Forecasting demand for newly launched products is a persistent challenge due to the absence of historical sales data, unpredictable market dynamics, and evolving consumer preferences. Traditional forecasting methods and ERP systems often fall short in addressing these complexities, especially in fast-paced industries like fashion, automotive, and electronics. Based on insights gathered from an industry-wide survey, this study identifies key limitations in current forecasting approaches and highlights the need for more adaptive, intelligent solutions. In response, this research proposes the conceptual development of a specialized AI and machine learning-powered forecasting assistant. This envisioned tool can assist organizations by generating context-aware forecasts through the analysis of diverse variables such as product attributes, industry benchmarks, market sentiment, and competitor activity—even in data-scarce environments. It is designed to potentially integrate with existing ERP systems and external data sources, offering real-time insights and automated scenario planning. An essential feature of the platform may be a user-friendly dashboard that allows companies to simulate multiple launch conditions, evaluate possible demand outcomes, and proactively plan inventory and distribution strategies. This functionality can be helpful to support efforts to minimize risks such as overproduction or stockouts and improve adaptability to market changes.