Occupational exposure to artificial intelligence (AI) identifies where technology may affect work, not how employment adjusts. Using microdata from Türkiye’s 2024 Household Labor Force Survey, this paper applies the Richmond (2026a) AI Jobs Transition Framework to a nationally representative sample of private sector workers. The framework links AI exposure with demand responsiveness (whether productivity gains expand demand enough to offset lower labor needs per unit of output) and human necessity (whether delivery requires human participation). It identifies four adjustment mechanisms: Automation Risk, Reorganize, Grow with AI, and Less Change. Reorganize accounts for 21.3 percent of private sector employment, compared with 12.3 percent in Automation Risk and 7.7 percent in Grow with AI; 58.7 percent falls into Less Change. Reorganize occupations combine high exposure, constrained demand, and high human necessity, indicating greater potential for adjustment through weaker hiring, lower labor intensity, or changes in work organization than through more direct labor-saving substitution. Exposure alone masks important differences in potential employment adjustment, which demand conditions and human necessity help distinguish.