Enhancing top managers' leadership with artificial intelligence: insights from a systematic literature review

Digital transformation (DT), led by the rise of digital technologies, has embodied a revolutionary change affecting all sectors and companies (Bresciani et al. 2021; Ferreira et al. 2019; Leso et al. 2023). According to several studies, DT refers to the use of emerging digital technologies for enhancing business processes, such as improving customer experience, optimizing operations, or creating new business models (Fitzgerald et al. 2014; Kraus et al. 2021; Vlačić et al. 2021). This concept has emerged as a response to the rapid evolution of digital technologies and the increasing demand for innovative solutions from consumers (Calderon-Monge and Ribeiro-Soriano 2023; Kraus et al. 2023; Verhoef et al. 2021). DT has consequently led to the introduction of a range of technological tools and processes, such as artificial intelligence (AI), machine learning, blockchain, and data analytics, which have become indispensable for businesses that want to succeed in an increasingly competitive and dynamic landscape (Chowdhury et al. 2023; Di Prima et al. 2024; Kraus et al. 2021). Several studies highlight the importance of understanding how AI disrupts organizational dynamics (Haenlein and Kaplan 2019; Kanbach et al. 2023; Naeem et al. 2024; Santana and Díaz-Fernández 2023). AI is the cornerstone of DT, offering unprecedented capabilities to analyze vast amounts of data, discover insights, and make data-driven decisions with accuracy and speed across industries (Åström et al. 2022; Berente et al. 2021; Kaplan and Haenlein 2019; Mikalef and Gupta 2021). In order to harness the potential of AI in strategic decisions within the organization, the leadership of top managers plays a crucial role in its implementation and use (Heyden et al. 2017; Ruokonen and Ritala 2023; Wrede et al. 2020). Indeed, according to a recent Harvard Business Review study, interviews with 600 organizations engaged in DT indicate a high level of confidence in how AI can be an excellent tool in supporting top managers' leadership (Hougaard et al. 2024). Top managers are the companies' key decision-makers, whose leadership plays a pivotal role in the DT process (Georgakakis et al. 2022; Heyden et al. 2017; Li and Shao 2023). The relevance of top managers was first highlighted by the theorists of upper echelons theory (UET), according to which the leaders' background, characteristics, and choices significantly influence strategy, decision-making, and organizational outcomes (Hambrick 2007; Hambrick and Mason 1984). Prior studies have also established noteworthy correlations between top executives and strategic corporate change (Hambrick et al. 1993; Wiersema and Bantel 1992). Moreover, several scholars propose that top managers influence organizations' innovation endeavors (Damanpour 1991; Smith and Tushman 2005; Wrede and Dauth 2020). Accordingly, firms' leaders play a critical role in addressing DT, promoting a digital culture, allocating resources effectively, and managing human resources (Avolio et al. 2000, 2014; Fernandez-Vidal et al. 2022; Gupta et al. 2023). Some recent studies conducted systematic literature reviews (SLRs) to examine the role of leadership in the context of DT (Lin 2024; McCarthy et al. 2022; Rêgo et al. 2021). Lin (2024) focused on the competencies that digital leaders need for leading DT, unveiling how top managers adapt technologically and provide leadership in digitalization. On the same line, McCarthy et al. (2022) investigate digital leadership by implementing advanced technologies, emphasizing a broader scope that includes managing digital infrastructure and supporting decision-making via information and communication technology. Furthermore, Rêgo et al. (2021) explore how corporate strategies evolve in response to DT, emphasizing the pivotal role of top managers. However, such systematic reviews leave an open gap. While they address DT and the role of top managers, these studies only touch on AI superficially, without making it a central focus. Thus, previous SLRs largely overlooked the disruptive influence of AI on leadership at the top manager level and its potential to drive a paradigm shift. When some studies emphasized the role of AI in business, they analyzed the impact of AI on managers' skills, managerial decision-making processes, and functions (Deepa et al. 2024; Kaushal et al. 2023). As a result, the available studies are too general and broad, as they do not go into the specifics of how AI affects the leadership of top managers.

