The adaptable enterprise: Why AI readiness is disruption readiness

Michael Ferris is Senior Vice President, Chief Operating Officer, and Chief Strategy Officer at Red Hat. In this role, he is focused on building the company’s global business strategy across all offerings and services, mergers and acquisitions, market-making partnerships, and internal operations.

For more than 25 years at Red Hat, Ferris has focused on the value of the company’s business model and serving the needs of customers, partners and open source communities. He served as the first product manager for Red Hat® Enterprise Linux®, and he later led the evolution of Red Hat’s subscriptions from capacity to consumption models. This allowed for Red Hat products on public cloud providers and led to the creation of Red Hat Certified Cloud and Service Provider Program, which includes more than 1,300 Red Hat partners and lets customers use multi- and hybrid-cloud paradigms across the entire Red Hat portfolio.

Ferris has also worked to define the company’s business architecture, leading strategy and business negotiations on high-profile partnerships as well. These efforts have enabled Red Hat to combine the open source development model with its value propositions and various subscription models to meet changing market needs. Ferris is responsible for leading Red Hat’s efforts to become an AI-focused organization.

Ferris is an inventor or co-inventor of more than 85 issued U.S. patents in the cloud computing space, all of which are covered under Red Hat’s patent promise in support and defense of open source development.

There is nothing normal about the pace of change we face today. Technological disruption is rarely predictable, but the days where leaders could wait and see what advancements are worthy of adoption are waning. In fact, according to recent IBM research, 64% of CEOs said that the risk of falling behind drives them to invest in some technologies before they have a clear understanding of the value they bring to the organization.¹

In the past, we often compared IT innovation to waves forming on the horizon—change we could see coming. Today’s pace, accelerated by growing technological capabilities and competitive demands, has turned that visible tide into a constantly churning sea of innovation. 

Artificial intelligence (AI) is a fundamental shift in the current, with the potential to reshape every industry it touches. The market for AI is projected to grow exponentially, with Gartner® indicating that, “worldwide generative AI (GenAI) spending is expected to total $644 billion in 2025, an increase of 76.4% from 2024.”² Leaders are under immense pressure to act on that opportunity. In the rush to adopt, many organizations risk making reactive, short-term decisions that create complexity and hinder long-term innovation.

Many of you are likely asking yourselves—and your leadership teams—what should your organization’s AI strategy be? You might already be developing your approach. But if you haven’t addressed your ability to adapt more broadly, you may find yourself quickly overcome. The first and most important question is not what your specific strategy should be, but instead: “How do we build an adaptable enterprise?”

Because an enterprise built to adapt to disruption is an enterprise built for AI (and everything that may come after).

An adaptable organization is one that adopts and actively maintains a culture and strategic mindset that rewards rapid innovation and provides the technologies, tools, and support that allows people to thrive (not just survive) in the face of change. Many leaders strive for resiliency—the ability to bounce back from disruption. But in the age of AI, bouncing back is not enough. We need to aim for something more: durability.

A durable enterprise doesn't just recover; it continuously delivers value while the world is changing around it. Its cultural and technological foundations allow it to adapt, evolve, and seize opportunities without being thrown off course by every new model, framework, or tool.

Durability is closely linked with adaptability, the ability to adjust strategy to shift resources, processes, and tools in response to dynamic global markets. Enterprises also need adaptable associates, which requires investments in learning and development as well as time for the experimentation  that leads to successful iteration. These aren’t new concepts; enterprises that couldn’t adapt to Linux, to virtual machines, or to cloud-native computing struggled to survive these paradigm shifts.

Getting these twin pillars right prepares your organization for change, giving teams the context and capability to act on your AI strategy. The work doesn't begin when you deploy a model; it starts with a clear-eyed view of your business goals. In other words, you must understand and define the problem or challenge first, and only then see if AI can answer it. 

