Jul 2026· Proceedings of the International Conference on Business Excellence· Vol 20, pp. 2105 - 2111· 0 citations· 18 references
TL;DR
The findings show that the adoption of AI can exacerbate organizational vulnerabilities by reducing human oversight, reinforcing existing inequalities and creating new forms of operational and ethical risk, and that inadequate governance frameworks and limited AI literacy among managers intensify these negative outcomes.
Abstract
Abstract The rapid adoption of artificial intelligence (AI) technologies within organizations has significantly changed the way they operate. While much of the existing discourse emphasizes the benefits of AI, significantly less attention has been paid to its negative implications for organizations. This paper addresses this gap by critically examining the negative effects of adopting AI at the organizational level. In this regard, this paper adopts a qualitative research methodology based on a systematic review of recent literature in the field, coupled with an exploratory analysis of real examples and case studies from international organizations, documented and analyzed by the author. The findings show that the adoption of AI can exacerbate organizational vulnerabilities by reducing human oversight, reinforcing existing inequalities and creating new forms of operational and ethical risk. Furthermore, the results indicate that inadequate governance frameworks and limited AI literacy among managers intensify these negative outcomes. The paper discusses the implications of these findings for organizational theory and practice, highlighting the need for balanced strategies for integrating AI. This paper makes an important contribution to the field of using new digital tools, especially AI technologies, within organizations, by providing a relevant analysis of the risks associated with them. In doing so, it promotes a more critical and nuanced understanding of the risks that come with adopting AI in organizations.
Artificial Intelligence (AI) often suffers from a "science-to-service gap," where high-performing models fail to translate into effective real-world decision-making. This systematic literature review investigates this divide, identifying three critical barriers: inadequate technical reasoning, organizational resistance, and stringent regulatory compliance. To bridge this gap, we propose a holistic analytical framework anchored in three interconnected pillars: the human–AI relationship, predicated on mutual trust and complementarity; organizational preparedness, necessitating comprehensive cultural transformation and workforce reskilling; and ethical regulation, prioritizing process transparency and robust accountability. Our findings reveal that successful AI integration extends beyond technical optimization, requiring cross-disciplinary strategies such as participative design and collaborative human–AI audits. By synthesizing these dimensions, this study provides a strategic roadmap for enterprises to navigate systemic challenges, fostering a transition from theoretical AI potential to actionable, empowered, and human-centric decision-making systems in complex operational environments. Research indicates that proper application of MCDM techniques can relate AI outputs to real-life decision-making by structuring, enforcing transparency, and justifying AI-scoring results for use within an MCDA (Multi-Criteria Decision Analysis) framework, as demonstrated in complex use cases such as transportation planning.
Karzan Ismael, Ali Mohammed Salih, Zryan Najat Rashid· Knowledge and Decision Syste...· 0 citations
In the era of the Fourth Industrial Revolution, the use of artificial intelligence (AI) in human resource management (HRM) is increasingly recognized as a powerful tool for improving organizational performance and efficiency. However, despite growing interest in this area, there is still limited and fragmented evidence explaining exactly how AI adoption leads to these improvements. Guided by the Technology Acceptance Model (TAM), the Resource-Based View (RBV), and Dynamic Capabilities Theory (DCT), this study investigates how AI adoption in HRM contributes to organizational performance and efficiency through improvements in HR process efficiency. A cross-sectional survey was conducted among 452 HR personnel and employees working in information technology (IT)-related organizations in Lagos State, South-West Nigeria. Three hypotheses were tested using path analysis. The findings reveal that AI adoption in HRM is positively associated with both organizational performance and organizational efficiency. In addition, HR process efficiency was found to play a mediating role in these relationships, indicating that improvements in HR processes are a key pathway through which AI generates organizational benefits. Overall, this study contributes to the HRM and information systems literature by providing deeper insight into AI-enhanced HRM and offering evidence to support the integration of AI into organizations’ day-to-day operations.
O. Akintola, S. O. Chukwuedo, Imad Yasir Nawaz et al.· Journal of Business and Digi...· 0 citations
Orientation: The global adoption of artificial intelligence (AI) is transforming organisations, including in South Africa, where human resource management (HRM) professionals increasingly use AI-enabled systems. While AI offers efficiency, it creates psychological challenges, notably the impostor phenomenon (IP), where professionals doubt their competence and credit success to external factors like AI.
Research purpose: This study explored how AI adoption is perceived by HRM practitioners to relate to experiences of the IP and examined the implications of IP for practitioners’ perceived professional confidence in AI-driven contexts.
Motivation for the study: Despite growing research on AI-in-HRM, its psychological impact remains understudied. Addressing this gap is vital to ensure AI adoption enhances performance without undermining professional confidence.
Research approach/design and method: A qualitative design was employed. Semi-structured online interviews were conducted with HR managers in South Africa (n = 15). Data were transcribed verbatim and analysed using thematic analysis to generate key themes.