Empirical research by Wrede et al. (2020) and Fernandez-Vidal et al. (2022) has begun to explore the new challenges that top managers face in the AI era, highlighting the importance of strategic foresight. Similarly, Jorzik et al. (2023) emphasize the critical role of top management in driving innovation through AI technologies, highlighting the influence of leadership on the evolution of business models. Moreover, Jorzik et al. (2024) also investigate the central role of top managers in creating new AI-driven business models. These studies make relevant contributions, exploring different topics and offering innovative ideas that enrich the existing literature. In general, such a growing literature debate appears to be fragmented, lacking a clear and cohesive vision of how AI radically transforms the leadership of top managers. This leaves a gap in the overall understanding of the topic that can be filled through an SLR.

Furthermore, UET, widely used in numerous studies to emphasize the strategic influence of top managers on business outcomes, still needs to be adequately expanded to fully consider the profound changes that AI can bring to leadership practices (Fernandez-Vidal et al. 2022; Wrede et al. 2020). This gap is particularly relevant since AI has the potential to revolutionize decision-making processes, operational strategies, and the skills needed by top managers (Jorzik et al. 2023). The advent of AI could radically alter leadership dynamics, raising questions about how leaders need to evolve and how AI could be integrated into decision-making processes to reflect new dimensions of analysis and prediction (Lin 2024; Schmidt et al. 2023; Schuster et al. 2023). In response, this article aims to provide an overview of the literature on how AI has impacted top managers' leadership.

An SLR is essential to map and analyze the topic thoroughly, identifying opportunities for future studies. Following the suggestion of Secundo et al. (2020) and Tandon et al. (2021), the SLR is conducted to address three primary research questions:

RQ1. What is the research profile of the literature on the impact of artificial intelligence on the leadership of top managers?

RQ2. Which research themes have been explored in the literature?

RQ3. What are the prospective research directions in this field?

In response to the research questions, this study seeks to present a new conceptualization, offering a comprehensive review to advance the field of study of UET. The aim is to provide new insights into how AI has transformed the leadership of top managers by employing bibliometrics and content analysis tools. We use bibliometric analysis to address RQ1, which focuses on understanding the research profile of the literature. This method allows us to identify the most influential articles, authors, countries, keywords, and journals, providing insights into the evolution of the field. For RQ2 and RQ3, we apply content analysis to explore the specific themes related to the impact of AI on top managers' leadership and identify future research directions. As a result, three research themes emerged grouping previous contributions according to: (1) AI-driven skills of top managers' leadership; (2) factors driving top managers' decision to adopt AI in organizations; and (3) the strategic use of AI.

The study offers contributions in two main directions. Firstly, the study aims to enhance the existing research on the influence of AI on leadership by presenting a new framework that emphasizes the strategic role of top managers in AI adoption (Schmidt et al. 2023; Schuster et al. 2023). It identifies the necessary leadership skills while considering organizational, technological, and sociocultural factors that shape the integration of AI. The framework highlights how these elements influence the effectiveness of AI-driven leadership and its strategic use. It provides a comprehensive view on how top managers can successfully navigate and lead AI initiatives. The second contribution lies in enriching UET by integrating AI as a crucial variable in top managers' leadership. Until now, UET has extensively explored the role of business leaders in driving the DT process, but AI's specific impact on their ability to lead and influence organizations has not been sufficiently examined (Fernandez-Vidal et al. 2022; Wrede et al. 2020). This study proposes an extension of UET, highlighting how AI can radically transform leadership and decision-making at the executive level. This advancement enables a better understanding of how leaders can leverage AI to navigate the complexities of DT, thereby enriching the theory itself.

This study offers crucial insights for top managers looking to harness the potential of AI in their strategic and operational roles. By considering organizational and individual dimensions, it provides a holistic view of the value of AI, guiding top managers toward a more integrated understanding of AI's role in organizations.

The role of top managers is a crucial issue in organizational management literature (Fernandez-Vidal et al. 2022; Georgakakis et al. 2022). Several management theories offer different perspectives on understanding top managers' decision-making processes. These include UET, the resource-based view (RBV; Barney 1991; Grant 1991), and new institutional theory (NIT; AlNuaimi et al. 2022; Biggart and Hamilton 1987; Dubey et al. 2018).

