Exploring the frontier of language-associated artificial intelligence in management

INTRODUCTION

The rapid evolution of artificial intelligence (AI) has recently ushered in a new era of technological advancement with implications for management. Among the most significant developments in this field are Large Language Models (LLMs) and Generative AI, which have emerged as powerful tools for understanding, generating, and manipulating human language with unprecedented sophistication (Hamaniuk, 2021; Wu et al., 2022). These technologies, rooted in the foundational work of pioneers like Alan Turing and the transformative breakthroughs of the past decade, are now at the forefront of a revolution in how organizations operate, make decisions, and interact with stakeholders.

Integrating AI-powered language technologies into management practices represents a frontier rich with potential yet fraught with challenges. These AI systems are reshaping the contemporary business landscape from strategic decision-making to customer engagement, from human resource management to supply chain optimization (Lokanan & Maddhesia, 2024; Plathottam et al., 2022). They promise enhanced efficiency, data-driven insights, and personalized experiences on multiple scales. However, as with any transformative technology, such technologies also raise critical questions about ethics, bias, transparency, and the changing nature of work (Stahl & Eke, 2024).

In this context, the Mackenzie Management Review (RAM) has called for papers for a special issue titled “Exploring the Frontier of Language-Associated Artificial Intelligence in Management.” This special issue seeks to gather diverse perspectives and cutting-edge research on the multifaceted implications of these technologies for management. The call covers a wide range of relevant areas for investigation, including the impact of LLMs on organizational decision-making, ethical considerations in AI deployment, the challenges of bias and fairness, the dynamics of human-AI collaboration, and the role of these technologies in shaping the future of work.

This special issue contributes to the growing body of knowledge at the intersection of AI and management by addressing several key objectives. First, it comprehensively assesses the current state of language-associated AI technologies and their applications in management contexts. Second, it critically examines the challenges and limitations of these technologies, including ethical dilemmas, regulatory concerns, and issues of transparency and accountability. Third, this review explores innovative approaches to leve-raging these technologies for sustainable and responsible management practices. Finally, it aspires to offer insights into the future trajectories of AI in management and its potential to transform organizational structures, processes, and cultures.

The articles in this special issue offer diverse perspectives and practical insights on the impact of language-associated AI in the management field. They offer a nuanced and multifaceted exploration of how language-associa-ted AI can reshape management theory and practice. From empirical studies on the impact of AI on decision-making processes to theoretical examinations of ethical frameworks for AI governance, from case studies of AI integration in organizations to critical analyses of the societal implications of these technologies, this collection of papers provides a comprehensive overview of the current state of the field and points towards future directions for research and practice.

At this critical juncture in the evolution of AI and its integration into management, this special issue serves as both a reflection on the progress made and a roadmap for the challenges and opportunities ahead. It aspires to contribute to a more informed, ethical, and effective management deployment of language-associated AI technologies, ultimately leading to more innovative, responsive, and responsible organizations in the AI era.

ARTIFICIAL INTELLIGENCE EVOLUTION

In his 1950 classic, Alan Turing published an article entitled “Computing Machinery and Intelligence.” In this work, he introduced the Turing Test, presented a criterion capable of determining whether a machine can be considered intelligent, and discussed this issue in detail (Turing, 2009). Although it is difficult to date the beginning of any past movement regarding AI as a research discipline, there is some consensus that the Dartmouth Summer Research Project of 1956 initiated AI as a research discipline (Moor, 2006). At the Dartmouth conference, researchers discussed the possibility of creating intelligent machines.

Classical authors have considered the so-called “AI spring” between the late 1950s and the early 1970s. During this period, there was great enthu-siasm and optimism regarding AI, mainly due to significant advances in research and development. In the 1960s, the first AI programs emerged, such as ELIZA, a chatbot capable of simulating human conversations (Weizenbaum, 1966). In 1972, an AI chess program called “Chess 4.0” was developed and defeated a chess master (Slate & Atkin, 1977).

However, as the 1970s progressed, AI development slowed con-siderably, primarily due to the difficulty of creating robust systems capable of solving highly complex problems. Another point worth noting is the lack of computing power at the time and the complexity of the issues faced (Buchanan, 2005). This period is known as the “AI Winter.” The 1980s-a kind of golden years-are considered one of the most fruitful decades for advances in Expert Systems (ESs). Several practical applications have been developed in different areas, such as industry, commerce, and services, emphasizing improving knowledge representation techniques. The knowledge-based systems are computer software applications that seek to replicate these experts’ problem-solving and decision-making approaches (Martinsons, 1995). This development, which occurred in the 1980s, paved the way for AI research areas that have driven the growth of ESs to this day, culminating in Learning Machines and Neural Networks, among others.

