When organizations discuss AI and performance management, the conversation almost always starts in the same place.
How should we evaluate employees who use AI?
Should we measure prompts? Productivity? Time saved? Output quality? AI adoption?
These are reasonable questions.
I just don’t think they’re the most interesting ones.
The assumption behind all of them is that performance management itself remains fundamentally valid and only the metrics need to change.
I’m not convinced that’s true.
I think AI is forcing us to rethink something much deeper:
What does exceptional performance actually look like when execution is no longer the scarce resource?
Performance Was Designed for a Different Economy
Modern performance management emerged in a world where producing work was expensive.
Writing software required significant engineering effort. Producing reports took hours. Research demanded specialized expertise. Creating presentations, documentation or financial models consumed real time and real capacity.
Because execution was difficult, organizations naturally rewarded people who produced more.
More features shipped.
More tickets closed.
More analyses delivered.
More projects completed.
Whether or not those metrics were perfect is a different debate.
They made sense for the environment they were designed for.
AI changes that environment.
Execution hasn’t disappeared.
It has simply become dramatically cheaper.
When Everyone Produces More, Production Stops Being the Differentiator
One of the biggest misconceptions surrounding AI is that higher productivity automatically creates competitive advantage.
It doesn’t.
Competitive advantage only exists when something is scarce.
Today, almost everyone can generate a first draft.
Almost everyone can produce code faster.
Almost everyone can summarize a meeting, analyze a spreadsheet or build a prototype in a fraction of the time it required two years ago.
If everyone becomes twice as productive, productivity itself stops being a differentiator.
Organizations eventually discover that the limiting factor isn’t producing work.
It’s deciding which work deserves to exist.
That’s a very different challenge.
AI Commoditizes Execution. Judgment Becomes the Premium Skill.
This is where I think many organizations are still looking in the wrong direction.
We’re trying to evaluate people based on outputs that AI increasingly influences, while paying surprisingly little attention to the capabilities that remain uniquely human.
The professionals creating the most value aren’t necessarily those generating the highest volume of work.
They’re the ones asking better questions.
Recognizing weak assumptions before they become expensive mistakes.
Knowing when AI is operating outside its capabilities.
Making trade-offs that models cannot make because they require business context rather than statistical prediction.
Most importantly, they’re deciding what shouldn’t be built.
Ironically, as software creation becomes easier, product judgment becomes more valuable.
The same applies to strategy, operations and leadership.
Execution is being democratized.
Judgment is becoming the competitive advantage.
The Best Employees Don’t Just Produce Work. They Increase Organizational Capability.
There’s another shift that I believe organizations will eventually have to recognize.
Performance has traditionally been treated as an individual attribute.
How much value did this person create?
AI changes that question.
Increasingly, the highest-performing people aren’t those who complete the most work themselves.
They’re the ones whose work enables everyone else to perform better.
The engineer who builds a reusable workflow.
The product manager who creates a decision framework adopted across teams.
The designer who develops a system that eliminates repetitive work for dozens of colleagues.
The leader who helps the organization learn faster.
Those contributions have always existed.
AI simply makes them dramatically more valuable.
Because when execution accelerates, organizational leverage compounds much faster than individual productivity.
What We’re Learning at Revolt
This has changed how we think about performance inside Revolt.
We’re far less interested in measuring who generates the most AI output than in understanding who improves the way the organization works.
Who creates reusable assets instead of isolated deliverables?
Who shares knowledge instead of accumulating it?
Who leaves behind systems, workflows and practices that make the next project easier than the previous one?
Those are increasingly the behaviors we want to encourage.
Not because output no longer matters.
But because output has become easier to generate than organizational capability.
That’s an important distinction.
The Definition of High Performance Is Changing
Every major technological shift eventually changes what organizations reward.
The industrial era rewarded efficiency.
The software era rewarded execution.
The AI era will reward something different.
Not the people who generate the most work.
The people who create the greatest leverage.
Because in a world where almost everyone has access to extraordinary execution tools, the real competitive advantage no longer comes from producing more.
It comes from helping the entire organization think, learn and operate better.
That’s a much harder capability to build.
It’s also the one I believe will define the next generation of high performers.
