McKinsey suggests that the time is ripe for an industrial revolution, and details a Deutsche Telekom case study -- "The costly build-to-order approach of typical IT infrastructures may soon be a thing of the past, thanks to advances in technology and to new management practices. Leading companies are moving toward a less complex model characterized by standard and reusable products, transparent pricing, and better use of IT resources. Companies can make their information technology systems up to 30 percent more productive by adopting a standardized model. To undertake this shift, CIOs must rethink the organization, architecture, and procurement processes of their IT organizations."
Enterprise AI adoption keeps running into the same wall: the moment sensitive data, model weights, or agent memory leave a tightly controlled environment, security teams lose visibility into what happens to them while the workload is actually running. Confidential computing exists to close that gap, and new research from ABI Research shows the technology has matured enough, across CPUs, GPUs, containers, and now agentic AI, to become a foundational requirement for enterprises building sovereign AI infrastructure: computing environments they control end to end rather than simply rent. Protection is Moving From CPUs to GPUs ABI Research finds that CPU-based confidential computing is closest to mass-market adoption, the product of years spent hardening chip-level isolation for general workloads. GPU-based confidential computing is now generating the strongest momentum, and for good reason. It addresses one of the largest unresolved gaps in AI security: protecting data and models while inf...