Guest editorial: Artificial Intelligence (AI) for management decision-making processes. From measurement to strategy

Work on this special issue began in early 2023 when artificial intelligence (AI) was increasingly recognised as a transformative force but had yet to reach its current widespread adoption and influence. Our objective was to explore the promising yet relatively unexplored role of AI in managerial decision-making. This field is increasingly capturing the interest of management scholars, practitioners and organisations. The rapid diffusion of AI technologies, supported by enhanced computing capabilities, lower costs of technological tools and broader accessibility, creates novel opportunities and challenges in organisational decision-making (Dwivedi et al., 2021). Consequently, we aim to understand whether AI integration has begun reshaping managerial practices and the direction it may take in the future.

AI'spotential to transform managerial decision-making processes highlights the need to expand the boundaries of management research. Integrated information systems have a significant influence on management practices and enhance organisational performance, requiring deeper integration with existing organisational processes and frameworks (Chapman and Kihn, 2009). Recent developments in machine learning and generative AI models further demonstrate AI'sability to augment human reasoning, creativity and foresight, rather than merely automate analytical processes (Farina et al., 2024; Patel and Lim, 2024).

For instance, AI enables personalised financial planning and banking services by supporting both customers and managers with real-time decision assistance (Avelar and Jordão, 2025). AI algorithms also empower organisations to project demand accurately, enabling innovative marketing and distribution approaches (Talwar and Koury, 2017). Moreover, AI provides crucial data-driven insights within supply chains, healthcare, financial services and other sectors, significantly enhancing decision-making accuracy and efficiency (Loftus et al., 2020; Min, 2010; Secinaro et al., 2021).

Despite these significant opportunities, management scholars often lag in interdisciplinary research that explores the implications of AI. While AI adoption has accelerated, a gap remains in understanding how organisations balance AI automation with managerial oversight and strategic decision-making. As suggested by Loureiro et al. (2021), there is a critical need for research addressing the impact of AI on internal and external organisational stakeholders, including ethical challenges and skills gaps. Recent studies also suggest that managerial readiness, trust and creativity play a decisive role in determining how AI is effectively integrated into organisational processes (Santoro et al., 2025). These insights underscore the need for management scholars to foster stronger interdisciplinary collaborations and integrate AI-related knowledge into their research and education.

This special issue attracted numerous submissions, and 24 papers were accepted through rigorous review. These contributions include structured literature reviews offering insights into existing research and emerging AI topics in managerial contexts. Additional articles explore sector-specific AI applications across various industries, addressing innovation, ethical considerations and the critical skills required for future professionals. As AI continues to evolve, understanding its sector-specific implications remains essential, particularly as industries navigate the challenges of automation, regulatory compliance and workforce adaptation.

In this editorial, we perform an in vivo analysis of the accepted articles to explore their thematic depth. We focus our analysis by addressing two research questions:

In subsequent sections, we present the main themes explicitly outlined in our call for papers. We then identify and discuss emergent themes identified from the selected articles. Finally, our discussion extends existing conceptual frameworks on AI adoption stages by suggesting new insights and proposing areas for future research.

To conduct our review, we imported the 24 accepted papers into NVivo. In the initial phase, coding was based on the themes explicitly outlined in the call for papers regarding the application and impacts of AI in managerial decision-making processes. Subsequently, we performed in vivo coding to identify emerging themes and topics not explicitly mentioned in the call for papers but highlighted by the authors of the accepted articles. To ensure reliability and coherence, two guest editors independently performed the coding and then discussed their findings to reach a joint agreement, following the approach suggested by Massaro et al. (2016). This method ensured interpretive consistency and allowed us to highlight both contributions that directly responded to the call's questions and newly identified research areas. Table 1 illustrates the main themes addressed by the papers accepted in this special issue, organised according to the key topics: integration and impact of AI in managerial decision-making, innovation and future skills and ethical considerations.

The subsequent sections provide concise summaries of the key findings from the published research papers, aligning with the research questions posed in the call for papers.

Several papers accepted in this special issue directly address how AI technologies reshape organisational decision-making, demonstrating AI'stransformative potential across various managerial contexts. Oppioli et al. (2025), through a structured literature review, systematically synthesise existing research and confirm that AI enhances forecasting accuracy, accelerates decision speed and improves managerial decision quality. Lanzalonga et al. (2025) empirically demonstrate the potential of AI and data-driven approaches in multi-utility companies, particularly in waste management operations, highlighting how AI facilitates circular economy practices through enhanced strategic and operational decision-making.

