Self-Service Data Prep: a Gartner Guide

The concept of “data in motion” is transforming the way organizations think about their technology stack and will determine which organizations can actually execute on AI and which are left drowning in endless streams of data.

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.

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.

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.

In this week’s real-time analytics news: Oracle announced new agentic AI features for Oracle AI Database.

A look beyond the artificial intelligence hype: What will it take for real value to start showing up as companies implement AI?

AI agents are transforming Order Management Systems (OMS) from a static rules-based engine to a dynamic intelligence-based strategist.

Networking will play a more strategic role in enabling real-time digital operations and supporting the next generation of AI-powered applications.