AI-BASED PREDICTIVE ANALYTICS FOR BUSINESS FORECASTING: A SECONDARY DATA STUDY

Abstract

In the modern digital era, organizations increasingly depend on Artificial Intelligence (AI) combined with predictive analytics to improve forecasting and support strategic planning. This study examines how AI-based predictive techniques contribute to forecasting business outcomes using secondary data from journals, reports, and academic sources. The research applies descriptive and analytical methods, along with comparison and theme-based evaluation.

The findings show that AI enhances prediction accuracy, speeds up decision-making, and improves overall business performance. Studies indicate that AI tools can increase decision speed by around 70%, improve forecast accuracy by approximately 65%, and raise operational efficiency by nearly 60%. Technologies such as machine learning, deep learning, and natural language processing assist in processing large datasets, discovering hidden patterns, and generating real-time insights. These capabilities help organizations make informed decisions, optimize resources, and adapt quickly to market changes.

AI-based forecasting is widely used in industries such as finance, retail, healthcare, and marketing. However, issues like poor data quality, high costs, lack of expertise, and data privacy concerns continue to limit its full adoption.