Matching Talent to the Jobs of Tomorrow A Guidebook for Public Employment Services 2025

2 Innovative solutions and use cases Step 5: Matching Accurately align job seekers with relevant opportunities to address labour market needs efficiently.Disruptive trends: tech provider perspectives The future of job matching will prioritize personalization, considering factors like qualifications, skills, age, family situation and work-life balance. This approach ensures more meaningful, tailored matches that address the diverse needs of the modern workforce.Innovative approaches AI technologies, including machine learning (ML) and deep learning (DL), use historical data and data analysis to predict optimal job matches. Big data fuels these models by providing the necessary data for effective learning. GenAI, particularly large language models (LLMs), enhances matching by offering clear, contextual explanations of how candidates align with roles, ensuring transparency. AI technologies can also enhance job matching on another level by analysing candidates’ preferences, motivation and willingness, enabling more tailored and mutually beneficial placements. Cost-effective solutions Public employment services can implement low-cost AI tools, such as open-source ML models, to support job matching by analysing skills and job requirements. Those models can then be based on any simple tabular data management tools, especially for smaller-scale applications or proof-of-concepts projects. Click on an icon below to find out more about each step Matching Matching Talent to the Jobs of Tomorrow: A Guidebook for Public Employment Services 15