Solutions for the Next Generation of 5G and IoT Network Deployments (White Paper)

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.

The transition to a truly agentic enterprise is not merely an IT upgrade. Such a transition is an architectural and philosophical metamorphosis. The organizations that scale with confidence are those that recognize this early.

Boring AI earns trust because it works. It removes friction from daily tasks, and over time, that trust becomes the foundation for more ambitious systems.

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

Neoclouds offer performance specialization, which increasingly is factored into what some call multi-compute strategies. In general, what that means is that enterprises are using hyperscalers for general workloads, neoclouds for model training, and edge infrastructure for inference.