The theorists of UET first emphasized the importance of top managers. Such a theoretical cornerstone, proposed by Hambrick and Mason (1984), posits that top executives' characteristics, values, experiences, and cognitive biases significantly influence organizational outcomes and performance. It emphasizes that top executives bring their unique perspectives and cognitive frames to the decision-making process (Hambrick 2007; Hambrick and Mason 1984; Hambrick et al. 1993). Top managers encompass all executive personnel crucially involved in a company's critical strategic decisions (Li and Shao 2023; Singh et al. 2023). Moreover, top managers are essential in defining an organization's vision, direction, and culture, significantly influencing strategic decision-making, resource allocation, and the company's success (Georgakakis et al. 2022; Samimi et al. 2022). Therefore, UET theorists suggest that by recognizing the influence of top managers on organizational outcomes, strategic management and organizational performance can be improved (Hambrick 2007; Hambrick and Mason 1984; Heyden et al. 2017).

The RBV offers a complementary perspective, focusing on the organization's internal resources and capabilities as sources of sustainable competitive advantage (Barney 1991; Grant 1991). According to the RBV, top managers are crucial in identifying, developing, and exploiting unique resources, such as technological expertise and AI-related innovation capabilities (Flood et al. 1997). On the other hand, NIT examines how external pressures, including regulatory, social, and cultural factors, influence organizational behavior and decision-making (Greenwood et al. 2008; Meyer and Rowan 1977). From this perspective, adopting AI could be seen as a response to regulatory and institutional expectations in an increasingly digitized environment (Dubey et al. 2018).

Given the study's objectives and the extensive supporting literature (Dhir et al. 2023; Georgakakis et al. 2017; Kallias et al. 2023), the most suitable theoretical lens is offered by UET. Although the RBV and NIT propose interesting complementary perspectives, UET allows for a more in-depth analysis of how leaders can understand and integrate advanced technologies such as AI. Therefore, according to previous studies (Fernandez-Vidal et al. 2022; Jorzik et al. 2023; Wrede et al. 2020), this theory provides a framework for studying the influence of top managers' leadership in strategic decisions and AI-related technological transformation.

Early research on UET focused on observable demographic variables such as age, level of education, previous work experience, length of tenure, and the composition of the management team (Hambrick 2007; Hambrick and Mason 1984; Hambrick et al. 1993). The aim was to establish correlations between these variables and strategic corporate outputs, such as innovation, strategic change, and financial performance (Certo et al. 2006). In later years, research began to consider less visible but equally relevant variables such as personality, values, perceptions, risk tolerance, and the motivations of top managers (Buyl et al. 2011; Carpenter et al. 2004). This expansion has led to a richer and more complex understanding of decision-making dynamics, highlighting how it is not only observable characteristics that influence strategic decisions but also leaders' deeper and less visible characteristics (Kallias et al. 2023; Nielsen 2010). The focus has also shifted to the context in which top managers operate (Dubey et al. 2018; Manner 2010). Organizational culture, institutional pressures, and power dynamics within the management team have become central to the UET field (Chuang et al. 2009; Dubey et al. 2018). This evolution shifted the focus from the individual characteristics of leaders to how these characteristics interact within a group of leaders, emphasizing the importance of group dynamics (Kallias et al. 2023). Leaders must balance conformity with innovation, striving to align organizational needs with external expectations and technological opportunities (Georgakakis et al. 2017). This tension between conformity and innovation is critical to top managers' decision-making processes (Dubey et al. 2018).

The study variables have broadened further with the advent of digital technologies and the increasing focus on diversity and inclusion (Fernandez-Vidal et al. 2022; Jorzik et al. 2023; Kallias et al. 2023; Wrede et al. 2020). Moreover, the impact of gender, ethnic, and functional diversity in management teams and how technologies influence leaders' decision-making processes are topics of considerable relevance (Firk et al. 2022).

