From Experimentation to Execution: Platform Engineering for Scalable Generative AI

Enterprise generative AI has moved past the point where model capability was the bottleneck. Most organizations have proven GenAI works in a pilot; the harder question is whether it can run reliably in production. Futurum’s 1H 2026 AI Decision-Makers Survey finds that roughly 52% of organizations remain in the awareness or experimentation stages of GenAI maturity, still piloting and refining rather than scaling. The gap isn’t model access — it’s the absence of the platform infrastructure needed to deploy, govern, and operate AI workloads alongside the rest of enterprise IT.

To close that gap, platform teams must treat GenAI as a new workload class rather than building a parallel AI stack. That means extending proven engineering disciplines, including GitOps, CI/CD, infrastructure-as-code, unified observability, and policy-as-code, to cover model serving, RAG pipelines, and agentic workflows. Doing so brings reproducibility, cost control, and governance to generative AI the same way these disciplines already govern the rest of production IT.

In our latest thought leadership report, From Experimentation to Execution: Platform Engineering for Scalable Generative AI, completed in partnership with Red Hat, Futurum Research covers why enterprise GenAI initiatives stall before reaching production and outlines the platform engineering practices organizations need to convert AI investment into repeatable business outcomes.

In this report, you will learn:

• Why roughly 52% of organizations remain stuck in the awareness or experimentation stages of GenAI maturity, and what’s blocking the move to production
• How GenAI becomes a composite, distributed system in production, introducing agentic workflows and inference economics that pilot tooling doesn’t address
• The platform engineering disciplines, including GitOps, CI/CD, unified observability, and policy-as-code, needed to run GenAI reliably at enterprise scale
• Strategic recommendations for extending existing platform capabilities, rather than building isolated AI stacks, to scale GenAI safely and efficiently

If you are interested in learning more, be sure to download your copy of From Experimentation to Execution: Platform Engineering for GenAI today.