Prove gen AI value in weeks, not years

If you lead an AI program, a data platform team, or any initiative tasked with squeezing business value from generative AI (gen AI), you already know why gen AI matters. Faster decisions, smarter customer experiences, more productive employees—the upsides are generally clear. According to a 2024 Boston Consulting Group survey, however, 74% of organizations stall when they try to turn isolated gen AI pilots into production-ready solutions. Enthusiasm is high, but momentum fades as soon as real-world constraints—such as governance, budget, and available skills—enter the picture.

From curiosity to credible results

Red Hat works with customers who face these gaps every day. The pattern is familiar: teams run a promising demo, and leadership nods, but weeks later, the project languishes because no one can prove concrete return on investment (ROI). A focused, outcome-driven proof of concept (PoC) is one way we help break that logjam.

Unlike a "technology demo," a Red Hat gen AI PoC is scoped to a single workflow with measurable stakes—think automating product descriptions or reducing supply chain forecasting time. We run it on the same open, hybrid-cloud platforms you will scale on later (Red Hat OpenShift AI, Red Hat Enterprise Linux AI, and Red Hat AI Inference Server), so success in the PoC more readily translates directly to production.

Three common barriers and how we help you overcome them 

The journey from an AI pilot to a full-scale production environment is often a difficult one. Many promising projects stall, unable to move past a number of common barriers. Removing these obstacles is the first step toward a successful AI adoption, and a Red Hat PoC can help with all three.

Pilots die in limbo: Teams often need to rewrite code or re-platform before going live, burning time and goodwill. Because OpenShift AI runs on any major cloud or on-prem cluster, the code you validate in a PoC deploys unchanged when you scale.

Skills gaps hinder adoption: Most companies can't spin up a brand-new AI operations team overnight. Our integrated toolchain and hands-on enablement let existing DevOps and site reliability engineering (SRE) staff run and monitor gen AI using tools they're familiar with—no heroics required.

Fuzzy KPIs can kill funding: Projects without defined success metrics quickly lose executive attention. Every Red Hat PoC starts with a jointly developed success plan that tracks improvements in cost, latency, or user satisfaction—evidence that an executive sponsor can champion.

Real impact: Customer success stories

One of our customers—A global media and entertainment leader—faced challenges inferencing their gen AI models across different hardware platforms. Through a Red Hat gen AI PoC, the company successfully deployed multiple models optimized for different computing platforms, including a number of accelerators.

With Red Hat's optimized inference solutions and model repository, the organization significantly streamlined deployments, reduced complexity and cost, and accelerated the time-to-production for their AI services. This allowed the internal platform team to confidently scale gen AI-driven experiences to millions of customers globally, improving operational agility and reducing technical overhead.

Another customer—a major global retail company—needed to manage over one million inference requests daily for complex supply chain models. Red Hat delivered a structured PoC designed to optimize inference capabilities, utilizing quantization techniques that reduced GPU compute utilization by 40%. This significantly reduced operational costs, increased efficiency, and enabled the retailer to more effectively handle these high-volume inference workloads. The success and measurable value from the initial PoC quickly led to us working with them across additional AI initiatives, establishing Red Hat as a strategic partner in the retailer's broader AI and data science roadmap.

A 4 step path from pilot to production

Getting an AI pilot into production requires more than just a good idea—it requires a clear strategy. By focusing on a few key principles, you can build a PoC that is ready to scale. Here's how:

1. Choose one high-stakes use case: Select a workflow where seconds, dollars, or customer satisfaction points truly matter
2. Define success up front: Agree on tangible metrics—cost per inference, task completion time, Net Promoter Score
3. Build on an open foundation: Deploy the PoC on Red Hat's hybrid-cloud platforms to avoid vendor lock-in later
4. Scale with confidence: Use the same pipelines, governance policies, and monitoring stack from PoC to production—no re-engineering required

Ready to see results?

If your gen AI initiative is stuck between curiosity and execution, working with Red Hat to develop a tightly scoped PoC can deliver measurable ROI in a matter of weeks, not years. Let’s map out a use case, success metrics, and timeline together.

Scan the QR code and apply for a gen AI PoC, and we’ll work with you to outline a no-cost PoC tailored to your use cases and business goals. Let us help you turn promising experiments into production impact—starting now.