Ask these questions to help discover your best course of action:

• How are your customers responding to what your organization offers today?
• How can you improve or expand those offerings, based on customer feedback?
• What technological trends or nascent innovations could impact (or disrupt) your capabilities or offer new opportunities?
• What improvements can you make to your internal processes to respond more efficiently or quickly? (Is speed what you need, or is it something else?)
• Are there other opportunities (now or on the horizon) to create more value for customers or greater efficiency or resiliency for the organization? 

Now that you’ve identified your challenges and established goals for your work, how do you build a foundation that doesn’t just bounce back in the face of change, but evolves and thrives through it? As Red Hat’s Chief Strategy Officer, I work alongside product teams to weave AI into our offerings and have a front row seat as our associates adopt and refine AI tools and processes. Through these experiences, I have been able to identify and explore common elements of successful efforts.  I believe these 4 focus areas are crucial for any organization that needs to better adapt to change, both culturally and technologically.

1. Create a clear, shared view of what you want AI to do

Before you can build, you must have a blueprint. AI can do many things, but what do you need it to do? Avoid the temptation to wander the technological wilderness hoping for a lightning strike of inspiration. Instead:

• Focus on business outcomes. Dig deeply into your teams’ feedback and customer conversations to identify specific, high-value business challenges or opportunities. Is it about improving developer productivity? Optimizing your supply chain? Creating a new, personalized customer experience? Aligning the strategy to a real outcome is key; you don’t develop an AI plan just because it’s expected. Have the problems to solve in mind at the outset.
• Establish clear key performance indicators (KPIs). Define what success looks like from the start. Any AI proofs-of-concept (POC) should be measured against clear benchmarks. This ensures your investments are tied to tangible value and helps you learn and iterate effectively. 

2. Build a culture of experimentation and expertise

Technology alone won't get you there. Durability and adaptability come from an organization's people. At Red Hat, our open culture is our greatest competitive advantage. Nurturing this culture means allowing employees to take risks, experiment, fail, adapt, and try again. This rapid, iterative approach prioritizes learning from failure. A willingness to try new things and acquire experience and expertise often leads to success. For AI, this type of mindset and culture is more critical than ever.

• Embrace open collaboration. The best ideas can come from anywhere. Foster an environment where it’s safe to experiment (even if it fails) and where tough questions are not just tolerated, but encouraged—no matter your role.
• Invest in your people. The AI talent gap is real. 42% of respondents to Bain & Company’s quarterly AI survey indicated that a lack of in-house expertise or resources prevents their organization from moving faster with generative AI technologies.³ In order for your existing workforce to be able to adapt and respond to change, they may require training and experiential learning. In some ways, employee growth is always critical to successful market innovation. At Red Hat today, we are investing heavily in AI tools and training, making them available to all associates. We also ensure teams have the time and space to experiment and explore AI applications together, which furthers our open, collaborative culture. We aren’t just looking for productivity gains, but to create deep, practical expertise. After all, we can’t recommend AI advancements to our customers without fully understanding the benefits and impacts they may bring.

3. Know your data and applications and where they reside

We've long said that the cloud is hybrid. The immense promise of AI means making it available wherever your applications reside—so AI must be hybrid, too. Your data—the crucial center of any AI model—already lives everywhere: in your datacenter, across multiple public clouds, and at the edge of the network.

• Bring AI to your data and apps. Keeping AI workloads close to their data sources and the applications they enhance reduces latency to make transactions more efficient, and can help teams manage and maintain security across their environments. A successful AI strategy must therefore be a hybrid cloud strategy. It must allow you to train, tune, and run models wherever your data and applications reside, without compromising on security, compliance, or data sovereignty. For many of our customers, this means a strategic approach with enough flexibility to run the business using any model, any hardware accelerator, in any cloud environment.
• Create a consistent foundation. A hybrid approach avoids isolated AI innovation. It requires a consistent platform that can span all of your environments, so you can manage your data, apps, and models in a unified (and replicable) way.

4. Modernize first, then move forward with AI

Trying to add AI to a legacy technology foundation can be like putting a rocket engine on a horse-drawn carriage, with predictably poor results. Legacy platforms and monolithic applications can stifle your AI ambitions. 

