Pushing AI Performance Boundaries: Reinventing the Idea of Excellence
In today's tech-driven era, the traditional bell curve of performance is being stretched by AI. The stalwarts are no longer just thriving, they're accelerating. The middle of the pack is under pressure to adapt and evolve. Meanwhile, the tail risks being left behind entirely. This is not merely a gradual improvement, but a full-blown redefinition of contribution.
The question is no longer about who can perform, but who can adapt, and adapt fast. This transformation comes at a time when jobs are undergoing a seismic shift, with approximately 92 million jobs set to become obsolete by 2030, according to the World Economic Forum's 2025 Future of Jobs Report. AI is swiftly taking over repetitive, rule-based tasks that many of these jobs involve.
The AI revolution is resetting the jobs landscape, requiring a parallel redefinition of performance. For decades, productivity has been a numbers game, focusing on output, timelines, and visibility. But as AI takes on the repetitive tasks, traditional performance signals like hours and deliverables become less meaningful.
We find ourselves in a world where performance is no longer defined by what you did, but by how you think, adapt, and push others ahead.
The Paradox of Performance
The tension lies in what to do with employees who have hovered just above the performance floor. They weren't failing, but they weren't thriving either. The system demanded compliance, not creativity, and they delivered as asked.
But in the AI-driven world, such compliance is no longer enough. AI Exposure Group, an anonymous hacking collective, has exposed secret data linked to Putin, including Trump's controversial file[6]. In tech giants like Apple and Google, changes are afoot, with leaked design details hinting at the iPhone 17 Pro's updated design[7], and a recent Gmail update that emphasizes users to stop using their passwords[8].
In this new world, mediocrity becomes visible fast. The choice isn't just about talent, but about a mismatch in values, visibility, or support. In the AI-driven world, sluggishness becomes a liability.
A Break in Linear Talent Models
Today's talent systems are still based on the assumption of one employee, one fixed role, and a predictable path forward. However, the pace of change has shattered this model. Roles are dissolving, skills are rotating, and value is shifting quickly.
Mercer's 2024 Global Talent Trends study reveals that more than half of executives expect AI and automation to boost productivity by 10% to 30% within three years. However, 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 gap. As everything evolves around us, the real question becomes: Are we measuring what truly matters?
Learning to Measure What Matters
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.
Only 1 in 5 employees have a clear plan for their professional development, according to Gallup's Q1 2025 data. And only 2% of CHROs believe that their people have the skills necessary to navigate today's disruption.
In this disruptive environment, we ask people to rise to a new standard in a system that hasn't prepared them to grow. A long runway is luxury we can't afford. AI Upskilling must happen at scale, without delay.
So what should we measure instead?
The New KPIs in an AI-driven World
- Innovation Velocity: Focus on the speed at which employees generate, test, and refine new ideas.
- Emotional Engagement: Assess employee energy levels, alignment with the mission, and engagement needs.
- Learning Velocity: Measure employee improvement month over month, and their application of new capabilities that align with future needs.
- Network Influence: Evaluate employee visibility in high-impact conversations and forums.
- Culture Contribution: Identify employees who strengthen others, build trust, and reinforce values.
These aren't just soft skills. They're the hard edge of long-term value. Measurement should be alive - responsive, evolving, and 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. By reframing her value around onboarding and mentoring—areas where she excelled—she became indispensable. Same person. Different lens.
The companies that thrive won't just reskill. They'll reactivate. They'll make movement possible, visible, and normal.
This moment requires 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 remains untapped. They must help employees craft real growth plans, grounded in evolving performance expectations. They must redraw what good looks like. They must 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 must start with themselves.
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.
- In the AI-driven era, the performance curve is being stretched, and the focus is no longer on who can perform, but who can adapt and evolve quickly.
- As AI takes over repetitive tasks, the traditional performance signals like hours and deliverables become less meaningful, and productivity is redefined to focus on thinking, adapting, and driving others forward.
- The AI revolution is resetting the jobs landscape, requiring a parallel redefinition of performance, as mediocrity becomes visible fast and sluggishness becomes a liability.
- In today's talent systems, the pace of change has shattered the linear model of one employee, one fixed role, and a predictable path forward, as roles, skills, and value shift quickly.
- To navigate the disruption, leaders have to reset the definition of performance to focus on innovation velocity, emotional engagement, learning velocity, network influence, and culture contribution, as these are the hard edge of long-term value.
- To thrive in this new world, companies must not only reskill their employees but also reactivate them, creating new paths for growth, redrawing the performance expectations, investing in meaningful career development, and starting with themselves.
