For years, organizations have treated strong work as evidence that the person producing it has developed the necessary capability. If an employee could analyze a problem, write a persuasive proposal or respond effectively to a customer, it was reasonable to assume that they understood much of the thinking behind the result. As artificial intelligence (AI) becomes increasingly involved in producing that work, the connection between a strong output and the employee’s underlying capability is becoming much less reliable.

AI is making it possible for people to complete increasingly complex work faster and with less effort, creating a measurable productivity gain while also making capability harder to assess. When employees can produce strong work without doing as much of the thinking that once helped them learn how to produce it, organizations may see performance improve even as the knowledge, judgment and experience underneath it develop more slowly.

Call it capability debt: The gap that can develop when AI-assisted performance advances faster than the human capability supporting it.

Why Capability Debt is Difficult to See

Like other forms of debt, capability debt may be useful in the short term and difficult to detect while things are going well. If the analysis is completed, the customer receives a strong response, the proposal is well structured or the employee finishes the task in half the time, the organization sees little reason for concern. What remains largely invisible is how much of the underlying work the employee understands and could perform if the situation changed or the AI’s response proved unreliable.

This gap is becoming more important as AI moves beyond administrative work and takes on activities that previously required employees to synthesize information, identify patterns, structure arguments, recommend actions and propose decisions. These capabilities allow people to perform work that might once have required considerably more knowledge or experience, which creates real value while also removing some of the thinking through which that knowledge and experience would have developed.

Research offers some reason for caution. A 2025 study by researchers at Carnegie Mellon University and Microsoft Research, involving 319 knowledge workers, found that greater confidence in generative AI was associated with less reported critical-thinking effort during AI-assisted work. The researchers also found that generative AI shifted critical-thinking effort away from information gathering, problem-solving and task execution toward information verification, response integration and task stewardship.

Organizations don’t need to require employees to perform work manually simply to preserve effort, particularly when AI can complete part of that work faster and more effectively. However, a strong AI-assisted output is no longer enough to show that the employee has developed the capability behind it.

The Gap Appears When the Work Changes

For routine work, there may be few immediate consequences because AI continues to produce an acceptable answer and the employee knows enough to use it. Capability debt becomes visible when the work stops being routine: a customer situation falls outside the usual pattern, the available information is incomplete or contradictory, an AI recommendation sounds plausible but rests on the wrong assumption, or a compliance issue requires someone to recognize that the standard answer doesn’t apply.

These situations depend on knowledge and judgment that may not be apparent during ordinary performance. Employees need to recognize when the AI has misunderstood the situation, determine what information is missing, explain why a recommendation should or should not be followed and continue working when the technology cannot supply a reliable answer.

Organizations therefore need to identify which capabilities employees must retain when AI performs part of the work. For example, employees would still need enough understanding to guide the technology, judge whether its output makes sense, spot problems and make the decisions the organization still expects them to own.

Strong Performance May No Longer Show Individual Capability

This changes an important assumption in learning and performance measurement. Traditionally, strong performance gave us reasonable evidence of what a person knew and could do. Increasingly, what we see may reflect the combined capability of the employee and the technology.

Learning leaders need to be clearer about what employees still need to be able to do without relying on AI. A financial adviser may use AI to summarize large volumes of information and identify possible recommendations, while still needing to recognize when a recommendation doesn’t fit the client’s circumstances. A manager may use AI to prepare for a difficult conversation, but still has to interpret what is happening as the conversation unfolds and decide when to change direction. A technical employee may rely on AI to generate a solution while retaining enough underlying understanding to recognize an unusual failure or question an assumption embedded in the response.

The answer will differ by role, which makes a broad requirement to “maintain human skills” too vague to be useful. Learning and development (L&D) and business leaders need to identify what employees must still know, notice, understand or judge in the moments when AI is doing part of the work, because those are the capabilities the organization continues to depend on even when they are no longer fully visible in everyday performance.

When Work No Longer Provides the Same Development

Some of these capabilities once developed almost invisibly through work. Employees encountered similar situations repeatedly, watched how experienced colleagues approached them, received corrections, experienced the consequences of their decisions and gradually became better at recognizing what mattered. A new employee drafting an analysis, for example, was not merely producing a document; they were learning which information deserved attention, how different pieces of evidence fit together and where an apparently reasonable conclusion could go wrong. This concern is especially relevant for early-career employees, as AI is already changing some of the developmental experiences people once gained through routine work.

When AI produces the first analysis, recommendation or draft, it may also remove some of those developmental experiences. The employee still completes the task and may even produce better work, but receives less practice doing some of the thinking we increasingly risk handing over to AI: forming an initial interpretation, making connections, noticing uncertainty or working through the consequences of a flawed assumption. If those abilities remain important to the role, their development can no longer be left entirely to the ordinary flow of work.

Replacing the Development That AI Removes

L&D may need to be more deliberate about how people continue to build those skills. That doesn’t necessarily mean creating another course. The right approach depends on what employees are no longer getting to practice in the work itself and what they still need to be able to do well.

Organizations could:

• Use short post-training refreshers that ask employees to reconsider a difficult decision several weeks later, strengthening knowledge that might otherwise fade when AI supplies the answer during daily work.
• Include unusual cases and exceptions in reinforcement learning instead of repeatedly rehearsing the standard situation, giving employees greater exposure to the conditions in which ordinary patterns stop being reliable.
• Give managers better questions for coaching employees through real decisions so that expert reasoning becomes more visible and employees learn why one response fits better than another.
• Configure AI coaching to challenge an employee’s reasoning before supplying a recommendation, allowing the technology to support development rather than immediately removing the need to think.
• Ask employees to form an initial judgment in selected situations before reviewing the AI’s analysis, making it possible to see what they notice, overlook or misunderstand without assistance.
• Create opportunities after significant work to examine what happened, what was missed and what employees should recognize the next time they encounter a similar situation.

The goal is not to preserve old ways of working simply because they once helped people learn. It’s to notice when AI has removed an experience that was also building an important capability and decide whether that development now needs to happen somewhere else. That may also mean protecting enough space for the thinking itself.

Protecting Capability Without Preserving Old Work

Capability debt may remain invisible while AI is producing good answers and productivity is improving. It becomes more apparent when the situation is unfamiliar, the answer is incomplete or someone has to make a judgment AI cannot make for them.

L&D can help organizations identify the knowledge and judgment people still need to develop, even when AI is carrying more of the work. That may require creating new opportunities to build capabilities that no longer develop as naturally through day-to-day experience.

The aim is not to preserve the old way of working. It’s to ensure that as AI carries more of the work, human capability doesn’t become the unpaid debt behind the productivity gain.