Currently (2010s-2020s), we are experiencing an era of growth in the so-called Modern AI, Including ChatGPT (a generative model) and new applications using an innovative technique called Deep Learning-advanced and deep machine learning capable of using neural networks with multiple layers to identify and learn complex patterns from large data sets (like big data). Modern AI offers numerous possibilities in various areas of knowledge, such as the healthcare, engineering, financial, marketing, and education industries, among many others.

LARGE LANGUAGE MODELS AND GENERATIVE AI

Generative AI characterizes recent AI models that can create new content, such as texts or code, images, and videos. These models learn patterns from large amounts of data and use this knowledge to generate new and similar content. Large Language Models (LLMs) are generative models that genera-te text or code. They are based on transformer architectures and gene-rative AI models for image and video use other technologies like Generative Adversarial Networks (GANs) (Goodfellow et al., 2014), Variational Autoencoders (VAEs) (Kingma & Welling, 2022), and diffusion models (Sohl-Dickstein et al., 2015).

A transformer model (Vaswani et al., 2017) is a neural network capable of learning context and, thus, meaning by identifying relationships in sequential data like the words in a sentence. The model uses a mathematical function called attention to detect distant data elements in a series that influence and depend on each other. The model was considered a “foun-dation model” in 2021 (Bommasani et al., 2022), driving a wave of advances in AI. Transformers revolutionized the field of natural language processing (NLP) and are the backbone architecture for state-of-the-art LLM (Chernyavskiy et al., 2021).

Fundamentally, transformers provide the computational framework, while LLMs are the massive models trained on this architecture to excel at language-related tasks. Generative Pre-trained Transformer (Chat GPT-3 and GPT-4) from Open AI, Gemini from Google, Claude by Anthropic, and Llama from Meta are examples of LLMs currently available and in continuous enhancements. In the generative AI models for image and video tools, among the most popular, we found DALL-E 3, Midjourney, Stable Diffusion, and Synthesia.

AI IN MANAGEMENT - BEFORE GENERATIVE AI

Artificial Intelligence (AI), with its ups and downs in development and popularity since its origins, has become an area of great interest and attention in the last two decades. Machine Learning, Neural Networks, Deep Learning, Computer Vision, and other related subsets of the broad area of AI are hot topics in the literature and companies’ development of new products and services (Duan et al., 2019).

Makridakis (2017) argues that the impact of AI on firms and society is comparable to the transformative effects of the industrial and digital revolutions. He outlines four potential scenarios for AI’s development, each depending on its degree of societal acceptance. Additionally, it presents survey results estimating the time frame for achieving Artificial General Intelligence or Superintelligence. In this sense, Jarrahi (2018) emphasizes AI’s role in enhancing decision-making by providing real-time information, preparing and analyzing data, and managing complexity. This allows humans to concentrate on creative and intuitive tasks, particularly in situations characterized by uncertainty and equivocality.

AI is usually associated with automation, which uses algorithms to replace humans in tasks, and augmentation, a collaboration between humans and machines/algorithms. Management needs to deal with this paradox, take advantage of AI capabilities to improve the company’s performance in repetitive tasks that it carries out efficiently through automation, and use AI to assist employees in carrying out their functions through augmentation, finding balance in the tension generated by the idea of employees being replaced by machines (Raisch & Krakowski, 2021).

The literature allows us to identify individual, group, and organizational factors that characterize the interaction between humans and AI resulting from using AI at work (Bankins et al., 2024). The study identified five key themes in the analyzed literature: human-AI collaboration, perceptions of algorithmic and human capabilities, worker attitudes towards AI, AI as a control mechanism in algorithmic management of platform-based work, and labor market implications of AI use, suggesting that it is essential to make the collaboration between AI and the workers a positive experience, to increase the adoption by workers, and to make clear where and what the AI tools are used.

Literature also predicts changes in the future of marketing caused by AI (Davenport et al., 2020). The data about the customer’s behavior, preferences, and activities on companies’ websites or social media is favorable for AI adoption to provide the user with the content product that best fits his profile. For example, AI is used for personalized engagement marketing (Kumar et al., 2019), chatbots, virtual assistants, and robots can be used to improve the customer experience on the shopping journey (Hoyer et al., 2020).

