Human-Ai Interface: Assessing The Role Of Explainable Ai (Xai) In Mitigating Knowledge Worker Resistance And Fostering Trust During Ai-Driven Business Process Automation
Aug 2026· Vol 3, pp. 20 - 35· 0 citations· 34 references
TL;DR
The study concludes that Explainable AI is not only a technological enhancement but also a strategic tool for promoting employee trust and supporting effective digital transformation and recommends that organizations prioritize explainability, invest in AI literacy and training, and develop transparent AI governance frameworks to encourage successful adoption of AI-driven business process automation.
The rapid adoption of Artificial Intelligence (AI) in data driven decision making has increased the complexity of analytical models, creating significant communication barriers between technical experts and nontechnical stakeholders. Limited understanding of AI generated insights often reduces trust, delays decision making, and restricts the effective adoption of AI supported recommendations. As organizations increasingly rely on explainable and human centered AI systems, effective communication has become essential to ensuring that AI out- puts are accessible, transparent, and meaningful for diverse stakeholder groups. This study aims to identify the key challenges in communicating complex AI model results and to develop practical communication strategies that enhance human trust among nontechnical stakeholders. A qualitative research design was employed using case studies, open ended surveys, expert interviews, and focus group discussions involving data scientists and nontechnical decision makers from business organizations. The collected data were analyzed through thematic analysis to identify recurring communication barriers and effective explanatory practices. The findings reveal that technical jargon, cognitive overload, and limited contextual explanations are the primary factors reducing stakeholder trust in AI generated insights. Conversely, explainable AI communication supported by intuitive data visualization, contextual storytelling, simplified summaries, and audience centered messaging significantly improves understanding, transparency, and stakeholder confidence across organizational contexts. The study proposes a human centered communication framework that strengthens trust in AI assisted decision making while promoting more inclusive and responsible technology adoption. These findings contribute to explainable AI research by demonstrating how effective communication can bridge the gap between technical complexity and human understanding, thereby generating meaningful humanistic impacts in organizational decision making and sustainable organizational innovation.
Dwi Apriliasari, Bintang Nandana Henry, Alexander Williams· Journal of Orange Technology· 0 citations
The increasing use of artificial intelligence (AI) is currently transforming HRM by challenging the very nature of HR decision making. The study explores the effect of artificial intelligence on human resource decision making in Nepalese organizations with a specific focus on ethical AI and human oversight. The quantitative research method was used to gather data from 260 respondents (HR professionals, line managers, and employees) who represent different sectors in Nepal through the developed structured questionnaires. Descriptive, reliability,Pearson’s product-moment moment correlation, multiple regression analyses, and bootstrapped mediation were performed to analyze the data. The results of the study revealed that adopting AI in human resource management, using AI-based analytics and automating HR processes will improve decision-making quality. Additionally, the indirect influence of general AI adoption, AI analytics, and decision-making effectiveness was partially mediated by ethical AI practices and moderated by human oversight of automated HR processes. On the contrary, there was no significant interaction between general AI adoption and human overseer-ship on decision‐making effectiveness. Overall, the study demonstrates that AI can be optimized in HR decision-making processes through ethical governance AI practices and human review. This includes the mediating role of ethical AI practices and the moderating role of human monitoring of AI-driven automated HR processes. This study contributes to the AI–HRM literature by providing empirical evidence from a developing economy context and some practical implications for responsible and human-enhanced AI decision making in organizations.
