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
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Author Information
Mitch Ashley is VP and Practice Lead for the CIO & Technology Buyers and Software Lifecycle Engineering practices at The Futurum Group. A multi-time CIO and CTO with 30+ years leading technical organizations, Mitch built and operated production systems spanning cybersecurity for the U.S. Department of Defense, PKI services for the broadband and 5G industries, SaaS platforms, large-scale telecom and banking systems, and a national broadband network. His work with AI began early, developing expert systems that diagnosed and repaired complex mainframe environments. That operator foundation grounds his analysis in operational consequence, covering the technology buyer's world of software engineering, cybersecurity, DevOps, cloud, and AI.
Brad Shimmin is Vice President and Practice Lead, Data Intelligence, Analytics, & Infrastructure at Futurum. He provides strategic direction and market analysis to help organizations maximize their investments in data and analytics. Currently, Brad is focused on helping companies establish an AI-first data strategy.
With over 30 years of experience in enterprise IT and emerging technologies, Brad is a distinguished thought leader specializing in data, analytics, artificial intelligence, and enterprise software development. Consulting with Fortune 100 vendors, Brad specializes in industry thought leadership, worldwide market analysis, client development, and strategic advisory services.
Brad earned his Bachelor of Arts from Utah State University, where he graduated Magna Cum Laude. Brad lives in Longmeadow, MA, with his beautiful wife and far too many LEGO sets.
Nick Patience is VP and Practice Lead for AI Platforms at The Futurum Group. Nick is a thought leader on AI development, deployment, and adoption - an area he has researched for 25 years. Before Futurum, Nick was a Managing Analyst with S&P Global Market Intelligence, responsible for 451 Research’s coverage of Data, AI, Analytics, Information Security, and Risk. Nick became part of S&P Global through its 2019 acquisition of 451 Research, a pioneering analyst firm that Nick co-founded in 1999. He is a sought-after speaker and advisor, known for his expertise in the drivers of AI adoption, industry use cases, and the infrastructure behind its development and deployment. Nick also spent three years as a product marketing lead at Recommind (now part of OpenText), a machine learning-driven eDiscovery software company. Nick is based in London.
