《世界經濟論壇&凱捷：2025匹配人才與未來職業：公共就業服務指南（英文版）（35頁）.pdf》由會員分享，可在線閱讀，更多相關《世界經濟論壇&凱捷：2025匹配人才與未來職業：公共就業服務指南（英文版）（35頁）.pdf（35頁珍藏版）》請在三個皮匠報告上搜索。

1、Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment ServicesF E B R U A R Y 2 0 2 5 I N S I G H T R E P O R TIn collaboration with CapgeminiThis report is interactiveLook out for this icon for pages that can be interacted with To ensure interactive capability,please download and

2、open this PDFwith Adobe Acrobat.Contents 2025 World Economic Forum.All rights reserved.No part of this publication may be reproduced or transmitted in any form or by any means,including photocopying and recording,or by any information storage and retrieval system.Disclaimer This document is publishe

3、d by the World Economic Forum as a contribution to a project,insight area or interaction.The findings,interpretations and conclusions expressed herein are a result of a collaborative process facilitated and endorsed by the World Economic Forum but whose results do not necessarily represent the views

4、 of the World Economic Forum,nor the entirety of its Members,Partners or other stakeholders.Images:Getty Images,MidjourneyForeword 4Executive summary 5Introduction 61 Framework for successful job matching 71.1 Job matching:definition and scope 81.2 Framework for successful 9 job matching2 Innovative

5、 solutions anduse cases 103 Case studies 22Case study 1:France 23Case study 2:Guatemala 24Case study 3:Nigeria 25Case study 4:Philippines 26Case study 5:Singapore 27Case study 6:Sweden 28Conclusion 29Appendix:Glossary 30Contributors 35Endnotes 36Matching Talent to theJobs of Tomorrow:AGuidebook for

6、Public Employment Services2ContentsForewordBy 2030,more than 20%of jobs globally are anticipated to undergo transformation due to disruptions in the labour market.This transformation will be driven by macrotrends such as the rapid adoption of frontier technologies,the green transition,demographic sh

7、ifts and geoeconomic fragmentation.According to the World Economic Forums Future of Jobs Report 2025,these shifts are expected to result in the creation of 170 million new jobs and,simultaneously,the displacement of 92 million others,underscoring the importance of efficient job matching for workers,

8、employers and public employment services alike to navigate this upheaval.1Improving the efficiency of job matching for job seekers and businesses will enable deeper labour market insights,stronger strategic workforce planning,more effective upskilling andreskilling programmes and,eventually,better e

9、mployment outcomes.Public employment services have a crucial role to play in enabling this process.To address this issue,the World Economic Forum and Capgemini have partnered to research best practices,challenges and solutions,focusing on public employment services operating at the forefront of labo

10、ur market disruptions.An extensive literature review,interviews with national employment services and a benchmark of technological solutions informed the design of this guidebook,which aims to be a practical resource for policy-makers.While there are no one-size-fits-all solutions,the guidebook addr

11、esses common challenges such as heterogeneity of jobs and skills languages and multiple sources of data.It additionally assesses emerging innovative solutions,including technological enablers such as artificial intelligence(AI)agents and blockchain solutions for certifications,mapping these factors

12、as part of the job matching journey.Whether policy-makers have already successfully embarked on this journey or are just beginning to prepare for it,the guidebook provides guidelines and illustrations on harnessing the best human capital and technological potential for matching talent to the jobs of

13、 tomorrow.Anne LebelChief Human Resources Officer,CapgeminiTill LeopoldHead,Work,Wages and Job Creation,World Economic ForumMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services3ContentsExecutive summaryA guidebook on innovative technology solutions for better labour marke

14、t outcomes.The rapid advancement of technologies such as artificial intelligence(AI)and machine learning(ML)is transforming job matching,enabling real-time labour market analysis,automated resume screening,and skills-based matching beyond traditional keyword searches.Public employment services have

15、long played a crucial role in connecting jobseekers with opportunities,driving workforce alignment,and advancing reskilling initiatives to improve employment outcomes.However,public employment services face significant barriers to effective job matching.These include skills misalignment due to the a

16、bsence of standardized frameworks,fragmented and inaccessible data sources that limit real-time labour market insights,and resistance to adopting emerging technologies.Technological innovations present an opportunity to overcome these obstacles.Yet,many public employment services worldwide have yet

17、to fully adopt advanced technologies to enhance efficiency and effectiveness in job matching.This guidebook offers a practical framework,actionable guidance and real-world case studies to support policy-makers in implementing and scaling technological solutions,ultimately driving better alignment be

18、tween skills andopportunities.A tailored data-driven frameworkThe guidebook outlines a five-step framework for public employment services to enhance job matching using data:(1)data access and collection,(2)data structuring and standardization,(3)data validation,(4)upskilling and reskilling,and(5)eff

19、ectivematching.Scalable solutions for better jobmatchingData is at the core of the framework,empowering public employment services to harness technologies such as AI and ML for more efficient and effective job matching.While these solutions can be capital-intensive and require a highly skilled workf

20、orce,countries can begin with cost-effective,low-tech options such as SMS-based tools.These practical approaches enable public employment services to address immediate needs while building capacity to adopt more advanced technologies.The guidebook highlights a range of advanced and cost-effective so

21、lutions for public employment services to improve the job matchingprocess.Insights from real-world case studiesImplementing innovative job matching requires a flexible approach to address the diverse needs of public employment services across countries.Six country-level case studies illustrate how p

22、ublic employment services with varying levels of technological maturity harness technologies to address their unique job matching challenges.These case studies highlight the importance of balancing innovative technologies with a human-centred approach to foster meaningful connections,minimize biases

23、 and ensure cultural relevance.Strengthening public-private collaboration is essential to align workforce supply with demand,and drive sector-wide innovation.The adoption of standardized skills frameworks provides a foundation for inclusive labour market insights and supports dynamic workforce plann

24、ing.Viewing job matching as an interconnected system allows for targeted improvements in one area to create ripple effects,ultimately boosting overall labour market efficiency.Through these strategies,public employment services can navigate the complexities of job matching and create a more agile an

25、d responsive labour market.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services4ContentsIntroductionIn particular,it targets public employment services seeking to support the matching of job seekers with opportunities at every level,including international organizations a

26、s well asnational,regional and local authorities.Effective job matching the process of connecting individuals of working age with meaningful employment faces mounting challenges.From misaligned skills frameworks and complex data workflows to concerns over data privacy and scepticism about emerging t

27、echnologies,the obstacles are many.This guidebook is designed to support policy-makers in enhancing job matching efficiency using emerging technological solutions.Chapter 2 shifts focus to technological innovations that can support job-matching activities today and in the future.A practical checklis

