Introduction

COVID19 pandemic radically transformed the world of work in India, especially in education technology sector. As lockdowns and social distancing made traditional classroom settings untenable, India witnessed an unprecedented shift towards online learning, a move that reshaped both the education as well as the employment landscape. Schools, colleges, and several training institutions rapidly moved towards digital platforms to continue operations, which in turn resulted in massive growth in edtech companies that provided online classes, interactive learning, and virtual tutoring. Digital collaboration tools in this crisis were an emerging aspect, which was no more a choice, but the only way to work from home1. As traditional teaching became infeasible, edtech companies rapidly adopted AI driven tools to enhance the digital learning experience. AI powered platforms enabled personalized learning paths for learners as well as helped employees to manage work effectively, which led to a sudden surge in AI demand. Additionally, in the last decade, AI has shown promising avenues encompassing all the sectors on a global level2,3. AI driven solutions have permeated nearly all sectors, from healthcare4 to financial forecasting5, power6, information technology7, manufacturing8, and education9, revolutionizing work models across the globe. Considering, promising edtech growth in India, with current worth $7.5 billion, is expected to reach $ 230 billion in 2030 as stated by Internet and Mobile Association of India (2025). Edtech organizations are delving into adaptive learning systems, chatbots, performance analytics, AI powered personalisation and intention to use metaverse10 indicating a pool of opportunities for further development. Research in South East Asian countries present the growth opportunities and challenges of AI adoption in edtech11,12,13. Recent studies in the same direction, identifies the immense scope for empirical studies for research and innovation, tailored to India’s unique challenges14,15,16. As the spiral growth and use of AI in edtech in India is fuelled by high digital adoption, several government initiates, shift to online learning and hybrid learning models, it is the opportune time to explore the AI driven processes and its influence on work outcomes. With growing demand for personalized and scalable education, AI has the potential to improve workplace efficiency, and adaptive learning outcomes, bridging educational gaps for building future ready educational ecosystem, especially in emerging economies like India.

Despite the relentless surge in AI investments, actual impact has often lagged expectations leaving companies to question their returns. An analysis of AI initiatives across various industries reveals that many organizations struggle to transition from experimental phases to meaningful impact17. This issue lies not only in the technology, but also in leadership preparedness to manage it. It requires an AI mindset that is adept at understanding, deploying, and integrating AI tools with everyday workflows. Therefore, AI is seen as an opportunity, and a challenge for many organizations. Simply allocating large budgets to AI will not guarantee effective AI utilization, rather what organizations need is strong AI focused leadership. Moreover, many companies struggle to move beyond isolated AI experiments, largely due to a lack of clear vision and guidance, and this guidance can be catered largely through AI savvy leadership. AI savvy leaders can bridge this gap by fostering a culture that embraces data driven innovation, and align AI initiatives with core business goals. It will help the employees to understand the way forward, and the complexities of AI encompassing from data quality to ethical considerations. This support can help employees navigate these challenges and unlock the real value of AI investments. AI savvy leadership can propel an organization forward in a competitive landscape. Research on AI savvy leadership remains limited, and this paper is an answer for organizations to look withing leadership capabilities and explore this unique skill further, for successfully utilizing AI, and elevating employee engagement.

The study contributes to a significant knowledge gap in fostering employee engagement at work among digital natives who are born in the cradle of technology, and are facing disengagement issues at workplaces, that too in the early phase of their careers. Digital natives refer to the generational cohort born after 1995, and are also known as Generation Z employees18,19,20. Recent study suggested AI as an integral factor for managing this tech glued generation21. This study is an attempt to help employers and academicians understand what work environment resonates with digital natives and hence, emphasize how employers can derive its benefits for meeting the organizational objectives.

The following sections provides a comprehensive explanation of hypotheses propositions including literature review, theoretical background, data analysis and the implications for theory and practice, followed by limitation and scope for further research.

Theoretical background

Drawing from two principal theoretical frameworks, Job Demands- Resources Theory- JD-R77and Technology Acceptance Model-TAM22, this study seeks to gain deeper insight into antecedents and consequences of AI utilization. Authors4,23 have discussed the applicability of JD-R theory in shedding light on the dynamics in the AI driven work environment. JD-R is referred as an explanatory framework explaining how digital natives can integrating AI at workplaces for reducing routine demands, and enhancing resources for spurring their engagement levels, which is a critical concern in human resource domain globally. The work offers novel conceptual framework advancing JD-R theory, capturing the impact of AI driven work processes. AI has the potential to reduce repetitive demands at work, while introducing cognitive, ethical, and learning challenges as well. Through JD-R lens, this paper critically examines how AI shapes demand, enrich resources and influence employee engagement in technology driven environment.

