A Multilingual Approach to Analyzing Talent Demand in a Specific Domain: Insights From Global Perspectives on Artificial Intelligence Talent Demand

Abstract

This paper introduces an innovative methodology for conducting demand analysis within various domains across multiple countries, presenting insights derived from a comprehensive analysis conducted in seven distinct nations. The proposed methodology provides a systematic process for gathering, processing, and analyzing textual data to discern global talent demand within specified domains. Three fundamental steps characterize the methodology: identification of areas of interest, calculation of relative demand, and execution of analysis and forecasting. Areas of interest within the job market are pinpointed through a combination of manual curation and automated techniques, ensuring the inclusion of only pertinent job postings. Relative demand for jobs within each specified domain is then computed for each country, furnishing a standardized metric for cross-country comparisons. Subsequently, analysis and forecasting unveil trends and patterns in job demand, enabling stakeholders to anticipate shifts in the job market landscape. Application of this methodology to scrutinize demand analysis across seven countries - the US, Canada, the UK, France, India, Singapore, and Australia - reveals substantial variations in demand across diverse regions, along with correlations and instances of skill shortages. These findings offer invaluable insights for policymakers, businesses, and researchers, facilitating informed decision-making and fostering growth and innovation within the specified domains.

Keywords

• Demand analysis
• AI skills
• job demand forecasting
• skills shortage