28、t is also provided,offering countries a step-by-step guide to advance through the various stages of job matching.Chapter 3 features six country case studies France,Guatemala,Nigeria,the Philippines,Singapore and Sweden showcasing best practices,challenges and solutions inreal-worldcontexts.Chapter 1

29、 of the guidebook presents a simple framework,outlining sequential steps and corresponding objectives that can be harnessed to enhance job-matching success and efficiency within public employment services.Additionally,a glossary of key technological terms offers accessible definitions to support und

30、erstanding ofthe concepts explored in thisguidebook.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services5ContentsFramework for successful job matching1A five-step framework to enhance job matching for public employment services focused on data access,standardization,valid

31、ation,upskilling and efficient matching.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services6ContentsJob matching refers to all activities that facilitate access to employment for individuals of working age,whether they are currently employed or actively seeking work.This

32、 process involves identifying suitable employment opportunities for individuals and ensuring that their skills and qualifications meet thedemands of thelabour market.The scope of job matching services varies across countries due to a complex interplay of technology adoption,economic conditions,and c

33、ultural and market dynamics.Countries may encounter a range of unique challenges,such as limited infrastructure,budgetary and regulatory constraints,and diverse levels of technological literacy among the population,allof which can hinder the effective adoption ofnew technologies that could greatly e

34、nhance the efficiency of job matching services.While these structural differences highlight the lack of a one-size-fits-all solution,the guidebook identifies key points of convergence in the definition and scope of job matching.1 Framework for successful job matchingJob matching:definition and scope

35、DefinitionCountry-specific needsScopeWhile the primary objective of job matching activities is to connect individuals with job opportunities within a national labour market or across borders,three complementary activitiesare commonly pursued through job matching:Market insights derived from data ana

36、lysis offer valuable information about labour market trends,employment patterns,skill demands and job locations.This data-driven approach enables policy-makers and employers to make informed decisions about workforce development andjobmatchingstrategies.Labour market insights Human capital planning

37、involves anticipating future labour market needs and preparing the workforce accordingly.This includes aggregating job offers and demand from diverse sources,andoffering valuable insights into emerging employment trends and opportunities.Human capital planning Upskilling and reskilling programmes ar

38、e structured training initiatives aimed at equipping individualswith the skills needed to meet the evolving demands of the labour market.Theseprogrammes,which can include vocational training,professional development courses andothereducational opportunities,play a crucial role in enhancing job match

39、ing services.Upskilling and reskillingMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services7Contents1 Framework for successful job matchingFramework for successful job matchingThis guidebook uses a simple framework,outlining five key steps for improving the success and eff

40、iciency of job matching for public employment services.Each step corresponds to akeyobjective of job matching,addressing common challenges.Step 1 Data access and collection:Gather comprehensive labour market data to analyse job demand and skill supply,tobetter understand current and future employmen

41、t needs.Step 2 Data structure and standardization:Create a common language by linking skills to jobs,certifications and credentials tofacilitate alignment between businesses and job seekers.Step 3 Data validation:Ensure data quality by verifying identities,credentials,skill proficiency levels and jo

42、bdescriptions,buildingtrust in the journey.Step 4 Upskilling and reskilling:Regularly update profiles with the latest learning and training credentials,ensuring that job seekers skills remain relevant,and increasing the accuracy andvalue ofskills matching.Step 5 Matching:Use clean,validated data inp

43、uts to generate accurate outputs,ultimately driving jobmatchingefficiency.Job matching success journeyMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services8ContentsInnovative solutions anduse cases2Exploring a diverse range of innovative and cost-effective approaches,provi

44、dingcomprehensive guidance across allstages of the job matching framework.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services9ContentsEach of the following pages is structured as follows:2 Innovative solutions anduse casesInnovative approaches for job matching processesH

45、ighlights advanced technologies enhancing job matching processes toaddress diverse needseffectively.Emphasizes practical and cost-effective technologiesto facilitate job matching processes,ensuringaccessibility and efficiency for all stakeholders.Cost-effective solutions for job matching processesDi

46、sruptive trends:tech provider perspectivesShares insights from tech providers on disruptive trends and innovations shaping thefuture of job matching.Outlines each steps objective from the public employment services perspectiveJob matching success journeyMatching Talent to theJobs of Tomorrow:AGuideb

47、ook for Public Employment Services10ContentsClick on an icon below to find out more about each step Access and collection2 Innovative solutions anduse casesStep 1:Data access and collection Collect accurate and timely data from businesses and job seekers to understand labour market dynamics.Innovati

48、ve approaches Datafrom businesses:Application programming interface(API)integrations with job boards can enable real-time vacancy updates and employer insights,while artificial intelligence(AI)-driven data analysis platforms can track labour market trends.Web scraping tools provide granular data fro

49、m employer sites andjob platforms.Data from job seekers:AI-driven profile analysers can automatically parse resumes and extract structured data,such as skills and qualifications.Combined with natural language processing(NLP),it processes unstructured data like resumes and cover letters to extract ke

50、y details.Chatbots can also assist job seekers by helping with submissions,answering queries and ensuring a seamless datacollectionprocess.Cost-effective solutionsData from businesses:Market surveys can be conductedto engage businesses and collect relevant information,along withforms and spreadsheet

51、s,which also provide a straightforward method for capturing data.Data from job seekers:SMS-based registration systems offer a practical way for job seekers to provide their information.Additionally,physical registration centres such askiosks or community offices can serve as data collectionhubs.Disr

52、uptive trends:tech provider perspectivesThe internet of things(IoT)will influence how data is collected.By harnessing interconnected devices,this technology will allow for real-time tracking of individuals performance on the job or during recruitment processes.AI can then be used to analyse the data

53、 and provide insight into where training is needed and what skills are lacking.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services11Contents Organize and standardize all the collected data into unified frameworks toenableefficient analysis.2 Innovative solutions anduse c

54、asesStep 2:Data structure and standardizationDisruptive trends:tech provider perspectivesPivot skill ontology technologies are key to the future of standardization,enabling a universal language that integrates diverse taxonomies.This allows public employment servicesto connect job seekers with oppor

55、tunities globally by accurately interpreting profiles across markets.Innovative approaches Public employment services could adopt a unified framework for labour market data,using tools like taxonomy management systems such as taxonomy-as-a-service(TaaS)and ontology editors to align with global stand

56、ards.NLP technologies can automate the structuring of unstructured data and address multilingual and domain-specific challenges,ensuring effective job andskills datastandardization.Cost-effective solutions Predefined taxonomies from global organizations such as the World Economic Forum,International

