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Generative AI has the potential to add up to $4.4 trillion annually to the global economy. But most organizations won’t see that value—not because of their models or infrastructure, but because of their data. Despite years of investment in data lakes, warehouses, and analytics tools, organizations are drowning in complexity. Data is scattered across siloed systems, riddled with duplication, and locked behind outdated batch processes.

We live in a world of events. The phone in your pocket is emitting data about your location, and receiving a notification to order your morning coffee from your favorite shop en route to work. Your thermostat knows you’re out for the day, and adjusts the temperature to save energy. Your refrigerator automatically orders a replacement water filter after serving a given amount of water. Railway sensors send a location event for cars passing by.

From automating routine tasks to providing real-time insights to inform complex decisions, AI agents and copilots are poised to become an integral part of enterprise operations. At least that’s true for the organizations that can figure out how to supply large language models (LLMs) with real-time, contextualized, and trustworthy data in a secure and scalable way.

This article was originally published on InfoWorld on Jan. 28, 2025 While large language models (LLMs) are useful, their real power emerges when they can act on insights, automating a broader range of problems. Reasoning agents have a long history in artificial intelligence (AI) research—they refer to a piece of software that can generalize what it has previously seen to apply in situations it hasn’t seen before.

In today’s data-driven world, the ability to turn raw data into actionable insights is no longer a nice to have—it’s a necessity to power exemplary citizen service. Singapore’s Smart Nation initiative is built on the idea that data, when utilized effectively, can transform public services and improve lives.

As real-time data processing becomes a cornerstone of modern applications, the ability to integrate machine learning model inference with Apache Flink offers developers a powerful tool for on-demand predictions in areas like fraud detection, customer personalization, predictive maintenance, and customer support. Flink enables developers to connect real-time data streams to external machine learning models through remote inference, where models are hosted on dedicated model servers and accessed via APIs.