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

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.

In this week’s real-time analytics news: IBM announced IBM Sovereign Core, AI-ready sovereign-enabled software for enterprises, governments, and service providers.

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

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.

While AI may offer marketing teams the promise of innovation and greater productivity, it also brings with it potential consequences for their public image, particularly among younger consumers who treat AI with a greater level of scrutiny.

Data Hydration covers the activities that support AI models being supplied with an adequate supply of essential information to allow them to thrive.

Organizations must rethink SAP transformation in the age of AI through the lens of Maslow’s Hammer, where companies over-rely on a familiar tool or method, leading them to see every problem as solvable with that single solution.

Data readiness must precede large-scale AI adoption in order to convert AI from a set of pilots into a predictable, repeatable, organization-wide capability.