The Power of Personalization: Driving Digital Banking Success (White Paper)

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.

AI can improve industrial inventory management where traditional systems struggle most. This includes forecasting intermittent demand, positioning inventory across multiple sites, improving execution accuracy, and moving surplus inventory from planning to action. In each case, the value comes from better decisions grounded in data.

Enterprises have built intelligence layers that can observe, predict, and recommend, but they have stopped short of allowing AI to execute decisions end-to-end. Here is what’s needed to overcome this.

In this week’s real-time analytics news: Apache Software Foundation (ASF) announced that Apache HugeGraph has become a Top-Level Project (TLP).

Hyperscale low-bandwidth IoT satellite constellations will soon power remote asset tracking, environmental monitoring, and agricultural telemetry.