Technology Acceptance Model22 is extensively utilized by scholars, academicians and practitioners for explaining various facets of adoption of technology. This research focuses on the pivotal role of integration of AI, within the framework of TAM, particularly concerning perceived usefulness. AI driven platforms offering personalized user experiences, with their potential to automate tasks directly influence employees’ perception of usefulness, thereby influencing adoption behaviours, with profound implication for digital transformation, and work environment. Additionally, TAM strongly resonates with digital natives in workforce segment, as their innate familiarity with technology shapes positive perceptions of usefulness, and ease of use. The understanding of how AI utilization through the lens of TAM affects employee engagement is deepened as a result of this research, building a bridge between AI savvy leadership and AI utilization, while adding to the applicability of TAM touching psychological and sociological aspects24,25,26.

To summarize, the integration of JD-R and TAM constructs a theoretical lens, through which the interplay between AI savvy leadership, AI utilization, collectively determine the influence on employee engagement in the edtech industry. The synergy of these two theories facilitates a holistic understanding of the socio-technological elements.

Hypotheses development

Artificial intelligence savvy leadership

Various leadership styles have been studied extensively in the past, including transformational and digital leadership, each offering unique insights into driving organizational success. Transformational leadership aligns teams with a shared vision, influencing dimensions of employee motivation, innovation, digital creativity and organizational growth27,28. Their approach focuses on driving efficiency, enforcing standardization processes and creating streamlined environments. Leaders with a mindset for digital transformation are considered as digital leaders29, who guide their businesses30, and play a crucial role in helping organizations navigate digital platforms and technologies31. However, with rapid advancements in artificial intelligence, the focus is shifting towards AI savvy leadership17, where AI savvy leaders are adopting a fundamentally different mindset to navigate ambiguity and acknowledge complexity as inevitable, while understanding the robust role of ethical consideration and readiness to adapt continuously. This new leadership paradigm emphasizes a deeper understanding of growth aspects of machine learning, predictive analysis, AI ethics, and strategic alliance unlike digital savvy leadership which primarily focus on digital tools. AI savvy leadership integrates AI into every aspect of business strategies, transforming operations at a fundamental level. As now AI is becoming the cornerstone of innovation, organizations with leaders who embrace this shift will gain a significant competitive edge in the future. Also, the availability of artificial intelligence tools at workplaces does not necessarily lead to its widespread utilization. This gap stems from lack of AI savvy leadership, which is more like a strategic and cultural alignment that goes beyond mere access to AI tools. Extant research associated technology competence in leaders with effective work outcomes such as employee engagement32,33. Given these underpinnings, the assertions are summarized in the following hypotheses:

H1

Artificial intelligence savvy leadership has a positive and significant impact on AI utilization.

H2

Artificial intelligence savvy leadership has a positive and significant impact on employee engagement.

AI utilization

Artificial intelligence technology includes program, systems, machines, algorithms, that replicate and augment human cognitive functions for making decisions based on data34. AI is now a top priority for many companies to gain an edge over others35,36. AI utilization is assisting companies to fasten the work, while ensuring automation crucial for internationalization process37. AI at work is driving growth, revenues and competitiveness38, and is transforming employee engagement by automating routine tasks, streamlining workflows, and offering real time insights. Recent studies have highlighted the significance of AI utilization for enhancing employee engagement39,40. Given these underpinnings the following hypotheses are proposed:

H3

AI utilization has a positive and significant impact on employee engagement.

H4

AI utilization mediates the relationship between artificial intelligence savvy leadership and employee engagement.

Employee engagement

Disengaged employees are a drain on resources which severely affects revenue and growth rates41. Extant studies have accentuated the significance of employee engagement and its positive impact such as reduced turnover, low absenteeism, better work performance, and productivity42,43,44,45. Kahn’s research on employee engagement is therefore gaining momentum. Researchers have defined employee engagement as the “emotional and intellectual commitment that employees have towards their work and organization”46,47. When employees are engaged, they harness their full selves to their work roles48. Recent researchers are now linking AI utilization with employee engagement40,49,50. The new challenge perhaps lies in leveraging AI more effectively and thoughtfully with a leadership that promotes a suitable culture for fostering innovation responsibly.

