Red Hat: How to Build Durable, AI‑Ready Enterprises

With AI accelerating disruption across industries, it’s prompting organisations to rethink what it means to be resilient. 

This is thrusting the need for readiness over recovery to the forefront, meaning businesses that build adaptability and durability into their operations, technology and culture will be best equipped to thrive amid constant change, while those that don’t risk being trapped in an endless cycle of “bouncing back”.

Michael Ferris, Senior Vice President, Chief Strategy Officer and Chief Operating Officer at Red Hat, says that, in the face of accelerating technological disruption, few leaders are as focused on turning resilience into readiness.

“AI readiness is disruption readiness and it hinges on your enterprise being adaptable and durable,” he explains. “An adaptable enterprise can evolve and seize opportunities as they arise. Its strategy and its people can be flexible. 

“A durable enterprise continuously delivers value while the world is changing around it. Its cultural and technological foundations allow it to thrive in the face of constant change. 

“This goes beyond simple resiliency, which is the ability to recover or ‘bounce back’ from disruption. With the speed of innovation we’re seeing in AI, just bouncing back means you’re already behind.”

In this Q&A with Technology Magazine, Michael explains why businesses must go beyond simple resilience by cultivating adaptability, durability and an experimental culture to achieve true AI readiness.

Beyond technology, what foundations make an organisation truly durable?

Technology alone is never the complete answer. I see true durability emerge when an organisation’s people are empowered within a culture of experimentation. This requires creating an environment where it is safe for associates to take calculated risks, experiment, fail, adapt and try again.

At Red Hat, we say our open culture is our greatest competitive advantage because it encourages this rapid, iterative learning. 

We foster open collaboration, believing the best ideas can come from anywhere. We invest in our people, providing the training and experiential learning necessary to build the deep, practical expertise required to navigate continuous change. 

This has given us real durability as we’ve navigated the Linux, cloud and now AI eras.

How can leadership and culture accelerate – or block – AI readiness?

Leadership and culture are two of the primary accelerators or blockers of AI readiness. 

A culture of experimentation, championed by leadership, can be really powerful. When leaders foster an environment that rewards experimentation, encourages open collaboration and invests in skill development, they empower their teams to embrace change. 

This is easier said than done, and I’m not saying that everyone will be on board with every change. But this helps enterprises create a mindset where new technologies like AI are seen as ways to augment and accelerate the work of teams.

On the other side of this – a culture that penalises failure – operates in silos and resists change will grind AI initiatives to a halt. 

If associates are afraid to experiment or lack the necessary skills, even the best tools won’t make the impact leadership teams are looking for. 

AI readiness begins with a cultural shift, not just a technological one.

What’s the first step on your roadmap to building AI-ready enterprises?

The first step isn't about technology, it's about clarity of purpose. 

Before building anything, you must create a clear, shared view of what you want AI to accomplish. What are specific, high-value business outcomes for your enterprise?

You might dig into customer feedback or team-level conversations to identify a real-world challenge or opportunity. Are you trying to boost developer productivity? Are you trying to create a new, personalised customer experience? 

There are so many use cases for AI. Aligning your AI ambitions to a tangible goal from the very beginning is an essential first step to create strategic focus.

How should companies measure progress to avoid falling into a “bounce back” loop?

Organisations should measure progress against tangible business value, not just recovery. Define what success looks like from the start. 

Any AI proof-of-concept must be measured against clear benchmarks that are directly tied to the business outcomes you identified in your first step. This ensures your investments create real value and allows you to learn and iterate effectively. 

As you get better at doing this, the goal is for your organisation to move beyond simply recovering from disruption into a state of continuous adaptation and value delivery. That is the hallmark of a durable enterprise.