57、 Labour Organization(ILO),O*NET,etc.and simple categorization using spreadsheets can provide effective results.Partnerships with language experts or educational institutions are another way to support efforts to standardize data in local andregional contexts.Click on an icon below to find out more a

58、bout each step Structure and standardizationMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services12Contents2 Innovative solutions anduse casesStep 3:Data validation Ensure the authenticity of all the data through secure verification processes forcandidates and job postings

59、.Disruptive trends:tech provider perspectivesAudio and video analysis technologies will enhance job matching by assessing candidates communication skills,language proficiency and behaviour through video resumes,interviews and recorded responses.Innovative approaches Blockchain can create secure,tamp

60、er-proof records of certifications,boosting employer confidence.Token-based incentives on the blockchain can encourage accurate skills verification and credential sharing.Furthermore,onlineskills assessment platforms offer a practical solution by enabling direct digital testing of hard and soft skil

61、ls.Cost-effective solutions QR codes and web links are low-cost,user-friendly technologies that provide quick access to information and verification.They can embed key data,such as validated certifications,skills or work history,into a scannable or clickableformat.Click on an icon below to find out

62、more about each step ValidationMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services13Contents2 Innovative solutions anduse casesStep 4:Upskilling and reskilling Provide targeted training and resources to bridge skill gaps and align job seekers with labour market demands.D

63、isruptive trends:tech provider perspectivesIntegrating co-pilot tools such as generative AI(genAI)agents into employment platforms will transform the value chain.Understanding job seekers needs and creating tailored resources,agents will act as real-time learning coaches.A network of interconnected

64、agents will enable public employment services to provide holistic learningsolutions.Innovative approaches AI can generate hyper-personalized training content,including text,video,assessments and interactive tutorials.This content can then be easily embedded into a learning experience platform(LXP)th

65、at public employment services can harness to provide upskilling and reskilling pathways for job seekers and eventually create hyper-personalized learning content.Cost-effective solutions Tech-enabled community-based learning centres can deliver in-person training effectively and at scale.Basic print

66、ed materials or SMS-based training programmes can reach job seekers in remote areas.More generally,open educational resources(OER)provide public employment services with an affordable way to offer training and skill development opportunities to job seekers.Learning management systems(LMS)now provide

67、 affordable platforms for delivering and managing basic training and upskilling programmes efficiently,allowing public employment services to easily share and create their own training.Click on an icon below to find out more about each step Upskilling and reskillingMatching Talent to theJobs of Tomo

68、rrow:AGuidebook for Public Employment Services14Contents2 Innovative solutions anduse casesStep 5:Matching Accurately align job seekers with relevant opportunities to address labourmarket needs efficiently.Disruptive trends:tech provider perspectivesThe future of job matching will prioritize persona

69、lization,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 modernworkforce.Innovative approachesAI technologies,including machine learning(ML)and deep learning(DL),use

70、 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

71、transparency.AI technologies can also enhance job matching on another level by analysing candidates preferences,motivation and willingness,enabling more tailored and mutually beneficialplacements.Cost-effective solutionsPublic employment services can implement low-cost AI tools,such as open-source M

72、L 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 MatchingMatching Talent

73、 to theJobs of Tomorrow:AGuidebook for Public Employment Services15Contents2 Innovative solutions anduse cases How to get started data access and collection OutcomesClear understanding of current labour market trendsIdentification of skill surpluses and deficitsStronger data partnerships across sect

74、ors ActivitiesDefine the objective,the ambition and the programme statementEstablish who your partners areEstablish data-sharing agreements with your partnersEnsure compliance with national data privacy and security regulationsDevelop standards for data accuracy,consistency and completeness Prerequi

75、sites Data governance to manage dataeffectivelyKnowledge of basic data analysis and visualization techniquesKnowledge of labour market trends and job market dynamicsFamiliarity with sources of labour data Key success factorsStrong partnerships with public and private stakeholdersComprehensive and re

76、liable data collectionAccurate analysis of skills demand and supplyBasicSophisticationAccessibilityForms and surveysCommunity outreach campaignsLocal business partnershipsRegistrationcentresSMSregistrationMarket insight platformsWeb scrapingAPIsJob postingportalsProfileanalysersNatural language proc

77、essingAdvancedSpecialized solutionsWidely accessibleMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services17Contents2 Innovative solutions anduse casesHow to get started data structure and standardization OutcomesA unified language for skills and job rolesBetter data compat

78、ibility across systemsReduced complexity in matching across industries and regions ActivitiesDevelop or adopt taxonomies and ontologiesTrain staff on how to map and align diverse data formats to the frameworkMap and align collected data to your standardized frameworkRegularly update your framework t

79、o reflect changing market trendsPilot the process with a small dataset to identify gaps before scaling Prerequisites Access to basic classification systemsClear understanding of national and sector-specific requirementsKnowledge in taxonomy and ontology managementSkills classification methodsLinguis

80、tic understanding for multilingual adaptation Key success factorsConsistent updates to taxonomiesCollaboration with industry experts to ensure relevanceSeamless integration with existing systemsBasicSophisticationAccessibilitySpreadsheetsOpentaxonomiesOntologyeditorsTaxonomy-as-a-serviceSpecializedl

81、anguageprocessing toolsNatural language processingAdvancedSpecialized solutionsWidely accessibleMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services18Contents2 Innovative solutions anduse casesHow to get started data validation OutcomesVerified and trusted candidate crede

82、ntialsReduced risk of skill misrepresentationImproved stakeholder confidence in the matching process ActivitiesEstablish partnerships with credentialing institutionsDevelop a process for verifying skills and qualificationsEducate job seekers and employers on how to participate in the validation proc

83、essProvide training to employers on using verification tools or assessing the authenticity of credentialsEnsure secure storage and handling ofvalidated credentials Prerequisites Existing credential frameworksBasic digital infrastructure for data verificationUnderstanding of skill assessment techniqu

84、esKnowledge of secure data handling practicesBasic familiarity with credential standards Key success factorsClear validation protocolsTransparency and trust-building among stakeholdersScalable solutions for large-scale implementationBasicSophisticationAccessibilityQR codesand linksSkill assessment p

85、latformsBlockchainVideo andaudio analysisToken-basedincentivesAdvancedSpecialized solutionsWidely accessibleMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services19Contents2 Innovative solutions anduse cases How to get started upskilling and reskilling OutcomesA more skille

86、d and adaptable workforceEnhanced employability for job seekersImproved alignment of workforce skills with market needs ActivitiesIdentify key gaps and focus on high demand skillsDevelop or adopt learning platforms,e.g.LXPs,or open educational resources(OER)Create personalized training pathways alig

