There are 3 key aspects that have led to proliferation of AI technologies such as GenerativeAI - algorithms, compute power and data. While significant progress has been made in refining algorithms and enhancing computing power, data still remains a complex challenge to deal with. Despite significant promise there is a big gap in potential and reality, which raises significant concerns around the success of GenAI projects in the near future. This is because, while foundational Gen AI models offer a much needed starting point, bringing us closer to the answer, the true value unlock would only happen when these foundational models are contextualized using proprietary enterprise data and individual customer data. This is where the importance of solving the data paradox becomes even more critical.