Artificial Intelligence in Business: Redefining Competencies, Finance, and Entrepreneurship

1. Why Does Artificial Intelligence Matter for Business, Finance, and Entrepreneurship?

Artificial intelligence (AI) is no longer only a technological innovation or an advanced computational tool; it has become a central driver of transformation in business, finance, entrepreneurship, human resources, organizational governance, and digital communication. The rise of generative AI, business intelligence, explainable AI, predictive analytics, social media analytics, machine learning, and immersive digital environments is changing the way organizations think, decide, produce, communicate, compete, and create value. In this sense, AI is not simply another stage of digitalization. It represents a deeper shift in managerial practices, organizational competencies, financial expectations, entrepreneurial behavior, information governance, and institutional readiness.

The relevance of AI in administrative sciences lies precisely in its capacity to connect technological progress with managerial and social transformation. Organizations are increasingly required to adopt AI tools while also developing the human, ethical, strategic, and institutional capabilities needed to use them responsibly. The managerial challenge is no longer whether AI will influence organizations, but how organizations can integrate AI in ways that generate sustainable value, improve decision-making, support innovation, protect trust, and preserve human-centered governance.

This Special Issue, was launched to examine this transformation from multiple perspectives. It brings together studies that investigate AI adoption, generative AI in SMEs, business intelligence, AI in banking, explainable AI in recruitment, AI-based social media analysis, Metaverse readiness, entrepreneurial perceptions, sustainable territorial development, and logistics efficiency. These contributions show that AI adoption is not a linear process. It depends on leadership, infrastructure, employee skills, financial trust, digital literacy, organizational readiness, ethical design, information quality, and the broader socio-economic context.

The articles included in this Special Issue also reveal that AI transformation cannot be fully understood through a single disciplinary lens. It requires dialog between management, finance, entrepreneurship, information systems, human resources, organizational behavior, public governance, sustainability studies, and digital communication research. In this respect, the Special Issue contributes to the growing body of research that sees AI not only as a technological tool, but also as a strategic, social, and institutional phenomenon.

2. Articles in This Special Issue

Khneyzer et al. (2026) examine the role of AI in sustainable territorial development, focusing on North Lebanon as a fragile and resource-constrained context. Their study shows that AI can support resource optimization, smart agriculture, urban mobility, disaster preparedness, and regional development. However, the authors also emphasize that such potential remains strongly conditioned by digital infrastructure, governance capacity, policy frameworks, funding, and digital literacy. This article situates AI within the broader question of territorial resilience. It demonstrates that AI cannot generate sustainable development by itself; it must be embedded in inclusive governance structures and adapted to local socio-economic realities.

Zhou et al. (2026) focus on AI in human resource management, particularly in recruitment processes. Their study shows that explainability in AI-based interviews enhances organizational attractiveness through perceived organizational support and perceived innovativeness. This contribution highlights an essential issue for organizations adopting AI in sensitive human-centered processes: transparency matters. Explainable AI is not only a technical requirement; it is also a signal of fairness, support, and credibility. The article therefore contributes to the understanding of how AI systems influence applicants’ perceptions and how organizations can use AI without weakening trust or employer reputation.

Abou Ltaif and Messarra (2026) extend the discussion of trust and digital communication; they examined the use of machine learning to detect pseudo-facts in Arabic social media content. Their study is based on 26,322 tweets related to a controversial judicial event in Lebanon and compares K-Nearest Neighbors, Naïve Bayes, and Logistic Regression models to classify content according to emotional and rational discourse. The findings show that Logistic Regression achieves the strongest classification performance, while emotional discourse represents more than 89% of the analyzed content. This contribution connects AI-based social media analysis with information risk assessment, institutional trust, and the emotional dynamics of digital environments. It also demonstrates that context-specific datasets can support effective AI classification in non-Western linguistic settings, particularly when misinformation and pseudo-facts affect public opinion and decision-making.

Hoang and Vu (2026) investigate AI in Vietnam’s banking sector through the concept of new quality productive forces. Their findings show that AI confidence positively contributes to productive transformation at the employee level, especially when supported by skill transformation. However, the study also reveals that work experience may weaken this relationship, suggesting that employees do not adapt to AI in the same way. This article underlines the importance of human capital development in AI transformation. It reminds managers that AI adoption requires more than technological deployment; it requires confidence-building, reskilling, and inclusive training strategies that consider differences among employee groups.

Peñarroya-Farell et al. (2025) provide a complex leadership perspective on generative AI adoption in SMEs. The authors integrated the Technology Acceptance Model, Temporal Motivation Theory, the Resource-Based View, and complexity theory, to show that generative AI adoption is shaped by leadership intention, motivation, and resource allocation. Their contribution is highly relevant for SMEs because these organizations often face limited resources, uncertainty, and organizational inertia. The article suggests that SME leaders must not only decide whether to adopt generative AI, but also determine how to align it with their strategic priorities and organizational capacities.

