Artificial Intelligence (AI)-driven Decision Support Systems (AI-DSS) have emerged as transformative tools in enhancing decision-making accuracy, operational efficiency, and innovation capacity across industries. This paper investigates the role and adoption of AI-DSS among startups and Micro, Small, and Medium Enterprises (MSMEs), with a particular emphasis on the fashion sector as a representative case. Through a qualitative methodology involving expert interviews and case analysis, the study identifies the extent of AI-DSS adoption, the key technical and organizational barriers, and the strategic outcomes realized by early adopters. Findings reveal that while startups demonstrate moderate to advanced usage of AI tools particularly for demand forecasting, personalization, and product design MSMEs often struggle with infrastructural limitations, lack of digital readiness, and cost concerns. The paper also highlights measurable improvements in decision speed, forecasting accuracy, and innovation output among AI-adopting firms. A modular and scalable AI-DSS framework is proposed to enable gradual integration, starting from data visualization to advanced predictive modeling. This research contributes to closing the AI readiness gap and suggests practical strategies to promote inclusive digital transformation in small-scale enterprises