From strategy to scale: Bringing AI to life in the enterprise

In my previous article, I explored how open source principles and collaborative ecosystems are shaping the future of AI. In this article, I want to focus more specifically on what it really takes to move AI off of the whiteboard and into real enterprise outcomes.

The reality is that getting from “we should be doing something with AI” to “we have a production-ready system running” is rarely straightforward. Most organizations don't have a blueprint. They're navigating this in real time—figuring out use cases, untangling infrastructure, aligning teams and managing risk. It is a lot to take on, and it's why building on the right platforms, services and skills matters.

A foundation that plays well with others

At the core of many enterprise IT environments, you will find  Red Hat Enterprise Linux (RHEL) and Red Hat OpenShift running critical workloads. These platforms offer more than just infrastructure—they're built on open source principles, and are designed to to integrate, adapt and evolve with your needs. Whether you're working with open source or proprietary tools, hardware partners or cloud providers, you're not boxed into a single path. You have the flexibility to build the solution that your environment demands.

When it comes to putting AI into production, Red Hat Enterprise Linux AI (RHEL AI) and Red Hat OpenShift AI offer this same flexibility. They’re engineered to support production use cases without requiring you to rebuild your environment from the ground up. 

• RHEL AI provides a foundation model platform for teams shaping their AI strategy. It includes access to IBM’s open source Granite family of large language models (LLMs) and supports model alignment and customization through the InstructLab toolkit.
• OpenShift AI is built for operationalizing and scaling AI at the enterprise level. It helps teams train, deploy, manage and monitor both predictive and generative AI (gen AI) models across hybrid cloud environments, with integrated machine learning operations (MLOps) capabilities.

Together, these provide a secure and consistent environment for building and expanding your AI initiatives without locking you into a single approach. 

Building AI takes more than tech

Even with the right tools, AI can still feel overwhelming. Most of the feedback we hear is not about the models themselves, but about knowing where to begin.

• Which use cases should we focus on?
• How will this fit into our existing systems?
• What is realistic based on our current skills, data and infrastructure?  

Here's where Red Hat can help. Red Hat AI Services are tailored engagements that are designed to help you set a clear direction for your AI initiatives and gain real momentum by working with Red Hat experts, whether you're just starting to ideate or are already building toward production.

Red Hat AI Services that help you move forward

• AI Discovery: For teams just getting started, we'll help you find your focus. What problems are you trying to solve? What systems, data or constraints do you need to consider? This engagement will help you develop a roadmap that is realistic, well scoped and grounded in your real environment.
• AI Assessment: Here we take a deeper dive. We'll look at your current state, map maturity levels, understand inferencing needs and identify the use cases that will deliver value fastest.
• AI Incubator: Ready to build something real? This collaborative, residency-style engagement pairs your team with Red Hat experts to prototype and validate your AI solution. With Red Hat’s platforms as the foundation, we help ensure your generative and predictive AI workloads have a strong security profile, and are compliant and built for production from the start.
• AI Platform Foundation: Once your AI strategy is locked in, we'll help you deploy your container-native platform, and make sure it’s optimized and something your team can confidently own going forward.
• MLOps Foundation: For teams scaling AI, in this engagement we focus on production-readiness. Think GitOps, automation, governance and everything else that can help make AI sustainable over the long term.

These consulting engagements will help you lay a real-world foundation for AI that can support growth long after the first project is launched. Sustaining that momentum over time, however, takes more than technology alone. It also requires building the right skills inside your organization. 

Keeping AI momentum: Building the skills to scale

Once you have your AI foundation in place, your focus should shift to making sure your team can continue building on it. This is where staying up-to-date with Red Hat Training and Certification comes in, helping teams build the practical expertise they need to turn AI plans into real, long-term progress.

There are many ways to get started. Red Hat offers a growing catalog of free technical overviews and hands-on courses to support teams with different goals, roles and levels of experience. Some courses go deeper and may assume familiarity with Python, OpenShift or machine learning concepts. Your team can explore what’s most relevant based on where you are now and what you’re working toward.

Here are a few options:

• Red Hat OpenShift AI Technical Overview (AI067) — A no-cost overview of the AI and machine learning landscape and the challenges of deploying applications
• Red Hat Enterprise Linux AI Technical Overview (AI096) — A no-cost introduction to Red Hat Enterprise Linux AI and the Granite family of LLMs
• Introduction to Python Programming and to Red Hat OpenShift AI (AI252)
• Creating Machine Learning Models with Python and Red Hat OpenShift AI (AI253)
• Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267)

For those who want to take it a step further and validate their skills, the Red Hat Certified Specialist in OpenShift AI credential shows you have the hands-on skills to build, deploy and manage AI and machine learning workloads at scale.

However your team chooses to build its skills — through training, certification or real-world experience — investing in people can help you keep your AI momentum going. Technology may lay the groundwork, but it’s the skills inside your organization that keep it moving. Red Hat supports both, with platforms you can trust, consulting that drives progress and training designed to grow real expertise at every stage of your journey. 

If you’re thinking about how to turn AI plans into real progress, we’d love to hear what you’re working toward. You can start by exploring the ebook or connecting with one of our experts to talk through your goals.