Successive studies, presented in Table 1, have advanced UET, proposing that top managers' leadership is crucial for companies to successfully achieve DT (Avolio and Kahai 2003; Avolio et al. 2014; Fernandez-Vidal et al. 2022; Li and Shao 2023; Wrede et al. 2020).

Further studies grounded in UET have delineated the necessary actions and skills expected of top managers to address the DT process effectively. Wrede et al. (2020) identified three essential actions that top managers must undertake: understanding digitization, establishing a structured framework for DT, and guiding change. Furthermore, Fernandez-Vidal et al. (2022) identified, through a top-down approach involving interviews with top managers, four critical capabilities required by managers to excel in the digitalization and integration of new technologies into current or new businesses: driving business transformation; adeptly navigating fluid and flexible organizational structures; managing complex talent dynamics; and prioritizing continuous learning.

However, a closer examination of these reveals that although they have explored how DT impacted top managers' leadership in business, they have not considered the challenges and opportunities presented by the integration of AI (Cortellazzo et al. 2019; Doornenbal et al. 2022; Firk et al. 2022; Schiuma et al. 2021). This gap in the literature is particularly striking given the increasing prominence of AI technologies in organizational contexts (Arias-Pérez and Vélez-Jaramillo 2022; Kraus et al. 2023; Koechling et al. 2023; Ulrich et al. 2024). Based on the definitions provided by previous papers, AI is defined as a computer with human-like capabilities, enabling it to perform tasks that conventionally require human intelligence (Dwivedi et al. 2021; Kaplan and Haenlein 2019; Kolbjørnsrud et al. 2017; Lee et al. 2019; Makarius et al. 2020; Mikalef and Gupta 2021). According to several studies, AI enables companies to reduce risk and gain a competitive advantage in today's data-driven economy, as also underlined by major surveys focusing on enterprises (Agrawal et al. 2020; Chowdhury et al. 2023; Secundo et al. 2024).

Given this background, an SLR is needed to consolidate and advance UET further in the AI context. Although UET has been widely used to examine the role of top managers in DT, there is a lack of in-depth understanding of how AI integration influences top managers' leadership. Furthermore, to the best of the authors' knowledge, no other systematic literature analysis has been conducted on this topic thus far. Therefore, this study aims to address this gap by conducting an SLR to unfold a new AI-driven approach for top managers' innovation strategy for successfully integrating AI into business processes.

As several scholars suggested (Bretas and Alon 2021; Kraus et al. 2022, 2024; Secundo et al. 2020), SLR enables researchers to conduct rigorous and reliable analyses of scientific sources. According to Hina et al. (2022) and Tandon et al. (2021), an SLR provides a structured approach to systematically collecting, critically evaluating, and synthesizing existing research, thereby ensuring transparency and reproducibility. The SLR approach also serves as a strategic tool for exploring the current state of the literature within a specific research domain while also identifying existing gaps to inspire future studies and advancements in knowledge (Kraus et al. 2022; Sauer and Seuring 2023; Tranfield et al. 2003). This structured approach allows the SLR process to minimize bias and produce replicable results, which is essential for the robustness of the methodology (Enholm et al. 2022; Saratchandra and Shrestha 2022; Thomas and Gupta 2022). More specifically, this research employs bibliometric and content analysis to investigate the research questions advanced in the introduction. On the one hand, bibliometric analysis is a quantitative approach involving statistical methods in analyzing large volumes of scientific data (Zupic and Čater 2015). This method provides a mapping to examine the detailed structure, dynamics, and relationships of scientific fields of interest (Donthu et al. 2021; Zupic and Čater 2015). Employing bibliometric analysis to address RQ1 allows an in-depth exploration of the evolutionary nuances and the emerging areas in that field, including articles, authors, countries, keywords, and journals. On the other hand, content analysis is instrumental for RQ2 and RQ3 in systematizing literature trends by identifying developed areas (hot spots) and underdeveloped topics (blind spots) within the literature (Gaur and Kumar 2018). It also enhances the comprehension of the reviewed articles, suggesting future research directions (Neuendorf 2002).