Bitetti et al. (2026) complement this perspective as they examined how generative AI creates value in SMEs. Their study identifies several functional applications of generative AI, including operational automation, data intelligence, market intelligence, linguistic expansion, market testing, and idea generation. The authors show that generative AI can support both efficiency-oriented and growth-oriented strategies. However, they also insist that value creation depends on technological, organizational, and environmental readiness. This article therefore shifts the discussion from AI adoption to AI value realization. It shows that generative AI creates value when its functions are clearly connected to the strategic objectives of the firm.

Alawamleh et al. (2026) analyze the influence of business intelligence on organizational performance in Jordanian banking institutions. Their findings show that business intelligence capabilities, business intelligence infrastructure, and collaboration capability have significant effects on organizational performance. The study also highlights the moderating role of employee BI experience, showing that the benefits of business intelligence depend partly on employees’ ability to use, interpret, and integrate data-driven tools. This contribution reinforces one of the central messages of the Special Issue: digital transformation requires not only systems and infrastructure, but also experience, learning, and organizational absorption capacity.

Crnogaj et al. (2026) examine entrepreneurs’ perceptions of AI, focusing on age differences. Their study reveals an asymmetrical age gap: younger entrepreneurs tend to perceive greater benefits and more positive business impacts from AI, while risk perceptions do not differ significantly across age groups. This finding offers an important nuance: differences in AI perception do not necessarily come from fear or resistance, but from different levels of opportunity recognition and future orientation. The article has important implications for entrepreneurship policy and training, as it suggests that AI-readiness initiatives should be differentiated according to age, experience, sector, and entrepreneurial profile.

Boustani (2026) studies Metaverse readiness in Lebanon through the lens of experiential and financial factors. The findings show that interest in immersive technologies and positive remote-work experience is associated with individual readiness to use the Metaverse for work, education, and professional activities. The study also highlights the role of perceived financial security in decentralized digital assets in shaping organizational expectations. The study focused on Lebanon as a developing and crisis-affected economy; the article contributes a context-sensitive understanding of readiness for immersive digital ecosystems. It shows that readiness is shaped not only by technological interest, but also by digital experience, financial trust, and perceptions of organizational benefit.

Alemu et al. (2026) explore the role of AI in logistics and port efficiency in the Sultanate of Oman. Their findings indicate that while automation and digital tracking are increasingly used, AI applications remain limited and often experimental. Where AI is adopted, it contributes to predictive maintenance, cargo flow optimization, customer service improvement, and strategic decision-making. However, wider implementation remains constrained by financial limitations, data integration challenges, and shortages of AI-skilled professionals. This article offers a practical view of AI adoption in a sector where efficiency, coordination, and predictive capacity are essential. It also highlights the need for collaboration between public authorities, financial institutions, technology actors, and higher education institutions.

3. Reflective Insights

The contributions gathered in this Special Issue confirm that AI is redefining competencies, finance, and entrepreneurship in a profound way. One of the strongest insights emerging from these studies is that AI adoption depends on readiness. Organizations need technological infrastructure, but they also need leadership, human capital, governance mechanisms, digital literacy, information quality, and trust. Without these conditions, AI risks remaining experimental, fragmented, or poorly integrated into organizational strategy.

Another insight is that AI value creation is not automatic. Generative AI, business intelligence, explainable AI, machine learning, and predictive systems can improve performance and innovation only when they are aligned with organizational goals. In SMEs, this means connecting AI tools to efficiency, growth, market expansion, or innovation strategies. In banking, it means linking AI and business intelligence to employee capabilities and organizational performance. In recruitment, it means designing transparent and explainable systems that strengthen rather than weaken trust. In social media analysis, it means using AI to identify information risks, detect pseudo-facts, and better understand the emotional mechanisms that influence public opinion. In territorial and logistics contexts, it means integrating AI into broader institutional and infrastructural development plans.

The Special Issue also shows that AI transformation is deeply human. Several articles demonstrate that employees, applicants, entrepreneurs, managers, citizens, and social media users do not respond to AI and digital information in the same way. Their perceptions are shaped by age, experience, skills, trust, confidence, emotional discourse, and institutional context. This suggests that AI strategies should avoid one-size-fits-all approaches. Instead, organizations and policymakers should develop inclusive and differentiated approaches that address the specific needs of different users and stakeholders.

The issue of trust also appears as a central condition of AI transformation. Trust is required in recruitment systems, financial technologies, business intelligence tools, decentralized digital assets, AI-based social media monitoring, and public governance applications. However, trust cannot be imposed. It must be built through transparency, explainability, data protection, ethical design, accountability, information integrity, and demonstrated usefulness. In this sense, responsible AI is not only a moral requirement; it is also a condition for successful adoption and long-term value creation.

Finally, the articles highlight the importance of context. AI adoption in Lebanon, Jordan, Vietnam, Oman, Slovenia, and European SMEs does not follow the same path. Local infrastructure, institutional capacity, economic conditions, sectoral needs, digital literacy, linguistic environments, and social dynamics all shape the possibilities and limits of AI transformation. This confirms that AI research in administrative sciences must remain attentive to geographical, institutional, sectoral, and cultural diversity.