【世界经济论坛】人才与未来工作匹配：公共就业服务指南-2025.2_35页_8mb

Matching Talent to the Jobs of Tomorrow: A Guidebook for Public Employment Services
February 2025 Insight Report

Overview
The report highlights how technological advancements are reshaping job matching processes, enabling real-time labor market analysis, automation, and skills-based alignment. By 2030, 20% of global jobs are expected to undergo transformation due to frontier technologies, the green transition, demographic changes, and geoeconomic shifts. These changes will create 170 million new jobs while displacing 92 million, underscoring the need for efficient job matching. Public employment services (PES) play a critical role in bridging this gap, but face challenges like fragmented data systems, skills misalignment, and resistance to adopting new technologies.

Key Frameworks and Solutions
The guidebook outlines a five-step framework centered on data:

1. Data Access and Collection: Gather labor market data from businesses and job seekers to understand current and future needs.
2. Data Structure and Standardization: Align skills, jobs, and credentials using standardized frameworks (e.g., taxonomies, ontologies).
3. Data Validation: Ensure data authenticity through secure verification processes and technologies like blockchain.
4. Upskilling and Reskilling: Provide targeted training programs to address skill gaps, leveraging AI-driven content and open educational resources.
5. Matching: Use validated data to generate accurate job placements, incorporating AI and machine learning for personalized recommendations.

Innovative solutions include AI-driven platforms for resume parsing and semantic analysis, blockchain for credential verification, and NLP tools to extract skill data. Cost-effective options like SMS-based systems, web scraping, and low-tech data collection methods are emphasized to address resource constraints.

Case Studies
Six case studies illustrate diverse approaches:

• France: France Travail integrates AI and semantic analysis to standardize job descriptions and skills frameworks. It emphasizes public-private partnerships (PPPs) and centralized data systems. Challenges include aligning AI with labor market realities and ensuring transparency.
• Guatemala: Tu Empleo, a PPP platform, combines manual and digital methods. Challenges include limited internet access, budget constraints, and resistance to digital adoption.
• Nigeria: The National Talent Export Programme (NATEP) uses AI and mobile-friendly tools to connect workers with global opportunities. Challenges involve inadequate infrastructure and low digital literacy.
• Philippines: The Department of Trade and Industry (DTI) develops digital job portals and integrates AI for improved efficiency. Barriers include fragmented data and outdated systems.
• Singapore: SkillsFuture Singapore employs AI and blockchain to track skills and credentials, focusing on lifelong learning. Challenges involve ensuring data quality and adapting to rapid technological changes.
• Sweden: Arbetsförmedlingen uses localized AI models and collaborates with innovation agencies. Challenges include incomplete job data and the need for continuous adaptation.

Challenges and Success Factors
Common hurdles across PES include:

• Fragmented data systems limiting real-time insights.
• Skills misalignment due to lack of standardized frameworks.
• Resistance to adopting technologies like AI.
• Budget and infrastructure limitations.
• Cultural and contextual relevance of solutions.

Success factors include:

• Strong public-private collaboration for resource sharing and innovation.
• Standardized skills frameworks to ensure transparency and inclusivity.
• Human-centric approaches to minimize biases and maintain trust.
• Iterative refinement of tools based on stakeholder feedback.
• Scalable, low-tech solutions as starting points for implementation.

Recommendations
The guidebook proposes four key strategies:

1. Adopt a data-driven framework to enhance efficiency.
2. Leverage AI and emerging technologies for personalized matching.
3. Foster collaboration between PES, employers, and education systems.
4. Balance technology with human expertise to ensure cultural relevance and adaptability.

Conclusion
Improving job matching requires viewing it as an interconnected system, where advancements in one stage (e.g., data validation) create ripple effects across the framework. Tailored solutions that address local labor market dynamics, combined with continuous innovation and stakeholder engagement, are essential for creating an agile and inclusive workforce. The report emphasizes that technology must complement, not replace, human judgment and empathy in addressing complex labor market needs.