Debate on artificial intelligence (AI) and employment has centred on the displacement of mid-career workers, leaving the entry point of careers comparatively unexamined. Junior roles rely on codifiable tasks such as drafting, data handling, scheduling and first-level support that generative AI can now perform, so firms may hire fewer juniors, raise experience requirements and reduce the mentoring through which novices become experienced workers. This paper develops the research foundation and a complete empirical design for studying how AI adoption affects early-career hiring pipelines, junior mentorship structures and groups concentrated in exposed roles, notably women in administrative and service occupations, across contrasting regional economies. It integrates task-based, human capital, mentoring, intersectionality and regional theory; identifies six specific gaps; articulates theoretical, empirical, methodological and policy contributions; and states eight falsifiable hypotheses. A sequential explanatory mixed-methods design combines a primary survey of early-career workers and employers (instrument provided in full), secondary labour-force and job-posting data analysed with difference-in-differences and shift-share methods, and qualitative interviews. A Regional Vulnerability and Resilience Index and a results-reporting framework are specified. Empirical estimates will be reported after fieldwork.

Manasvi Pramod Kulkarni, "Asymmetric Disruption Across Demographics and Regional Economies: How Artificial Intelligence Reshapes Early-Career Hiring Pipelines, Junior Mentorship, and Gender-Concentrated Administrative and Service Roles" International Journal of Scientific Research and Engineering Development, V9(5): Page(1967-1988) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.