87、ned with your job market requirementsPromote awareness campaigns to encourage upskilling initiativesMonitor and evaluate the effectiveness of training programmes Prerequisites A baseline understanding of skills in demandPartnerships with training providersCurriculum development and training methodol

88、ogiesUnderstanding of labour market needsKnowledge of learning platforms for skill-building Key success factorsAlignment between training programmes and market needsAccess to scalable and tailored learning resourcesEffective communication between public employment services and educationalpartnersBas

89、icSophisticationAccessibilityLearning management systemOpen educational resourcesSMS-based training programmesLearning experience platformGenAIGenAIagentsSkill gapanalysis toolsAdvancedSpecialized solutionsWidely accessibleMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Servi

90、ces20Contents2 Innovative solutions anduse casesHow to get started matching OutcomesFaster and more accurate job matchingActionable data intelligence for further labour market insightsEnhanced cross-border matching capabilities ActivitiesBuild or adopt a system to match job seekers to vacanciesGathe

91、r job seeker and employer feedback to refine matching criteriaIntegrate AI tools,when possible,to improve match qualityUpdate matching systems with real-time labour market trendsPromote successful matches to build trust in the system Prerequisites Access to robust datasets for matchingInitial implem

92、entation of structuring and validation stagesAlgorithm design and implementationKnowledge of market dynamics and demand-supply alignment Key success factorsTransparent and explainable matching processesRegular updates to algorithms based on feedbackResponsiveness to evolving labour markettrendsBasic

93、SophisticationAccessibilityTabular datamanagement toolsOpen-source machine learning modelsLarge language modelsGenAIAI(ML and DL technologies and tools)AdvancedSpecialized solutionsWidely accessibleMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services21ContentsCase studies

94、3Case studies from six countries France,Guatemala,Nigeria,the Philippines,Singapore and Sweden illustrate how technology is reshaping job matching practices.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services22Contents2 Innovative solutions anduse casesC A S E S T U D Y

95、1France About France TravailFrance Travail is a French public employment service.Its mission is to support job seekers in their search for work and to assist companies in their recruitment efforts.Its close to 54,500 employees carry out this mission they are mobilized to anticipate trends,innovate a

96、nd convene key stakeholders involved.Job matching scopeFrance Travail offers tailored job matching services for job seekers and employers across sectors.It supports career transitions,beneficiaries of the Revenu de Solidarit Active(RSA)*,and recruitment alignment through advanced AI tools that stand

97、ardize job descriptions and identify needed skills.By considering factors like location,working conditions and transferable skills,it enhances job matches.Since 1989,its national jobs and skills repository,ROME,which was strongly reviewed in 2024,has provided an updated framework for understanding t

98、he labour market and improving recruitment efficiency.Technology use casesFrance Travail combines advanced digital tools and human expertise to improve job matching.Using AI,semantic analysis and generative AI,itidentifies skill similarities,generates detailed job descriptions and enhances job recom

99、mendations.Recent additions,like conversational agents in mobile channels,provide personalized guidance for job seekers.By integrating data from state services,professional partners and private job boards,France Travail ensures centralized,clean and structured information.Public-private partnerships

100、(PPPs)and scalable infrastructure process large datasets,delivering responsive,Key success factors Collaborative approach:France Travail actively partners with other public service entities,local employment networks and business associations to develop widely recognized and validated job description

101、s,skills frameworks and shared tools,ensuring alignment and credibility across stakeholders.Data integration:The organization adoptsthe“Tell Us Once”principle to reduce data redundancy by efficiently using existing structured and unstructured data from other public services,streamlining processes an

102、d reducing administrative burdens.Technological innovations and continuous improvement:France Travails emphasis on experimentation has improved job matching services,achieving faster job description generation and higher response rates from job seekers.Innovations like conversational agents enhance

103、efficiency,engagement andinclusivity.data-driven solutions for the evolving labour market.Despite its technological advancements,France Travail emphasizes the critical role of human insight in building trust and ensuring successful jobplacements.ChallengesFrance Travail faces a range of challenges i

104、n adopting and implementing advanced technological solutions.A critical concern is ensuring that technologies align effectively with the practical realities of the job market.Translating the complexity of job market dynamics into IT frameworks,including AI,requires close collaboration between job ma

105、rket experts and technical developers.Ensuring transparency and fairness is equally critical,requiring AI systems to be explainable and free from bias.For instance,if past hiring practices favoured specific demographics,AI systems might unintentionally perpetuate these biases in its recommendations.

106、Moreover,the organization must ensure that technology complements(rather than replaces)human expertise,which remains essential to cultivating trust andmaintaining the quality and integrity ofjobmatching services.*RSA is a social welfare benefit in France designed to ensure a minimum income for indiv

107、iduals or households with insufficient resources.The amount provided varies depending on the household composition.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services23Contents2 Innovative solutions anduse casesC A S E S T U D Y 2Guatemala Job matching scopeIn Guatemala,

108、job matching services are delivered through a PPP.The Ministry of Labour manages the Tu Empleo platform,which connects job seekers with opportunities.Meanwhile,Guatemala Moving Forward focuses on providing training in English,programming and management skills to meet the demand of emerging job oppor

109、tunities.The public sector ensures broad access and regulatory compliance,while the private sector provides technology and market insights,streamlining job matching and improving the employment marketseffectiveness.Technology use casesGuatemala is gradually adopting technological solutions for job m

110、atching,with the primary public platform,Tu Empleo,currently using a web interface and web-based forms for data collection and job postings.However,there is a clear shift towards incorporating more advanced technologies,such as AI and data analytics,to improve efficiency and accuracy.The Ministry of

111、 Labour looks to benchmarks such as the Future Up platform in Costa Rica,currently being developed by the Inter-American Development Bank(IDB),which aims to integrate AI to analyse data more effectively,assessjob seekers skills and provide personalized recommendations for trainingprogrammes.Challeng

112、esGuatemala faces significant challenges in job matching,including reliance on manual operations,lack of integrated systems and legal complexities related to licensing agreements and procurement laws.Budget constraints and low technological literacy further hinder progress,especially in rural areas

113、with limited internet access.Only 4%of workers extensively use digital tools,while low educational attainment and high dropout rates create the potential of ayoung underutilized population.4 These issues contribute to mismatches between job market demands and workforce skills,emphasizing the need fo

114、r user-friendly technologies accessible to both employers and employees.Key success factors PPPs:Guatemala is prioritizing PPPs to improve understanding of labour market needs and drive technological change and improve job matchingservices.About Guatemala MovingForwardGuatemala Moving Forward(Guatem

