a strong basis for later structural model study.

Figure 1 presents the measurement model developed in SmartPLS

Examining Results of Hypothesized Direct Effects of the Constructs

Table.1 indicates that the majority of routes connecting the constructs were statistically significant, with p-values falling below the conventional significance threshold of 0.05. Paths from Attracting and Selecting Talents (AST) to Adoption of Artificial Intelligence (AI) and Knowledge Sharing (KS); from Career Succession (CS) to AI and KS; from Developing Talents (DT) to AI and KS; and from Empowering Talents (ET) to AI and KS were statistically significant. Nonetheless, the relationships from Retaining Talents (RT) to AI and KS were not statistically significant, as their p-values beyond the 0.05 level. This section presents the outcomes of path analysis about the aforementioned hypotheses inside the structural model:

Table.1 Examining Results of Hypothesized Direct Effects of the Constructs

CONCLUSION

The study’s structural model analysis shows that personnel management techniques have a big effect on how Saudi Arabia’s burgeoning tech firms use Artificial Intelligence (AI). Knowledge exchange also plays a complicated role in this.  Attracting and selecting talents (AST), developing talents (DT), and empowering talents (ET) were three of the five main parts of talent management that had strong and statistically significant positive effects on AI adoption. This means that startups that focus on hiring, training, and empowering their employees are more likely to effectively use AI technologies.  Also, these three factors made it much easier for employees to share knowledge, which supports the premise that strategic human capital practices create a collaborative knowledge environment that is necessary for innovation.  It’s interesting that career succession (CS) has a big negative effect on both AI adoption and knowledge sharing. This could mean that succession planning processes in these fast-changing startup contexts aren’t working as well as they could be.  On the other hand, retention tactics (RT) had no significant effect on either AI adoption or knowledge sharing. This suggests that keeping personnel without also implementing developmental or engagement activities does not help technology move forward.  Most importantly, sharing knowledge made it harder for AI to be adopted in this case. This could be because people didn’t want to share expertise because of competition or the culture of the firm.  These results show how complicated talent-driven innovation is in emerging countries and stress the necessity for integrated, forward-looking talent management approaches that not only build internal skills but also make sure that cultural and strategic elements are in line with each other to make AI integration successful.

RECOMMENDATION

This report gives Saudi Arabian tech companies a number of useful suggestions for improving their talent management methods so that they may successfully implement AI.  First, companies should focus on structured ways to find and hire people who not only have the technical skills they need, but also the capacity to adapt and work well with others.  This makes sure that the people and the digital transformation agenda are on the same page.  Second, programs for developing talent should be given more attention. These should focus on digital literacy, cross-functional skills, and continual learning, all of which are very important for sharing knowledge and getting ready for AI.  Third, giving employees more authority through decentralized decision-making and innovative autonomy was a major reason why companies adopted AI. This shows how important it is for executives to build trust and give employees more freedom.

Also, the study found a surprise negative link between sharing knowledge and using AI, which suggests that not all activities that include sharing knowledge lead to new ideas.  So, startups should spend money on structured knowledge management systems, digital collaboration platforms, and company cultures that encourage deliberate and high-quality information sharing.  Finally, making team members feel safe and trusted can make knowledge exchange work better as a link between talent strategy and AI deployment.  All of these suggestions support a comprehensive, people-centered way to deal with digital transformation in the Saudi tech sector.

Suggested Future Research

Examine moderating factors such as leadership style and organizational culture to better understand their impact on talent management, knowledge sharing, and AI adoption relationships.

Conduct longitudinal studies to observe changes over time and gain deeper insights into how talent strategies influence digital transformation.

Perform comparative studies across different countries or regions to test the generalizability of the findings beyond Saudi Arabia.

Incorporate additional variables like innovation behavior and organizational agility to enrich the research model.

Utilize mixed-methods approaches to provide a more comprehensive understanding of the role of human capital in AI adoption and organizational success.

REFERENCE

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