A Business-First Approach to Autonomous Monitoring (Special Report)

Companies that develop or support products in the field need to transition from reactive to predictive maintenance and service. Such a transition is possible using ubiquitous connectivity and predictive analytics on machine data. Here’s how.

Although edge architectures differ fundamentally from conventional cloud and on-premises environments, edge isn’t an alternative to traditional infrastructure strategies as much as it is an augmentation of them.

The relationships banks have with their customers can last for decades, but too many banks fail to use the data they have to provide personalized digital banking services. 

Every transaction, order, customer interaction, and internal operation depends on decisions. At a time when businesses are under pressure to improve efficiencies, cut costs, and be more responsive, automating decisions becomes essential. While many businesses have picked off the low-hanging fruit when automating, most need to add more. Specifically, businesses want to automate complex decisions, involving multiple steps, multiple data sources and many rules.

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

Agentic AI changes where value is created in the software stack. When analysts discuss the “death of SaaS,” they rarely mean that cloud applications will disappear. Instead, they mean the SaaS model may lose its central role in enterprise computing.

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.

Domain-specific LLMs are how businesses move from AI curiosity to AI utility where outcomes are measurable, adoption actually sticks, and teams trust what the model tells them.