Evaluating the role of AI in predictive incident management in IT organizations in Sri Lanka

Abstract

The recent development of Artificial Intelligence (AI) has transformed Information Technology (IT) operations, particularly predictive incident management. This study explores the implementation of AI in predictive incident management systems in IT organizations in Sri Lanka. The traditional incident management systems are reactive by nature, fixing issues after they have happened, causing downtime and operational inefficiency. On the other hand, predictive incident management systems powered by AI utilize machine learning, data analytics, and anomaly detection to predict potential IT incidents in advance so that preemptive action can be taken. The research looks into the impact of AI adoption on machine learning, user satisfaction, and organizational trust in AI systems. The research adopts quantitative research design, gathering data from 340 IT professionals through a structured survey. Key determinants examined include AI awareness, data quality, system integration, training, and transparency of AI decision-making. The findings suggest that AI awareness and system integration are significant drivers of predictive incident management performance improvement, while organizational culture, data privacy, and transparency of AI decision-making are critical to trust development. Additionally, data quality issues, integration with current legacy systems, and resistance to AI adoption are emphasized. The study highlights the imperative need to surmount these hindrances for successful implementation of AI in predictive incident management. The research contributes to the current knowledge of AI application in emerging economies and provides actionable suggestions to Sri Lankan IT organizations that aim to improve their incident management systems.