This chapter examines the fundamental distinction between AI-driven job elimination and job transformation, challenging the prevailing narrative that positions these as equivalent processes. Through analysis of contemporary labor market data and organizational case studies, we demonstrate that AI’s impact follows distinct patterns based on underlying competency structures rather than job categories or hierarchical positions. The chapter introduces the Competency Shift Framework, which explains why certain roles face elimination, while others undergo transformation through the lens of codifiable versus contextual competencies. Drawing from McKinsey Global Institute research on occupational transitions and emerging empirical evidence from AI-adopting organizations, we reveal that jobs comprising primarily codifiable competencies face elimination risks, while roles involving mixed competency profiles undergo transformation requiring new human-AI collaboration skills. This analysis moves beyond simplistic automation forecasts to provide organizations with a nuanced understanding of how AI reshapes work, establishing the theoretical foundation for subsequent chapters’ examination of skill acquisition disruption and strategic workforce planning.