The Definitive Guide to Continuous Intelligence

The shift toward real-time decision-making at the edge is an evolution in how businesses operate. Closing the latency gap means smarter, safer, and more resilient operations.

AI agents represent an opportunity to go beyond isolated analytics projects and move toward self-improving, autonomous workflows that directly drive operational performance.

Scaling AI in industry requires the right technology, a practical understanding of industrial operations, and open interoperability with an organization’s existing data infrastructure.

The enterprise data stack is at an inflection point. Storing and streaming data is no longer enough. Businesses need a combination of streaming technologies, high-performance in-memory databases, and adaptive intelligence to support real-time enterprise operations.

Enterprise delivery is entering a phase where AI agents operate with autonomy, making decisions, initiating workflows, and even coordinating across functions without waiting for human approval.

The IBM acquisition of Confluent is indicative of the tech world’s fixation on real-time data streaming. However, the more pressing issue is the overwhelming volume of unstructured documents that hold the critical context that real-time AI often overlooks.

Small businesses should focus on AI’s strengths, explore how it can boost employee performance, and avoid letting the technology have the last word in any area of operations.

In this week’s real-time analytics news: The European Commission prods the creation of frontier general-purpose AI systems.