The way we measure performance has always been tied to the nature of work — and that nature has changed dramatically over time.
First era → Operational work
Tasks were repetitive, well-defined, and measurable.
Quantity mattered more than anything else (with a baseline of quality).
It was simple: assign the task, check if it's done, measure output. Performance was visible almost instantly.
Second era → Knowledge work
Things got more complex — expertise and judgment started to matter.
Here, quality became far more important than quantity (though both still played a role).
Businesses and clients were willing to pay a premium for quality.
Timelines stretched, but that was fine if the final project met the standard.
Performance measurement was still manageable: look at the outcome, assess quality, and you knew whether the person delivered.
Third era → Novel work (today, in the age of AI)
AI has changed the game — quality and quantity can now be generated at scale, on demand, 24×7.
The differentiator isn't how much you produce, or even how polished it looks — machines already excel at that.
The differentiator is creativity: solving new problems in new ways.
And that's far trickier to measure. Creativity doesn't run on predictable timelines. You can't guarantee novelty just by working harder or longer. A person could put in endless hours and still not create something truly new.
Which means performance measurement has become slower and riskier. Businesses may have to wait much longer before they know whether someone can deliver in this "novel work" world.
So what's the answer?
I believe we need to shift focus away from lagging indicators (outputs, outcomes) and start looking at leading indicators — early signs that a person has the mindset and skills to thrive in this new environment.
Here are some early indicators:
1. Work ethics – discipline, consistency, ownership of outcomes.
2. Contribution in meetings – do they bring valuable perspectives, even if you need to draw it out of them?
3. Comfort with conflicting thoughts – can they handle ambiguity and contradictions without stalling?
4. Contextual understanding – do they grasp what's happening around them and shape solutions that actually fit the situation?
5. Articulation – can they communicate ideas with clarity? In tomorrow's world, articulation will matter more than knowledge, because knowledge is already democratized.
In short → managing performance in the AI era means measuring what machines cannot.
Not just IQ, but also EQ and SQ.
Not just end results, but the early signals of creativity, adaptability, and alignment.
This is harder — no doubt. But it's also necessary. Because without evolving our performance measurement, we'll keep applying old metrics to a new world — and miss what really drives value today.
What do you think — are leaders and organizations ready to rewire performance management for this reality?