The Pros and Cons of Continually Assessing Performance

2026-06-15 · Source: Feeds - HBR.org · Field: Business & Management — Human Resources & Workforce Development, Corporate Strategy & Leadership, Operations & Process Management · Depth: Fundamental Awareness, long

Summary

The article "The Pros and Cons of Continually Assessing Performance" by Sangeet Paul Choudary and John Winsor, published June 15, 2026, examines the necessity and challenges of continuous performance assessment systems in an era where AI rapidly redefines human-machine labor division. Traditional job-based and skills-based models are becoming obsolete as AI commoditizes skills and shifts task requirements. The authors highlight examples like airlines using advanced data-monitoring systems to assess pilots' real-time judgment and Meta's controversial software for capturing employee activity for AI training. They propose a three-part architecture for effective continuous assessment: changing what constitutes evidence of capability (e.g., Microsoft's Skills Agent), analyzing work at the individual task level (e.g., Stripe's "minions" coding agents, GitHub Copilot metrics), and closing the loop from insight to action (e.g., Adecco's r.Potential, Cresta's Agent Assist). The goal is organizational learning, not surveillance, requiring clear governance.

Key takeaway

For HR Professionals and AI/ML Directors evaluating workforce strategy, you must transition from periodic reviews to continuous assessment systems. Your organization needs to capture real-time work signals and analyze tasks at a granular level to understand evolving human-AI collaboration. Implement transparent governance to ensure these systems foster learning and adaptation, rather than perceived surveillance, enabling dynamic capability matching and faster organizational readiness in the AI era.

Key insights

AI's impact on work necessitates continuous, task-level performance assessment for dynamic capability matching and organizational adaptation.

Principles

• Assessment must support growth, not just measurement.
• Capability evidence must be real-time work signals.
• Governance defines system legitimacy and purpose.

Method

Implement continuous assessment by: 1) Changing capability evidence to real-time work signals. 2) Analyzing work at the individual task level. 3) Closing the loop from insight to action, adapting people inside the workflow.

In practice

• Monitor code commits, customer calls, tool usage.
• Analyze work at individual task level with AI agents.
• Implement AI-enabled coaching in workflows.

Topics

• Continuous Performance Assessment
• AI Workforce Transformation
• Human-AI Collaboration
• Skills Taxonomy
• Task-Level Analysis
• Organizational Readiness

Best for: Executive, Director of AI/ML, HR Professional, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Feeds - HBR.org.