AI is often ill-suited to enterprise scenarios, not primarily due to the technology being insufficiently advanced, but more because of a lack of engineering capability to "make the technology work." ...

Enterprise AI may be the quieter side of the AI boom, but it's where some of the most significant opportunities are emerging. Palantir is positioning itself at the center of that transformation.

As Meta unveils its powerful on-device reasoner, a wider industry trend emerges where small, specialized models are solving enterprise challenges around cost, privacy, and control.

Abstract: Traffic data contains deep domain-specific knowledge, making labeling challenging, and the lack of labeled data adversely impacts the accuracy of learning-based traffic analysis. The ...

Microsoft Fabric expands as industry analysts reveal critical criteria enterprises need for evaluating AI-ready data ...

We’re in a hinge moment for AI. The experiments are over and the real work has begun. Centralizing data, once the finish line, is now the starting point. The definition of “AI readiness” is evolving ...

MCP is more than protocol, it’s a strategic enabler. It gives AI agents the structure they need to interact with enterprise ...

Trust remains the defining challenge of the AI era—and the clearest path to advantage. Organizations that get it right will ...

The Next Frontier. In contrast to traditional supply chains, which react to problems after they emerge, cognitive supply ...

Expectations are like elbows - stop it - everyone has them. When it comes to the Washington Commanders, it seems just about everybody has an opinion or assertion as to how good or bad they’ll be in ...

Abstract: Currently, the poor spatial resolution (10–50 km) and accuracy of satellite-based precipitation products (SPP) limit their applications at regional scales. To overcome these issues, a hybrid ...