Pluralsight’s new Cloud Ready program tackles skills gap behind enterprise AI failures

Enterprises have been spending heavily on cloud infrastructure for years. Teams sign contracts with hyperscalers, migrate workloads, and issue announcements. What rarely follows is the part where employees actually know how to run everything that just went live. Pluralsight is launching a new program called Cloud Ready that targets precisely that gap, arriving at a moment when the consequences of ignoring it are becoming harder for organizations to dismiss.

The numbers behind the launch paint a picture that many technology leaders will recognize uncomfortably well. Pluralsight’s research shows that three quarters of cloud leaders expect spending to increase over the next two years, yet only around half believe those investments are generating the outcomes they intended.

Gartner adds further weight to that concern, forecasting that AI workloads could consume half of all cloud resources by 2029. Against that backdrop, Pluralsight finds that just 14 percent of organizations currently carry the operational maturity to support AI effectively. That gap between ambition and readiness is exactly where Cloud Ready aims to operate.

The program pulls together assessments, instructor-led courses, certification preparation, and cloud sandbox environments into a structured workforce readiness framework covering architecture, operations, security, engineering, and AI modernization roles.

Notably, the emphasis on hands-on lab environments reflects a genuine shift in what enterprises want from training providers right now. Passing a certification exam carries less weight than demonstrating the ability to execute tasks inside working cloud and AI environments before anyone touches production systems. In regulated industries especially, the distance between understanding concepts and applying them under real operational conditions has become something risk teams actively worry about.

What makes this launch feel different from a typical training product announcement is the problem Pluralsight acknowledges openly. Organizations treated cloud migration as a procurement exercise for years. They moved infrastructure without building teams capable of governing it, optimizing costs across it, or securing the complex AI workloads now running on top. Consequently, many enterprises carry significant operational debt sitting just underneath their AI ambitions.

AI compounds that debt further because inference workloads, agentic systems, and large-scale data pipelines all demand cloud skills that most teams have not yet fully developed.

At that point, workforce readiness stops functioning as a training budget conversation and starts looking a great deal more like an infrastructure risk that boards are beginning to ask pointed questions about.