Poster #2 - Beyond Technical Metrics: Redesigning Performance Appraisals to Incorporate Relational Competency in the Age of AI

• In Event: Friday Poster Reception

Abstract

Performance appraisal systems in public organizations remain heavily weighted toward technical metrics, reflecting a design philosophy rooted in an era when human labor was the primary driver of technical output. As artificial intelligence increasingly augments technical work across government, including data processing, drafting, scheduling, and analysis, a critical gap has emerged: federal performance appraisal systems have not kept pace with this workforce transformation. Form CD-541, the U.S. Department of Commerce's performance management instrument, allocates 80% of performance weight to technical outputs with zero relational competency indicators. Yet AI now augments these same technical tasks across every department. Despite this shift, the redesign of performance appraisal remains entirely absent from the AI-HR conversation, a critical gap this research seeks to fill. This study asks: How should public sector performance appraisal systems be redesigned to systematically incorporate relational competency alongside technical metrics in an AI-enabled workforce environment? When AI automates and augments technical tasks, the remaining human value lies in judgment, collaboration, ethics, and relational capacity. Current appraisal systems are not equipped to measure these qualities, thereby creating accountability gaps and contributing to toxic workplace cultures. This research argues that the integration of AI in public organizations is not simply a technological shift; it is a workforce transformation that demands a parallel evolution in how employee performance is defined, measured, and rewarded. Using an AI-assisted prototype redesign of Form CD-541 as a conceptual model, this research proposes a balanced appraisal framework allocating 40% to technical outputs, 40% to relational competency, and 20% to professional growth. Data collection will include semi-structured interviews with federal HR practitioners to assess construct validity and feasibility of implementation. Proposed measurement tools include 360-degree feedback from the immediate chain of command, peer-to-peer assessment, and behavioral observation scales to document relational behavior over time. Preliminary findings from the prototype redesign demonstrate that relational competency can be systematically incorporated into existing federal appraisal instruments without abandoning technical performance standards. Policy implications include improved equity and accountability, mitigating workplace misconduct through structured relational accountability, reducing reliance on EEO complaints as a last resort, making data-driven leadership decisions for Senior Executive Service appointments, and shifting from punishment-oriented to development-focused evaluation practices. This research contributes to public HRM scholarship by proposing a replicable framework for federal agencies and offering OPM a concrete pathway to modernize performance management in the age of AI, measuring what AI cannot replace: human relational capacity.

Author

• Omololu Adeyemi, University of Baltimore
  Presenting Author