A TDWI expert panel discussion highlighted the critical interplay between data maturity and the success of AI initiatives, emphasizing that organizations with low data maturity struggle with AI deployment. The panel identified key barriers, such as lack of integration between data analytics and AI platforms and insufficient data governance, stressing that AI without quality data is ineffective and data without AI is underutilized. The discussion underscored the importance of involving business units in AI implementation to unlock real value, moving away from treating AI as a purely technical endeavor. The panel advocated for developing targeted micro-genAI applications to address specific business problems, leveraging cloud providers for infrastructure to focus on outcomes. The conversation also noted a trend towards using multiple specialized AI models for complex tasks rather than relying on a single model, underscoring the need for flexible, integrated data infrastructure. Successful AI initiatives start with a solid data foundation, ensuring data is reliable and accessible, which enables organizations to iterate quickly on AI applications while maintaining data quality and governance. The integration of data and AI platforms is becoming increasingly crucial, with the overarching message that organizations need to merge their data and AI strategies for future readiness.