A study on the use of AI in Human Resource Management (HRM) identifies the use of AI in multiple HRM functions (Budhwar et al., 2022). AI is already used for human resource planning, recruitment and selection, training and development, compensation and benefits calculations, and performance management. The potential of AI in HRM is far from being fully realized due to the complexity of HR functions, lack of data, liability issues associated with fairness and other ethical and legal constraints, and adverse employee reactions to management decisions through data-driven algorithms (Tambe et al., 2019).

The adoption of AI techniques in supply chain management (SCM) is frequent (Toorajipour et al., 2021; Riahi et al., 2021). Toorajipour et al. (2021) selected articles using AI on marketing, logistics, production, and supply chain subfields, showing the diversity of AI techniques used. Riahi et al. (2021) show the distribution of studies using descriptive, predictive, and prescriptive analytics, a standard classification for the methods used in the SCM context. They also divided the studies addressing the distinct supply chain processes and summarized the AI techniques. Smart Logistics uses AI and other technologies, like big data, IoT, and robots, to automate logistic processes (Feng & Ye, 2021).

LLM AND GENERATIVE AI OPPORTUNITIES AND CHALLENGES IN MANAGEMENT

Generative AI and large language models (LLMs) significantly transform how organizations operate and make decisions across various domains. These technological advancements provide new tools that enable companies to optimize processes, enhance communication, and increase efficiency in their operations. Figure 1 illustrates the potential applications of generative AI in organizations.

Figure 1
The potential application of generative AI for organizations

Generative AI significantly enhances strategic decision-making by providing data-driven insights that enable managers to navigate complex scenarios and make well-informed choices, especially in dynamic business environments where rapid adaptation is crucial. Additionally, it transforms organizational knowledge management (Guo et al., 2020), facilitates data storage, transformation, and distribution, promotes continuous learning, and empowers employees to make autonomous, informed decisions, improving operational efficiency.

In functional areas, generative AI is vital in automating customer interactions, particularly in customer service and marketing. It also streamlines human resource management processes, such as resume screening and onboarding, allowing teams to focus on strategic and creative tasks (Votto et al., 2021). This automation reduces time spent on repetitive tasks and minimizes human errors, leading to smoother and more effective operations. Administrative tasks, such as scheduling and document generation, are increasingly automated by generative AI, freeing up valuable time for employees to concentrate on activities that require critical thinking and innovation. This reconfiguration of administrative functions boosts productivity and shifts the focus toward more strategic endeavors.

Several key themes underscore the technological advancements and organizational shifts driven by Large Language Models (LLMs) and generative AI. These include process automation, data-driven decision-making at both strategic and operational levels, and personalized marketing and customer engagement. For instance, AI-powered predictive market analysis enables managers to forecast trends and make informed investment and product development decisions. Moreover, advanced chatbots improve customer support by offering real-time service and resolving issues without human intervention, enhancing efficiency and reducing costs (Pinochet et al., 2024a). A central theme is the automation of managerial tasks in complex environments, with the augmentation approach suggesting that AI enhances rather than replaces managerial capabilities. This task automation (Davis & Marcus, 2015; Raisch & Krakowski, 2021) allows AI to process large datasets and generate real-time insights, transforming decision-making across strategic, functional, and administrative domains.

In marketing and customer experience, personalization has become a critical area where LLMs analyze vast amounts of data to identify patterns and create targeted content, resulting in higher engagement and conversion rates (Arun Kumar et al., 2022). AI-driven tools also facilitate real-time sentiment analysis and customer feedback, enabling companies to quickly and accurately adjust their strategies to meet consumer needs. Integrating interactive AI and mixed realities is increasingly vital in enhancing customer experience and engagement (Sung et al., 2021). Significant impacts are also being seen in knowledge management and organizational learning due to LLMs. AI-assisted knowledge management systems use generative AI to index, classify, and suggest relevant information to employees, improving knowledge dissemination and retention. Customized training content tailored to users’ knowledge levels and learning styles is also being developed, promoting a more effective and personalized learning environment (Nguyen & Malik, 2022; Pinochet et al., 2023).

Integrating generative AI into business processes promotes operational optimization and enables new working methods, facilitating digital transformation and improving operational efficiency (Eubanks, 2022). Business process automation (BPA) exemplifies how LLMs optimize tasks such as report generation, data entry, and financial analysis, reducing time and mini-mizing errors. Moreover, AI adoption fosters cultural transformation within organizations as employees learn to collaborate with AI systems and integrate these tools into daily routines.

