Editorial
AI accelerates the work. Your work systems and performance metrics must keep pace to support and fairly reward human contributions. Leaders must rethink the environment, jobs and performance metrics to reflect the human capabilities that elevate AI’s outputs to business value.
“Just three of you?”
“Three humans. Our AI systems do the work of about 50 people.”
Mahershala Ali and Glenn Close had this exchange in the movie “Swan Song,” when Close’s character explains how a team of three is an entire workforce of a human cloning lab.
When the movie came out in 2021, this scene felt like a far-fetched future. Rewatching it over the weekend hit differently in light of the recent waves of AI layoffs. It made me wonder whether senior leaders have actually done the work to update organizational structures, systems and performance metrics when machine intelligence is a close collaborator.
After two decades in organizational effectiveness roles, I can tell you candidly: this foundational work requires patience, honesty and the courage to confront what most organizations ignore. The payoff is a more capable, resilient organization. But only if senior executives are willing to invest the time and effort in the slow, unglamorous work that delivers lasting results.
AI Is About to Reveal How Broken Work Really Is
Annual reviews are here. Overall, these metrics fail to consider that individual performance happens inside an environment. This organizational context typically includes constantly shifting goals, scope creep and decision-making changes. Managers rarely have full visibility into the conditions shaping the work and end up assessing employees without understanding the actual constraints.
Adding AI will speed up deliverables, but also create more performance friction if the systems that support the actual work stay the same. Teams may produce faster with AI, yet approval bottlenecks, unclear decision rights or constant shifts in priorities will still slow productivity. This results in more rework, confusion and performance signals that don’t reflect the full employee contribution to achieving results. Without addressing broken supporting systems, the best AI investments won’t pay out what leaders expect.
Before introducing new AI workflows and performance metrics to fairly evaluate employee impact, organizations must get their house in order and review the ecosystem surrounding the work:
- Shadow work created to compensate for broken processes.
- Organizational friction caused by bureaucracy and silos.
- Work-system waste buried in rework and redundant tasks.
- Cognitive load tax triggered by constant tool-switching and shifting priorities.
As McKinsey puts it: “AI is not just reshaping tasks. It is reshaping organizations.” AI will not repair the cracks. It will only expose them. This is why senior teams need to address the real drivers of performance before deploying machine intelligence at scale to assess the true value of human contributions and the ROI of AI investments.
First: Rebuild What Performance Metrics Measure
For performance metrics to be accurate in an AI-enabled workplace, it’s necessary to rebuild a sustainable foundation that starts with three equally important elements:
- Organizational Design: Start with an honest audit of the environment where performance happens and fix anything that creates emotional labor for individual or team contribution.
- Job Redesign: Shape roles around outcomes and contribution with clear guidelines of what is augmented by digital collaborators and what is valued as human contribution.
- Skills-matching: Define and develop the human skills that will act as a performance differentiator of human enhancement. These are the skills that elevate the outputs that AI tools produce.
This is the deeper, foundational change that leadership must undertake before they can design metrics to evaluate what AI has co-created.
Next: Design Performance Metrics that Reflect Human-AI Collaboration
Today’s performance metrics still focus on individual outputs, productivity and timely completion. These metrics do not reflect how work is actually created in an AI-enabled environment. While every organization is unique, this blueprint provides a starting point to set standards that distinctly measure the value created through human contribution on AI-produced outputs:
- Provide Clear Guidelines on the Human-AI Division of Labor: Define what AI handles and what humans own at both the individual and team levels.
- Measure Human Differentiators: Focus on the skills that elevate AI’s outputs. MIT’s EPOCH framework (Empathy, Presence, Opinion, Creativity, Hope) outlines the uniquely human capabilities that become more valuable when collaborating with AI tools.
- Make Quality Outcomes the End Goal: Shift from tracking deliverables and productivity to assessing the impact of human contribution that turned AI outputs into business value (e.g., a better decision, a reduced risk, minimized errors, etc.).
The importance of this shift in redesigning performance metrics is real. According to the World Economic Forum, “AI could contribute up to $15.7 trillion to the global economy by 2030, primarily by amplifying human capabilities. This rapid evolution demands a shift in how we measure and develop skills.”
Once these standards are defined, leaders can align them with redesigned jobs and the skills required for success. It’s a chicken-and-egg problem that will require iteration: set clear guidelines early and refine as the work continues to evolve.
When done well, this approach enables leaders to evaluate AI-augmented performance more accurately and reward human contributions more equitably while creating the conditions for achievable, sustainable outcomes.
Reimagined Performance Metrics in the AI Era
“Swan Song” got one thing right: AI can do the work of 50 people (or more!). What AI can’t replicate yet is the person who shaped the final outcome, judged the risk or softened the tone for an irate customer. AI will provide the output, but humans will elevate it as a meaningful outcome. That’s where organizational value is created and where performance should be rewarded. And unless leaders fix the broken systems surrounding the work, they’ll miss the full potential of their AI investments and the human contribution behind the AI-enabled results.
This is the foundation of performance value metrics in the AI era: AI accelerates the work and humans enhance its impact when the jobs and surrounding systems are also intentionally redesigned. Only then can leaders measure business impact with accuracy and fairness. This is the kind of disciplined, unglamorous work that makes organizations more resilient.
That’s the plot twist that "Swan Song" missed, but the one shift leaders can’t afford to ignore. Performance ratings may take on a whole new meaning. What counted as “Exceeds Expectations” or a solid “Meets” may become obsolete as these metrics evolve to measure skills and human contribution far beyond deliverables.
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Editor's Note: What other aspects of our workplaces do we need to rethink in light of AI?
- Outcome-Based Workforce Planning: New Model or New Language? — From jobs to skills to outcomes, the language keeps evolving, but the work hasn’t. Is this progress or just better packaging?
- AI Is Making the Hiring Crisis Worse — Using AI as a cure-all creates a doom loop: candidates and hiring managers game each other with AI, and hiring stays broken.
- Start With the System, Not With the Task: A Lean Framework for HR AI Implementation — When you automate drudgery, you optimize the past. Start with purpose and system design to create space for reinvention.
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