Job Analysis

Abstract

Despite predictions of the “death of jobs” due to economic shifts and technological changes, job analysis remains the foundational “backbone” of human resource management. This chapter demonstrates how traditional job analysis methods are evolving to make use of machine learning and artificial intelligence (AI) while maintaining their core purpose: systematically documenting job tasks and identifying required knowledge, skills, abilities, and other characteristics. The chapter outlines diverse applications of job analysis, from creating position descriptions and developing selection processes to supporting career exploration and succession planning. Two primary methodological approaches are examined: inductive methods (job/task analysis and the critical incident technique) that generate job-specific information, and deductive methods (like the U.S. Department of Labor’s Occupational Information Network, or O*NET) that apply standardized taxonomies across occupations for comparison purposes. The chapter also presents an eight-stage AI-enhanced job analysis lifecycle that extends from strategic planning through ongoing maintenance, demonstrating how technology complements rather than replaces traditional methods. Human oversight remains essential for contextual understanding, bias detection, and ensuring methodological rigor. Recent innovations in job analysis include using machine learning to predict occupational profiles and developing frameworks for capturing technology-related worker requirements. The future of job analysis lies in thoughtfully integrating human expertise with technological capabilities, maintaining the systematic foundation necessary for informed talent management decisions, while dramatically improving efficiency and scalability in our rapidly evolving work environment.