Research methodology

Data collection and procedure

The data for this study were collected through a structured questionnaire distributed among digital natives employed in EdTech companies within the Delhi NCR region. Initially, the questionnaire was pilot-tested among 71 digital natives in August, 2024 to ensure clarity and enhance reliability. The three constructs namely AI savvy leadership, AI utilization, and employee engagement are reflective, implying that the observed indicators represent the underlying latent variables. In pilot study, five-point Likert scale was used to capture responses. However, to improve response accuracy and fix biases visible in the pilot study, seven-point Likert scale was incorporated to improve data variability and reliability51. Following these revisions, the final questionnaire was administered to 355 digital natives. The data collection commenced on September 2, 2024, and concluded on November 15, 2024. However, after careful data screening, 74 responses were discarded due to missing or unengaged data. Consequently, the final analysis included 281 valid responses. The final sample size of 281 respondents is deemed adequate for structural equation modeling, exceeding the recommended minimum of 10 respondents per questionnaire item52.

This study was approved by the Institutional Advisory Committee of the Jagan Institute of Management Studies, Delhi, India. Further, all procedures performed in this study involving human participants were in accordance with the ethical standards of the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed written consent was obtained from all individual participants included in the study. The research involved no personal identifiers, posed minimal risk to participants, and complied with ethical standards for social science research. All participants were adults (18 years or older) and provided informed written consent prior to participation. Participation was entirely voluntary, and confidentiality and anonymity were strictly maintained.

Instrumentation

The following scales were utilized to measure the three constructs and test the hypotheses of the proposed relationships in the research model. Each statement was measured on a seven-point Likert scale where 1 stands for strongly disagree to 7 stands for strongly agree.

Artificial Intelligence Savvy Leadership: Three items were adapted from the scale to measure AISL49, tapping into aligning emerging AI applications with strategic goals and ethical considerations. This was done after considering the expert opinions of researchers and managers in different organizations. Cronbach’s alpha for AISL is 0.869, thereby validating the reliability of the scale.

AI Utilization: AIU is measured using five items adapted from the instrument53 after considering the expert opinions of experienced managers and academicians. Cronbach’s alpha of the same is 0.882.

Employee Engagement: EE is measured using the instrument54. Out of nine items, 6 items were dropped as factor loadings were less than 0.708. 3 items were then considered. The Cronbach’s alpha of EE is 0.867, thereby validating the reliability of the scale.

Data analysis

Respondents’ profile

The year of birth was categorized under two options- above 1995 and below 1995. Responses of those above 1995 were considered as, it falls under digital natives’ cohort. Some of the respondents mentioned their designations and roles such as Sales Manager, Product Designer, Business Development Executive, Software Development Test Engineer, Account Executive, Graphic Designer, Solutions Engineer, Assistant HR Manager, and Program Manager. Table 1 shows the detailed analysis of social demographic profile of the respondents.

Table 1 Socio demographic profile of Respondents.
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Evaluation of measurement model

Preliminary analysis was performed to check the suitability of the data including Cronbach’s alpha, skewness and kurtosis, multicollinearity, response method bias, common method bias, outer loadings of all items, composite reliability and average variance extracted. Internal consistency was measured by assessing Cronbach’s alpha. The alpha values of three constructs namely AI savvy leadership, AI utilization and employee engagement are 0.869, 0.867 and 0.882 respectively, thus indicating high level of internal consistency55,56,57. The skewness and kurtosis indices of the items are in the prescribed range of ± 1, indicating considerably normality in the data58,59. Split-half technique was used to check for response bias when completing the questionnaire. The range of the Variance Inflation Factor (VIF) is between 1.79 and 2.90, which is close to 3 and lower, also falls in the range of 1 and 5, indicating absence of multi- collinearity among the predictors60,61. All the outer loadings of 11 items are above the threshold value of 0.708, supporting sufficient levels of construct validity62. Three approaches were for assessing convergent validity- Average Variance Extracted (AVE), Composite Reliability (CR) and factor loadings63. To further examine convergent validity, CR should be greater than 0.70, the average variance extracted from every construct should be greater than 0.50, and the value of CR for each construct should be higher than the value of AVE64. The coefficient of CR for each construct in this research study is higher than the cutoff value of 0.7052. The value of AVE for each construct is also higher than the threshold value of 0.50. Also, the value of CR in this study is higher than the value of AVE. Thus, it can be concluded that the proposed measurement model has good convergent validity52,60,64. All the analysis were executed on Smart PLS 4. The details are mentioned in Table 2 below:

Table 2 Measurement model on construct Validity.
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Both groups, including early respondents and late respondents, follow the same pattern65. The independent sample t-test results did not indicate any significant difference in the mean values of the two groups, therefore non response bias was found in this research. To potentially mitigate the impact of common method bias Harman’s single factor test was utilized, the total variance extracted is 25.13%, lower than 50% of the total variance, thereby representing absence of common method bias in the study66. Fornell & Larcker and Heterotrait Monotrait (HTMT) ratio of correlation criterion was utilized to examine the discriminant validity. Table 3 shows that all the diagonal values are greater than the non- diagonal values64. HTMT criteria claims that the values should be ≤ 0.867. Table 4 shows that all the values are ≤ 0.8, which fulfills the criteria of HTMT ratio of correlation68. As provided in Tables 3 and 4, all the items in the measurement model achieved discriminant validity, indicating the constructs are distinctly different from one another.

Table 3 Measurement model on discriminant validity (Fornell Larcker).
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Table 4 Measurement model on discriminant validity (HTMT).
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Evaluation of structural model

In the regression model presented in Fig. 1, AI utilization and employee engagement are the outcome constructs and AI savvy leadership in the proposed hypothesized model is treated as predictor. After assessing measurement model, hypothesis testing including bootstrapping technique, resampling at 5000 with a bias-corrected confidence interval at a 95% confidence level69,70, was run on Smart PLS 4 to explore path coefficients, R2, Q2, f2, goodness of fit- Standardized Root Mean Square Residual (SRMR), and Normed Fit Index (NFI). Structural equation modelling statistical analysis is employed before the path analysis to verify the validity, and reliability of the measurement model71. To measure the adequacy of the model, the constructs of convergent and discriminant validity have been utilized.

Table 5 provide below is the structural model. The implications mentioned in the table depicts two out of three hypotheses were supported. H1 reflects that AI savvy leadership (β = 0.504, t = 11.332, p < 0.05), has a positive and significant impact on AI utilization, having effect size of 0.349. H2 reflects that AI savvy leadership does not impact employee engagement (β = 0.027, t = 0.419, p > 0.05), having an effect size of 0.004. H3 reflects that AI utilization has a positive and significant impact on employee engagement (β = 0.299, t = 4.451, p < 0.05), having an effect size of 0.079. R2 represents the amount of variance in the endogenous construct, explained by all the exogenous constructs to it. In this model R2 of AI utilization and employee engagement, are 0.251, 0.096 respectively, which depicts reasonable power72. Q2 of AI utilization and employee engagement is 24.2% and 2.2% respectively, reflecting predictive relevance of the model.

Table 5 Structural Model.
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Considering model fit indices, the structural model has an acceptable fit. The results of the model fitness, indicate a good fit as the SRMR is 0.036 which is ≤ 0.0873 and NFI is 0.927 which touches the threshold value between 0.8 and 174,75,76. Mediation analysis is utilized to examine the mediating relationship of AI utilization between artificial intelligence savvy leadership and employee engagement.

Fig. 1
figure 1

Hypothesized model. Source Authors’ Compilation.

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The results of the mediation analysis, H4 presented in Table 6 aim to investigate the indirect effects of artificial intelligence savvy leadership on employee engagement mediated by AI utilization. The analysis reveals an indirect effect of 0.151, with a t value of 3.895, indicating a statistically significant strong mediation effect. The direct effect of artificial intelligence savvy leadership on employee engagement is 0.027, with a t value of 0.419, and the total effect (combining both direct and indirect effects) is 0.178, with a t value of 3.041. The Variance Accounted For (VAF) is 84.83%, indicating full mediation exists of AI utilization between artificial intelligence savvy leadership and employee engagement, as direct impact is not significant. The mediation is classified as full as only indirect path is significant. Thus, H4 is supported.