To overcome these barriers to advancement, think about ways your organization can modernize to prepare for AI enablement:

• Automate and simplify. Automation is a cultural and technological precursor to AI. Getting your teams comfortable with automated workflows builds the mindset needed for AI adoption—viewing technology not as a loss of control, but as an enabler of innovation.
• Embrace a modern platform. Moving from proprietary, siloed systems to an open, flexible, and consistent hybrid cloud platform offers advancements beyond AI. Technologies like Linux®, containers, and Kubernetes (the container orchestration engine that is also part of Red Hat® OpenShift®) provide the adaptable foundation required to build, deploy, and manage modern applications—including the AI-infused applications of the future.

A great deal of AI innovation is happening in open source communities. This is thrilling, but it can also be chaotic. How do you choose the right technology for your organization when there are so many projects—from Llama Stack to Agent2Agent?

 IT leaders have tens, if not hundreds, of millions of dollars in existing investments. You don’t want to (and likely cannot) abandon these production environments to chase every early or evolving technological advance. Open source innovation isn’t just about ushering in the next generation of IT; it is also at the forefront of bridging traditional systems with the AI future. 

Model Context Protocol (MCP) is an example of this kind of open source community project. MCP enables AI agents to take advantage of existing resources to accomplish tasks of varying complexity. This specifically includes technologies built before AI became an enterprise imperative. MCP provides a client-server architecture based on open protocols that allows the AI access to the same tools and applications that human teams use. The future vision of a truly autonomous AI, acting as an engineer or developer, can only become a reality if we can bridge these divides.

This is where Red Hat's historical role as an advocate for enterprise organizations within open source and technology communities provides a powerful advantage. In the early days of Linux, the rapid pace of OS innovation was too fast for enterprises to adopt. They could not keep up with the rapid—and somewhat unpredictable—release schedule. Red Hat owes its success to taking that incredible open source innovation and making it stable, reliable, and (eventually) available via subscription as Red Hat Enterprise Linux. Today, we are doing the same—whether it is in support of a specific-but-common need like MCP, or underpinning the technological advancement of large-scale industry change agents like AI.

While much of the industry emphasis currently centers on AI models, this is just the beginning when it comes to enterprise AI. The desired outcome of most AI engagements today is at the point of inference—the moment when a model provides an answer. This is where the true value of AI emerges, but it’s also where the weaknesses of an AI strategy can appear. Gartner points out that “by 2028, as the market matures, more than 80% of data center workload accelerators will be specifically deployed for inference as opposed to training use.”⁴

This growing demand is why Red Hat intends to make AI a more manageable experience for IT leaders—just like we did with Linux and other open source technologies. Red Hat AI Inference Server, built on the proven vLLM project, and emerging community technologies like llm-d are components of the ecosystem Red Hat is helping build around the execution phase of AI. This includes enterprise inference technologies that are supported within a known lifecycle, just like you would expect of any other enterprise IT platform. Red Hat also works to foster and contribute to upstream innovation that makes it easier to efficiently run AI workloads at scale.

We are approaching agentic technologies and systems in this same way. MCP, for example, can revolutionize how AI agents operate, but MCP servers need to encompass the appropriate security protocols, compliance requirements, and reliability that enterprise IT organizations expect. With this in mind, Red Hat engages with and within these communities, to help advance the most mature open source AI innovations so that they might meet the rigorous demands of modern enterprises and production systems.

Red Hat makes complexity consumable for the enterprise, balancing what’s next with what’s needed right now. We have helped organizations all over the world become more adaptable and durable, setting them on a course to modernize their environments and move forward.

The transition from the pre-AI era to the AI era is a moment of profound 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. Red Hat is ready to help you.

Ready to explore enterprise AI and open innovation?

Access hands-on resources to help practitioners build their skills or review strategic guidance for decision makers. Get a handle on basic concepts or dive more deeply into demos, news and insights, and customer success stories.
 

ai.redhat.com

Want guidance on other technology challenges?

Discover more expert perspectives in The Executive’s Collection—where Red Hat leaders and industry analysts help you make smarter decisions and stay ahead of disruption.
 

redhat.com/executives-collection