Sustainable AI: Innovations for Energy-Efficient Machine Learning Models

Abstract

This study investigates the integration of artificial intelligence (AI) and machine learning (ML) into Salesforce development to optimize code and configuration processes. The primary purpose was to evaluate how AI-powered recommendation engines enhance development efficiency, code quality, and user satisfaction. Using a combination of simulated data and empirical analysis, the study developed an AI recommendation engine and assessed its impact on key performance metrics including development speed, error rates, and customization accuracy. Major findings reveal that AI integration led to a significant reduction in development time (from 100 to 65 units) and defect density (from 3.8 to 1.9 defects per 1,000 lines of code), while improving customization accuracy and user satisfaction. The analysis demonstrates that AI tools streamline development processes and enhance code quality, leading to faster and more reliable outcomes. These findings support the hypothesis that AI significantly benefits Salesforce development by increasing efficiency and effectiveness. The study underscores the value of AI in software customization and highlights areas for further research.