The growing use of LLMs and generative AI raises critical ethical and governance issues. As companies adopt these technologies, there is a pressing need for robust governance frameworks and ethical considerations, including transparency, fairness, and accountability in AI use, avoiding biases, and complying with regulations. Many organizations are establishing AI ethics and governance committees to oversee the ethical implementation of AI technologies.

Human resource management and the nature of work are transforming significantly due to generative AI, particularly in recruitment and talent retention processes (Colbert et al., 2016). AI tools analyze resumes, conduct automated interviews, and predict candidate suitability, reducing human bias and improving efficiency. Sentiment analysis and employee engagement tools help organizations identify issues early and improve talent satisfaction and retention. The introduction of generative AI necessitates a reassessment of traditional management concepts, influencing recruitment, training, and development practices and allowing for greater personalization and efficiency in human resource operations (Pinochet et al., 2024b).

Therefore, AI rapidly transforms organizational operations and interactions, enhancing customer experience and increasing operational efficiency. Emerging trends show the growing integration of conversational AI, the development of continuous and adaptive learning, the combination of gene-rative AI with other advanced technologies, and the increasing importance of social responsibility and transparency in the use of AI. These trends highlight AI’s potential to innovate and optimize processes while emphasizing the need for responsible and ethical practices to ensure trust and fairness in applying these technologies.

To strategically implement AI in organizations, it is essential to develop internal competencies by investing in training and skill development for employees, ensuring they can effectively use these technologies. Additionally, forming partnerships with startups and academic institutions is crucial for keeping the organization at the forefront of innovations in AI and machine learning (ML). The establishment of AI ethics and governance committees is also vital to ensure the ethical use of AI and compliance with regulations. Finally, fostering an environment of continuous experimentation and innovation enables the organization to adapt to new technologies and remain competitive quickly.

CRITICAL PERSPECTIVES AND IMPLICATIONS OF AI IN CONTEMPORARY MANAGEMENT

The rapid advancement and integration of Artificial Intelligence (AI), particularly Large Language Models (LLMs) and Generative AI, into contemporary management practices present unprecedented opportunities and significant challenges. These technologies fundamentally alter decision-making processes in organizations, enabling managers to make more informed and data-driven decisions by processing vast amounts of data and generating real-time insights. However, this shift raises critical questions about balancing human judgment and AI-driven recommendations. Jarrahi (2018) notes that AI should be viewed as a tool for augmenting human decision-making rather than replacing it entirely, underscoring the need for a symbiotic relationship between human managers and AI systems.

Integrating AI into management practices raises pressing ethical concerns, particularly regarding bias, fairness, and transparency in AI-driven decisions. Studies on gender bias in job postings highlight the potential for AI to perpetuate or exacerbate existing biases, emphasizing the need for robust ethical frameworks and governance structures. Organizations must develop comprehensive AI ethics policies and establish oversight committees to ensure responsible AI use, aligning with broader corporate social responsibility goals.

Furthermore, the advent of AI is reshaping the nature of work and the skills required in the modern workplace. While AI can automate routine tasks, it also demands new skills related to AI management and interpretation. This shift necessitates reevaluating traditional management concepts and practices, as Black & van Esch (2020) and Cullinane & Cushen (2019) noted. Organizations must focus on upskilling and reskilling their workforce to adapt to this new technological landscape, emphasizing critical thinking, creativity, and emotional intelligence that complement AI capabilities.

Integrating AI technologies will likely lead to significant organizational structure and culture changes. As AI takes over routine tasks, organizations may need to redesign their hierarchies and workflows. This transformation calls for a cultural shift towards embracing AI as a collaborative tool rather than a threat. Leaders must foster a continuous learning and adaptation culture, encouraging employees to view AI as an opportunity for enhanced productivity and innovation.

AI, particularly generative AI and LLMs, offers unprecedented opportunities for personalization in marketing and customer service. As Kumar et al. (2019) highlighted, AI enables highly targeted marketing strategies and improved customer experiences. However, this level of personalization raises concerns about privacy and data security. Organizations must balance leveraging AI for enhanced customer engagement and respecting individual privacy rights. Regarding knowledge management, AI technologies are revolutionizing how organizations store, retrieve, and disseminate knowledge. As Guo et al. (2020) suggest, AI-powered systems can significantly enhance organizational learning and innovation. However, this capability also raises questions about the ownership and control of knowledge within organizations and the potential loss of tacit knowledge that AI systems may need help to capture.