Table 6 Mediation of AI utilization between artificial intelligence savvy leadership and employee engagement.
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Discussion

This study examined how AI-savvy leadership influences employee engagement through the mediating role of AI utilization among digital natives working in India’s EdTech sector. Drawing upon the Technology Acceptance Model (TAM) and Job Demands–Resources (JD-R) theory, the findings provide valuable insights into the mechanisms by which leadership and technology adoption jointly shape work experiences in technology-driven environments.

Hypothesis 1

proposed that AI-savvy leadership has a positive and significant impact on AI utilization. This hypothesis was supported, as evidenced by a strong path coefficient, indicating that leaders who demonstrate technical proficiency and communicate the strategic value of AI tools are more likely to foster their effective use among employees. This outcome reinforces TAM’s central proposition that external variables—such as leadership practices—influence perceptions of usefulness and, in turn, drive behavioral adoption26. It also aligns with prior research demonstrating that leadership behaviors play a critical role in guiding employees through technological transitions30,32.

Conversely, Hypothesis 2, which posited a direct positive relationship between AI-savvy leadership and employee engagement, was not supported. This non-significant finding suggests that merely having AI-savvy leaders is insufficient to enhance engagement unless their actions translate into concrete technology utilization that employees find meaningful. This dynamic is consistent with JD-R theory, which emphasizes that job resources—such as AI systems—must be effectively embedded into workflows to generate motivational outcomes77. Additionally, the result resonates with the study, which assessed the impact of AI-related practices on engagement is mediated by perceptions of autonomy and competence rather than leadership alone40. Cultural context may also play a role, as employees in collectivist societies like India often look to group norms and tangible practices more than individual leader behaviors when determining engagement78.

Hypothesis 3

predicted that AI utilization would have a positive and significant impact on employee engagement. This hypothesis was supported, underscoring that employees who actively use AI tools report higher engagement levels. This outcome highlights AI’s potential as a job resource that alleviates repetitive demands, increases autonomy, and fosters learning opportunities—mechanisms central to JD-R theory23. The finding also aligns with the recent work aligning acceptance of digital tools with improved perceptions of competence and efficacy, thereby enhancing engagement79.

Finally, Hypothesis 4 proposed that AI utilization mediates the relationship between AI-savvy leadership and employee engagement. The results revealed full mediation, with AI utilization fully accounting for the pathway between leadership and engagement outcomes. This mediation effect advances TAM by empirically demonstrating how leadership functions as an antecedent to perceived usefulness and adoption behavior, which in turn lead to positive work experiences25.

Overall, the findings underscore the importance of fostering not only AI-savvy leadership but also concrete practices that enable employees to effectively use AI in ways that enrich their work experiences. Future research should explore how these dynamics unfold across different cultural contexts and industries to further understand how leadership and technology shape the evolving world of work.

Theoretical implications

The findings of the study rendered several theoretical and has three key takeaways for academicians and scholars. In human resource domain workplace practices have significantly transformed and will continue to evolve with the advent of AI. Earlier digitalization laid the foundation of a more connected and efficient workplace theories and now the need of the hour is to incorporate AI into HR models to resonate with generation cohort of digital natives. The four implications for theory are delineated below.

First, the integration of JD-R and TAM facilitates a holistic understanding of the socio-technological elements, while empirically validating the interplay between AI savvy leadership, AI utilization collectively influencing employee engagement. The work offers novel conceptual framework advancing JD-R theory4,23, capturing the impact of AI driven work processes. The paper critically examines how AI is crucial for influencing employee engagement in technology driven environment. The study also adds to Technology Acceptance Model given by Davis in 1989 for explaining various facets of adoption of technology, particularly concerning perceived usefulness on employee engagement79. Additionally, TAM strongly resonates with digital natives in workforce segment, as their innate familiarity with technology shapes positive perceptions of usefulness, and ease of use.

Second, the research adds to leadership theories, accentuating the benefits of AI savvy leadership as a new leadership paradigm, emphasizing the importance of understanding and leveraging AI tools to empower digital natives and improve AI utilization and employee engagement. As organization increasingly adopt AI technologies, the emergence of AI savvy leadership needs to become a focal point of researchers17. Interestingly, the results reflect no direct influence of AI savvy leadership on employee engagement, indicating that AI savvy leaders encourage AI utilization in work processes resulting in an engaged workforce. The full mediation of AI utilization between AI savvy leadership and employee engagement unfolds that when leaders integrate AI tools in daily workforce and cultivate tech-positive culture, this alignment empowers employees resulting in better work engagement. The academicians need to develop theories on AI savvy leadership for in 5.0 industry environment to redefine future leadership skills development for managing the emerging workforce.