Further contributions include Esposito et al. (2025), who investigate cognitive AI systems for optimising operational decision-making in waste management, highlighting key advantages such as cost reduction and increased efficiency. Giuggioli et al. (2025) demonstrate AI'ssubstantial impact on entrepreneurial finance through innovative decision-support methods for start-up evaluation, significantly enhancing investment decisions. Avelar and Jordão (2025) analyse AI'sinfluence on financial analysis and decision-making across global stock exchanges, emphasising AI-driven forecasting models that support strategic investment decisions.

Ma and Chang (2025) extend this discussion by examining AI'srole in digital supply chain transformation, demonstrating how AI and big data analytics enhance agility and decision-making in automotive supply chains. Additionally, Biloslavo et al. (2025) explore how AI revolutionises strategic planning processes within volatile and uncertain (VUCA) environments, offering valuable conceptual insights and managerial frameworks. Bhatnagr and Rajesh (2025) contribute further by assessing AI'simpact on digital banking apps, focusing on its ability to improve user experience, satisfaction and overall organisational performance. Similarly, Divya et al. (2025) provide empirical insights into the mediating role of leadership in AI integration, revealing how effective leadership strategies enhance AI'simpact on employee engagement and organisational outcomes. Khanfar et al. (2025) offer a structured synthesis of the key technological and organisational factors influencing AI adoption, emphasising its role in long-term digital transformation strategies. Misra et al. (2025) and Guan et al. (2025) reinforce these findings by examining how localised and humanised AI applications shape organisational decision-making, offering insights into their implications for managerial efficiency and decision quality.

In addition, several recent contributions further enrich the debate on how AI transforms managerial cognition and decision-making mechanisms. Savastano et al. (2025) explore managerial perceptions of enterprise chatbots, highlighting their strategic role in enhancing communication flows, data-driven reasoning and organisational efficiency, while stressing the importance of managerial training for successful adoption. Santoro et al. (2025) demonstrate how AI-powered growth-hacking facilitates experimentation, creativity and automation, thereby supporting more agile and evidence-based decision-making processes. Cimino et al. (2025) focus on the appropriation of generative AI tools, such as ChatGPT, by innovation managers, demonstrating how innovation orientation and individual creativity influence AI customisation and, consequently, decision-making quality.

From a performance-oriented perspective, Giachino et al. (2025) and Neiroukh et al. (2025) empirically validate the positive relationship between AI-driven decision-making capabilities and firm performance, identifying decision speed and quality as key mediating mechanisms. Finally, Rizzo et al. (2025) address the behavioural dimension of AI integration, revealing that managers' trust and willingness to rely on AI-generated advice depend on their social comparison orientation, suggesting that psychological and cultural factors critically shape AI-supported decision-making.

Therefore, collectively, these studies confirm AI'sprofound impact on decision-making processes, underscoring the need for further research on best practices for AI integration, optimising its strategic value and refining human-AI collaboration within organisations.

One of the central goals of this special issue was to explore how AI-driven innovations are reshaping managerial approaches and the skills necessary for professionals to navigate an increasingly AI-enhanced work environment. Many of the papers included in this issue provide valuable insights into these shifts, highlighting both the advantages and challenges that organisations face when adopting AI technologies.

Cavazza et al. (2025) offer an in-depth analysis of how AI is transforming business models in the agricultural sector. Their research focuses on vertical farming, illustrating how automation and predictive analytics are not only improving operational efficiency but also redefining key decision-making processes. These changes are prompting professionals in the field to develop new technological competencies to stay competitive. Similarly, Barile et al. (2024) examine the growing role of robo-advisory services in the banking industry. Their study sheds light on how AI-powered financial advisory platforms are gradually assuming responsibilities traditionally held by human advisors, necessitating a shift in skill sets among finance professionals to collaborate effectively with AI-driven systems.

Another contribution comes from Al-Khatib (2025), who investigates how the hospitality industry is responding to the adoption of AI. This study highlights the critical role of organisational readiness and workforce upskilling in ensuring a smooth transition to AI-enhanced service delivery. Ma and Chang (2025) contribute to this discussion by analysing AI'simpact on supply chain agility. Their findings demonstrate how big data analytics, combined with AI-driven forecasting, is streamlining logistics, inventory management and strategic planning across interconnected business networks.