The rise of AI in management presents new avenues for research and necessitates changes in management education. Researchers must explore the long-term impacts of AI on organizational performance, employee well-being, and societal outcomes. Management education programs must incorporate AI-related skills and knowledge, preparing future managers to work effectively with AI systems and navigate the ethical and strategic challenges they present.

Researchers and practitioners must adopt a critical and balanced perspective as AI continues evolving and integrating into management practices. While embracing AI’s potential to enhance efficiency and decision-making, organizations must also be mindful of its limitations and potential negative consequences. Future research should focus on developing frameworks for responsible AI adoption, exploring AI’s long-term impacts on organizational dynamics, and investigating ways to foster effective human-AI collaboration in management contexts.

The journey towards AI integration in management is complex and multifaceted. It requires a collaborative effort from researchers, practitioners, policymakers, and ethicists to ensure that AI technologies are deployed in ways that benefit organizations, employees, and society at large. As we navi-gate this new frontier, maintaining a human-centered approach to AI adoption will be crucial in realizing its full potential while mitigating its risks. By addressing these critical perspectives and implications, organizations can harness the power of AI to drive innovation, improve decision-making, and create more adaptive and responsive management practices for the future.

ACCEPTED PAPERS PRESENTATION

This special issue contains five articles that delve into the multifaceted implications of language-associated AI technologies in management.

The article “Large Language Models (LLMs): A Systematic Study in Administration and Business” provides potential insights for researchers and professionals in the field of LLMs and Administration and Business by analyzing the characteristics of academic production related to such subjects. In a consistent analysis of articles published between 2000 and 2024, the article demonstrates that most articles involve computational modeling and empirical analysis and refer to the validation of existing technologies, methods, or tools. The article reinforces that, despite the advances in the use and application of LLMs, some challenges persist, mainly those involving ethics and data privacy and the management of research biases involving natu-ral language processing.

The article “The Future Avenues of Artificial Intelligence and Decision-Making in Business Management” provides a comprehensive understanding of this transformative intersection’s theoretical foundations, research trajectories, and emerging themes. The authors conducted a bibliometric analysis of 494 journal articles on the intersection of artificial intelligence and decision-making in business management. As a result, the article consistently maps this intersection, highlighting the pioneering studies in the literature and the most cited references and presenting four main themes addressed by the articles: industry and society impact, business strategies, technological applications, and decision systems. The authors also present a well-structured future research agenda.

The article “Evolution of the Use of Conversational Agents in Business Education: Past, Present, and Future” analyzed publications on artificial intelligence, education, business, and conversational agents or chatbots in the Scopus database. The article provides insights into the thematic landscape of artificial intelligence-related research across multiple domains. The research findings reveal five thematic trends at the intersection of the themes: student-centered learning in higher education, interactive methods using the natural language processing approach, technological solutions and ChatGPT in a university context, enhancing education through intelligent platforms, and challenges for the social and academic integration of artificial intelligence tools. The authors also present a future research agenda.

The paper “Gender Bias in Large Language Models: a Job Posting Analysis” evaluated potential gender biases in job postings. The authors used different LLM embeddings to evaluate potential gender biases generated in job postings from two large platforms, LinkedIn and Vagas.com. The results demonstrated that the degree of consistency between architectures varies significantly as the words contained in the job descriptions and the two gender vectors are changed. This means that even pre-trained models may not be reliable in understanding gender bias. For the authors, the lack of consistency indicates that the assessment of gender bias may differ depending on the parameters.

Finally, the article “The (lack of) ethics at Generative AI on Education and Research in Management” discusses the effects of generative artificial intelligence on teaching and research. As the authors examine the effects of technology on people, they use the research lens of virtue ethics. Based on the PRISMA method for collecting and selecting articles, it is evident that using generative artificial intelligence for student learning and researcher training in virtues and character is in its infancy. The article categorizes the impacts of generative artificial intelligence on teaching and research, mapping articles focused on the agent or actions and classifying their underlying ethical perspectives in recognition of the utilitarian principles and regulations that govern the ethics of artificial intelligence.

FINAL CONSIDERATIONS

As we conclude this exploration of AI in management, particularly the impact of Large Language Models (LLMs) and Generative AI, it is evident that these technologies are at the forefront of a transformative era in organi-zational practices. They offer opportunities for innovation, efficiency, and strategic decision-making but also present significant challenges and limitations that demand careful consideration. The future of management in the age of AI will depend on how effectively organizations navigate these complex dynamics.

