Our journey to AI-centricity, part 2: Crafting a strategy that scales

In the first part of this series, we discussed the messy and challenging work of fixing our foundation—standardizing on Red Hat OpenShift and cleaning up years of fragmented data. With that foundation in place, we faced a new challenge: how to integrate AI into how Red Hatters work without creating new internal barriers or security risks.

Moving from policy to participation

When gen AI first arrived, we made a mistake common to many enterprises: we led with a policy of "no." Our first move was to release a dense legal document so restrictive that it inadvertently discouraged people from exploring the technology altogether. Instead of a collaborative rollout that got our users excited about what’s possible, we created a culture of apprehension.

The result was a surge in shadow AI. Associates were still using the tools—the demand was too high to ignore—but they were doing it in the dark, using non-sanctioned tools without any governance or protection. We realized that by trying to over-control the journey, we had actually increased our risk and stifled the very innovation we needed. We had to pivot from a policing-the-technology mindset to one of empowering our people to use it safely.

We’ve since shifted toward a culture of curiosity. We now encourage every Red Hatter to experiment with both in-house and third-party LLMs that we provide. The goal is to let AI handle the repetitive, time-consuming tasks that slow us down. This frees up our people to focus on the creative problem-solving and strategic thinking that actually moves Red Hat forward.

The 3 layers of our AI strategy

A diagram titled 'Evolving to people-powered, AI-centric' illustrating Red Hat's AI strategy, which includes the 3 layers of 'Associate AI empowerment,' 'AI for business transformation,' and 'AI insights,' built on a foundational layer of 'Data - Knowledge - Models - Processes - Security'.

To scale these efforts, we’ve organized our internal AI work into 3 distinct pillars that help us navigate the same AI journey many of you are on today:

• Associate empowerment: We focus on providing the tools and training every associate needs to see how AI can augment their specific role. Whether it's assisting in coding or summarizing complex documents, this layer is about giving associates the opportunity to make their daily work more efficient and satisfying.
• Business transformation: We continue to identify bottlenecks in enterprise-wide processes, like sales workflows and expense management, that are ripe for AI automation. We use a mix of our own products and third-party Software-as-a-Service (SaaS) tools to remove that operational friction and streamline how the company functions.
• AI insights: This is our experimental layer. We’re building a network of AI assistants and agents that understand our specific business context. This will allow us to ask complex questions—like real-time forecasting—by safely incorporating our data within models that operate under our defined protection standards.

If there’s one thing we’ve learned on this journey, it’s that you shouldn't wait for a perfect plan to start. We’ve adopted a few key principles to keep us moving:

• Lead with a bold vision; execute with MVPs: Don't let the pursuit of a massive, all-encompassing solution stop you from finding small wins today. Use Minimum Viable Projects (MVPs) to prove value and build momentum.
• The "upstream first" mindset: We contribute our findings back into our products, delivering the fixes and features we need internally and that eventually benefit our customers.
• Prioritize choice: Define a strategy that uses the best tool for the job—whether it's open source or proprietary—without locking your data into a single vendor.

We’re still learning, experimenting, and iterating every day. That’s the open source way, and it’s the only way we know how to build a future that works.