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Introduction

AI has become integrated into business strategies, enhancing capabilities in customer interaction, operational efficiency, and decision-making. According to a McKinsey survey (2023), organizations classified as high performers are fully embracing artificial intelligence, utilizing both generative AI and traditional AI technologies. These high-performing organizations are implementing generative AI across more business functions than other organizations, particularly in product and service development and risk and supply chain management. Consequently, AI is now recognized as one of the top emerging sets of technologies, crucial for executing a digital-first strategy and ensuring competitiveness in today's market (Enholm et al., 2021). However, alongside these advancements, there are growing concerns about the unintended consequences of AI technologies (Benlian et al., 2022; Cheng et al., 2022ab).

Academically, AI's impact has been studied across various fields, including information systems (Gursoy et al., 2019), tourism and hospitality (Grundner & Neuhofer, 2021), marketing (Syam and Sharma, 2018; Barari, 2024; Huang and Rust, 2021), financial management (Culkin and Das, 2017), and education (Ivanov, 2023). These studies highlight AI's potential to revolutionize interactions between businesses and stakeholders, creating significant business value by improving efficiency and effectiveness (Akerkar, 2019). However, these advancements come with significant challenges that must be addressed. Discrimination and bias are among the most critical issues (Ntoutsi et al., 2020). AI systems, which often rely on historical data, can inadvertently perpetuate existing biases, leading to unfair treatment of certain groups. This is particularly troubling in sectors like hiring, where AI-driven recruitment tools may disadvantage candidates based on race, gender, or socioeconomic background (Alt, 2018). The potential for AI to reinforce stereotypes and discriminatory practices underscores the urgent need for research into bias mitigation strategies and the development of fair AI systems. AI implementation also brings the challenge of job displacement (Lazaroiu & Rogalska, 2023). Automation and AI-driven processes can potentially replace human labor across various sectors, leading to significant job losses and economic upheaval (Kulkov, 2021; Svetlana et al., 2022). While AI can increase productivity and reduce operational costs, it also threatens the livelihoods of millions of workers, particularly those in routine and manual jobs. This economic impact necessitates a closer examination of how AI affects employment and how society can adapt to this technological shift by investing in reskilling and education programs.

Furthermore, the increasing use of AI technologies can exacerbate inequality. As AI becomes a critical driver of competitive advantage, companies with more resources can leverage it to widen the gap between themselves and smaller, less technologically advanced firms (Zhang et al., 2021). The growing divide affects businesses and society, potentially leading to increased economic disparity and social unrest. Therefore, it is crucial to investigate how AI contributes to inequality and explore solutions that ensure equitable access to AI technologies and their benefits. The darker aspects of AI are not limited to discrimination, job displacement, and inequality. They also encompass ethical dilemmas such as accountability and transparency. As AI systems become more autonomous, determining responsibility for their actions becomes increasingly complex. This raises questions about who should be held accountable when AI systems cause harm or make unethical decisions. Additionally, the opacity of many AI algorithms makes it difficult for users to understand how decisions are made, challenging the principles of transparency and trust in AI-driven processes (Gigerenzer, 2022; Guercini, 2023a). These challenges underscore the necessity for a comprehensive examination of AI's impact on individuals, organizations, and society.

List of topic areas

• The Dual Impact of AI on Business Processes
• Behavioral, Psychological, and Cultural Dimensions
• Trust, Transparency, and Accountability in AI Systems
• Emerging AI Technologies and Their Business Implications
• Potential future scenarios for AI in business and the implications for industry practices
• Ethical and Social Implications
• Regulatory and Governance Challenges
• Case Studies and Practical Insights
• Lessons learned from organizations that have successfully navigated the challenges of AI adoption.

Guest Editors

Matteo Cristofaro,
University of Rome Tor Vergata, Italy,
[email protected]

Simone Guercini,
University of Florence, Italy,
[email protected]

Submissions Information

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Key Deadlines

Opening date for manuscripts submissions: 30 June 2025
Closing date for manuscripts submission: 30 October 2025