115、ala No Se Detiene)2 is a PPP aimed at attracting foreign investment,boosting the economy and creating jobs to improve living conditions.Running through 2032,the initiative focuses on human capital development,enhancing skills aligned with future workforce needs to help Guatemalans access opportuniti

116、es in high-growth industries.3Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services24Contents2 Innovative solutions anduse casesC A S E S T U D Y 3Nigeria About National Talent Export ProgrammeThe National Talent Export Programme(NATEP)was established in 2023 to capitalize

117、 on Nigerias talent pool and drive economic growth through outsourcing and physical talent export.Its mission is to build a sustainable talent pipeline,harness the countrys human capital andposition Nigeria as a global talent hub.NATEP aims to create 1 million jobs and increase foreign exchange earn

118、ings through strategic partnerships and advanced technology.5 Job matching scopeIn Nigeria,public job matching services target technology-driven sectors like insurance,healthcare,banking,artisanship,the creative industry and tech to drive economic transformation and global competitiveness.The proces

119、s involves outsourcing local and international remote work and physical talent exports for global opportunities.The goal is to align skilled labour supply with sector demands,both internationally and domestically,driving economic growth,innovation and workforce adaptability in a rapidly evolving glo

120、baleconomy.Technology use casesTechnological solutions are central to Nigerias efforts to streamline job matching,aiming to create or facilitate employment for 1million individuals over five years.AI tools enhance efficiency by screening candidates,assessing skills and verifying identities through n

121、ational databases,optimizing and scaling the job matching process.Supported by PPPs and third-party contracting,Nigeria plans to expand its systems with personalized training and mobile-friendly technologies,taking advantage of widespread mobile phone use.The adoption of a skills taxonomy will furth

122、er improve job matching by providing a structured framework to monitor and assess workforce capabilities.ChallengesNigeria faces significant challenges in implementing efficient job matching processes,including limited budgets,unreliable internet connectivity,unstable power supply and low digital li

123、teracy.To address these issues,Nigeria is focusing on strengthening infrastructure and promoting digital upskilling initiatives.User resistance and limited in-house capacity further hinder the adoption and management of technology-enabled systems,highlighting the needfor lightweight,mobile-friendlys

124、olutions.Key success factors Public policies and PPPs:Government mandates and public-private collaborations encourage the adoption of innovative solutions for employment services.Upskilling programmes:Comprehensive training programmes are essential for equipping job seekers with the skills required

125、for evolving labour market demands.Technological innovation:Advanced AI-driven tools are critical to streamlining candidate screening,skill assessment and identity verification,ensuring precise and efficient jobmatching.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services

126、25Contents2 Innovative solutions anduse casesC A S E S T U D Y 4Philippines About the Department ofTrade and IndustryThe Department of Trade and Industry(DTI)in the Philippines is a government agency dedicated todriving trade and industry development.It plays a pivotal role in supporting businesses,

127、attracting investments and ensuring consumer protection.DTIs mission is to drivea competitive and innovative industrial sector,facilitate inclusive economic growth and generate employment through effectivepolicies and programmes.6 Job matching scopeThe Philippines job-matching services integrate gov

128、ernment-led efforts with private sector initiatives to create a comprehensive employment ecosystem.The national government is currently developing a digital portal designed to streamline job matching across diverse sectors,including opportunities abroad.This platform enhances connectivity between jo

129、b seekers and employers through features like user registration,profile verification and detailed job preference settings.Assessing this information,the system will efficiently identify and match candidates with themost suitable employment opportunities.Technology use casesThe national government in

130、 the Philippines uses online portals and mobile applications to deliver job matching services,offering job alerts and virtual skill-building resources.The current system employs non-AI filtering to connect job seekers with opportunities,developed and managed through in-house efforts,local ICT collab

131、orations and third-party consultants.Future plans include integrating AI-driven features to enhance job matching precision by considering factors like personality traits and cultural alignment for a more comprehensive employment service.Key success factors Government supportfor digital transformatio

132、n:Initiatives such as the establishment of the Centre for AI Research and the inclusion of innovation as a pillar in the competitiveness index for cities and municipalities drive progress in digital employment solutions.PPPs:Collaborative efforts between government and private entities ensure the re

133、levance and effective implementation of tailored solutions across various sectors.ChallengesThe effective deployment of solutions for job matching in the Philippines encounters several challenges.A critical gap in comprehensive and up-to-date data on skills demand results from fragmented and outdate

134、d data collection systems,limited coverage of rural and informal sectors,and infrequent labour market studies that do not keep pace with evolving needs.These issues hinder accurate matching between job seekers and opportunities.Inadequate technological infrastructure and limited internet access,part

135、icularly in rural areas,also restrict the reach and effectiveness of digital platforms.Budget constraints impede the development,deployment and scaling of advanced technologies.Existing systems often have complex user interfaces that discourage engagement from job seekers andemployers.Moreover,ineff

136、iciencies in profile verification and authenticity checks lead to delays and obstacles in hiring processes.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services26Contents2 Innovative solutions anduse casesC A S E S T U D Y 5Singapore About SkillsFuture SingaporeSkillsFutur

137、e Singapore(SSG)is a government agency that drives and coordinates the implementation of the national SkillsFuture movement,promotes a culture of lifelong learning and strengthens the ecosystem of training and adult education in Singapore.Through a holistic suite of national SkillsFuture initiatives

138、,SSG enables Singaporeans to take charge of their learning journeys pursuit of skills mastery.SSG also works with key stakeholders to ensure that students and adults have access to high-quality and industry-relevant training that meets the demands of different sectors of the economy for aninnovative

139、 and productiveworkforce.7 Job matching scopeSkillsFuture Singapore focuses on skills as the core of its job matching services,aligning workers with opportunities based on employer demand and promoting informed education and training decisions.Businesses benefit from strategic workforce planning and

140、 investment in skills development.SSG adopts an ecosystem approach by ensuring adequate,high-quality training programmes are available,providing online and in-person career-skills advisory services,reducing barriers to participating in training through modular and flexible learning,upskilling adult

141、educators and investing in research and evaluation.A robust skills taxonomy ensures seamless alignment across the ecosystem,enhancing job matching,sustaining low unemployment and maintaining economiccompetitiveness.Technology use casesSkillsFuture Singapore employs advanced technological solutions,s

142、uch as cloud computing and ML,to enhance job matching and workforce planning.SSG also deploys chatbots to provide a conversational interface to users.The integration of these technologies is critical for efficiently managing large datasets and delivering user-friendly interfaces for both individuals

