As artificial intelligence assumes more routine workplace responsibilities, Julio Avael III provides a useful point of reference for examining a less visible consequence of automation: what happens to human expertise when employees no longer regularly perform the tasks AI has absorbed? Organizations can gain speed, consistency, and efficiency from artificial labor, but those gains can introduce another challenge if important human capabilities gradually weaken through lack of use.
The issue is not whether organizations should automate repetitive work. The more important question is how to capture the advantages of automation without allowing the knowledge needed to supervise, challenge, and replace automated processes when necessary to disappear.
Automation Changes More Than Headcount
Workplace automation is often evaluated according to measurable outcomes.
Organizations may examine whether a system:
- Reduces processing time
- Handles greater volumes of work
- Decreases repetitive tasks
- Improves consistency
- Reduces operational bottlenecks
- Allows employees to focus elsewhere
These improvements can make automation attractive.
However, transferring a task from an employee to an AI system also changes how frequently people practice the knowledge associated with that task.
If employees stop performing a function for months or years, proficiency may naturally decline. That creates a potential tradeoff between immediate efficiency and long-term organizational capability.
Routine Work Can Be a Training Ground
Repetitive tasks are frequently among the first candidates for automation because they appear to require less human judgment.
Yet routine work can serve another purpose.
Performing ordinary tasks repeatedly allows less experienced employees to learn how a process functions. Over time, patterns become familiar, unusual situations become easier to recognize, and practical judgment develops.
An employee may begin by handling straightforward cases before eventually becoming capable of managing complicated exceptions.
When AI takes over the straightforward cases, organizations need to consider where that learning will occur.
Removing repetitive work may improve productivity while unintentionally removing part of the pathway through which expertise traditionally develops.
Skills Can Weaken When They Are Not Used
Professional capabilities require practice.
Someone may understand a process conceptually while becoming less efficient at actually performing it after a long period without direct involvement.
This matters when automation handles a function almost continuously but occasionally requires human intervention.
The employee receiving an unusual case may theoretically understand the process while lacking recent hands-on experience.
That creates a difficult situation. The cases handed back to humans may be precisely the cases requiring the greatest judgment, even though employees have had fewer opportunities to practice the underlying work.
AI Can Change the Nature of Human Expertise
Automation does not necessarily eliminate the need for skilled employees. It can change which skills matter most.
When AI performs routine execution, employees may spend more time on:
- Reviewing exceptions
- Interpreting unusual circumstances
- Validating automated outputs
- Resolving conflicting information
- Escalating complex cases
- Exercising judgment
- Communicating decisions
These responsibilities can be more demanding than the tasks that were automated.
The future value of human expertise may therefore lie increasingly in understanding when a normal process does not apply.
That requires employees to understand both the automated system and the underlying work.
Exception Handling Becomes More Important
Automated systems generally perform best when circumstances resemble the situations they were designed to handle.
The challenge comes from exceptions.
Incomplete information, unusual requests, conflicting data, unexpected circumstances, or system failures may require intervention.
As automation expands, employees can become responsible for a smaller number of cases that are disproportionately complicated.
Organizations therefore need people capable of answering questions such as:
- Why did the automated process stop?
- Is the output reasonable?
- What information is missing?
- Does this case require a different approach?
- Should the automated recommendation be overridden?
- Who should receive the issue next?
These abilities require more than knowing which button to select.
They require genuine understanding of the process.
Automation Bias Can Make Skill Loss Harder to Notice
A reliable AI system can gradually change how employees approach its output.
When recommendations are correct most of the time, reviewing them carefully can begin to feel unnecessary.
This can lead to automation bias, where people become increasingly inclined to accept automated results without sufficient independent evaluation.
The problem may remain invisible while the system performs normally.
It becomes more apparent when an unusual situation requires someone to recognize that the automated answer is inappropriate.
Maintaining human expertise therefore involves preserving the ability to question technology, not simply operate it.
Entry-Level Work May Need to Be Reconsidered
One of the broader workplace implications of AI involves entry-level development.
Many careers have traditionally followed a progression from simpler responsibilities toward more complex ones.
Routine work gives employees opportunities to learn terminology, understand procedures, observe patterns, make low-consequence decisions, and receive feedback.
If artificial labor performs much of that work, organizations may need alternative ways to develop future specialists and managers.
Possible approaches can include:
- Structured training
- Simulated scenarios
- Rotational assignments
- Supervised exception handling
- Case reviews
- Mentoring
- Periodic hands-on practice
Automation can remove tasks without automatically replacing the developmental experience those tasks once provided.
Human Oversight Requires Real Knowledge
Organizations sometimes address AI risk by requiring human review.
But human oversight is meaningful only when the reviewer understands the subject well enough to identify a questionable result.
If employees become entirely dependent on automated recommendations, oversight can become superficial.
A reviewer might approve an output because the system generated it rather than because the result has been independently evaluated.
Meaningful oversight requires employees to retain enough expertise to disagree.