Further enriching this debate, Savastano et al. (2025) emphasise the strategic importance of enterprise chatbots as enablers of digital transformation and managerial learning. Their study highlights the need for managers to acquire new competencies in data interpretation, communication design and ethical AI governance, thereby fostering the development of hybrid digital-human skill sets.

In a complementary way, Santoro et al. (2025) explore the integration of AI in growth-hacking strategies, identifying how automation and experimentation are reshaping marketing and innovation processes. Their findings underline the need for professionals to develop analytical, creative and technological capabilities to manage AI-driven experimentation cycles effectively. By connecting AI tools with creativity and data-driven insight, this research underscores how AI transforms not only business models but also the competencies required to sustain continuous innovation.

Cimino et al. (2024) contribute to the discussion from the perspective of innovation management, demonstrating how the adoption of generative AI tools, such as ChatGPT, depends on innovation orientation and individual creativity. Their results indicate that managers capable of adapting and customising AI systems gain not only a competitive advantage but also new cognitive and problem-solving skills, an essential step toward building AI-augmented decision ecosystems.

Several studies also delve into the human-AI collaboration necessary to maximise AI'spotential in decision-making. Divya et al. (2025) explore how leadership influences the effectiveness of AI adoption, emphasising that strong leadership strategies can help bridge the gap between AI capabilities and employee engagement. Bhatnagr and Rajesh (2025) further this discussion by examining AI-driven decision-making in digital banking, emphasising how AI'simpact on user satisfaction and trust influences customer engagement and financial decision-making.

Misra et al. (2025) extend this analysis by focusing on AI chatbots in banking, exploring both the benefits and challenges of integrating AI-driven customer service. While these systems enhance customer engagement, they also raise ethical questions related to trust, personalisation and data privacy. Additionally, Khanfar et al. (2025) provide a structured synthesis of the technological and organisational factors shaping AI skill development, outlining the future competencies necessary for AI-driven business environments.

These studies highlight that the diffusion of AI is redefining professional skill sets across industries, demanding a combination of technological literacy, creative adaptability and ethical awareness. Additionally, the published papers illustrate AI'srole as a catalyst for innovation, emphasising the need for organisations and professionals to develop adaptive capabilities and new skill sets. Future research should continue exploring the intersection between AI innovations and human capital development, particularly in sectors undergoing rapid digital transformation. Ultimately, an ongoing investigation is necessary to refine AI-human collaboration models and ensure that AI integration promotes efficiency and ethical responsibility in managerial practices.

While AI offers significant advantages in managerial decision-making and innovation, several papers in this special issue highlight its ethical challenges and potential risks. If not carefully managed, AI-driven processes may introduce biases, reduce transparency and exacerbate inequalities in decision-making structures. In this section, we explore how researchers have critically examined the ethical implications of AI adoption in various organisational contexts using the special issue results.

Rezaei et al. (2025) explicitly investigate the ethical concerns surrounding AI-driven knowledge-sharing practices within organisations. They highlight challenges related to data privacy, transparency, accountability and bias, emphasising that improper AI implementation could reinforce existing inequalities rather than mitigate them. Similarly, Wan and Chen (2025) explore the ethical risks associated with the humanisation of AI service robots, particularly in the hospitality and retail industries. Their findings suggest hyper-anthropomorphised AI could lead to user misconduct and unintended ethical dilemmas, necessitating regulatory and organisational oversight.

Biloslavo et al. (2025) conceptualise AI'sinfluence on strategic planning in uncertain environments (VUCA) and its associated ethical risks. They argue that AI-driven decision-making frameworks could introduce hidden biases, reduce human agency and raise concerns about accountability in critical managerial choices. Similarly, Bhatnagr and Rajesh (2025) examine the ethical dimensions of AI use in digital banking applications, focusing on user trust, psychological influence and the potential manipulative effects of AI-driven customer interactions.

