NVIDIA launched NVIDIA Nemotron 3 Super, a 120 billion parameter open model with 12 billion active parameters designed to run complex agentic AI systems at scale.

In this week’s real-time analytics news: IBM launched Db2 Genius Hub, which brings autonomous capabilities to Db2.

In this week’s real-time analytics news: Red Hat announced Red Hat AI Enterprise and the Red Hat AI Factory with NVIDIA.

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.

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.

The next wave of product intelligence will be defined by solving the underlying operational and infrastructure challenges that allow AI to act intelligently in real time.

When scaling AI, rather than looking solely at the component level, organizations need to undertake system-level emulations that reflect the operating environment of an AI data center to optimize performance.

The future of AI will be defined by how it integrates into our decision-making processes. Only explainable AI will enable that.

In 18 months, organizations will fall into two groups, those that locked down AI usage and those explaining failures to regulators and shareholders.

AI exposure management makes AI TRiSM and OWASP protections actionable at enterprise scale.

A unified semantic layer standardizes business definitions across systems, creating a shared contract for how metrics are defined, documented, and governed. That contract applies to dashboards, notebooks, APIs, and AI systems alike.