Real-time AI, also known as proactive intelligence, enables systems to process data and make decisions instantaneously, which is crucial for applications such as autonomous vehicles, fraud detection, and emergency response prioritization. Unlike traditional AI that relies on batch processing with significant delays, real-time AI operates with minimal latency, bridging the gap between insight and action in milliseconds. Event streaming is a key component, providing a persistent and replayable log of events that allows AI models to learn from historical patterns while reacting to current data. Technologies like Redpanda, Apache Kafka, and Amazon Kinesis support this approach, and the optimal architecture for real-time AI involves layered optimizations to balance latency, persistence, and computational complexity. Depending on the use case, real-time AI can be deployed on devices, at the edge, or centrally in the cloud, with each location offering different trade-offs between latency and computational power. Industries such as finance, cybersecurity, and gaming benefit significantly from real-time AI by reducing the time between insight and action, thus enhancing decision-making, risk management, and user engagement. The future of real-time AI is set to include advancements like Agentic AI, which autonomously plans and acts, and Retrieval-Augmented Generation (RAG), which generates accurate responses by retrieving relevant information, promising to further reshape industries and opportunities.