That means organizations need to consider not only whether humans remain involved but also whether those humans remain capable of exercising informed judgment.
Institutional Knowledge Can Also Decline
Skill erosion is not limited to individual employees.
Organizations themselves can lose knowledge.
Before automation, a process may be understood by several employees who perform it regularly. As technology takes responsibility for that process, fewer people may remain familiar with the details.
Over time, documentation may become outdated and experienced employees may leave.
Eventually, the organization may know how to operate the automated system without fully understanding the process underneath it.
That distinction becomes important during:
- System outages
- Vendor changes
- Major updates
- Unexpected errors
- Unusual cases
- Process redesigns
Organizations should know which capabilities are becoming dependent on technology and whether enough internal knowledge remains to manage that dependence.
System Failure Is a Test of Human Capability
The value of retained expertise becomes especially clear when technology becomes unavailable.
An automated system may fail because of a technical problem, integration issue, data problem, or external disruption.
If a critical workflow depends almost entirely on automation, the organization needs to know what happens next.
- Can essential functions continue?
- Can employees identify which work should receive priority?
- Does anyone still understand the underlying process?
- Are manual procedures documented?
These questions are part of operational resilience.
A backup process has limited value if nobody remains capable of performing it.
Organizations Need to Decide Which Skills Must Be Preserved
Not every manual capability needs to remain unchanged after automation.
Preserving every legacy process would undermine many of the efficiencies organizations hope to achieve.
Instead, leaders can identify capabilities that remain strategically important.
These might include skills necessary for:
- Handling exceptions
- Validating important decisions
- Maintaining regulatory or organizational requirements
- Responding to outages
- Training future employees
- Evaluating AI performance
- Redesigning workflows
This creates a more deliberate approach to human capability.
The question becomes not whether every old task should survive, but which knowledge the organization cannot afford to lose.
Periodic Practice Can Help Maintain Capability
Some skills may require intentional practice once AI handles the majority of routine work.
Organizations already use this principle in other contexts. Emergency procedures, for example, may be practiced even when emergencies are uncommon.
A similar approach can be useful for critical automated workflows.
Periodic exercises might involve asking employees to:
- Work through sample cases
- Review unusual historical situations
- Explain automated decisions
- Practice manual procedures
- Identify errors in simulated outputs
- Respond to hypothetical system failures
The objective is not to create unnecessary work.
It is to ensure that essential knowledge remains available when automation cannot handle a situation independently.
AI Literacy Is Not the Same as Domain Expertise
As AI becomes common in workplaces, organizations increasingly emphasize AI literacy.
Understanding how to use artificial intelligence is valuable, but it should not be confused with understanding the work itself.
An employee may become highly effective at interacting with an AI system while having limited knowledge of how to evaluate the resulting output.
Strong human oversight requires both.
Employees need enough technological understanding to recognize what an AI system can and cannot reasonably do. They also need sufficient subject knowledge to determine whether its conclusions make sense.
AI literacy and domain expertise should therefore develop together rather than one replacing the other.
Managers Need to Monitor Capability, Not Just Productivity
Automation can make productivity improvements relatively easy to measure.
If processing time declines or output increases, the benefits are visible.
Human skill erosion is harder to measure because it develops gradually.
Managers may therefore need to consider additional questions:
- Can employees explain how the underlying process works?
- Are people comfortable challenging automated outputs?
- Can unusual cases be handled effectively?
- Are newer employees developing sufficient expertise?
- Does critical knowledge depend on only a few individuals?
- Could essential work continue during a system disruption?
These questions provide another dimension for evaluating whether automation is actually strengthening the organization over the long term.
The Goal Is Not to Preserve Work for Its Own Sake
Concern about skill erosion should not become an argument against automation.
Organizations have good reasons to automate repetitive and inefficient processes.
The objective is to distinguish between work that can disappear safely and knowledge that remains necessary.
If AI can perform a routine function more efficiently, continuing to require employees to perform it solely because it was traditionally done manually may provide little value.
However, if eliminating that task also eliminates the only practical way employees learn an important capability, another development mechanism may be necessary.
This distinction allows organizations to pursue efficiency without ignoring long-term expertise.
Final Thoughts
The expansion of artificial labor creates an important workplace paradox. As AI becomes more capable of performing routine work, human employees may be asked to handle fewer ordinary cases and a greater proportion of difficult exceptions. At the same time, fewer opportunities to perform the underlying work can make expertise harder to develop and maintain.
Organizations therefore need to think beyond which tasks can be automated.
They also need to determine which human capabilities must survive after automation occurs.
Training, periodic practice, meaningful human review, documentation, exception handling, and deliberate knowledge transfer can all help preserve those capabilities.
The long-term measure of successful AI adoption should not be efficiency alone. A resilient organization should be able to benefit from artificial labor while retaining people who understand the work deeply enough to supervise technology, challenge questionable outputs, respond when systems fail, and make sound decisions when circumstances fall outside the automated routine.