Third, this paper plays a pivotal role in filling the gap in existing body of literature of employee engagement and AI utilization. Prior research has linked career development80, rewards and recognition81, compensation82, job crafting83, perceived organizational support10,84 with employee engagement. The comprehensive framework with empirical evidence in this study will advance theoretical foundation of employee engagement in the upcoming decade, emphasizing how technology needs to be integrated for to make HR theories more contemporary within organizational context especially in developing economies. Lastly, this study offers new addition to generational cohort studies on digital natives and its implications for HR at workplaces. It is an extension to the research done by authors10,85,86,87,88,89,90, underscoring the dire need to cater to the unique expectations of digital natives for achieving effective and efficient work outcomes. This study takes a step forward and found antecedents to digital natives’ employee engagement resonating the technology fluency of this cohort.

Managerial implications

The paper offers practitioners and managers with tools to effectively integrate artificial intelligence for deriving positive work outcomes (Table 7). The research on AI and its influence on HR functions is still in its nascent age. However, considering its vast potential in the coming decade AI can address disengagement issues at modern workplaces, especially in emerging economies like India. This research offers two managerial implications to effectively utilize AI investments and create an engaged workforce of digital natives.

First, companies need to acknowledge that AI can enhance workplace efficiency provided the processes are guided by AI savvy leaders who understand strategic potential and ethical consideration17. Leaders equipped with AI inclination and orientation can harness its potential, while positioning their organizations competitively in an increasingly AI driven world. The findings reveal lucid benefit of having AI savvy leadership on AI utilization and employee engagement, making it extremely relevant for companies to develop, as well as look out for AI savvy leaders, to facilitate smooth AI integration for having engaged digital natives.

Second, various industries are facing challenges regarding dipping engagement levels of employees. Previous studies offered antecedents to employee engagement including career development80, rewards and recognition81, compensation82, job crafting83 and perceived organizational justice10. This study unfolds the robust role of AI as the emerging catalyst of employee engagement. Managers can streamline work processes and empower the youngest demographic dividend by fostering autonomy and enabling them to focus on meaningful task through AI enabled infrastructure and support. Additionally, AI resonates with digital natives, as this generation thrives on technology and rely on AI personalized experiences. To sum up by harnessing AI capabilities, though effective AI utilization, backed by AI savvy leadership at workplaces, managers can drive employee engagement and shape the AI driven future of work in dynamic ways.

Table 7 Summary of managerial Implications.
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Limitations and scope for future research

Though the research unfolded two antecedents of fostering employee engagement though the lens of AI integration at work, there is some further scope of this study. First, the cross-sectional design of the study poses a significant limitation. It is crucial to go for a longitudinal study to understand the changes over time across different time periods, for revealing patterns, establishing causal relationships, and development trends rather than a snapshot approach like cross sectional91. Second, this research focused on the edtech sector in the Delhi NCR region of India with limited sample size, offering insights specific to the area. However, for greater generalizability further stories could be conducted on a greater sample size, across various regions and sectors, that would allow for comparative analysis revealing regional and sectoral variations, for enhancing the applicability of the findings across diverse context92. Third, the study is susceptible to the negative effects of CMV, as Harman’s single source factor test is unlikely to explain the observed patterns. Employing robust statistical techniques can mitigate or eliminate common method bias93,94. Lastly, as this study provides meaningful insights within Indian context, thereby restricting generalizability. Comparative analysis with South Asian Countries could further enrich the findings shedding light on cross-regional perspectives, while also offering broader understanding of regions having mature AI ecosystems.

Conclusion

Integration of artificial intelligence in the new world of work has the potential to engage digital natives by having AI savvy leadership in place. The access to AI tools allows digital natives to focus more on meaningful and complex activities. The study elucidates that AI savvy leadership plays a pivotal role in fostering effective AI utilization, which in turn enhances employee engagement across teams. AI savvy leaders can better integrate intelligent tools with workflows, strategically aligning AI capabilities with digital natives’ needs and the organizational goals. This transformation can employ this new generation of young workers to focus on high value tasks elevating their sense of purpose and fulfilment. When leaders champion AI adoption and provide the necessary support through AI savvy leadership, digital natives are more likely to embrace AI driven processes, while contributing beyond their defined roles.