In 2019, I wrote a blog called Reality Check on Promotions. My argument was that promotions depend on three things: performance, potential, and feasibility. Being excellent at your current job doesn’t automatically mean you are ready for the next one. And being ready doesn’t mean there is a position available.
I still believe that. But the framework assumes we know what the next job looks like. Seven years later, I am not sure we can make that assumption anymore.
What exactly are we promoting people into?
Consider two people. One manages 200 employees delivering a business process. The other manages ten people and a collection of AI agents delivering better outcomes at substantially lower cost.
Who deserves the bigger title? Who should earn more?
Most companies would look at the org chart before answering. Headcount has been a convenient proxy for responsibility, status, and compensation. That proxy is becoming less useful.
But AI challenges all three parts of my original framework.
Performance
Strong output has traditionally been evidence of individual capability. Not perfect evidence, but useful.
AI makes that signal less reliable.
Two analysts can produce equally impressive recommendations. One understands the business, challenges the assumptions, and uses AI to accelerate the work. The other accepts a plausible answer without understanding its limitations. Their presentations might even look identical.
I would absolutely reward people who use AI to deliver better outcomes. Productivity matters. There is no virtue in doing manually what technology can do better.
But rewarding outcomes and assessing readiness are different decisions. Reward the results, but don’t automatically treat them as evidence that someone is ready for greater authority.
Potential
This is the bigger problem.
We have traditionally developed people through increasingly difficult assignments. They did the work, made mistakes, received feedback, and gradually earned the right to make bigger decisions.
In The Corner Case, I explore what happens when AI removes the routine work that also helped people develop expertise.
There are two problems, not one.
First, we may stop producing the experiences needed for the next level. Second, AI-assisted output makes it harder to recognize who has developed genuine judgment.
Consider a junior analyst who delivers an excellent recommendation. Would she have caught an incorrect assumption? Could she defend the recommendation when challenged? Does she understand what could go wrong. Reviewing the presentation by itself won’t tell us.
We need to create opportunities to exercise judgment deliberately, and evaluate how people make decisions, not just what they deliver. And we need to keep revisiting what we mean by potential. Being ready for today’s senior job may not prepare someone for the job that exists five years from now.
Feasibility
AI lets individuals create more value without proportionately larger teams. At the same time, organizations can operate with fewer management layers.
The gap between individual contribution and organizational position will widen.
We have had technical and managerial career ladders for decades. But managerial roles still tend to carry greater authority, compensation, and prestige.
That was already questionable. AI makes the mismatch much larger.
An exceptional engineer or analyst equipped with AI might generate more economic value than their manager. Why should that person need a management title to earn more?
Measuring individual contribution remains difficult, especially when people and AI systems work together. We should evaluate outcomes owned and decisions made, not simply count deliverables. That won’t eliminate disagreements about fairness, but headcount isn’t a particularly good measure of value either.
Companies will need ways to increase compensation and responsibility without waiting for management vacancies. Otherwise, their best AI-enabled contributors may leave for organizations willing to reward their economic impact without requiring them to manage a team.
Legacy HR grading systems tie pay bands directly to direct reports. As long as compensation scales with span of control rather than economic impact, companies will keep forcing their best AI-enabled builders into bad management roles
Who develops the next generation?
There is another problem companies cannot solve individually.
Every company has an incentive to automate routine work. But if that work also develops future experts, the industry could eventually find itself short of experienced people.
Each company improves its economics while collectively weakening the talent pipeline. Hiring experienced people from competitors doesn’t solve that problem. It just moves the scarcity around.
Companies will need to invest deliberately in development through rotations, supervised decisions, simulations, and opportunities to handle difficult work. Those investments may not produce an immediate productivity return.
Someone still has to pay for them !
In the past, junior work was the tuition an enterprise paid to produce senior talent. Automating that work removes the tuition cost, but it also cancels the classes
Hierarchy doesn’t disappear. Organizations still need people who allocate capital, resolve conflicts, and accept accountability.
In Migrating Scarcity, I argued that when something becomes abundant, scarcity moves to the next constraint. As AI makes execution and analysis cheaper, judgment, problem selection, and accountability become more important.
Those capabilities don’t necessarily correspond to the next box on an org chart.
The old promotion framework assumed a relatively stable set of jobs arranged in a hierarchy. AI challenges the stability of both the jobs and the hierarchy.
We may be entering a world where promotion is no longer the primary measure of career progress. If that happens, the question isn’t just who deserves the next promotion. It’s whether the next promotion is even the right thing to offer.
