By George Hall
Is there a judgment divide between those trained to ‘follow’ the map and those developed to ‘redraw’ it?
A recent lawsuit against the Texas A&M University System has reignited debate about academic freedom. Four professors and a national faculty organization argue that university policies restricting the “advocacy” of certain ideas about race, gender, and sexuality are so vague that faculty cannot know what they are permitted to teach. Among the examples cited is a philosophy course that included Plato’s Symposium, raising difficult questions about where “teaching” ends and “advocacy” begins (Priest, 2026).
Although those questions belong in higher education, they point toward a different question that Learning & Development professionals have barely begun to ask:
As organizations embrace skills-based learning, who decides how much understanding people actually need?
That question may prove more important than the lawsuit itself.
The Promise of Skills-Based Learning
The movement toward skills-based learning has been one of the most important developments in our profession. It has shifted attention from credentials to demonstrated capability. It is opening opportunities for people whose abilities exceed their résumés. It has focused learning on performance rather than seat time and helped organizations understand the capabilities they possess.
Those are substantial benefits.
But every design philosophy solves one problem while creating another.
The hidden question in a skills-based world is not simply: What skills should people have?
It is: What kind of human capability are we trying to develop?
That is a serious and much larger question, which raises other questions:
- Are we developing people primarily to execute established processes accurately and efficiently?
- Or are we also developing them to interpret unfamiliar situations, exercise judgment, question assumptions, recognize consequences, and act when no procedure fully applies?
- Do we want workers who can perform only within a system as it currently exists?
- Or people who understand the system well enough to recognize when it is failing, adapt when conditions change, and help redesign it?
Human capability, at its best, surely includes more than proficiency. It includes the ability to make sense of complexity, connect action to context, distinguish compliance from responsibility, and decide what should be done when the correct answer has not already been defined.
Without these crucial capabilities, people may become highly competent at carrying out decisions they do not understand, applying rules to situations they cannot interpret, and sustaining systems they are unequipped to question. The organization may become more efficient while becoming less intelligent, less adaptable, and less capable of recognizing its own mistakes.
Every learning architecture contains an answer to this question. It expresses a view of the person being developed: as an operator, a problem solver, a responsible decision-maker, a systems thinker, or some combination of these. This is where traditional education and skills-based learning begin from different places:
- Traditional education asks: What should an educated person understand?
- Skills-based learning asks: What must this person be able to do?
Both questions matter: Understanding without the capacity to act is insufficient. But action without sufficient understanding can become dangerous.
The problem begins when “able to do” is quietly reduced to “able to execute what someone else has already decided.” At that point, skills-based learning becomes something much narrower: Task-based learning.
When Performance Becomes the Goal
Once performance becomes the primary objective, organizations naturally begin asking:
- What is the minimum someone needs to know to perform successfully?
- What does it cost to deliver this capability?
These are perfectly reasonable business questions. It is also the beginning of a profound shift:
- Learning increasingly becomes focused on procedures, workflows, competencies, certifications, compliance, and measurable behaviors
- Knowledge that does not contribute directly to immediate performance begins to look unnecessary
Like many others, I first encountered this logic through HR-sponsored educational-reimbursement programs. Courses were routinely denied funding if they were not directly related to an employee’s current role. Learning that might prepare someone for a future position, expand their judgment, or help them understand the organization more broadly was viewed as an expense—and was not seen as necessary. The policy appeared financially prudent, but it carried an implicit definition of learning: Education was valuable only to the extent that it improved the work a person was already assigned to perform. Now, someone might reasonably say: “My employees don’t need Plato. They need Excel, customer service, AI literacy, project management, and cybersecurity.”
Fair enough. They are probably right. Most employees do not need Plato to perform today’s work. But Plato is not the issue. The issue is what disappears once organizations begin defining learning entirely by its immediate usefulness.