Integrating LLMs and Generative AI into management practices brings interconnected challenges and opportunities. Foremost among these is the need for robust ethical AI governance frameworks. As these technologies become more deeply embedded in decision-making processes, transparency, fairness, and accountability are paramount. Organizations that successfully develop and implement such frameworks can mitigate risks, position themselves as leaders in responsible AI use, gain competitive advantages, and strengthen stakeholder trust.

Alongside the ethical challenges is the pressing need to upskill and reskill the workforce. The AI revolution requires a significant shift in organi-zational capabilities, demanding both the development of existing talent and the attraction of new expertise. While this presents a challenge, it also offers an opportunity to create more dynamic, adaptable organizations capable of effectively leveraging human-AI collaboration. Those organizations that excel in this area will likely emerge as leaders in their respective fields.

The potential for personalization offered by LLMs and Generative AI in customer interactions and marketing strategies is immense, yet growing concerns over data privacy and security temper it. Striking the right balance between these competing demands represents a significant challenge and an opportunity for innovation in customer engagement practices. Organizations must navigate this terrain carefully, seeking ways to harness AI-driven personalization while upholding rigorous ethical standards and respecting individual privacy rights.

As AI technologies evolve, they open new avenues for innovation across all business operations, from product development to service delivery and business model innovation. Organizations that effectively harness these technologies for innovation may gain significant competitive advantages. However, realizing this potential requires overcoming several limitations of LLMs and Generative AI in management contexts.

One of the most persistent challenges is the issue of bias and fairness. Despite significant advancements, AI systems still struggle with inherent biases in their training data, which can lead to unfair or discriminatory outcomes in critical areas such as recruitment, performance evaluation, and customer interactions. The lack of consistency in bias assessment, as highlighted in studies on gender bias in job postings, underscores the complexity of this issue and the need for ongoing research and development. Another major limitation is the need for deep contextual understanding and common-sense reasoning in LLMs. While these systems excel at processing and generating language, they often need help grasping complex organizational scenarios’ nuanced dynamics. This limitation can result in misinterpretations or inappropriate responses in situations that require a sophisticated understanding of context.

The ‘black box’ nature of many AI models, including LLMs, poses challenges for management decision-making, particularly in contexts where transparency and accountability are crucial. The inability to fully explain how these models arrive at their conclusions limits their application in sensitive management areas and raises questions about their reliability and trustworthiness. Data privacy and security concerns remain significant barriers to the widespread adoption of LLMs and Generative AI in management. These technologies often require access to large amounts of potentially sensitive data, and ensuring the privacy and security of this information-especially given evolving regulations-remains a significant concern for many organizations.

Many organizations need help effectively integrating AI technologies into their IT infrastructure and business processes. This integration hurdle can significantly hinder the realization of AI’s full potential in management and requires careful planning and execution to overcome. Additionally, there is a risk that overreliance on AI systems could lead to workforce deskilling in certain areas. Balancing AI augmentation with human skill development is a critical challenge that organizations must address to ensure long-term success and resilience.

Looking ahead, integrating LLMs and Generative AI into management practices is not merely a technological challenge but a multifaceted endeavor that touches on ethics, culture, skills, and organizational design. The path forward will require continuous learning, adaptation, and a steadfast commitment to responsible innovation. Organizations must remain vigilant in addressing these technologies’ limitations and ethical concerns while exploring their vast potential to enhance decision-making, drive innovation, and crea-te value.

In conclusion, while LLMs and Generative AI offer transformative potential for management practices, they also present significant challenges that must be carefully navigated. The future of AI in management will likely be shaped by how effectively organizations address these limitations while capi-talizing on the opportunities presented. It will require a collaborative effort from researchers, practitioners, policymakers, and ethicists to ensure these powerful technologies are deployed in beneficial, ethical, and sustaina-ble ways. At this critical juncture, we hope this special issue has contributed to the ongoing dialogue and will inspire further research and thoughtful implementation of AI technologies in management, paving the way for more innovative, responsive, and responsible organizations in the AI era.

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• EDITORIAL BOARD
  Editor-in-chief
  Fellipe Silva Martins
  Technical support
  Vitória Batista Santos Silva

• EDITORIAL PRODUCTION
  Publishing coordination
  Jéssica Dametta
  Editorial intern
  Bruna Silva de Angelis
  Language editor
  Irina Migliari (Bardo Editorial)
  Layout designer
  Emap
  Graphic designer
  Libro

Publication Dates

• Publication in this collection
  16 Dec 2024
• Date of issue
  2024

History

• Received
  04 Aug 2024
• Accepted
  05 Aug 2024