世界经济论坛-人才与未来工作匹配_公共就业服务指南（英）-2025.2_35页_9mb

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

Core Content

This guidebook from the World Economic Forum and Capgemini provides a comprehensive framework and practical insights for public employment services (PES) to improve job matching in the context of a rapidly evolving labour market. It highlights the importance of aligning skills with job opportunities, leveraging technology, and addressing systemic challenges to enhance employment outcomes.

Main Points

1. The Need for Improved Job Matching

• By 2030, over 20% of global jobs are expected to be transformed due to technological, environmental, and demographic changes.
• The Future of Jobs Report 2025 indicates that 170 million new jobs will be created, while 92 million existing roles may be displaced.
• Effective job matching is critical for workers, employers, and PES to navigate this transformation and ensure sustainable employment outcomes.

2. Framework for Successful Job Matching

The guidebook outlines a five-step framework to enhance job matching through data:

1. Data Access and Collection – Gather comprehensive and timely data from both job seekers and employers.
2. Data Structure and Standardization – Create unified frameworks to align job and skills data.
3. Data Validation – Ensure data authenticity through secure verification and skill assessment.
4. Upskilling and Reskilling – Provide targeted training to bridge skill gaps and improve employability.
5. Matching – Use validated data to generate accurate and efficient job placements.

3. Innovative and Cost-Effective Solutions

• Innovative Approaches:
  • AI and machine learning for real-time data analysis and personalized training content.
  • Blockchain for secure certification records and tamper-proof verification.
  • NLP tools for parsing unstructured data like resumes and cover letters.
  • IoT for performance tracking during recruitment and employment.
• Cost-Effective Solutions:
  • SMS-based registration systems for data collection.
  • Simple categorization using spreadsheets.
  • Open educational resources (OER) and low-cost AI tools.
  • Community-based learning centers and physical data hubs.

4. Disruptive Trends in Job Matching

• Personalization: Future job matching will focus on tailoring opportunities based on individual qualifications, skills, age, family situation, and work-life balance.
• AI Integration: Generative AI (genAI) agents and co-pilot tools will play a key role in providing real-time learning support and improving match accuracy.
• Standardization: Unified skill ontologies and taxonomies will help create a global language for job and skills data, enabling better cross-border matching.

Key Information

• Challenges: PES face issues like fragmented data, lack of standardized frameworks, and resistance to adopting new technologies.
• Solutions: The guidebook emphasizes the use of technology to overcome these challenges, including AI, blockchain, and NLP.
• Case Studies: It includes six country-specific examples (France, Guatemala, Nigeria, the Philippines, Singapore, Sweden) to illustrate how PES can implement these solutions in diverse contexts.
• Human-Centered Approach: While technology is essential, the guidebook underscores the importance of maintaining a human-centered approach to ensure cultural relevance, minimize bias, and build trust.
• Collaboration: Public-private partnerships are highlighted as crucial for aligning workforce supply with demand and driving sector-wide innovation.
• Implementation Guidance: The guidebook provides actionable steps for each stage of the framework, from data collection to matching, and outlines key success factors for each.

Structure of the Guidebook

• Chapter 1: Outlines the five-step framework for job matching.
• Chapter 2: Discusses innovative and cost-effective technological solutions.
• Chapter 3: Features real-world case studies from six countries to demonstrate practical applications.

Conclusion

The guidebook serves as a practical resource for policymakers to implement and scale technological solutions in job matching. It emphasizes the need for a data-driven, flexible, and human-centered approach to ensure that public employment services can effectively meet the demands of the future labour market.

Appendices

• Glossary: Provides definitions of key technological terms.
• Contributors: Lists the individuals and organizations involved in the research and development of the guidebook.

This guidebook is interactive, and to access its full capabilities, users are encouraged to download and open the PDF using Adobe Acrobat.