Data and AI teams are often eager to start building. A new technology emerges, an exciting idea surfaces, and before long a project is underway. Yet many initiatives struggle to deliver the expected value, not because of poor implementation, but because insufficient attention was given to understanding the problem being solved. In this session, the Ideate First approach is introduced as a way of exploring opportunities before committing to solutions. It is demonstrated how movement from assumptions to insights can be supported through structured ideation, and how divergent thinking can be used to create a broader understanding of both problems and opportunities before convergence takes place. The roles of desirability, viability, and feasibility are explored, together with the tensions that can arise between these perspectives. By examining how problem spaces can be explored and framed before development begins, a different perspective on innovation, data, and AI initiatives is presented. This session is intended for professionals involved in data, AI, technology, product development, and business transformation who are interested in understanding how early-stage exploration can influence later outcomes.