I approached this challenge by first understanding the dataset and the business questions it aimed to answer. I cleaned and validated the data using Python (Pandas), checked for missing values, duplicates, and data types, and then merged the fact and dimension tables into a star schema for analysis. I carried out exploratory data analysis (EDA) to identify trends in AI adoption, workforce displacement, job creation, industry preparedness, and country-level AI readiness. After deriving key insights, I built an interactive Power BI dashboard using DAX measures, slicers, maps, and visualizations to present the findings in a clear, business-focused way. Throughout the project, I used Python (Pandas), Power BI, DAX, and Microsoft Excel for data preparation, analysis, modeling, and visualization. One of the most interesting insights was that AI adoption accelerated significantly after the emergence of Generative AI in late 2022. Despite productivity gains, job displacement consistently exceeded job creation across the period analyzed, highlighting the growing need for workforce reskilling. I also found that Manufacturing and Public Health were among the industries most susceptible to automation, while Manual Skilled Trades had the highest AI replaceability score. At the country level, stronger digital infrastructure generally aligned with higher AI adoption, although more mature AI policies did not always translate into better job replacement outcomes. This challenge was especially rewarding because I discovered it with only a few days remaining before the deadline. Completing the project under such tight time constraints pushed me to improve not only my technical skills but also my ability to prioritize tasks, tell a compelling data story, and deliver a complete analytical solution.

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