Employee performance management by Randy Bean, Erik Strauss and Randeep SinghJuly 6, 2026iiievgeniy/Getty ImagesPostSummary. Even as they adopt AI, companies are measuring employee performance with familiar metrics of success: productivity, goal completion, and efficiency. As such, employees who rely heavily on AI mayLeer en españolLer em portuguêsPostWhat does “good performance” look like when outputs are produced by people working with AI? And how can leaders ensure that speed and efficiency don’t come at the expense of judgment and accountability?PostRead more on Employee performance management or related topics AI and machine learning, Analytics and data science and Technology and analytics
Performance Management Needs New Metrics in the AI Era
Even as they adopt AI, companies are measuring employee performance with familiar metrics of success: productivity, goal completion, and efficiency. As such, employees who rely heavily on AI may appear highly productive, while those who slow down to verify assumptions, challenge outputs, or correct errors might appear less efficient precisely when they’re adding the most value. To effectively measure how employees perform working with AI tools, companies should adopt a three-layer measurement framework for AI-era performance that enables organizations to distinguish between metrics for human contribution, AI system and agent metrics, and human-AI systems. In doing so, they need to adopt new sets of metrics that better reflect the new reality of work.
99 words~1 min read