143、 and employers.Other innovations include acareer skills passport,which serves as a career-and-skills registry for individuals,Standardization:The adoption of a standardized skills language enhances the efficiency of job matching services and workforce planning.Data integration:Various data sources,i

144、ncluding job postings and employment data,are integrated to offer a comprehensive view of job demand and workforce skills.Accessibility:The deployment of intuitive and accessible tools has significantly improved user engagement and outcomes.Monitoring and continuous improvement:Success is measured b

145、oth quantitatively(e.g.the number of users and participating companies)and qualitatively(e.g.user satisfaction and the alignment of skills with job market demands).Continuous monitoring and adaptation ensure tools remain relevant to the evolving job market.and specialized tools designed to help smal

146、l-and medium-sized enterprises(SMEs)profile their workforcescapabilities.ChallengesBesides the well-established national skills taxonomy,institutional users are also free to adopt other taxonomies embedded within human resources technology(HRTech)platforms.However,while large companies often maintai

147、n their own taxonomies,they align them with the national framework when hiring locally to minimize inconsistencies and support data integration.Additionally,SSG encourages the private sector to innovate and design tools to meet the diverse needs and interaction preferences of individuals and employe

148、rs.Ensuring transparency in underlying methodologies and incorporating iterative refinements based on user feedback have been critical for continuously enhancing userinterfaces.Key success factors Government support and PPPs:Strong governmental backing ensures the effective rollout of technological

149、solutions,while collaboration with sector agencies,trade associations and professional bodies enhances their adoption and integration across industries.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services27Contents2 Innovative solutions anduse casesC A S E S T U D Y 6Swed

150、en Job matching scopeArbetsfrmedlingen supports job seekers,especially those facing barriers such as low skills,asylum status or immigration challenges,in finding suitable opportunities.The agency offers programmes like internships and targeted training to aid workforce integration.A robust job and

151、skills taxonomy ensures precise alignment between job seeker skills and employer needs,enhancing job matching efficiency and cultivating an inclusive employment ecosystem.Technology use casesArbetsfrmedlingen uses advanced technological solutions,including NLP and LLMs,to enhance job matching.These

152、technologies extract relevant information from job advertisements to match it with job seekers profiles.The agency has developed in-house modelstailored specifically to the Swedish language and local requirements.This approach addresses the lack of suitable external solutions,ensuring high-quality a

153、nd context-specific models.Additionally,Arbetsfrmedlingen collaborates with public innovation agencies tofurther developthesetechnologies.Monitoring and continuous improvement:While quantitative data tracking remains a challenge,qualitative feedback from job seekers suggests increased satisfaction a

154、nd better alignment of skills with job opportunities.Continuous refinement of the system based on feedback is key to ensuring its success.ChallengesArbetsfrmedlingen has faced several challenges that impact job matching efficiency.One major issue is that job advertisements often lack detailed and ac

155、curate data,particularly around skill requirements,making precise matching difficult.The absence of follow-up data on job seekers post-service outcomes further complicates the evaluation of long-term effectiveness.Finally,the rapid pace of technological advancements necessitates ongoing adaptation a

156、nd investment in new tools and methods.Addressing these challenges is essential to improve the efficacy of Arbetsfrmedlingens employment services.Key success factors Government support and PPPs:Strong collaboration with other public agencies enhances resource sharing and strategic alignment.PPPs bri

157、ng additional expertise and technological capabilities to enhance job matching services.Technological innovations and continuous improvement:The integration of AI and NLP significantly enhances the accuracy and efficiency of job matching by providing a deeper understanding of both job ads and job se

158、ekers profiles.Continuous innovation is vital to maintaining and improving the effectiveness ofjob matching.About ArbetsfrmedlingenArbetsfrmedlingen is Swedens national public employment service.Its mission is to contribute to a well-functioning labour market by supporting businesses in recruitment

159、efforts and providing individuals particularlythose facing challenges inentering the labour market with the tools and programmes needed to enhance their employability and secure suitable job opportunities.Through collaboration with municipalities and other stakeholders,the agency aims to improve the

160、 efficiency and effectiveness of its services.8Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services28Contents Creating a more dynamic and responsive job market requires a foundational focus on public-private collaboration.Partnerships between public employment services,pr

161、ivate sector companies and the education sector are essential to align workforce supply and demand effectively.Moreover,as the job matching landscape evolves,collaboration across sectors ensures that technological innovation supports,rather than replaces,human potential.By aligning strategies,co-des

162、igning job matching and learning technologies,cultivating trust and embracing flexibility,stakeholders can create systems that meet the demands of a complex and ever-changing workforce.The development of shared standardized skills frameworks and strategies drives improved integration of labour marke

163、t insights,enabling solutions that are both inclusive and adaptable to changing needs.The job matching efficiency journeyshould not be seen as a rigid or isolated process Conclusionbut rather as an interconnected system where each stage supports and enhances the rest.The use of one technology in one

164、 step often creates a ripple effect,improving the overall efficiency and outcomes of the entire framework.This interconnection highlights the importance of viewing progress as cumulative,with even small improvements in one stage potentially leading to broader systemic benefits across the wider job m

165、atching process.Innovative job matching systems must balance advanced technologies with a human-first approach.While AI and data-driven tools enhance precision and scalability,empathy and human judgment remain central to cultivating meaningful connections,avoiding bias and respecting individual aspi

166、rations.Equally important is recognizing that one size does not fit all;tailored solutions that address the unique needs of different labour markets,including cultural and technological considerations,are crucial for ensuring relevance and effectiveness.This is where cognitive diversity becomes a ke

167、y enabler.By assembling teams with varied expertise and problem-solving approaches,policy-makers can design tailored frameworks that address the unique needs of their labour markets.This approach ensures solutions are both culturally relevant and effective,usingdiverse perspectives to create innovat

168、ive and adaptable strategies forimproving workforce alignment.This guidebook offers four key recommendations for the success of job matching journeys:Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services29ContentsAppendix:GlossaryMatching Talent to theJobs of Tomorrow:AGui

169、debook for Public Employment Services30ContentsGlossary(1/4)TermGeneric definitionApplied to job matchingAgentAn agent is an autonomous AI unit that communicates with other agents,makes decisions,and plans and performs tasks.It is customizable for integration with language models,humans,tools or the

170、ir combinations.An agent can automate tasks in job matching such as analysing job descriptions and candidate profiles and generating personalized resources.It can streamline onboarding as an intermediary.Application programming interface(API)An API is a set of rules and protocols that allows differe

