From AI Pilot to Production: A Step-by-Step Guide to Platform Modernization with Azure Red Hat OpenShift

Introduction

Artificial intelligence is rapidly transitioning from experimental pilot projects to mission-critical production systems. However, this shift introduces a new challenge: how to manage hundreds of AI initiatives with consistent identity, governance, and security while maintaining scalability. At Red Hat Summit 2026, Microsoft and Red Hat showcased how Microsoft Azure Red Hat OpenShift serves as the secure, scalable foundation for production AI workloads. This guide walks you through the steps to replicate this success, using the real-world example of Banco Bradesco, a leading Latin American financial institution that moved beyond AI experimentation to a production-ready enterprise AI platform unifying governance across more than 200 initiatives.

From AI Pilot to Production: A Step-by-Step Guide to Platform Modernization with Azure Red Hat OpenShift — Source: azure.microsoft.com

What You Need

An active Microsoft Azure subscription with appropriate permissions to provision resources.
Access to Azure Red Hat OpenShift (ARO) – a jointly managed, enterprise-grade Kubernetes platform.
A set of AI pilot projects (or existing experiments) that need to be operationalized.
Defined governance policies (identity, access, security) aligned with your organization’s compliance requirements.
Integration with Azure Active Directory (now Microsoft Entra ID) for identity management.
Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.
A cross-functional team including platform engineers, security specialists, and AI/ML engineers.

Step-by-Step Guide

Step 1: Assess Your Current AI Pilot Landscape

Before moving to production, take inventory of your existing AI pilots. Identify which ones have clear business value, how they are currently deployed (e.g., on virtual machines, notebooks, or ad hoc containers), and where governance gaps exist. For example, at Banco Bradesco, over 200 AI initiatives were running in isolation without unified security or policy controls. Document each pilot’s dependencies, data sources, and compliance requirements. This assessment becomes your roadmap for productionization.

Step 2: Establish a Unified Governance and Security Framework

One of the key obstacles in scaling AI is enforcing consistent identity and security across all workloads. Using Azure’s native capabilities, define a governance framework that integrates with Azure Policy, Azure Role-Based Access Control (RBAC), and Microsoft Defender for Cloud. On Azure Red Hat OpenShift, this means configuring the cluster to use Azure Active Directory for authentication, setting role bindings, and applying policy enforcements that prevent unauthorized changes. This step ensures that every AI workload—from development to production—adheres to the same security posture, a critical requirement for regulated industries like finance.

Step 3: Deploy Azure Red Hat OpenShift as Your Production Foundation

Provision an Azure Red Hat OpenShift cluster in a region close to your users and data. ARO combines the enterprise-grade Kubernetes distribution from Red Hat with the security, compliance, and scalability of Azure. During deployment, you’ll choose the size, configure networking (e.g., virtual networks, private endpoints), and enable add-ons such as Azure Monitor and Azure Policy for Kubernetes. The key advantage of ARO is that both Microsoft and Red Hat jointly support it, giving you a single point of contact for troubleshooting. Banco Bradesco used ARO as the secure-focused foundation to unify all its AI initiatives under one platform.

Step 4: Migrate AI Workloads from Pilots to Production

Containerize each AI model or service using Docker, ensuring that dependencies (libraries, frameworks, model artifacts) are reproducible. Then, create Kubernetes manifests or Helm charts to deploy these containers on ARO. Set up CI/CD pipelines using tools like Azure DevOps or GitHub Actions to automate testing, security scanning, and deployment. Implement canary releases or blue/green deployments to minimize risk. Each workload should be registered with the governance framework established in Step 2, enforcing consistent identity and policy. Banco Bradesco’s move from experimentation to production required migrating over 200 initiatives onto ARO, each with its own governance and security settings.

Step 5: Scale and Optimize with Continuous Monitoring

Production AI demands observability. Enable Azure Monitor and Azure Log Analytics to track resource utilization, model performance, and error rates. Use Azure Cost Management to budget and optimize spending. Implement horizontal pod autoscaling based on workload demand. For AI-specific metrics like inference latency, use custom metrics and dashboards. At Banco Bradesco, scaling to hundreds of AI services required tight integration with Azure identity and policy capabilities to maintain governance even as usage grew.

Step 6: Recognize and Leverage Partner Achievements

Celebrate milestones and learn from recognized leaders. At Red Hat Summit 2026, Microsoft received the Red Hat Ecosystem Innovation Award for Platform Modernization and an honorable mention for North American Hybrid Cloud Everywhere. This award highlighted how Microsoft’s collaboration with Red Hat delivers measurable customer outcomes. One of the standout examples is Banco Bradesco, which moved beyond proof-of-concept to a full production AI platform. By aligning with such partner ecosystems, your organization can accelerate its own modernization journey.

Tips for Success

Start small, then scale: Don’t try to migrate all pilots at once. Pick two or three high-value, low-complexity projects first to prove the process.
Invest in governance early: It’s much harder to retrofit security after production than to build it in from the start. Use Azure Policy for Kubernetes from day one.
Leverage joint support: Azure Red Hat OpenShift is a first-party Azure service with co-engineering by Microsoft and Red Hat. Use their shared documentation and support channels for faster issue resolution.
Integrate with existing Azure services: Take advantage of Azure’s identity, monitoring, and security services to avoid reinventing the wheel.
Document and share learnings: Create internal case studies like Banco Bradesco’s story to build momentum and secure executive buy-in for further modernization.
Monitor costs: AI workloads can be resource-intensive. Set budget alerts and use spot instances for non-critical batch jobs when possible.

By following these steps, your organization can move from AI pilots to a secure, governed production environment using Azure Red Hat OpenShift—just as Banco Bradesco did. The journey requires careful planning, but the payoff is a scalable, enterprise-ready AI platform backed by an award-winning partnership.

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