In line with Bretas and Alon (2021) and Massaro et al. (2016), and as applied by Secundo et al. (2020), the researchers adopted a structured approach consisting of three methodological steps to explore the literature on AI's impacts on top managers' leadership.

The following content analysis examines and classifies the collected articles to identify the main themes and emerging trends in top managers' AI-driven leadership. An in-depth text analysis aims to provide a comprehensive overview of the recurring concepts and ideas found in the considered documents. As mentioned above, this study outlines three distinct clusters: (1) AI-driven skills of top managers' leadership; (2) factors driving top managers' decision to adopt AI in organizations; and (3) the strategic use of AI. Based on previous studies (Alon et al. 2020; Bargoni et al. 2023; Secinaro et al. 2022), the clusters were labeled according to the content of the articles, the similarity of topics, and the context, and each was delineated by specific thematic categories and subcategories as outlined below.

The analysis of the above clusters represents an innovative step towards an in-depth understanding of the growing research field on the influence of AI on top managers' leadership. Through this detailed analysis, the study has comprehensively mapped out the various dimensions and dynamics characterizing this emerging field. Each cluster, delineated based on the articles in the sample, revealed a series of categories and subcategories that offer a detailed overview of the topics addressed. Based on these results, the research developed a framework that synthesizes the main conclusions drawn from the content analysis, providing a clear and organized structure for understanding how previous studies have addressed this topic. In Fig. 5, the framework also serves as a starting point for future studies to further explore how AI revolutionizes top managers' leadership.

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The figure indicates that the strategic use of AI requires an integrated approach, combining top managers' leadership skills with organizational, technological, and sociocultural factors (Georgakakis et al. 2022). This represents an evolution of UET in the current technological context, expanding the theory's focus to include new dimensions related to AI (Fernandez-Vidal et al. 2022; Hambrick 2007). This advancement is significant as it recognizes that, in the DT era, technological skills and the ability to navigate complex contexts become increasingly crucial (Jorzik et al. 2023; Wrede et al. 2020). Previous studies through UET have considered factors such as age, educational background, and professional experience indicators of leaders' ability to make strategic decisions (Certo et al. 2006; Hambrick and Mason 1984). However, integrating AI into business decisions requires a different skill set, such as the ability to analyze data, agility in responding quickly to technological change, and emotional and social intelligence to drive organizational change in an often uncertain and complex environment (Jorzik et al. 2023; Wrede et al. 2020). As highlighted in the content analysis, these new skills can be extensions of the psychological characteristics already analyzed through UET (Hambrick 2007; Kallias et al. 2023).

A further significant aspect highlighted by the figure and cluster analysis is the interaction between the individual characteristics of leaders and organizational, technological, and sociocultural factors (Dubey et al. 2018; Georgakakis et al. 2017). According to UET, strategic choices result from a complex interaction between the characteristics of top managers and the context in which they operate (Dubey et al. 2018; Hambrick and Mason 1984). In the age of AI, this interaction becomes particularly evident (Fernandez-Vidal et al. 2022; Jorzik et al. 2023). The digital culture and strategy of an organization can amplify or mitigate the impact of the individual characteristics of leaders (AlNuaimi et al. 2022; Georgakakis et al. 2022). An organization with a solid digital culture may facilitate AI adoption, even if leaders lack direct experience with such technologies (Wrede et al. 2020). Similarly, the availability of high-quality data and advanced machine learning algorithms can influence leaders' strategic decisions (Jorzik et al. 2024; Kallias et al. 2023).

According to the articles reviewed, this interaction between the individual characteristics of top managers and organizational factors can be analyzed through a lens that integrates UET with NIT and the RBV (AlNuaimi et al. 2022; Barney 1991). This perspective highlights how strategic decisions, such as AI adoption, are influenced not only by the characteristics of leaders but also by the internal resources and institutional pressures that shape an organization's operating environment, thus extending the traditional understanding of UET into the digital age (Fernandez-Vidal et al. 2022; Jorzik et al. 2023). In particular, NIT emphasizes the role of institutions, norms, and cultures in shaping organizational decisions (Greenwood et al. 2008; Meyer and Rowan 1977). The findings suggest that sociocultural factors such as social acceptance of AI and the ethical use of such technologies influence the decision of top managers to adopt AI (Dubey et al. 2018; Enholm et al. 2022). Moreover, according to NIT, top managers' use of AI is related to gaining a competitive advantage and aligning with external norms and expectations, thereby responding to the pressures of their operating environment (AlNuaimi et al. 2022). In parallel, the RBV emphasizes the importance of internal resources, such as high-quality data and technological infrastructure, highlighting how leaders leverage these resources to generate and sustain competitive advantage through AI (Barney 1991; Flood et al. 1997; Grant 1991).

