Automation can remove tasks. Organizations must preserve how people become capable.
Imagine a company that becomes very good at automating junior work.
Reports that once took hours appear within minutes. Research speeds up. Routine analysis largely disappears. Productivity rises, and the savings are real.
A decade later, several senior employees retire. Experienced managers move on. The company turns to the next generation and finds fewer people ready.
Something happened during those efficient years.
The company automated work that also happened to be training people.
AI Is Only Part of the Story
Lyon Kassab raises this problem in AI Is Not the Biggest Threat to the Future Workforce. Declining Critical Thinking Is.
He is not arguing against artificial intelligence. AI can remove tedious work and make people more productive. His concern begins when technology substitutes for human development.
A student can produce a good answer without understanding the question. A worker can generate an impressive analysis without knowing whether its reasoning holds together.
Those are not the same achievements.
Kassab connects this problem with changes already happening in the workplace. Many tasks that AI handles well belong to junior employees. Yet those jobs have traditionally done something besides getting routine work finished.
They gave beginners somewhere to begin.
Junior analysts became experienced analysts. Coordinators moved into management. Young professionals picked up judgment through ordinary work, mistakes, observation, and correction.
Take away those tasks, and we may unknowingly take away some of the learning.
The Report Was Never the Only Product
Consider a junior analyst preparing a monthly report.
At first, the assignment looks simple. Gather information, check the numbers, organize the findings, and send the report upward.
But watch what happens over several years.
The analyst learns where unreliable information tends to appear. Certain numbers begin to look suspicious before anyone points them out. Questions from senior managers become easier to anticipate.
What once required constant checking gradually becomes judgment.
So the company was producing two things.
It was producing reports.
It was also producing analysts.
AI might produce a better report in a fraction of the time. That is valuable. But the faster report does not automatically create the experienced analyst.
This is where the real problem begins.
Let the Old Task Disappear
None of this means companies should preserve inefficient work.
That would miss the point.
Some repetitive jobs teach almost nothing. Some traditional training survives only because nobody has bothered to redesign it.
Automate those tasks.
The useful question comes before automation.
What did people learn while doing this work?
Sometimes the answer will be, not much. Fine. Let the task go.
Other work develops pattern recognition, professional instinct, communication, or judgment. Beginners encounter exceptions. They see experienced people respond. They slowly learn what the textbook could not teach.
If that capability still matters, the organization needs another way to develop it.
The task can disappear.
The learning cannot.
Coaches Have Been Solving This Problem for Years
Sports offer a useful comparison.
A basketball coach does not become sentimental about an old drill. If a better exercise develops the same skill, the coach changes the training.
What matters is not the drill.
It is the player who emerges from it.
Perhaps an exercise develops footwork. Another improves timing. Another forces decisions under pressure. The coach chooses the experience according to the capability being developed.
Workplaces could approach automation in much the same way.
When AI removes an activity, identify what that activity used to teach. Then design another experience that develops the same capability, perhaps better.
That changes the role of training.
It is no longer something added after the real work.
Training becomes part of how the organization prepares itself for the future.
The Story Does Not End With the Beginner
Suppose a company does this exceptionally well.
Young employees receive difficult but manageable assignments. Experienced colleagues coach them. Mistakes become material for discussion instead of reasons for embarrassment.
Years pass.
The beginners become good at what they do.
Now the organization faces another transition, one that is easy to overlook.
Who trains the next group?
Capability locked inside one person eventually leaves with that person. Retirement, resignation, illness, or a better job offer can take years of accumulated experience out the door.
A durable organization needs something more than skilled people.
It needs skilled people who can develop others.
When Training Starts to Multiply
This is familiar in the traditional crafts.
An apprentice begins by watching. Simple tasks come next. The master corrects errors and gradually hands over harder work.
Eventually, the apprentice works independently.
But that is not how the craft survives.
It survives when yesterday's apprentice becomes tomorrow's master.
The carpenter teaches another carpenter. The nurse guides a younger nurse. The architect reviews the work of a junior architect and explains why something must change.
Knowledge has moved from one person to another.
Eventually, some of those learners learn how to teach.
Now capability begins to multiply.
The pathway might look roughly like this.
Learn → Practice → Master → Teach → Develop Teachers → Repeat
The important movement is near the end.
Training transfers capability.
Training trainers allows capability to spread.
Leadership Has the Same Problem
Organizations often celebrate exceptional leaders.
That makes sense. Strong leaders can make difficult decisions and guide people through uncertainty.
But there is a revealing question to ask about any great leader.
What happens after that leader leaves?
If performance collapses, the organization may have depended on the person more than it developed leadership.
A leader's work is incomplete if leadership ends with them.
