<![CDATA[This study forecasts tourism employment trends in India at both national and regional levels while examining the role of automation in reshaping sectoral employment structures. Utilizing the Auto Regressive Integrated Moving Average (ARIMA) model, the research analyzes annual secondary employment data for India (2009–2023) and Himachal Pradesh (2014–2024). Model adequacy is confirmed through standard residual diagnostics and goodness-of-fit measures, supplemented by a qualitative analysis of how digitalization influences skill requirements.Findings indicate a steady upward trend in national tourism employment through 2030, suggesting sustained expansion despite pandemic-related disruptions. Conversely, Himachal Pradesh exhibits significant fluctuations driven by seasonality, climatic vulnerability, and informal labor dependence. The analysis further reveals that automation acts as a complementary force, transforming routine tasks and increasing demand for hybrid skills rather than reducing overall headcount. Originality lies in the dual-level forecasting approach combined with an automation-based workforce interpretation, extending research beyond traditional demand metrics. While the study is limited by the use of annual data and potential structural breaks from COVID-19, it offers critical implications for policymakers. The results highlight the need for region-specific workforce planning and strategic digital adoption to enhance resilience in vulnerable areas like hill states. Future research should consider machine learning models and granular datasets to better capture seasonal dynamics..]]>
