How the IT Architect Needs to Consider API Management (Free Webinar)

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).

Real-time analytics is often approached as a technology upgrade. In practice, it is an organizational outcome. Organizations that succeed treat real-time analytics as the final stage of a deliberate progression.

Far from accelerating Excel’s extinction, AI agents are extending Excel’s lifespan by making it more accessible and more powerful.

As distributed AI workloads scale across geographically dispersed GPU clusters, submarine fiber-optic cables become critical infrastructure for model training, real-time inference, cross-border data replication, and multi-region

Effective CISOs focus on making informed, defensible cloud risk decisions that support innovation without sacrificing control.

Security must scale alongside AI development rather than lagging behind. This requires rethinking AppSec and risk management as a continuous practice driven by intelligence.

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.