171、nt software applications to communicate and share data with one another,enabling seamless integration and functionality across platforms.An API can facilitate the integration of public employment services systems with external platforms,such as job boards,educational platforms or employer databases,

172、allowing for real-time data exchange on job vacancies,skills and qualifications.Artificial intelligence(AI)AI is a broad field of computer science focused on creating systems that can perform tasks that would typically require human intelligence.This includes a wide range of capabilities such as lea

173、rning,problem-solving,perception,language understanding,etc.9 AI is an overall concept encompassing a wide variety of AI subsets that analyse large sets of candidate and job data to automate the process of matching the right talent to the right opportunities.This makes recruitment faster and more pr

174、ecise than manual filtering.Big dataThis is an all-encompassing term for large,complex digital data sets that require equally complex technological means to be stored,analysed,managed and processed with substantial computing power.10 Big data is an overarching concept that fuels predictive algorithm

175、s by analysing vast candidate pools and job data,enabling faster and more accurate matches compared to smaller datasets.BlockchainA blockchain is a distributed ledger which maintains all transactions and assets and is updated by a number of counterparties.11 Blockchain cannot be easily tampered with

176、,mostlybecause it is decentralized and uses a secure and linkeddata structure.Blockchain can be used to maintain transparent and immutable records of candidates credentials,work histories and skills,enhancing trust and eliminating the risk of data manipulation.ChatbotA chatbot is a computer program

177、designed to simulate conversation with a human user(usually over the internet)especially one used to provide information or assistance to the user as part of an automated service.12Chatbots can guide candidates through job applications,answer queries or even assess basic skills.Unlike static web for

178、ms,they create a more interactive experience while reducing recruiter workload.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services31ContentsGlossary(2/4)TermGeneric definitionApplied to job matchingDeep learning(DL)DL is a type of machine learning(ML)that enables machine

179、s to mimic human behaviour by learning from data and identifying patterns.13DL can analyse video resumes,interpret nuanced resume language and match candidates by identifying deeper skill-job connections.It can also handle advanced tasks like image and speech recognition,though it is more computatio

180、nally intensive than standard ML.Generative AI(GenAI)GenAI is a category of AI that can create new content such as text,images,videos and music.14GenAI can create personalized job recommendations,interview questions and learning content based on specific skills and gaps.Unlike ML or DL,it focuses on

181、 content creation,adding creativity and adaptability to job matching.Inference modelThis is a statistical model used to predict or infer the properties of a population based on sample data,especially useful in scenarios where direct measurement of all data points is impractical.These models can pred

182、ict the likelihood of a candidates success in a role based on historical data,helping recruiters make data-driven hiring decisions.Internet of things(IoT)The inter-networking of physical devices and objects whose state can be altered via the internet.15IoT facilitates the use of interconnected devic

183、es to collect real-time data on labour market trends,workplace environments and industry demands.By using IoT data,public employment services can gain precise insights into emerging job opportunities,workforce needs and skills requirements.InteroperabilityInteroperability refers to the ability of so

184、ftware or hardware systems or components to operate together successfully with minimal effort by an end user.16Interoperability can ensure that tools like job boards,applicant tracking systems,and learning platforms share data effortlessly,enabling comprehensive talentmanagement.Large language model

185、(LLM)LLMs are a class of language models that use DL algorithms and are trained on extremely large textual datasets that can be multiple terabytes in size.17LLMs can process resumes,answer candidate queries and enhance chatbots for real-time career guidance.Specializing in language comprehension and

186、 generation,they excel in tasks involving communication or text-heavy data in job matching.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services32ContentsGlossary(3/4)TermGeneric definitionApplied to job matchingLearning experience platforms(LXP)Platforms that deliver pers

187、onalized learning experiences to users by using AI to recommend learning resources based on their needs,preferences and pastbehaviour.LXPs can help workers and job seekers upskill by recommending training aligned with their career goals and available job openings,unlike traditional systems that foll

188、ow a one-size-fits-all approach.Learning management systems(LMS)An LMS is a software platform used to create,deliver and manage educational content and training programmes.LMSs can bridge skill gaps by offering training courses to align candidate skills with job requirements.They focus more on struc

189、tured learning than LXPs,which prioritize personalization.Machine learning(ML)ML is a branch of AI and computer science that focuses on the development of systems that can learn and adapt without following explicit instructions.ML imitates the way humans learn,gradually improving its accuracy by usi

190、ng algorithms and statistical models to analyse and draw inferences from patterns in data.18ML algorithms can analyse past hiring data to predict which candidates might be a good fit based on skills,experience and job descriptions.They focus on identifying patterns and making predictions from struct

191、ured or unstructured data.Natural language processing(NLP)NLP refers to the ability of a machine to process,analyse and mimic human language,either spoken or written.19NLP can extract key information from resumes and job descriptions,speeding up and improving accuracy compared to manual keyword sear

192、ches.OntologyOntology refers to defining the properties of subject areas and how they are related by defining a set of concepts,terminologies and categories that represent the subject.It maps out skills,roles and qualifications to standardize how candidates are assessed.Unlike taxonomy,ontology enco

193、mpasses relationships between concepts.Open educational resources(OER)OERs are learning,teaching and research materials in any format and medium that reside in the public domain or are under copyright that have been released under an open license,permitting no-cost access,re-use,re-purpose,adaptatio

194、n and redistribution by others.20OERs can provide job seekers with accessible learning resources to upgrade skills based on market demands.Public employment services can use OERs for cost-effective training,improving employability and reducing barriers to skilldevelopment.Matching Talent to theJobs

195、of Tomorrow:AGuidebook for Public Employment Services33ContentsGlossary(4/4)TermGeneric definitionApplied to job matchingParsingThe process of analysing a string of symbols.TermGeneric definitionApplied to job matchingParsingThe process of analysing a string of symbols.Parsing is the ability to extr

196、act skills,experiences and contact details from textual data like resumes.Pivot ontologyA pivot ontology is a centralized framework used to align and integrate multiple,diverse ontologies.It acts as an intermediary that standardizes how different datasets or systems interpret and communicate concept

197、s,ensuring interoperability and consistency.A pivot ontology can standardize diverse taxonomies from different countries by mapping them to a unified framework.Semantic analysisSemantic analysis is the process of understanding the meaning and interpretation of words,phrases and sentences in the cont

198、ext of the languages they are used in.Semantic analysis ensures better job candidate alignment by illuminating the intent and context of job descriptions and resumes,unlike keyword-based matching.Skills taxonomyThis is a structured classification of skills,used to standardize and organize skills dat