Unknown authors· KVM Research Journal· 0 citations
AI has increasingly found its place in Management Information Systems (MIS), allowing organizations to process a huge amount of information and analyze it to find trends, predict business results, and enable decision-making. Yet, most AI technologies function as almost "black boxes," generating results without explaining the underlying logic behind them. This may lead to concerns about accountability, fairness, and ownership of decisions, hampering trust in the technology and limiting its acceptance in the company. Explainable Artificial Intelligence (XAI) solves these challenges through the provision of clear and intelligible accounts of the predictions and recommendations made by AI. This study investigates the significance of XAI in improving the executives’ decision-making process with greater transparency, trust, accountability, and the human–AI collaboration. By applying a conceptual and literature research methodology, the paper proposes a new MIS framework using XAI, where quality of data, transparency of model, relevance of the explanation, and human supervision advance the executives’ degree of trust and decision-making quality.The study claims that XAI must be regarded not only as a technical tool but as an organisational capability enabling managers to assess AI suggestions critically and act on them wisely. The research establishes the significance of user-centric explanations, governance systems, ongoing monitoring, and human responsibility. Findings indicate that explainable AI can boost managers’ confidence and enhance the quality, speed, and defensibility of strategic choices if the provided explanations are accurate, meaningful, easy to comprehend, and correlating with the goals of the organisation.
Virendra Gomase, Suhas B. Dhande, P. Natu· International journal of com...· 0 citations
Assessing the effects of technology reliability (RL), credibility (CR) and technical competence (TEC) on HR professionals’ trust and, subsequently, their intent to deploy AI tools reveals that technology RL, CR and TEC each enhance trust in AI.
R. Arora, Neha Kumari Siradhana· South Asian Journal of Human...· 0 citations
The fast pace of instantiating the Artificial Intelligence (AI) in digital services has changed the manner in which organisations provide personalised, effective, and data-driven solutions in various fields like e-commerce, healthcare, and education. Even after these improvements, AI implementation by users is still not consistent, mainly because of the issues surrounding the areas of transparency, fairness, accuracy, and control. This lack of transparency, which is commonly called the black-box problem with many AI systems, has decreased the trust level and disposition of their users. To address this, Explainable Artificial Intelligence (XAI) has been proposed as a highly important concept to increase the level of transparency and user comprehension in AI-driven decisions. This research will explore the impact of the main XAI characteristics, such as the transparency of the algorithm, the perceived fairness, the perceived accuracy, and the perceived control on the perceived value of the users and, consequently, on their willingness to use AI-driven services. The paper relies on the Technology Acceptance Model (TAM) and the Theory of Consumption Values that suggested a combined model where perceptions of value serve as a mediating variable between the features of the AI systems and their intention to use them. The quantitative research design was used, and the primary data were gathered through the use of a structured questionnaire and a sample of 200 respondents in the National Capital Region (NCR) of India. The research adds to current literature, combining XAI characteristics with value-based and technology acceptance models, thus providing a more in-depth insight into the AI adoption in the new markets. Managerially, the findings demonstrate the need to create AI systems that are accurate and transparent, as well as fair and user-focused, to increase the perception of value and instigate user acceptance. On balance, it is possible to note that the study highlights the critical importance of the perceived value as a key process that connects the explainable features of AI to user adoption intentions.
This study investigates how organizational members concurrently perceive the benefits of artificial intelligence (AI) for knowledge management processes (KMPs) and the challenges involved in implementing AI within knowledge management systems (KMSs). Based on survey data from 378 respondents across diverse sectors and roles, the research employs validated instruments measuring perceptions of AI’s contribution to knowledge acquisition, documentation, sharing, and application, as well as perceived human, technological, financial, and ethical‑regulatory barriers. The results show a consistent positive relationship between perceived AI usefulness and perceived implementation barriers: individuals who attribute greater value to AI-enhanced knowledge processes also express heightened awareness of the complexities required to integrate AI into organizational systems. Knowledge documentation presents the strongest associations with all barrier categories, while knowledge sharing exhibits the weakest. Human‑related barriers emerge as the most pervasive across all processes, indicating the central role of employee readiness and organizational culture in shaping AI-enabled KM. These findings reveal a dual perception in which optimism regarding AI’s potential coexists with recognition of the organizational adjustments it demands. The study contributes to a more integrated understanding of AI adoption in KM, emphasizing that effective implementation requires aligning technological capabilities with human, cultural, and governance considerations.
M. Nakash, E. Bolisani· European Conference on Knowl...· 0 citations
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