{"id":28187,"date":"2025-09-29T12:46:41","date_gmt":"2025-09-29T10:46:41","guid":{"rendered":"https:\/\/datanets.ro\/red-hat-openshift-ai-the-complete-platform-for-organizations-looking-to-build-and-scale-ai-applications\/"},"modified":"2025-09-29T12:48:51","modified_gmt":"2025-09-29T10:48:51","slug":"red-hat-openshift-ai-datanet-systems","status":"publish","type":"post","link":"https:\/\/datanets.ro\/en\/red-hat-openshift-ai-datanet-systems\/","title":{"rendered":"Red Hat OpenShift AI: The Complete Platform for Organizations Looking to Build and Scale AI Applications"},"content":{"rendered":"

Artificial intelligence is experiencing an unprecedented wave of accelerated adoption across the business landscape. According to McKinsey\u2019s State of AI 2025 report, 78% of companies are already applying AI in at least one business function\u2014up significantly from just 55% in 2023. This surge reflects not only the growing appetite for innovation but also the increasing pressure to embed AI as a core component of everyday operations.<\/strong><\/p>\n

Yet, the journey from strategy to execution remains complex. McKinsey\u2019s Superagency in the Workplace 2025<\/em> study highlights the gap: while 92% of organizations plan to increase their AI investments over the next three years, only 1% of executives believe their company has achieved the maturity required to fully integrate AI into workflows and consistently capture value. In addition, 47% of senior leaders acknowledge that GenAI application development is advancing too slowly\u2014even though 69% of organizations began investing as early as last year. (source McKinsey<\/a>).<\/p>\n

The key to accelerating this transition lies in IT infrastructure. To operationalize AI at scale, organizations need flexible platforms for orchestration and virtualization. Technologies like Kubernetes and Docker, coupled with multi-cloud and hybrid environments, enable containerized applications, rapid deployment, auto-scaling, and intelligent load balancing. But infrastructure alone is not enough. Modern AI initiatives also require MLOps capabilities\u2014robust data pipelines, lifecycle model management, version control, and seamless integration with business applications. Equally critical is a strong foundation of data security and governance, ensuring end-to-end encryption, strict access controls, and compliance with regulations such as GDPR and NIS2.<\/p>\n

 <\/p>\n

\"\"<\/p>\n

 <\/p>\n

To help organizations overcome these challenges, Datanet Systems<\/strong> recommends Red Hat OpenShift<\/strong> and Red Hat OpenShift AI<\/strong> – globally recognized platforms for container orchestration and AI application management. Through its partnership with Red Hat, Datanet now brings these proven technologies to enterprises in Romania, empowering them with the infrastructure and capabilities needed to accelerate AI adoption, scale innovation, and unlock business value faster.<\/p>\n

 <\/p>\n

Step One: Virtualization with Red Hat OpenShift<\/strong><\/h2>\n

 <\/p>\n

Powered by Kubernetes, Red Hat OpenShift has become the containerization platform of choice for enterprises worldwide, with more than 3,000 organizations already relying on it. One of the key differentiators is its unified management of both containers and virtual machines (VMs), which enables organizations to simplify application deployment and lifecycle management across hybrid and multi-cloud environments\u2014while ensuring built-in security and governance.<\/p>\n

Enterprises consistently report that OpenShift accelerates container orchestration, streamlines the migration of traditional workloads to modern AI\/ML stacks, and automates development, deployment, and operations. The result: greater scalability, consistency, and performance\u2014whether in the cloud, on-premises, or at the edge.<\/p>\n

Red Hat OpenShift Virtualization at a glance:<\/p>\n