In the rapidly evolving landscape of AI development, the demand for high-quality, diverse training data is paramount. However, acquiring and utilising real-world data often presents challenges related to scarcity, privacy concerns, and inherent biases. This presentation explores the transformative potential of Generative AI (GENAI) models in addressing these critical issues. I will delve into the mechanisms by which GENAI models can generate synthetic datasets that are not only realistic but also tailored to specific needs. This capability unlocks new opportunities for training robust AI models while mitigating the risks associated with sensitive or limited real-world data. Additionally, we will examine how synthetic data can contribute to debiasing AI systems and ensuring fairness and inclusivity. Through practical examples and insights, this talk highlights the pivotal role of GENAI in shaping the future of AI development, where data limitations no longer impede progress.