We often think of performance as a bell curve: some stars at the top, a strong middle, and a struggling tail. But AI isn’t just nudging that curve. It’s stretching it. The stars are accelerating with augmentation. The middle is under pressure to adapt. And the tail? It’s at risk of dropping off entirely.
This is no longer about gradual improvement. It’s a full-scale redefinition of what contribution means. The question is no longer “Who performs?” It’s “Who adapts—and how fast?”
And its coming at a time of seismic shifts in the workplace. Approximately 92 million jobs (representing about 8% of total employment) will be completely obsolete in 2030, the World Economic Forum projected in its 2025 Future of Jobs Report. Many of these jobs involve repetitive or rules-based tasks, exactly the kinds AI is rapidly absorbing.
This is the AI leap. And it’s already here.
The redefinition of jobs will require the redefinition of performance. For decades, productivity was a numbers game. More output, tighter timelines, greater visibility. It rewarded motion over meaning. We measured hours and deliverables as signals of performance. But now, as AI takes over repetitive rules-based tasks, those signals mean less and less.
We’re entering a world where performance won’t be defined by what you did—but by how you thought, how you adapted and how you moved others forward.
The Paradox Of Performance
Here’s the tension. What do we do with people who’ve hovered just above the performance floor? They weren’t failing. But they weren’t thriving either. The system asked for compliance not creativity—and they delivered exactly that.
Now, that’s no longer enough.
Do we give them more time? Coach them into different fits? Or quietly phase them out?
It’s not just a performance decision. It’s an ethical one. It’s not always a question of talent. Sometimes it’s a mismatch of value, visibility or support. But in this new world, quiet mediocrity becomes visible fast.
We’ve seen this movie before. The cotton gin. The typewriter. The internet. Every wave of disruption creates more value—but not for everyone. What’s different this time is the speed. The AI leap is exponential. The burden on leadership is to act not just strategically, but humanely.
Linear Talent Models Are Breaking
Today’s talent systems still assume one employee, one fixed role, and a predictable path forward. But the pace of change has shattered that model. Roles are dissolving. Skills are rotating. Value is shifting fast.
According to Mercer’s 2024 Global Talent Trends study, more than half of executives expect AI and automation to boost productivity by 10% to 30% within three years. Two in five expect even greater gains. But nearly 60% say technology is advancing faster than they can retrain their people, and 74% worry their talent can’t pivot fast enough.
This isn’t just a productivity gap. It’s a measurement one. When everything around you is evolving, the real question becomes: Are you measuring what actually matters? And as the old adage goes — you can’t manage what you don’t measure.
Where To Focus When Machines Take The Tasks
If AI is resetting the definition of work, leaders have to reset the definition of performance. But here’s the problem—most employees don’t even know what’s expected of them next.
According to Gallup’s Q1 2025 data, only 1 in 5 employees say they have a clear plan for their professional development. Even more troubling, only 2% of CHROs believe their people have the skills needed to navigate today’s disruption.
We’re asking people to rise to a new standard in a system that hasn’t prepared them to grow. A disruptive environment like this doesn’t allow for long runways. Upskilling has to happen at scale and without delay.
So what should we measure instead?
As AI takes over operational tasks, human productivity must focus on what machines can’t replicate. These five dimensions offer a better lens. They’re not exhaustive. They’re indicative. And they’ll evolve.
In a world of accelerating disruption, what we measure today may not reflect what matters tomorrow. Leaders should expect performance indicators to shift. Not adapting metrics in a world like this is like using a compass in a storm while the terrain keeps shifting—it points somewhere, but not always where the ground still exists.
1. Innovation Velocity
How often are people generating, testing and refining new ideas? How fast do they move from spark to pilot?
- Number of ideas submitted, prototyped or launched per quarter
- Time between idea generation and implementation
- Experiments run and lessons harvested
- Frequency of innovation-focused forums or feedback cycles
2. Emotional Engagement
Are people energized by the mission? Do they bring more than what’s required? Are there engagement needs met?
- Pulse scores on employee egagement, purpose, energy and alignment
- Voluntary participation in cross-team efforts
- Peer-nominated contributions that go beyond the role
- Consistency of measurable discretionary effort during change or challenge
3. Learning Velocity
Are employees improving month over month? Are they applying new capabilities that align with future needs?
- Skills gained and applied in real projects
- Completion rates of upskilling or reskilling programs
- Progress in stretch assignments or rotations
- Feedback from peers and managers on applied growth
4. Network Influence
Whose thinking spreads beyond their formal remit?
- Volume and quality of cross-functional collaboration
- Requests for input on decisions outside their domain
- Visibility in high-impact conversations or forums
- Recognition from peers across the organization
5. Culture Contribution
Who strengthens others, builds trust and reinforces values?
- Retention and engagement scores within teams
- Mentorship, onboarding or peer support activities
- Recognition for behaviors aligned with culture
- Feedback on psychological safety and inclusion
These aren’t just soft skills. They’re the hard edge of long-term value. And like everything else in a disruptive era, they won’t stay still. Measurement should be alive—responsive, evolving, real. Static metrics in a dynamic world are like old maps for new continents. Useful until they aren’t.
AI is already playing a role in this redefinition. An MIT Sloan study found that 60% of managers believe their KPIs need to improve. Yet only a third are using AI to rethink what they measure. The result? Nine in ten of those who do say their KPIs have already improved.
Redrawing The Performance Lens
Not everyone will make the leap. But we owe it to people to make the leap visible. We can’t raise expectations without creating new paths.
I once coached a manager who had quietly coasted for years. She wasn’t failing—but she wasn’t growing. When her role was restructured, she panicked. But once we reframed her value around onboarding and mentoring—spaces where she excelled—she became indispensable. Same person. Different lens.
Not everyone needs to be let go. Some just need to be re-seen.
The companies that thrive won’t just reskill. They’ll reactivate. They’ll make movement possible, visible and normal.
This isn’t just a business challenge. It’s personal. Over the past year, I’ve redrawn how I evaluate my own work. I’ve asked harder questions: what’s the value I’m creating, not just the volume I’m producing? Writing more isn’t the goal. Resonating more is.
I’ve shifted from broadcasting to dialoguing. From volume to depth. Fewer ideas, shared more intentionally. More coaching conversations. More listening. That shift hasn’t just changed my outcomes. It’s reshaped what I expect from myself, and how I define success in others.
What Leaders Can Do Now
This moment calls for more than productivity tweaks. It calls for a redefinition. Leaders must shift from valuing activity to measuring true outcomes. They need to invest in the middle—where AI pressure hits hardest and potential often runs untapped. It’s not enough to point out performance gaps.
Great leaders don’t assume development is happening. They help employees craft real growth plans, grounded in evolving performance expectations. They redraw what good looks like. They invest in opening up meaningful career paths, retiring those that are redundant or performative, and designing new ones that break from the old career ladder. And they start with themselves. Because in times like these, what you model carries further than what you measure.
The AI leap is stretching the performance curve. The real question isn’t whether you’ll adapt. It’s who you’ll help stretch and rise with you.