Guan et al. (2025) offer a unique perspective by analysing how cognitive biases, particularly the Dunning–Kruger effect, influence AI acceptance among managers. Their research suggests that integrating AI may lead to overconfidence in AI-generated recommendations, thereby reducing critical human oversight in decision-making processes. At the same time, Misra et al. (2025) extend this analysis by discussing localised and humanised AI chatbot technologies in financial services, emphasising the ethical dilemmas related to user privacy, informed consent and the potential for AI to manipulate consumer behaviour. Therefore, the accepted studies emphasise the importance of developing robust ethical frameworks for AI adoption, ensuring that AI integration remains aligned with principles of fairness, accountability and transparency. Future research should explore regulatory measures, corporate governance mechanisms and best practices that mitigate AI'sethical risks while maximising its potential for responsible decision-making and innovation.

This special issue contributes to advancing the discourse on AI'sintegration in managerial decision-making, its role in driving innovations and the ethical considerations accompanying its widespread adoption. The accepted papers illustrate how AI reshapes organisational and trans-organisational structures, presenting opportunities and challenges that require further investigation.

Several contributions in this special issue explore AI'simpact across industries and networks, highlighting its transformative potential at a trans-organisational level. Ma and Chang (2025) provide a comprehensive analysis of AI-driven digital transformation in supply chain management, illustrating how AI enhances efficiency, agility and predictive capabilities across multiple firms in the automotive industry. Similarly, Avelar and Jordão (2025) investigate the application of AI in financial forecasting, emphasising how AI-generated insights influence investment strategies in global stock exchanges. Their findings suggest that AI adoption in financial analysis is not confined to single organisations but extends across interconnected global markets, shaping financial decision-making at an industry-wide level.

Furthermore, Oppioli et al. (2025) and Khanfar et al. (2025) offer a systematic literature review on AI adoption across organisations, synthesising key factors influencing AI integration at the organisational and inter-organisational levels. Their findings reveal that while AI adoption is increasing, its success depends on industry-specific technological readiness and the upskilling of the workforce. Biloslavo et al. (2025) discuss AI'srole in strategic planning in volatile environments, offering a conceptual framework for organisations navigating uncertain and rapidly evolving business landscapes. Therefore, these studies highlight that AI'simpact transcends organisational boundaries, necessitating collaboration among firms, regulators and industries to maximise AI'spotential.

Building on this discussion, Cimino et al. (2024) extend the trans-organisational perspective by examining how innovation managers appropriate generative AI tools, such as ChatGPT, to support creativity, knowledge sharing and cross-boundary collaboration. Their findings show that the capacity to adapt and customise AI systems fosters not only internal innovation but also inter-organisational learning and collective problem-solving within broader innovation ecosystems.

Similarly, Giachino et al. (2025) and Neiroukh et al. (2025) emphasise the strategic relevance of AI capabilities that enable firms to enhance coordination and responsiveness across networks. Both studies reveal that AI-driven decision-making improves not only organisational performance but also the quality and speed of inter-firm decisions, reinforcing data-driven cooperation and agility in complex business environments.

From a behavioural perspective, Rizzo et al. (2025) introduce a novel dimension to understanding AI use across organisational boundaries. Their research shows that managers' trust in AI-generated advice is influenced by social comparison orientation, with lower comparison tendencies associated with higher openness toward AI recommendations. This insight highlights the psychological and cultural factors that influence AI adoption and collaboration across interconnected organisational settings.

Therefore, these studies highlight that AI'simpact transcends organisational boundaries, necessitating collaboration among firms, regulators and industries to maximise AI'spotential. Taken together, these studies demonstrate that AI increasingly operates as connective tissue within modern business networks. Its influence extends beyond operational efficiency, reshaping how knowledge circulates, how trust in AI-mediated decisions develops and how inter-firm collaboration unfolds. To harness this potential, cross-industry cooperation and effective governance mechanisms are crucial for striking a balance between technological advancement and ethical and strategic alignment.

Other papers focus on AI'simpact within specific organisational settings, showcasing how AI adoption influences internal decision-making structures and managerial practices. Cavazza et al. (2025) examine the role of AI in the agricultural sector, demonstrating how AI-powered technologies are redefining business models and operational strategies in vertical farming. Similarly, Barile et al. (2025) explore AI-driven innovations in the banking sector, particularly in robo-advisory services, highlighting the shift from traditional human-led financial advice to AI-enhanced customer interactions.

