Why Hands-On Learning Matters More Than Ever in the Age of AI

I recently sat down with Danny Abdo, Chief Operations and Product Officer, and Corey Hynes, Executive Chairman and Founder of Skillable, a hands-on learning platform designed to help organizations build and validate real-world technical skills. We discussed how the platform came to be and why practice-based learning is essential in the AI era.

As AI reshapes how IT professionals work, one thing is becoming clear: reading documentation or watching videos is no longer enough. To keep pace with rapidly changing tools, professionals need safe, realistic environments where they can experiment, fail, and learn by doing.

Hands-on learning has always mattered in IT, but AI raises the stakes: its outputs can be probabilistic, tools and interfaces change quickly, and the “right” answer often depends on context. That means competence is less about memorizing steps and more about practicing complete workflows. I.e., prompting, validating results, troubleshooting failures, and knowing when not to trust the machine.

“If there’s one truth that’s become brutally obvious, it’s that the old methods of ‘read a thing’ or ‘watch a video’ just aren’t cutting it anymore,” said Corey. “You have to practice with these tools.”

From cloud to AI: A fundamental shift in skills development

The rise of AI mirrors earlier technology shifts, such as the move to cloud computing but at a much faster pace. Tools evolve so quickly that traditional learning and development cycles struggle to keep up.

Corey explained that organizations are facing a familiar challenge. When cloud computing emerged, many rushed to train everyone on “cloud skills” without clarity on what those skills really meant. AI introduces even more uncertainty, alongside entirely new ways of thinking about work.

“We’re in a world now where you have to put your hands on the tool and experiment,” Corey said. “In doing so, you evolve not just how you work, but how you think about interacting with software.”

Danny added that AI is also blurring the line between technical and non-technical roles. Skills such as critical thinking, creativity, and judgement are becoming just as important as knowing how to use specific tools.

“The people getting the best outcomes aren’t just technical,” Abdo explained. “They’re combining IT skills with durable skills like quality of judgement and problem-solving.”

How Skillable started and the problem it set out to solve

Skillable’s origins stretch back more than two decades. Corey traced the company’s beginnings to his work writing certification exam questions for Microsoft, where proving the correctness of answers required setting up real software environments and testing them directly.

That experience sparked a simple but powerful idea: learners should always have access to real systems, not just theoretical explanations.

“Instead of talking about software, why not let people actually use it?” Corey said.

Over time, this concept evolved from classroom-based virtual machines into a large-scale, cloud-based platform. Today, Skillable delivers secure, time-limited lab environments where learners can practice real tasks and demonstrate genuine proficiency without risking production systems.

Learning through challenges, not checklists

At the core of Skillable’s platform is challenge-based learning. Rather than following rigid step-by-step instructions, learners are given outcomes to achieve, such as configuring a cloud resource or responding to a security incident.

The platform evaluates success using multiple methods, including automated scripts, activity recordings, and AI-powered analysis.

“We’re not just asking whether someone did the task,” Corey explained. “We’re also looking at how they did it. Did they struggle? Did they complete it quickly? That tells us a lot about mastery.”

This outcome-focused approach is especially important in cloud and AI environments, where user interfaces and workflows change frequently.

AI changes what we assess, not why we assess

As AI becomes a normal part of daily work, Skillable’s approach to assessment is evolving. Instead of testing whether someone can write scripts from memory, organizations increasingly want to know whether learners can use AI effectively and recognise when it produces incorrect results.

“It shifts from procedures to outcomes,” Danny said. “We’re assessing judgement and decision-making, not just whether someone followed a set of steps.”

This reflects a broader change in roles across the industry. Hynes described the emergence of “coordinators”, professionals who both produce and manage work by directing AI tools and agents. These roles demand higher-level thinking and a deeper understanding of systems as a whole.

In a world where AI can speed up execution but not replace judgement, the most valuable skill is still knowing what to do and proving you can do it in realistic conditions. Practice-first learning and outcome-based assessment help teams build that capability faster and adapt to a fast pace of change.

You can check out the full interview with Danny and Corey below.