INTEGRATING AI, DATA SCIENCE, AND DECISION ANALYTICS FOR CLIMATE-RESILIENT BUSINESS STRATEGY: A MULTI-CRITERIA ENGINEERING APPROACH

Abstract

There are growing threats to business in most industries, especially as a result of climate change, which requires strong, flexible, and long-term approaches. In this study, an integrated framework that integrates Artificial Intelligence (AI), Data Science and Multi-Criteria Decision Analytics (MCDA) will be introduced to assist in the formulation of business strategies that remain climate resilient. The study analyzes the resilience performance of three cases in the industry, such as energy, manufacturing and logistics, using a mixture of modeling, forecasting through scenarios and decision criteria weighting. Analytic Hierarchy Process (AHP) and Technique order of preference by similarity to ideal solution (TOPSIS) are utilized to construct decision matrices to evaluate strategies in the climate risk conditions. Business vulnerability indices are predicted based on AI models like the Random Forest and Gradient Boosting Regressors of the projected climate stressors working environment of temperature rise, carbon tax challenges, and supply chain shocks. The findings show that adaptive operation design, green innovation, and investment in digital infrastructure stand consistently at the first place across the sectors as the resilience enhancer. A scalable interdisciplinary type of engineering offers an effective roadmap to industries who require climate-friendly and computationally efficient ways of managing their data-informed strategic planning.