Therefore, this holistic perspective aims to enrich the discussion on UET by offering a framework that considers the characteristics of leaders and the resources and infrastructures available within organizations to gain a competitive advantage in the target market (Fernandez-Vidal et al. 2022; Georgakakis et al. 2022).

To answer the third research question, the study presents an extensive agenda for future research, delineating several promising future research questions (FRQs) based on the gaps in the articles. Table 5 summarizes the identified opportunities for future research concerning the correlation between top managers' leadership and AI, focusing on addressing knowledge gaps and highlighting particularly stimulating areas for further exploration.

These proposed future research avenues focus on how AI has influenced top managers' leadership, a critical area that requires further exploration. Our SLR revealed that most studies examine these variables from organizational, individual, and strategic perspectives. Consequently, we identified several research gaps in the articles in the SLR and proposed specific FRQs that could significantly enhance the understanding of this topic.

Recent studies have highlighted strategic leadership as a critical area for future research, especially regarding AI-driven innovation (Samimi et al. 2022; Singh et al. 2023). Strategic leadership is focused on achieving strategic outcomes for various economic, environmental, or social organizations, including multinationals and SMEs (Carter and Greer 2013; Vera et al. 2022). It is more comprehensive than a single leadership style (e.g., transformational, transactional, or servant leadership) or a specific set of values (e.g., self-centered or other-centered). Strategic leadership refers to the functions the top management team carries out that aim to impact the organization strategically (Samimi et al. 2022). Therefore, future research could examine such leadership styles and analyze how they relate to AI innovation in companies (Singh et al. 2023). Future research avenues could also explore leadership strategies that address resistance to AI adoption, focusing on frameworks that integrate strategic vision and emotional and social intelligence. Furthermore, the role of leadership in cultivating a culture of innovation remains underdeveloped, with few studies exploring how calculated risk-taking and experimentation can be encouraged in a digitally transforming environment (Singh et al. 2023; Wrede et al. 2020). The research also highlights that leadership in the AI era must go beyond traditional models of top-down communication (He et al. 2023; Newstead et al. 2023). Instead, leaders must navigate complex, digitally enabled environments where hierarchical barriers are minimized and foster a shared digital culture, which is essential for achieving sustainable competitive advantage (Fernandez-Vidal et al. 2022). This invites further exploration into leadership strategies that combine emotional and social intelligence with technological fluency to drive change.

The second research gap emerges from the challenges associated with integrating diverse organizational structures– traditional, hierarchical, and network-based– into a cohesive whole capable of responding dynamically to market shifts. Organizations face increasing pressure to be agile and resilient in the face of uncertainty, yet the structural designs that promote this duality remain insufficiently understood (AlNuaimi et al. 2022). Recent studies have suggested that hybrid organizational forms, which integrate functional silos with flatter, decentralized decision-making processes, may offer solutions (Chowdhury et al. 2023; Davison et al. 2023; Fernandez-Vidal et al. 2022). However, how these structures can be effectively implemented to foster cross-functional collaboration and knowledge sharing remains unanswered. This gap in the literature is critical, as failure to optimize structural alignment could impede the flexibility needed to adapt to rapid changes in technology and customer demands (Schwarzmüller et al. 2018).

