The Skills That Actually Matter in the AI Age (& What’s Obsolete)

Solutions Review’s Executive Editor Tim King offers commentary on the skills that actually matter in the AI age and what’s obsolete, based on the recent Insight Jam panel of experts.

The opening panel of Insight Jam Q1 Mini Jam didn’t just ask what skills matter in the age of AI—it exposed that the very definition of skill is being rewritten in real time. Beneath the surface of conversations about prompting, tooling, and technical fluency was a deeper and more disruptive realization: we are not simply upgrading skills—we are redefining capability itself.

At the center of this reset is a dangerous illusion—one that organizations, educators, and individuals are all currently grappling with. As Dr. Courtney Ultig framed it early in the discussion, we are mistaking tool literacy for real capability. The ability to generate outputs with AI—documents, analyses, even strategy—can create the appearance of expertise. But producing work is not the same as understanding it. Knowing where to click, how to refine an output, or how to prompt effectively is not equivalent to knowing why something works, when it fails, or how to adapt it in a new context.

This distinction is not academic—it is foundational. In an AI-driven environment, where outputs are abundant and instant, understanding becomes the scarce resource.

Michelle Ament pushed this even further, arguing that AI is not just augmenting work—it is exposing what has been missing all along. As organizations leaned heavily into knowledge accumulation over the past two decades, they underinvested in the development of human intelligence: judgment, discernment, curiosity, creativity, and self-awareness. AI is now forcing that imbalance into the open. When machines can generate answers instantly, the human role shifts from answering questions to evaluating answers.

This is where the great skills reset truly begins.

The conversation around prompting offers a perfect microcosm of this shift. Initially framed as a critical technical skill—“prompt engineering”—the panel quickly reframed it as something more fundamental. Prompting, at its core, is not about syntax or structure. It is about problem framing. As Dr. Courtney noted, the real skill lies in how a person defines the problem before seeking an answer. The prompt is simply an expression of that thinking.

Over time, even the concept of prompting itself is dissolving. As Michael Atkinson observed, interaction with AI is becoming less about issuing commands and more about engaging in dialogue. The interface is evolving from tool to collaborator. In that world, the durable skill is not prompting—it is communication, clarity of thought, and the ability to guide a conversation toward a meaningful outcome.

This evolution exposes another critical layer of the skills reset: context matters more than ever. Keith McCormack highlighted a subtle but powerful distinction—using AI in a domain where you have expertise versus one where you do not. In the former, you can quickly identify errors, challenge assumptions, and refine outputs. In the latter, you are vulnerable to accepting plausible but incorrect information. This creates a new kind of risk: AI doesn’t just amplify capability—it amplifies blind spots.

As a result, expertise is not disappearing—it is becoming more important, but in a different way. It is no longer about generating answers from scratch. It is about interrogating answers, validating sources, and understanding underlying assumptions. The skill is not creation—it is discernment.

This brings us to one of the most profound questions raised during the panel: if AI can generate answers instantly, what does it mean to truly understand something?

The answer, across the discussion, converged on a single idea: understanding is demonstrated through application. It is not enough to recognize or reproduce information. True understanding shows up when an individual can apply knowledge in a novel context, critique its limitations, defend their reasoning, and build something new from it. In other words, understanding becomes visible only when it is used under pressure.

This has massive implications for education and workforce development. Traditional models—rooted in memorization and static assessment—are no longer sufficient. As Jason Gouia pointed out, even foundational frameworks like Bloom’s taxonomy are being disrupted. We are entering a world where individuals can “create” outputs without fully understanding the underlying concepts. The sequence of learning is being scrambled. Creation no longer guarantees comprehension.

This forces a re-evaluation of how we measure capability. Michelle raised a critical gap: we currently lack effective systems to assess human skills like judgment, discernment, and critical thinking. These are the very capabilities that will define success in the AI age, yet they are the hardest to quantify. Without new models for measurement and development, organizations risk over-indexing on what is easy to train and track—technical skills—at the expense of what actually matters.

And that over-indexing is already happening.

The panel acknowledged that much of the current focus on technical training is driven by visibility and momentum. AI labs and early adopters are heavily concentrated in technical domains, particularly software development. This creates a distorted signal—one that suggests technical proficiency is the primary path to relevance. But as Michael noted, this is a temporary imbalance. As AI capabilities expand, the bottleneck will shift from technical execution to human judgment and decision-making.

In that future, the competitive advantage will not come from knowing how to use AI tools—it will come from knowing when, why, and whether to use them at all.

Looking ahead 5 to 10 years, the panel’s answers began to converge around a new definition of human advantage. Not knowledge. Not even technical skill. But a combination of deeply human capabilities:

• Learning agility — the ability to continuously adapt in a rapidly changing environment
• Resilience — the capacity to navigate uncertainty and disruption
• Self-awareness — understanding one’s strengths, limitations, and role in a system
• Judgment and discernment — evaluating information and making sound decisions
• Communication and influence — articulating ideas and aligning others
• Curiosity and creativity — exploring new possibilities and generating novel solutions

Perhaps most importantly, several panelists challenged the premise of the question itself. As Michelle argued, we are not simply evolving within a “knowledge economy.” We are transitioning into an intelligence age. In this new paradigm, the value of an individual is not defined by what they know, but by how effectively they can use their uniquely human capabilities to create value alongside intelligent systems.

This is the great skills reset.

It is not about learning more tools. It is not about becoming faster or more efficient. It is about becoming more human in the ways that matter most.

And that is both the challenge—and the opportunity—of the AI age.