Publication Details
Abstract
The increasing adoption of Artificial Intelligence (AI) in business process automation has transformed organizational operations by improving efficiency, accuracy, and decision-making. However, many knowledge workers remain reluctant to embrace AI systems due to concerns about transparency, fairness, accountability, and the fear of job displacement. Explainable Artificial Intelligence (XAI) has emerged as a promising solution by making AI decisions more understandable and transparent to users. This study examines the role of XAI in reducing knowledge worker resistance and enhancing trust during the implementation of AI-driven business process automation. A mixed-methods research design was adopted, combining quantitative survey data with qualitative insights from knowledge workers across selected organizations in Nigeria. Data were analyzed using descriptive statistics, regression analysis, and thematic analysis to explore the relationships among explainability, trust, resistance to AI adoption, and organizational acceptance. The findings indicate that AI systems with higher levels of explainability significantly improve users' trust and confidence while reducing resistance to AI adoption. The study further reveals that transparency, clear communication, and employee involvement during AI implementation positively influence acceptance and successful integration of AI technologies in organizational processes. 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. It 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.