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The pace of IT’s evolution is staggering—can you feel it? Just a few years ago, the focus was on decoupling monolithic legacy applications into microservices architectures. While that transformation remains important, today’s conversations have shifted to adopting large language models (LLMs) that unlock actionable insights and deliver competitive advantages across industries. The urgency is undeniable, and it’s pushing businesses to explore and implement AI as they strive to remain relevant. But here’s the critical question: Are you truly prepared to embrace AI, or are outdated practices holding you back?

Lessons from The Phoenix Project

In the acclaimed book The Phoenix Project by Gene Kim, Kevin Behr and George Spafford, readers meet Brent Geller, the quintessential overburdened IT engineer. Brent is brilliant, the go-to resource when systems fail and the ultimate problem solver. However, his indispensable knowledge turns him into a bottleneck, stalling progress and limiting innovation. Sound familiar?

Many organizations unknowingly depend on their “Brents,” creating single points of failure that hinder modernization and growth. These bottlenecks are warning signs that the organization’s systems, processes and culture may not be ready for the future.

The AI readiness gap

AI is transformative, but as an IT leader it’s time to reflect on your organization’s readiness:

  • Can your team adopt and train LLMs to generate insights, identify anomalies and mitigate risks while still managing manual, time-consuming tasks like provisioning VMs, configuring firewalls and patching systems?
  • Is your IT organization proactive and automation-focused, or are you stuck in reactive, legacy workflows?

From my experience working with customers exploring AI, one pattern stands out: Organizations that embrace IT automation are leading the AI race.These forward-thinking companies have adopted modern practices like defining desired states in YAML files and automating workflows with advanced tools. By eliminating toil, they’ve enabled their teams to focus on upskilling and adopting cutting-edge methodologies, such as machine learning operations (MLOps). Taking advantage of containerized models on Kubernetes clusters with platforms like Red Hat OpenShift AI, these organizations are unlocking AI’s transformative potential.

The power of automation and culture

Tools like Red Hat Ansible Automation Platform can help reduce the “Brent Effect,” but tools alone aren’t enough. Success requires a cultural shift toward automation and modernization. Transitioning from manual processes to Configuration as Code (CaC) empowers IT teams to:

  • Automate repetitive tasks
  • Focus on strategic, high-value projects
  • Build a robust foundation for AI-driven innovation

Additionally, fostering a community of practice can accelerate cultural change. Collaboration, shared learning and collective problem-solving help break down disparate team structures and align teams around automation goals. If you haven’t already, I highly recommend reading my esteemed colleague Frank Wu’s blog on building a successful automation community of practice. His tips and best practices provide valuable insights to overcome cultural hurdles and clear the path toward modernization.

A path forward

In 2025, here’s how you can prepare your IT organization to thrive in the AI era:

  1. Automate IT processes: Standardize Day 2 operations by using YAML for tasks like IP provisioning, firewall configurations and system hardening (e.g., applying CIS benchmarks)
  2. Integrate ITIL and agile practices: Boost your IT operations by combining the best of traditional ITIL practices—such as change control and incident management—with the speed and adaptability of modern Scaled Agile Frameworks (SAFe). Take advantage of a GitOps strategy to bridge the gap, fostering faster, more reliable workflows powered by Git actions integrated with Event-Driven Ansible, while fostering both stability and innovation
  3. Adopt event-driven self-healing: Take advantage of tools like Event-Driven Ansible to automate anomaly detection and remediation from event sources, helping systems run efficiently and autonomously

Focusing on these areas will help you eliminate bottlenecks, reduce dependencies on single points of failure and position your organization to dominate in an AI-driven world.

Keep up or stay ahead

The evolution of IT is accelerating, and the stakes have never been higher. Are you ready to go beyond legacy practices, embrace automation and use AI to propel your business forward?

IT is changing at record speeds—don’t just keep up. Stay ahead.

Where to go next

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Red Hat Enterprise Linux AI | Product Trial

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About the author

Ben is a Senior Ansible Solution Architect with a wealth of expertise spanning more than 25 years in the realm of constructing and automating UNIX and Linux based distributed computing systems. During his distinguished career, Ben devoted two decades to the Federal Reserve, where he led the design and construction of pivotal infrastructure for crucial systems, including bank payment systems, Treasury Auction Application systems, and Federal Automated Clearing House infrastructure. He currently calls Atlanta, GA, home, where he resides with his wife and children, enjoying southern living.
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