Why customers are choosing Red Hat AI for real business outcomes

Why customers are choosing Red Hat AI for real business outcomes 4 business reasons customers choose Red Hat AI 1. Freedom to deploy where the business needs it 2. Open innovation without locking into a single path 3. Security-focus, sovereignty, and operational control 4. Operational consistency across teams Where this shows up in real business use cases A decision framework for evaluating AI solutions Final thoughts The adaptable enterprise: Why AI readiness is disruption readiness About the author Megan Jones More like this Red Hat and NVIDIA: Setting standards for high-performance AI inference Red Hat AI tops MLPerf Inference v6.0 with vLLM on Qwen3-VL, Whisper, and GPT-OSS-120B Technically Speaking | Build a production-ready AI toolbox Technically Speaking | Platform engineering for AI agents Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Most leaders I speak with are well past the hype cycle of AI. The question is no longer whether AI matters. The question is how to move from experimentation to production in a way that is security-focused, supportable, and repeatable across teams. From where I sit—leading strategy and operations for AI Platform Core Components (AIPCC), an engineering function within Red Hat’s AI Engineering organization—that shift changes everything. The conversation moves from a tooling decision to an operating model decision. A strong AI platform is the foundation that helps teams ship AI-enabled capabilities on schedule, operate them reliably, and do it in a way that aligns with governance, enterprise risk, and long-term cost control. With the recent launch of Red Hat AI Enterprise , that foundation is becoming more explicit, bringing together model lifecycle, inference, and operational management in a way that reflects what customers are actually trying to do: scale AI responsibly across hybrid environments. Most organizations are not building AI in a single environment.