Main findings: Participant accounts suggest that AI adoption heightened feelings of self-doubt among HRM professionals, with many attributing workplace achievements to AI systems, luck or external support. Participants described IP as eroding their confidence, reducing ownership of achievements and affecting work engagement.
Practical/managerial implications: HR leaders should embed AI training within human resource development (HRD) programmes, implement performance appraisal systems that recognise both human and AI contributions, and design interventions to strengthen self-efficacy.
Contribution/value-add: This study offers interpretive insights into how IP is experienced in AI-driven contexts and offers actionable insights for fostering resilience and adaptability among HRM professionals in South Africa.
Faraaz Omar, Tendency Beretu· Sa Journal of Human Resource...· 0 citations
This article explores how both organizations and “talents” (talented employees) are responding to the adaptation of artificial intelligence (AI).
We follow a phenomenon-based approach to describe the key trends and challenges influenced by the new work arrangements and seek to address the who, how, where and why issues. Specifically, we discuss the impact of organizations' digital transformation on talent identification, and show how these changes configure new management challenges from a talent management (TM) perspective.
Our research highlights how AI is impacting people and organizations everywhere as the technology continues to advance, whereas the introduction of AI in the workplace is fundamentally changing existing work arrangements.
Our article examines how the TM agenda evolves as organizations adopt AI and highlights the role that employees play in the implementation of AI in the workplace–new developments that are recently neglected in the literature. The article highlights that the integration of AI is revolutionizing TM by improving the methods used to identify, develop and retain talent and highlights the role of TM in pioneering managerial responses to AI integration to maximize organizational effectiveness. The article enriches the literature by adding new research perspectives which enhance our understanding of how AI is reshaping TM agenda.
M. Latukha, H. Scullion· Person-centered review· 0 citations
As artificial intelligence (AI) permeates modern workplaces, the need for ethical governance, specifically corporate-responsible AI (CRAI), has become paramount. However, empirical research has been hampered by the lack of a validated instrument to assess CRAI from the perspective of employees. To bridge this gap, this study first creates and validates a novel measurement scale for CRAI through a rigorous multi-stage development process. Using this newly developed instrument, we subsequently investigate how CRAI fosters employees’ knowledge-sharing behavior (KSB) by integrating organizational behavior theories and knowledge-based perspectives. Data were collected using a three-wave, time-lagged design from 405 working professionals in South Korea. The findings indicate that CRAI positively influences KSB, with psychological safety serving as a partial mediator. This outcome suggests that employees’ sense of psychological safety acts as a key mechanism for translating ethical AI governance into collaborative knowledge exchange. Furthermore, the results reveal that organizationally prescribed perfectionism (OPP) moderates the relationship between CRAI and psychological safety; specifically, rigid performance demands undermine the trust-building potential of responsible AI practices. This study contributes to the literature by providing a psychometrically sound tool for future CRAI research and by demonstrating that ethical AI encourages knowledge sharing most effectively when synchronized with a supportive, rather than perfectionistic, work culture.
Byung‐Jik Kim, Yeon-Jun Choi, Julak Lee· Humanities and Social Scienc...· 0 citations
This study examines how artificial intelligence (AI) and digital transformation are reshaping organizational culture, with particular attention to the Middle East and the Gulf Cooperation Council (GCC) states, where state-led national visions have accelerated adoption. The objective is to identify the cultural mechanisms—values, leadership paradigms, trust, and ways of working—through which AI functions as both a catalyst and a conduit for adaptive change, and to assess how these mechanisms are expressed in a region undergoing rapid, top-down and bottom-up digitalization. Methodologically, the paper adopts an integrative review and synthesis design, combining a structured review of peer-reviewed literature (2021–2026) with documentary analysis of national AI strategies and regional human-capital and CEO surveys covering Saudi Arabia, the United Arab Emirates (UAE), Qatar, Bahrain, Kuwait, and Oman. Evidence is organized within a socio-technical framework linking AI drivers to cultural mediators and organizational outcomes. Results indicate that AI adoption is associated with measurable shifts toward data-driven decision-making, agile and learning-oriented norms, and a heightened salience of ethics and governance, while regional evidence shows exceptionally high workforce engagement—only 6–7% of employees in Saudi Arabia and the UAE never use AI at work, versus 23% globally—and a strong upskilling appetite. Persistent tensions include skills shortages, uneven scaling beyond pilots, and the need to reconcile automation with human-centric values and workforce nationalization. The study concludes that cultural transformation is not a by-product but a prerequisite of successful AI integration, and that change capacity, trust, and inclusive leadership are decisive. The paper offers a consolidated framework and a regionally grounded evidence base for leaders steering culture through the AI transition.
Sadik M. Amr· World Journal of Advanced Re...· 0 citations