Authors: Shilpa Hiwale, Dr B V V Siva Prasa
Abstract: The rapid growth in both the volume and complexity of enterprise data has significantly accelerated the adoption of Artificial Intelligence (AI), particularly within the life sciences industry. This paper explores how AI-driven data ecosystems can enable commercial excellence by integrating predictive, prescriptive, and cognitive analytics within a unified framework. The study combines quantitative analysis of customer, sales, and operational datasets with insights from academic research and real-world industry practices. The findings suggest that organizations adopting integrated AI ecosystems are better positioned to enhance forecasting accuracy, improve customer engagement, and enable faster, more informed decision-making. The data used were business-related datasets sourced from Kaggle and data were gathered using a quantitative and analytical research approach. Using Python-based machine learning frameworks, about 50,000 records of customer, sales, demand, churn and operational data were analyzed. Different analytical models such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Artificial Neural Networks were used to discover the customer behavior, sales forecasting, customer segmentation, and prediction of risk. The results show that AI-powered analytics have a significant impact on improving the accuracy of predictions, customer retention, business intelligence, and operational efficiency. The most significant factors influencing customer churn were the customers' satisfaction and the customer segmentation and demand forecasting for marketing targeting and resource optimization. The study also shows that AI-powered analytical systems can aid in intelligent decision-making by converting vast amounts of business information into commercial intelligence that is useful for business decisions. The proposed data ecosystem framework will leverage AI to provide predictive, prescriptive and cognitive analytics that will enhance the performance and competitiveness of organizations. The study adds to the body of literature on AI-powered business transformation and offers valuable insights for organizations aiming to adopt data-driven approaches for sustainable commercial success.
DOI: https://doi.org/10.5281/zenodo.20666345