Savastano et al. (2025) provide new insights into the adoption of enterprise chatbots from a managerial perspective. Their study outlines how chatbots are increasingly integrated into internal decision-making and communication systems, supporting knowledge sharing, coordination and data-driven management. By mapping managers' perceptions across industries, they identify both opportunities, such as efficiency gains, improved decision-making speed and enhanced collaboration, as well as barriers related to technological readiness and skill gaps. The authors also propose a strategic framework for successful chatbot implementation, stressing the importance of managerial education and change management in ensuring that AI tools strengthen, rather than replace, human judgement.

In a complementary way, Santoro et al. (2025) investigate how AI technologies are operationalised within growth-hacking strategies. Their multiple-case study across firms and digital consultancies reveals that AI serves as both a creative and analytical enabler, supporting experimentation, automation and rapid decision cycles in marketing and innovation processes. The findings emphasise that AI-powered growth hacking requires cross-functional collaboration and the development of hybrid competences that merge data analytics, creativity and strategic thinking.

Misra et al. (2025) provide a case study on AI-driven chatbot integration in banking, emphasising how AI revolutionises customer service and decision-making at the managerial level. Their research reveals hyper-personalised AI interactions' ethical and operational challenges, raising questions about trust, privacy and customer autonomy. Additionally, Wan and Chen (2025) investigate AI-powered service robots in the hospitality industry, identifying potential risks related to human-AI interactions and the ethical dilemmas emerging from AI'sincreasing role in the service sector.

Together, these contributions demonstrate that AI adoption within specific organisational contexts, whether through chatbots, automation or creative experimentation, redefines the managerial skill set and decision environment. Therefore, the accepted set of studies demonstrates that while AI enhances efficiency and decision-making within organisations, its implementation presents sector-specific challenges that require tailored strategies.

When this special issue was written and launched in early 2023, AI adoption steadily grew, but its impact on managerial decision-making remained relatively limited compared to today's landscape. However, during the development of this special issue, generative AI experienced an unprecedented surge in adoption (Cimino et al., 2024). The release of OpenAI's ChatGPT in late 2022 triggered a wave of interest and investment in AI-powered tools, prompting major technological players to follow suit (OpenAI, 2022). Microsoft integrated AI capabilities into its products through Copilot, embedding AI-driven assistance across its productivity applications (Mehdi, 2024). Similarly, Google launched Gemini, a multimodal AI system designed to enhance search and enterprise applications (Imran and Almusharraf, 2024), and DeepSeek, a free and more affordable AI chatbot, in January 2025. It released an updated model of generative AI with mathematics and coding skills (BBC, 2025).

This stream in generative AI adoption significantly altered the landscape of AI applications in managerial decision-making. Organisations across various industries have begun leveraging AI for operational efficiency, strategic planning, customer engagement and human resource management. Consequently, the research presented in this special issue became even more relevant, providing insights into the evolving role of AI in organisations and the implications for decision-making frameworks.

While the studies in this special issue provide valuable insights into AI'sintegration across various domains, they also reveal significant challenges that warrant further investigation. First, the ethical risks associated with AI decision-making remain a critical concern. Studies such as those by Rezaei et al. (2025) and Guan et al. (2025) highlight the potential for AI to reinforce biases and reduce transparency, signalling the need for stronger regulatory frameworks and ethical guidelines. Equally, Rizzo et al. (2025) remind us that psychological factors, such as managerial trust and social comparison orientation, may subtly shape how decision-makers engage with AI-generated advice, adding behavioural complexity to the ethics of AI-assisted decision-making.

Second, AI-driven transformations are progressing at varying rates across industries, resulting in disparities in AI adoption and effectiveness. Some industries, such as finance and supply chain management, exhibit rapid AI integration, while others, including healthcare and public administration, face barriers related to data accessibility, regulatory constraints and organisational resistance. Recent studies (e.g., Santoro et al. (2025), Savastano et al. (2025)) demonstrate that even within advanced sectors, the success of AI implementation depends on managerial readiness, hybrid skill development and cross-functional collaboration, suggesting that capability-building, rather than technology alone, is a decisive factor in achieving impact.

Furthermore, the works of Giachino et al. (2025) and Neiroukh et al. (2025) reveal that AI'scontribution to performance emerges through improved decision-making speed and quality – two dimensions that future research should further quantify across different organisational and cultural settings. This performance lens should be integrated with innovation-oriented perspectives, such as those advanced by Cimino et al. (2024), who call attention to how generative AI tools are reconfiguring managerial cognition, creativity and collaborative knowledge work.