199、a across platforms and industries.It standardizes how skills are categorized and matched,ensuring consistency.Unlike ontologies,it does not capture relationships or contexts.TokenA token is a digital asset created and managed on a blockchain platform.Tokens can represent a wide range of assets or ut

200、ilities,such as currency,ownership rights or access to specific services,and they are secured by the blockchains decentralized and tamper-proof system.Tokens can serve as a digital incentive or credential.For example,tokens may be awarded to individuals for verifying their skills or contributing acc

201、urate data.Unstructured dataThis refers to information that does not have a predefined format or organization,making it difficult to process and analyse using traditional methods.Processing unstructured data requires advanced technologies(like NLP,ML,DL,etc.)to extract key insights,such as identifyi

202、ng hidden skills in resumes or understanding nuanced job requirements.Matching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services34ContentsContributorsWorld Economic ForumXimena Jtiva Insight Lead,Education,Skills and LearningTill Leopold Head,Work,Wages and Job CreationShuvasis

203、h Sharma Insights Specialist,Work,Wages andJobCreationThe authors are extremely grateful to colleagues at the World Economic Forum and Capgemini who have provided invaluable insights and support over the course of this project.At the World Economic Forum:Neil Allison,Alison Eaglesham,Genesis Elhusse

204、in,Sam Grayling,Aarushi Singhania and Steffica Warwick.At Capgemini:Mark Howarth,Alex Marandon,Catherine Paquet and Helena Vilcans.CapgeminiStphanie Bertrand Director,Workforce and OrganizationClaudia Crummenerl Global Head,Advisory for Strategic PartnershipsDawn Dalton Consultant,Workforce and Orga

205、nization,Capgemini InventRobin Hartmann Consultant,frogAmy Kowalska Senior Manager,Workforce and OrganizationAntoine Schenesse Consultant,Workforce and OrganizationNeil Shastri Senior Director,Workforce and OrganizationJohann Zillmann Vice-President,Enterprise TransformationAcknowledgementsThe autho

206、rs would like to thank the following individuals for their insights and contributions to the reports case studies:Olufemi Adeluyi Assistant Director,National Information Technology Development Agency(NITDA)Catherine Beauvois Project Director,Skills 4.0,JOP and Behavioral Science Development,France T

207、ravailLigia Chinchilla Executive Director,Mesa Capital HumanoAnders Danell Head,AI Center Department,ArbetsfrmedlingenJo-Dann N.Darong Director III,Competitiveness and Innovation Group,Department of Trade and Industry,ThePhilippinesEghosa Ero National Talent Export Programme(NATEP)Soon-Joo Gog Chief

208、 Skills Officer,SkillsFuture Karolina Jedrzejewska Product Manager,Applied AI,ArbetsfrmedlingenHlne Rohaut-Marchal Data&AI Programme Manager,France TravailProductionLouis Chaplin Editor,Studio MikoLaurence Denmark Creative Director,Studio MikoWill Liley Editor,Studio MikoCat Slaymaker Designer,Studi

209、o MikoOliver Turner Designer,Studio MikoMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services35Contents1.World Economic Forum.(2025).Future of Jobs Report 2025.https:/reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf.2.Guatemala Moving Forward.(n.d.).Guatemala No

210、 Se Detiene.https:/guatemalanosedetiene.gt/en/.3.Guatemala Moving Forward.(2024).Guatemala Moving Forward.Results Presentation.https:/guatemalanosedetiene.gt/wp-content/uploads/2024/06/June-2024-English.pdf.4.World Bank.(2024,10 December).The Future of Work in Guatemala.https:/www.worldbank.org/en/n

211、ews/infographic/2024/09/11/futuro-trabajo-guatemala#:text=Limited%20adoption%20and%20diffusion%20of,progress%20can%20bring%20to%20Guatemala.5.National Talent Export Programme.(n.d.).About Us.https:/natep.gov.ng/about-us/.6.Republic of the Philippines,Department of Trade and Industry.(n.d.).About the

212、 Department of Trade and Industry.https:/www.dti.gov.ph/about/.7.SkillsFuture Singapore.(n.d.).Skills Demand for the Future Economy 2023/24.https:/www.skillsfuture.gov.sg/docs/default-source/skills-report-2023/sdfe-2023.pdf.8.Arbetsfrmedlingen.(2024).Our business.https:/arbetsformedlingen.se/om-oss/

213、var-verksamhet.9.International Labour Organization(ILO).(n.d.).ILO Thesaurus.https:/metadata.ilo.org/thesaurus/1491829989.html.10.European Commission.(2023).EU-U.S.Terminology and Taxonomy for Artificial Intelligence,pp.4.https:/digital-strategy.ec.europa.eu/en/library/eu-us-terminology-and-taxonomy

214、-artificial-intelligence.11.European Union Agency For Cybersecurity.(2017,18 January).Distributed Ledger Technology&Cybersecurity Improving information security in the financial sector.https:/www.enisa.europa.eu/news/enisa-news/enisa-report-on-blockchain-technology-and-security.12.European Commissio

215、n.(2023).EU-U.S.Terminology and Taxonomy for Artificial Intelligence,pp.11.https:/digital-strategy.ec.europa.eu/en/library/eu-us-terminology-and-taxonomy-artificial-intelligence.13.World Bank.(2023).The Use of Advanced Technology in Job Matching Platforms,pp.5.https:/thedocs.worldbank.org/en/doc/ceb

216、5c5792ad0d874e9b1c3cc71362f46-0460012023/original/Digital-Job-Matching-Platforms-IS4YE-Draft-Note-for-Discussion.pdf.14.The Organisation for Economic Cooperation and Development(OECD).(n.d.)Generative A.https:/www.oecd.org/en/topics/generative-ai.html#:text=Generative%20AI%20(GenAI)%20is%20a,Large%2

217、0Language%20Models%20(LLMs).15.The Organisation for Economic Cooperation and Development(OECD).(2023).Measuring the Internet of Things.https:/www.oecd.org/en/publications/measuring-the-internet-of-things_021333b7-en.html#:text=This%20report%20provides%20new%20evidence,all%20examples%20of%20IoT%20app

218、lications.16.European Commission.(2023).EU-U.S.Terminology and Taxonomy for Artificial Intelligence,pp.10.https:/digital-strategy.ec.europa.eu/en/library/eu-us-terminology-and-taxonomy-artificial-intelligence.17.Ibid.18.Ibid.19.Ibid.20.United Nations Educational,Scientific and Cultural Organization(UNESCO).Open Educational Resources.https:/www.unesco.org/en/open-educational-resources.EndnotesMatching Talent to theJobs of Tomorrow:AGuidebook for Public Employment Services36Contents