IBM at Red Hat Summit 2025

AI adoption often stalls—not because of AI itself, but due to the IT bottlenecks surrounding it. Cloud provisioning is slow, app deployments are complex and manual workflows create a reactive environment. As AI models evolve, these outdated processes can’t keep up, delaying innovation and making AI scaling a cumbersome task.

This session explores how AI-powered automation transforms IT operations into self-optimizing systems. Explore demos of the latest integrations with Red Hat solutions that power dynamic resource allocation, automated workflows and intelligent monitoring. Learn how these integrations enable you to build an AI-ready IT environment where cloud and app ecosystems move at the speed of AI innovation.

AI innovation is often constrained by rigid infrastructure and fragmented AI pipelines. As AI models grow more complex, training, deployment and orchestration processes become disjointed, slowing the entire AI lifecycle. The challenge lies in creating an open AI ecosystem that removes these bottlenecks and accelerates model-to-production workflows.

In this session, see technical demonstrations of how to break down AI silos and enable flexible, cross-environment AI pipelines while streamlining model training, tuning, and deployment across cloud and on-prem environments. The result is an adaptive AI platform that fosters innovation—allowing AI to scale efficiently, securely, and without barriers.

AI models rarely operate in isolation. They span public cloud, on prem and edge environments—creating challenges in workload mobility, security and data control. Fragmented infrastructures slow down AI pipelines, introduce silos, and increase the complexity of scaling AI models across environments.

This session focuses on building an intentionally designed hybrid cloud infrastructure that adapts to dynamic workloads. Learn how secure, high-performance computing will support AI model training and deployment. Discover how to drive application modernization seamlessly across hybrid cloud environments. See how to deploy storage solutions that enhance AI workloads with flash-optimized architecture and intelligent data placement. The result is a unified AI infrastructure that scales horizontally and vertically—running AI models where data lives, whether at the edge, on prem or in the cloud.