Abstract
Forecasting accuracy is a significant source of IT performance and decision-making. The traditional statistical methods are unable to explain the nonlinear dynamic behavior of IT companies. This study examines the predictive analytics and ML can improve IT sales forecasting in terms of precision and responsiveness. The performance is compared with ML models such as Random Forest, Neural Networks and Gradient Boosting with traditional models like AutoRegressive Integrated Moving Average (ARIMA) and linear regression case studies are processed using open evaluation. The five theoretical models are used which includes Resource-Based View (RBV), Technology Acceptance Model (TAM), Dynamic Capabilities, Diffusion of Innovation (DoI) and Information Processing Theory (IPT) have a goal of implementing ML solutions for organizational adoption, decision support and strategic alignment. Salesforce Einstein, IBM Watson, AWS Forecast and Microsoft Azure AI case studies are used in real-life IT systems including their benefits, limits and organizational difficulties. This research represents actual data and theoretical concepts in the use of predictive analysis based on the machine learning for IT corporate decision-making purposes.
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