AI-Driven Decision-Making to Enhance Organizational Efficiency through Automation

Abstract

This study will focus on the effects that Artificial Intelligence (AI)-driven decision making and automation can have on efficiency within organizations from different industries. With a fast-growing amount of information about various companies and their operations, the need for more efficient and better-informed decision-making is inevitable. Technologies related to AI, such as machine learning algorithms, decision support systems, and RPA, can facilitate this process. The study used a mixed method approach by conducting a quantitative analysis of performance measures along with qualitative data collection from managers through interviews and survey tools. The data was gathered over a period of one year from medium and larger size organizations using organizational performance measures along with logs of AI systems Regression analysis and correlation analysis were applied to determine the relationship between the adoption level of AI and organizational performance. Significant progress can be noted after implementing AI, including reductions in process cycle time (34.4%), decision-making time (42.3%), and operating costs (18.6%), as well as an improvement in resource utilization (20.6%). The regression analysis showed that there was a highly significant positive relationship between the scores of AI implementation and performance results (R² = 0.68, p < 0.01). Differences among sectors indicate that manufacturing and IT services are the sectors that derive more benefits from using AI in their operations. The research is significant in theory and practice. Limitations pertain to sample size, industry, and time frame. Research may extend to examining longitudinal effects, international comparisons, and the effects of AI on decision-making.