We identified a significant research gap in empirical investigations of the skills required by top managers, particularly in balancing the development of hard (technical) and soft (interpersonal) skills in the AI age. While many studies focus on the technical competencies required for AI integration, few address the importance of complementary soft skills for strategic leadership in the AI context (Fernandez-Vidal et al. 2022; Samimi et al. 2022; Schmidt et al. 2023; Singh et al. 2023). The gap also lies in how top managers can assess the existing AI skills within their teams and identify the areas where upskilling or reskilling is necessary. Moreover, there is limited research on the design and implementation of training programs that effectively bridge the gap between outdated skills and emerging technological requirements (Schuster et al. 2023; Singh et al. 2023). A related concern is how top managers can foster a culture of continuous learning within their organization, particularly as digital skill requirements evolve at an accelerated pace (Wrede et al. 2020). This gap is exacerbated by the rapid obsolescence of specific technical skills and the growing importance of hybrid skills that combine technical know-how with creative and strategic thinking. Research must delve deeper into how organizations can structure adaptive learning ecosystems in response to the skill demands of the digital economy.

A further research gap arises from the need to understand better the strategic benefits and challenges associated with collaborating with external ecosystems, such as startups, academic institutions, and other external organizations, in AI development. While several studies highlight the potential for innovation and growth through such collaborations, there is a lack of empirical evidence on how companies can effectively identify, attract, and integrate talent from these external ecosystems (AlNuaimi et al. 2022; Chowdhury et al. 2023; Varma et al. 2023). Furthermore, managing external talent, particularly regarding maintaining alignment with internal organizational culture and goals, presents a largely unexplored area (Fernandez-Vidal et al. 2022). The challenges of talent integration, retention, and development within AI-driven ecosystems are further compounded by this sector's rapidly shifting skill requirements (Odugbesan et al. 2023). Exploring the dynamics of external partnerships and how they can be leveraged for innovation and talent acquisition is critical. Further research should investigate the long-term impacts of such collaborations on organizational performance, focusing on the co-evolution of internal and external talent strategies. This includes not only talent acquisition but also the governance structures that support sustained partnerships.

The last research gap focuses on the ethical implications of AI, particularly regarding algorithmic biases and the need for responsible AI practices. Although ethical concerns regarding AI are gaining traction in academic and professional discourse, many organizations struggle to integrate ethical evaluations into their everyday decision-making processes related to AI (Parry et al. 2016; Varma et al. 2023). The role of the top manager becomes pivotal in this context, as they are responsible for shaping the organizational culture and governance structures that can effectively address these ethical concerns (Watson et al. 2021). The tension between fostering AI innovation and adhering to ethical norms without stifling technological progress is an area that warrants deeper investigation (Jorzik et al. 2023; Wrede et al. 2020). Top managers are at the heart of this tension (Larson and DeChurch 2020). Addressing it demands strategic foresight and the courage to make decisions that may delay immediate technological advancements in favor of long-term sustainability and ethical integrity (Schiuma et al. 2021). This responsibility extends beyond technical aspects, but it requires top managers to cultivate an environment where ethical dialogue is encouraged, and employees feel empowered to raise concerns about potential biases or unethical outcomes in AI systems (Parry et al. 2016). Therefore, future research could focus on developing a framework to illustrate how AI influences top managers' decision-making through an ethical lens. As AI becomes increasingly embedded in decision-making processes, top managers must navigate complex ethical dilemmas that impact their organization at multiple levels (Larson and DeChurch 2020). Their ability to make informed, principled decisions will be a defining factor in the success of responsible AI adoption.

Moreover, an integrated inductive-deductive approach can be pursued by combining qualitative and quantitative research endeavors. This methodology analyzes mediating and moderating factors influencing AI-driven leadership among top managers and explores how these can be leveraged to create sustainable competitive advantages. Finally, the study could be validated and enriched by examining the insights in different cultural or organizational contexts.

This SLR examines how AI has revolutionized top business managers' leadership using bibliometric and content analysis techniques. The study reveals connections between countries, keywords, and journals and identifies the field's most relevant authors and articles. It also offers guidance for future researchers by identifying three clusters in which this topic can be analyzed: (1) AI-driven skills of top managers' leadership; (2) factors driving top managers' decision to adopt AI in organizations; (3) the strategic use of AI. Finally, the study proposes a top managers' leadership AI-driven framework for successfully integrating AI in businesses.