Ultimately, as AI adoption continues to expand, human-AI collaboration models will require further refinement. Studies in this issue suggest that AI is not replacing human decision-makers but augmenting managerial capabilities. However, achieving an optimal balance between AI-driven automation and human oversight remains challenging. This balance demands a multidimensional approach, combining technical explainability, ethical safeguards and the cultivation of trust at both organisational and inter-organisational levels. Future research should investigate best practices for human-AI collaboration, ensuring that AI enhances decision-making without diminishing human accountability and agency.

Building on these insights, Table 2 presents a structured research agenda outlining key areas for future exploration. The research themes are categorised into three major clusters: AI integration in managerial decision-making, AI-driven innovations and future skills development, ethical considerations and the dark side of AI. Each cluster features specific research questions that highlight pressing challenges and emerging opportunities for scholars and practitioners.

To conclude, we reflect on the motivations outlined in the call for papers. AI has rapidly transitioned from an emerging technological tool to a fundamental driver of transformation in managerial decision-making, operational efficiency and strategic innovation. This special issue was structured around key research questions, including the integration of AI into decision-making, its role in driving business innovations and future skills development and the ethical implications of its adoption.

Despite the extensive research included in this special issue, several areas remain underexplored. While multiple papers have addressed the role of AI in specific and trans-organisational settings, the findings also reveal that the effectiveness of AI adoption depends on managerial readiness, organisational culture and the ability to develop hybrid competencies that merge data-driven reasoning with creativity and strategic judgement. Future research should further investigate the impact of AI on regulatory environments, governance structures and broader macroeconomic implications. The intersection of AI and global labour markets, particularly how AI-driven automation reshapes workforce dynamics across different sectors, requires deeper exploration. Moreover, the ethical dilemmas surrounding AI, such as bias in decision-making, transparency issues and regulatory governance, continue to demand academic and practical attention.

A notable trend emerging from this special issue is the increasing role of generative AI and advanced machine learning models in reshaping managerial processes. The rapid adoption of AI-powered tools by organisations worldwide, such as OpenAI's ChatGPT, Microsoft Copilot, DeepSeek and Perplexity, underscores the urgent need for scholars and practitioners to study AI'sevolving capabilities in real-time. As shown by several contributions, generative AI is not only transforming cognitive processes and creativity (e.g., in innovation management) but also fostering new forms of collaboration that extend across organisational boundaries. However, while these advancements present immense opportunities, they also introduce significant challenges, particularly regarding bias, ethical concerns and the necessity of regulatory oversight. Ensuring the responsible deployment of AI remains a priority for both researchers and practitioners.

Additionally, this special issue demonstrates that AI research, while expanding, still retains exploratory characteristics. Several studies in this issue take a conceptual or literature review approach, reflecting the need for further empirical inquiry. Future research should focus on comparative studies across industries, cross-regional analyses and investigations into AI adoption in private and public sector organisations. More granular, data-driven investigations could provide valuable insights into AI'slong-term effects on decision-making and business operations. In this regard, future work should also examine how psychological and behavioural dimensions, such as trust in AI systems and social comparison tendencies, affect managerial reliance on algorithmic advice and influence inter-firm collaboration.

A key takeaway from this special issue is the evolving relationship between AI and managerial decision-making. While AI adoption has accelerated, critical gaps remain in understanding how AI systems influence executive leadership, stakeholder engagement and corporate governance. The studies collectively highlight the need for integrative frameworks that combine ethical AI design, leadership development and human-AI interaction models, grounded in transparency and trust. Future research should explore the psychological and behavioural dimensions of AI-assisted decision-making, ensuring that AI complements rather than replaces human expertise.

Ultimately, this special issue contributes to the growing academic discourse on AI by shedding light on its integration into managerial practices and its implications for organisations. However, as AI evolves, academic inquiry must keep pace with technological advancements, ensuring that research remains relevant to theoretical and practical applications. Future studies should adopt interdisciplinary approaches and real-world experimentation to understand how AI can promote sustainable, ethical and inclusive innovation across various industries. Business leaders must proactively engage with AI adoption strategies while balancing ethical considerations and governance frameworks. We encourage scholars to explore further the themes outlined, expanding the knowledge base on AI'stransformative potential and addressing the remaining critical gaps.