The more durable achievement is creating people who can take responsibility, exercise judgment, and eventually develop leaders themselves.
The same principle reaches far beyond management.
Senior engineers develop younger engineers. Experienced nurses develop nurses. Teachers develop future teachers. Craftspeople develop craftspeople.
Expertise lasts when people know how to pass it forward.
An Organization Consumes Capability Every Day
We usually describe organizations by what they produce.
A hospital provides care. A school educates students. A construction company builds. A business sells goods or services.
Yet every organization is consuming something while doing that work.
Human capability.
People use judgment, experience, relationships, technical knowledge, and practical skill every day. Those resources do not replenish themselves automatically.
People retire. Employees leave. Technologies change. Yesterday's expertise becomes less useful.
An organization can therefore appear healthy while slowly spending down its human capability.
The decline may remain invisible for years.
Then the experienced people leave.
Only then does everyone notice what was lost.
AI Could Make Training Better
There is no reason AI must weaken this system.
Used differently, it could make training far richer.
A beginner could attempt a problem before asking AI for help. The machine could then produce another approach. Learner and coach could compare both.
Why did they differ?
What assumption changed the answer?
What did the learner notice that AI missed?
What did AI catch that the learner overlooked?
AI could also generate simulations and unusual cases. Beginners could practice situations that might otherwise take years to encounter.
That is very different from asking AI for the answer immediately.
In one case, AI replaces the struggle.
In the other, AI improves the practice.
Management Changes When Machines Produce More
Managers have traditionally spent much of their time managing output.
Is the project on schedule? Was the report completed? Did the team reach its target?
Those questions are not going away.
But AI creates room for another responsibility.
A manager should also notice whether people are getting better.
Who can now solve something they could not solve last year? Who is ready for harder responsibility? Who needs experience rather than another lecture?
And eventually, who is ready to coach somebody else?
This is closer to what good coaches already do. They do not simply demand better performance. They create the conditions through which better performance becomes possible.
That may become one of management's most important jobs.
The Numbers Can Fool Us
Automation gives managers an immediate reward.
Four hours become twenty minutes. A team handles twice the workload. Costs decline.
Capability develops on a different clock.
Suppose the four-hour task also exposed junior workers to problems they needed to understand. Removing it causes no obvious damage this quarter.
Five years later, the organization has fewer people ready for senior work.
Ten years later, everyone calls it a talent shortage.
The shortage may have begun years earlier, when an efficiency decision quietly removed a learning pathway.
No productivity dashboard would have shown it.
Add One Question to Every Automation Decision
When considering automation, organizations naturally ask:
Can AI do this task?
Keep asking that.
Then add another question.
What were people becoming capable of while doing it?
If the answer is nothing important, automate the work and move on.
If the work develops valuable judgment, redesign the learning pathway before removing it.
Maybe beginners need simulations.
Maybe they should attempt the problem before using AI.
Perhaps teams need stronger apprenticeships or rotations. Experienced workers might need formal coaching responsibilities.
There will not be one universal answer.
Different work develops people differently.
The important thing is noticing that something needs replacing.
Beyond Training Workers
This takes us beyond protecting entry-level jobs.
Jobs will change. Some should disappear. Others will emerge in forms we cannot yet predict.
Trying to preserve yesterday's workplace would solve the wrong problem.
What matters is preserving the pathway underneath it.
Beginners need somewhere to begin.
Practitioners need enough experience to become experts.
Experts need opportunities to become coaches and leaders.
Some of those coaches must eventually learn how to develop new coaches.
That is how an organization renews itself.
Not by keeping every old job.
By keeping capability moving from one generation to another.
The Competitive Advantage Hiding Behind AI
Most discussions about AI competition focus on technology.
Which company has the better model? Who can automate faster? Who can produce more with fewer people?
Those questions matter.
There is another one worth watching.
Which organizations will become unusually good at developing people?
The strongest ones may go further. They will develop people who know how to develop others.
That creates something technology alone cannot guarantee.
Renewal.
A durable organization does not merely inherit expertise from the people who built it.
It recreates expertise in those who come next.
AI can help us produce more work with less effort.
Our harder responsibility is making sure people still learn, practice, master, teach, and pass capability forward.
The organizations that solve that problem will possess something more valuable than today's productivity gain.
They will know how to create tomorrow's capable people.
Credits
Inspired by AI Is Not the Biggest Threat to the Future Workforce. Declining Critical Thinking Is. by Lyon Kassab.
Kassab identifies declining critical thinking and shrinking entry-level development pathways as workforce risks. This essay extends that insight into coaching, leadership development, and capability multiplication.
Tags
#Artificial_Intelligence #Future_of_Work #Leadership #Learning #Organizational_Development
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