How AI Is Forcing a Rethink of Learning and Development

3 minutes ago

6 min read

Nearly every CEO I speak with lately asks some version of the same question.

“What does AI actually mean for my workforce?”

Not in an obvious way but in a quiet, practical, late night thinking kind of way. They are not worried about robots taking jobs. They are worried about something harder to place. They feel the organization is about to bend, but cannot fully see it yet. Honestly, this concern is justified. However, the mistake most leaders make is starting this conversation at the wrong level; Tools. Vendors. Models. Product names. What is harder to place is where the real change is happening. The shift is in how work is structured, how decisions move through the organization, and where judgment still matters.

Stop Talking About LLMs.

Start Talking About Probability.

When most people hear “AI” or “LLMs,” they imagine intelligence; Thinking, Reasoning, Something human-like. We’re not remotely there yet. What these systems actually are is much simpler and much more dangerous if misunderstood. They are probability engines.

They predict the most likely next answer based on patterns in enormous amounts of data. They do not know if something is true. They know if something looks right. See the distinction?

A human expert asks, “Is this correct?”

An AI system asks, “Is this likely?”

Most of the time, those answers overlap. Sometimes, they do not. And when they do not, the LLM does not know it is wrong. This is why AI feels impressive and unsettling at the same time. It speaks confidently. It fills gaps. It does not hesitate. LLMs are probabilistic reasoning engines that optimize for likelihood, not correctness. Humans still own accountability.

So the leadership question is not, “Can AI reason?” It is, “Where do we allow probability to act without supervision?”

It Is Not Job Loss.

It Is Work Compression.

The second mistake leaders make is framing AI as job replacement. That is not how it shows up in reality. What AI actually does is compress work. Tasks that once required several people now require fewer handoffs;

Drafting
Coordination
Summarizing
Reporting
Planning

It shrinks friction thus, layers around work disappear. This is why the first cracks will appear in middle management, program coordination, internal operations, and reporting roles. Not because those people are weak, but because those roles focus on managing friction. LLMs can cut the hidden tax of coordination costs which usually lies within the middle layers of an organization. The hidden tax in modern orgs:

Meetings
Status updates
Alignment decks
Follow-ups
“Who owns this?”
“Where is this stuck?”

Where will Organizations Break First

There are 4 areas/roles where LLMs will disrupt organizations in the next year. It is due to their current responsibility focus.

Middle Management
Program coordinators/PMO layers
Internal Ops roles
Reporting/Analytics layers

Middle Management

What they do today (uncomfortable truth)

In most tech orgs, middle managers spend:

30–40% status gathering
20–30% translating exec intent
10–20% reporting upward
10–20% actual people development -> This is only part difficult to automate

AIs can already;

Pull data from Jira, GitHub, Slack
Summarize progress
Flag risks
Draft plans
Translate goals into tasks

The result:

Execs don’t need intermediaries for visibility
Teams don’t need intermediaries for alignment
The “information router” role collapses

What survives

Managers who coach
Managers who decide
Managers who handle conflict
Managers who own outcomes

What dies:

Status relays
Ticket shepherds
Meeting factories

This is already happening quietly.

Program coordination / PMO layers

What these roles do

Track dependencies
Run ceremonies
Maintain timelines
Herd cross-functional cats
Produce artifacts to prove work exists

Why is this layer fragile? The work is;

Structured
Repetitive
Text-heavy
System-integrated
- LLMs are built for this.

Systems can now;

Monitor multiple systems
Detect slippage
Auto-update plans
Draft comms
Propose re-prioritization

Humans then approve exceptions, but there’s no need to manage the whole thing.

The result

Fewer coordinators
More senior PMs
Wider scope per PM
Less ceremony, more execution

This is one of the fastest eroding layers.

Internal ops “glue” teams

What this includes

HR ops
Finance ops
RevOps
IT ops
Enablement teams

Their collective primary focus is to, “Make the org function despite fragmented systems.” LLMs rule this area because they sit above tools:

They don’t care if data lives in 6 systems
They normalize language
They fill gaps humans used to bridge

That reduces the need for:

Manual handoff
Reconciliation work
Human middleware

What changes

Ops teams shrink
Remaining roles become architects, not doers
Fewer junior ops roles exist

Erosion of this layer moves slower due to the risk with compliance and possible legacy systems.

Reporting & Analytics layers

What these roles do

Pull data
Build dashboards
Explain variance
Create narratives for leadership

LLMs already do 70% of this. This means executives no longer need;

Static dashboards
Weekly reports
PowerPoint narratives

AND LLMs can answer, “What changed? Why? What should we do?”. Obviously AI responses to “what should we do”, should be questioned and analyzed by an experienced human.

What survives

People who design metrics
People who challenge assumptions
People who interpret second-order effects

What goes away:

Dashboard babysitters
Manual reporting cycles

This is already happening.

What is happening in the workforce today is there will be fewer layers and more responsibility per role. A senior leader can now oversee far more output than before. That sounds efficient until you realize the consequences.

As Output Scales

Risk Scales Faster

AI increases speed. It increases volume. It increases reach. It also increases the blast radius of mistakes.

A junior error used to be caught by a manager. A manager error used to be caught by a director. An AI assisted error can move from idea to execution without passing through as many human hands. This is because AI does not feel risk.

Then, organizations respond to such risks in predictable ways. They tighten approvals. They elevate sign off. They rely more heavily on experienced judgment. What will happen is the concentrated need for strong Senior Leaders.

This is the part many CEOs miss.

Here is where the fly hits the ointment, you have a very different workforce problem.

Why Succession Planning Is Quietly Breaking

If you read this far you probably can see the issue. AI removes a lot of junior type work. The easy repetition. The first drafts. The coordination tasks. The safe mistakes. All of us senior people learned from doing those repetitive tasks. We were all once Daniel-san washing Mr. Miyagi’s car.

If you remove the junior responsibilities and do not replace them with something intentional, you create a skill gap. Fewer juniors grow into seniors. Fewer seniors are ready to step into leadership. Succession plans look fine on paper and fail in practice.

Learning and Development becomes the Real Bottleneck

In five years, the companies that struggle will not be the ones that threw the baby out with the bathwater. They adopted AI too quickly before thinking about what their workforce will look like in a few years. You may not know what will happen next year with technology. Yet, you can understand if you don’t train and develop your junior team to move into leadership roles you will be SOL.

Training and development can no longer be about tools and skills. It has to be about judgment and competencies. Senior teams must know;

How to decide when to act.
How to know when to stop.
How to escalate.
How to own outcomes when automation fails.

Those are human skills which are developed and trained. LLMs can’t and shouldn’t be making those decisions.

Workforce Strategy Becomes Competitive Advantage

The organizations that win will do three things differently.

They will be explicit about where AI is allowed to operate independently and where it never is.
They will redesign roles around decision ownership, not task completion.
They will treat development and mentoring as infrastructure, not perks.

This is not about being conservative with technology. It is about being deliberate with your people. The future workforce is not smaller. It is more polarized. Fewer juniors. Fewer middle layers. Fewer but stronger seniors with wider spans of responsibility. Higher consequences for poor judgment.

That future is not theoretical. It is already arriving quietly.

The companies that acknowledge it early will shape it. The ones that do not will feel it when something breaks and they realize no one is ready to step in.