This capstone project develops a structured decision-making model to evaluate the feasibility of automation in industrial processes. While initial efforts to collaborate with major UAE organizations such as RTA, DEWA, Emirates Post, and Civil Defense did not yield responses, the project scope shifted to designing an automation decision framework supported by real-world case studies. The aim is to provide industries with a reliable model to determine when automation is justified and beneficial.Ten case studies from diverse sectors including furniture manufacturing, textiles, chemical processing, accounts payable automation, and automotive production were analyzed using ten decision criteria such as annual demand, labor cost, task repetition, product variety, and error rates. The findings highlight critical thresholds (e.g., labor cost > $15/hour, task repetition > 70%, annual demand > 10,000 units/year) that strongly influence automation outcomes. Results across cases demonstrated significant benefits: error reductions from 3–5% to near zero, productivity increases of up to fivefold, and processing time reductions of up to 87%. The developed Automation Decision Model offers a practical, data-driven framework to support industrial firms in making cost-effective, scalable, and sustainable automation choices, aligning with Industry 4.0 transformation goals and long-term competitiveness.