The Hidden Cost
The ‘hidden cost’ of skills-based learning is not that people become less skilled. In many ways, they become more skilled. The hidden cost is that organizations may gradually stop developing the capabilities that allow people to recognize when the system itself needs to change:
- Interpretation
- Judgment
- Context
- Ethical reasoning
- Systems thinking
These are not ornamental capabilities. Far from it: the World Economic Forum continues to identify analytical thinking as the core skill most sought by employers, while systems thinking is rising in importance as technological change makes decisions more complex (World Economic Forum, 2025). These capabilities may appear less urgent when the environment is stable and predictable. But they become indispensable in common situations:
- A customer presents a situation the script does not anticipate
- An AI recommendation is technically plausible but contextually wrong
- A regulation changes in a way that is subtle or easy to overlook
- A long-standing process begins producing unintended consequences
Organizations soon discover that they have developed people to operate the system—but lack people who are able to recognize problems or identify that the system itself has become unreliable. Moreover, employees may become exceptionally gifted at reproducing the very processes, assumptions, and decisions that created these problems. Organizations always face predictable consequences when they build execution capacity without sufficient interpretive capacity to question what is being executed. Is hindsight still 20:20?
The Judgment Divide
When organizations reduce learning to performance, judgment does not disappear. It simply moves:
- Senior leaders attend strategy sessions
- Consultants analyze emerging trends
- System designers determine how work should be done
- AI systems, increasingly, recommend what should happen next
And then everyone learns how to respond to these outputs. Over time, of course, organizations begin creating two very different kinds of workers:
- Some workers are developed to interpret reality
- Others are developed to comply with the interpretations that are produced for them
That distinction has very little to do with intelligence. It has everything to do with learning design: The deeper divide is not simply between those who ‘think’ and those who ‘perform.’ It is between those developed and authorized to ‘interpret reality’ and those rewarded for ‘remaining inside interpretations produced elsewhere.’
Organizations, in fact, decide not only who needs judgment, but whose judgment will be heard, supported, and permitted to change the system.
More than a decade ago, I interviewed Seth Godin and Barry Oshry for OD Practitioner magazine. In that interview, I examined the lingering influence of the industrial model of work.
Godin argued that industrial systems were designed to reward people who followed instructions, met specifications, and performed reliably. Yet value in a post-industrial economy increasingly comes from people who can see what others do not—and create a new map when the existing one no longer fits reality.
Oshry added an equally important qualification: Exercising judgment also requires systems awareness—the ability to understand power, relationships, and the organizational context through which change must move (Hall, 2013).
Now, this creates a contradiction that L&D can no longer easily avoid: Why are we still designing learning systems that produce the very compliance the post-industrial economy no longer needs?
AI Raises the Stakes
Artificial intelligence makes this contradiction even harder to ignore: The map now appears intelligent.
Although an employee may know exactly how to operate an AI system:
- Can they recognize when it is wrong?
- Can they challenge its assumptions?
- Can they explain why an answer is technically plausible but practically flawed?
- Can they distinguish a confident recommendation from a sound judgment?
Or have they simply learned to press the red button? There is an enormous difference between operating a system and judging it—one produces efficiency while the other produces resilience. When an AI recommendation arrives with enough speed, confidence, and technical authority, compliance can easily masquerade as expertise. Do we need people who can think critically about context, limitations, and unexpected consequences – or simply follow an AI-generated script?
The Question L&D Should Be Asking
For years, our profession has asked: What skills should people learn? That still remains an important question. But I believe the next decade will demand a very different one: Who are we developing—and authorizing—to exercise judgment?
Ultimately, every learning architecture answers two questions, whether intentionally or not:
- Who is being developed to interpret reality?
- What happens when they challenge the interpretation they are given?
The future divide may not be between people with degrees and people with skills. It may be between workers developed to interpret reality and workers developed to comply with interpretations produced for them. The danger is not that workers will stop reading Plato. The danger is that organizations will quietly redefine capability itself. Not as the ability to understand, interpret, question, and respond to reality. But as the ability to perform an approved task.
That would make organizations extraordinarily efficient. Whether it would make them wiser is a very different question.
References
Hall, G. (2013). Linchpin leadership. OD Practitioner, 45(4), 32–37.
Priest, J. (2026, August 4). Professors sue Texas A&M System over limits on teaching race, gender. The Texas Tribune.
Tabassi, E. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology.
Image credit: kimberrywood
