How to modernize IT skills for the AI transition

Technology is moving so quickly—particularly when it comes to emerging AI tools—that engineers are left feeling like they cannot keep up. More than 60% of organizations now prioritize modernizing their infrastructure to work faster and deliver new services. At the same time, gen AI is expected to support up to 30% of knowledge work by 2027—but only a small percentage of employees feel prepared to use it effectively.¹ In other words, technology investments are accelerating, but workforce readiness is not keeping pace. Consequently, organizations must make continuous technology skills training a high priority.

Traditional training models are no longer sufficient

Yesterday’s training methods—like fixed, rigid, multiday courses and static checklists—are not designed for today’s pace. Instead of helping, they slow things down when speed and adaptability are required. Traditional training models fail because they don't match how engineers actually work. Learners get stuck in rigid, multiday courses that are outdated before they even finish, while IT leaders are left guessing which skills their teams actually have when a deployment fails.

Even with widespread adoption of skills frameworks, many organizations struggle to put those skills into practice. Skills are tracked but not always translated into action, and learning exists but is not consistently tied to performance.

The result is inefficiency: Organizations spend critical budget resources on training courses, but their engineers still struggle to deploy applications to production.

AI is redefining learning 

Engineers are increasingly using AI on their own to learn and solve problems on the job: Rather than memorizing commands, they use AI-driven platforms to dynamically adjust environments when they encounter challenges during configuration tasks. Training should mirror this real-world workflow, shifting from static content delivery to an intelligent, data-driven learning system that increases relevance, accelerates skill development, and provides measurable outcomes.

Learning professionals can no longer prove their value by pointing at the number of learners who have completed a course. Forward-thinking organizations aim to measure how training can improve a number of specific metrics:

• Increased daily developer productivity
• Real-world skills advancement across modern tooling 
• The speed of overall technology adoptions
• Tangible performance outcomes

Such improvements translate directly to improved outcomes like reduced deployment failure rates or faster cluster provisioning. However, only a minority of organizations effectively connect learning initiatives to such business goals. Organizations build real momentum when they can draw a straight line from training to day-to-day team performance, and to do that they need real-time data on what their teams can actually build. Red Hat® Learning Subscription supports this shift through:

• Certification programs that validate real-world skills.
• Assessments that measure knowledge and its practical application.
• Progress tracking that provides visibility into learner development.

This visibility allows organizations to answer critical questions:

• Are skills levels increasing?
• Is performance changing?
• Is training delivering real business value?

By connecting learning to measurable outcomes, organizations can spend their training budgets where they will have the most impact and align training with strategic priorities.

Training should not just teach IT staff how to pass a test; the goal should be to turn developers working in isolation into a team of engineers that can confidently manage hybrid cloud at scale. AI investments that are not matched by evolving team skills will end up as expensive experiments.

According to Gartner®, “analysis of CIO perspectives on AI talent identifies four workforce blind spots that quietly erode AI value:” 

• “Versatilists remain structurally underrepresented. IT workforces skew toward specialists, with only about one-quarter of IT full-time employees (FTEs) qualifying as versatilists who combine technical depth with cross-domain translation skills. This imbalance limits the organization’s ability to integrate AI across business contexts.”
• “Human-centric capabilities remain undervalued. Skills such as trust-building, persuasion and ethical judgment rank near the bottom of talent priorities. As AI shifts human work toward decision making and sense making, this disconnect increases the risk of overreliance on AI outputs.”
• “The ‘messy middle’ lags in AI readiness, creating a persistent value bottleneck. While CIOs report that just over half of the enterprise workforce is ready to derive value from AI, frontline managers, middle managers and non-IT workers trail significantly in preparedness.”
• “Accountability for AI workforce outcomes remains fragmented. Compared with shared ownership models, CIO-led workforce enablement delivers dramatically stronger results across talent outcomes, including skill acquisition, productivity and revenue per employee.”³

Red Hat Learning Subscription helps organizations overcome these talent bottlenecks by:

• Developing multiskilled teams with role-based learning paths and hands-on labs across Red Hat technologies. This helps learners build expertise across infrastructure, automation, containers, and AI.
• Strengthening technical judgment through real-world, interactive labs that develop the problem-solving and architectural skills needed to deploy and manage AI securely.
• Bridging the "messy middle" by equipping managers and technical teams with the practical skills to translate AI strategy into operational execution.

By combining expert content, hands-on learning, and measurable skill development, Red Hat Learning Subscription helps organizations build engineers who can confidently deploy, secure, and scale AI across hybrid cloud environments.

Modern learning must integrate into daily workflows. Employees need immediate access to knowledge that supports real tasks, not disconnected training events. Blended learning and on-the-job learning are now dominant delivery models, reflecting the shift toward a continuous, embedded experience. Red Hat Learning Subscription supports learning in the flow of work through:

• Hands-on labs that simulate real-world environments.
• Scenario-based learning aligned to job functions.
• On-demand access for just-in-time skill development.

AI-driven learning represents a structural change in how teams build their technical capability. Red Hat Learning Subscription has embraced this model, in which engineers learn in real time instead of relying on static, multiday courses that struggle to keep pace with rapid software release cycles. Teams receive immediate guidance as they configure hybrid cloud infrastructure, access bite-sized modules that solve specific technical bottlenecks, and follow dynamically updated paths that evolve alongside Red Hat’s platform updates.

This model removes roadblocks across the learning lifecycle so that learners can:

• Pinpoint team skill gaps instantly using built-in diagnostic assessments.
• Practice complex tasks in live, sandboxed cloud environments instead of static slides.
• Track progress directly toward official Red Hat certifications to prove real-world readiness.

The learning assistant, an AI capability embedded in Red Hat Learning Subscription, reinforces hands-on learning by providing contextual guidance and explanations as learners work through real-world scenarios.

AI is not only transforming how people learn, it is reshaping what they need to learn. AI literacy and automation readiness are now key skill priorities for organizations. They need more adaptable, multiskilled teams and now prioritize technical decision-making over mere tool proficiency. Red Hat Learning Subscription addresses this shift with:

• Structured learning paths for cloud environments, automation, and AI-related technologies.
• Continuous updates aligned to evolving industry requirements.
• Hands-on experiences that reinforce the practical application of multiple skills, helping organizations build more adaptable teams with cross-domain capabilities.
• Scenario-based learning and certification programs that focus on technical decision-making and architectural judgment, such as choosing optimal automation strategies, managing hybrid cloud governance, or configuring AI models with Red Hat OpenShift®.

This focus translates that learning into real capability. Learners develop skills across key domains, including:

• Containerization and Kubernetes.
• Automation and DevOps practices.
• Hybrid cloud and open source technologies.

Ready to build a learning strategy that actually keeps pace with your goals? See how our Red Hat Learning Subscription tiers work, or take a custom skills assessment for your team.