As artificial intelligence (AI) advances from experimental phases to practical applications, the critical role of data becomes increasingly evident, with AI's effectiveness heavily reliant on real-time, reliable, and accessible data. Traditional data management approaches, involving complex and costly processes, create barriers such as data silos and long latencies, impeding AI's potential. Data streaming offers a revolutionary solution by providing live data across operational and analytical systems, enabling AI to act instantaneously, simplifying infrastructure, and reducing costs. This shift in data strategy supports emerging trends where business processes are increasingly managed by software and AI, and facilitates integration between real-time and static data. The impact of data streaming is already visible across industries like finance, retail, and manufacturing, where it enhances real-time fraud detection, dynamic pricing, and predictive maintenance. Despite initial resistance similar to historical technological advancements, data streaming is poised to become standard practice, essential for harnessing AI's full value, with frameworks like Confluent's Data Streaming Organization aiding its enterprise implementation.