Current AI systems predominantly rely on batch processing, which is cost-effective but limits the ability to capture real-time insights and adapt to unforeseen changes. This traditional approach involves periodically retraining models with collected data, often leading to siloed data and inefficiencies. In contrast, real-time AI brings AI directly to data, allowing systems to respond quickly and precisely to individual user actions. This requires significant changes to existing data architecture, focusing on real-time data management, model serving, and monitoring. Implementing real-time AI involves setting up systems for immediate data processing, leveraging NoSQL databases for low-latency queries, and ensuring robust monitoring to address issues like data drift and training-server skew. Companies like TikTok have successfully adopted real-time AI to enhance user experience, and solutions like Astra DB provide scalable and cost-effective NoSQL database support for such architectures.