Building an adaptable enterprise: A guide to AI readiness

The speed and diversity of today's technological advances create a constant challenge for IT leaders. You simply cannot afford to wait and see which advances fall by the wayside or end up worthy of adoption. The pressure to invest in technologies like AI is immense, but chasing the latest breakthroughs without first defining a clear strategy is a risk that can delay long-term innovation.

The key question you must address isn't what your specific AI plan should be, it's how you can build an enterprise capable of adapting to any disruption. This means moving beyond merely reacting to and recovering from change to being able to continuously deliver value while the world is changing around you. You need to achieve enterprise durability and adaptability.

4 strategies to build an adaptable enterprise

Building an adaptable, AI-ready foundation begins with a clear-eyed view of your business goals. You must define the problem or challenge first, and only then determine if AI can provide a solution. This strategic mindset is supported by 4 critical focus areas.

1. Focus on the challenge, not the technology

Before you build, create a strategic plan. Avoid pursuing AI innovation without a defined business purpose. Instead, identify specific, high-value business challenges. Are you focused on improving developer productivity? Optimizing your supply chain? Creating a new customer experience?

Align your AI strategy to a tangible business outcome and define what success looks like by setting clear key performance indicators (KPIs). By doing this, you directly tie your investments to tangible value and are able to measure, learn, and iterate effectively.

2. Invest in your people

An adaptable organization is not built on technology alone, it also depends on your people. You need to nurture a culture that encourages experimentation and accepts failure as a part of adaptation. With a real and growing AI talent gap in the industry, you also have to invest in your people to build the necessary expertise.

At Red Hat, we are investing heavily in AI tools and training for our associates, giving them the time and space to explore AI applications together. This approach creates the deep, practical expertise that helps prepare your workforce to respond to change.

3. Look to the hybrid cloud

To unlock the full potential of AI, it should be available wherever your data and applications reside—in your datacenter, across multiple public clouds, and at the edge. This means your AI strategy must be a hybrid cloud strategy. You need to train, tune, and run models anywhere while maintaining a strong security posture, compliance, and data sovereignty.

This hybrid approach helps avoid isolated AI innovation in your organization and gives you the flexibility to use any model, any accelerator, and any cloud. A consistent platform that spans all of these environments helps you manage your data, applications, and models in a unified and replicable way.

4. Modernize your foundation

Legacy platforms and monolithic applications can quickly stifle AI ambitions. To overcome these barriers, you should look to modernizing your foundation and embracing automation. Getting your teams comfortable with automated workflows helps build the mindset needed for AI adoption, positioning technology as an enabler of innovation rather than replacement for human ingenuity.

Additionally, moving from proprietary, disparate systems to a modern, open, and flexible hybrid cloud platform will provide the adaptable foundation required to build, deploy, and manage modern applications—including the AI-infused applications of the future.

Tame the AI chaos with the open source advantage

An enormous amount of AI innovation is happening in open source communities, which can feel chaotic. This is where Red Hat’s historical role as an open source pioneer with 30 years of experience can provide a powerful advantage to our partners and customers.

In the early days of Linux, we made rapid open source innovation stable and reliable for the enterprise. We are doing the same today with AI. Red Hat engages within open source communities to advance mature AI innovations so they can meet the rigorous demands of enterprises.

For example, Model Context Protocol (MCP) is a community project that allows AI agents to take advantage of existing resources and applications that your human teams use. Red Hat works with and contributes to these communities so projects like MCP support the security protocols, compliance requirements, and reliability expected of modern production systems.

Open your future

The transition to the AI era is a moment of immense opportunity. Building a durable and adaptable enterprise is not a one-time project, it’s an ongoing commitment. It’s about having the right culture, the right platform, and the right partner.

Ready to learn more about how to build an adaptable enterprise and prepare for what’s next? Download the full e-book, "The adaptable enterprise: Why AI readiness is disruption readiness," for a complete blueprint.

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