This article is part of:Centre for the New Economy and Society

• The global labour market is transforming due to shifting demographics, new technology and economic disruption.
• Conventional job-matching systems are becoming outdated as a result, leading to prolonged unemployment and reduced productivity.
• A recent report, Matching Talent to the Jobs of Tomorrow, explains how public employment services can use technology to better match jobs with talent.

By 2030, more than 20% of jobs are expected to transform due to advances in digital technology, demographic shifts and economic disruption. This is already reshaping how talent is found, trained and deployed, requiring new strategies to ensure alignment between skills and opportunities.

Conventional job-matching systems often fail to capture the full range of a candidate's skills. They rely heavily on keyword matches and rigid job titles, overlooking candidates’ transferrable skills, growth potential and adaptability. This outdated approach leads to skills mismatches, prolonged unemployment and reduced productivity.

Moreover, inconsistent skills taxonomies and fragmented labour market data make it challenging for stakeholders – governments, employers and education providers – to exchange clear and actionable information on skills needs. It also prevents them from responding to fast-changing labour market demands.

Without standardized frameworks, it's difficult to ensure that everyone is speaking the same language when it comes to skills and competencies. This lack of cohesion hinders both individual career progression and broader economic resilience.

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Towards a smarter, skills-based ecosystem

Technology is transforming how we connect people with jobs. Tools like artificial intelligence (AI), machine learning and blockchain are helping to build dynamic, data-rich systems that more closely align workers’ skills with market demands.

A report, Matching Talent to the Jobs of Tomorrow, published in February 2025 by the World Economic Forum and Capgemini, lays out a five-step roadmap to help public employment services use technology more effectively. Here are the highlights:

1. It all starts with data

Smart systems thrive on good data. With AI, organizations can scan job ads and CVs in real time using natural language processing (NLP) to pull out keywords, skills and competencies. This continuous data flow gives a clearer picture of what employers are looking for – and how those needs are shifting.

2. Speak the same language

Right now, the way we describe jobs and skills isn’t always consistent. Standardizing this language across industries can make the job-matching process faster and more efficient. NLP tools can align taxonomies across public and private sectors, ensuring global interoperability. This helps job seekers, employers and education providers communicate effectively.

3. Build trust in the data

Verifying skills and qualifications is essential to prevent delays during the hiring process. Blockchain-based credentialing can create secure, tamper-proof records of certifications, while digital assessments provide scalable mechanisms for skills verification. These tools increase trust in candidate profiles, reduce hiring inefficiencies and support global talent mobility.

4. Personalize the learning journey

AI-powered learning platforms can tailor reskilling and upskilling to each person. By analysing existing skills and career goals, they recommend learning paths and adjust them in real time as job market needs evolve. This approach fosters lifelong learning and workforce adaptability.

5. Smarter job matching

Machine learning models combine candidate data with real-world hiring outcomes to improve predictive accuracy. Large language models (LLMs) help contextualize skills and work history, delivering more relevant job recommendations and personalized career guidance for candidates and employers.

Technologies driving the transition

The shift toward smarter job matching is powered by a set of key technologies that are reshaping how we assess and connect talent with opportunity.

NLP and AI are central to data extraction and interpretation, for example, powering real-time insights and enabling multilingual functionality. AI-driven Gap Analysis can also be used to identify emerging skill needs, informing training programme design and guiding institutional investment.

Blockchain ensures the integrity and portability of credentials, reducing reliance on manual background checks. Finally, dynamic skills frameworks, updated continuously through machine learning, help keep taxonomies relevant as new jobs and technologies emerge.

But successful job matching isn’t just about technology – it’s also about how we implement it. Public-private collaboration ensures alignment between workforce policy, education systems and employer needs. Context-specific design tailors job-matching systems to local digital infrastructure and socioeconomic conditions, enabling scalable innovation. And human-centered systems integrate digital tools with expert oversight, supporting inclusivity and trust.

Building inclusive, future-ready labour markets

Smarter job-matching systems represent a strategic opportunity to reshape how skills are developed, recognized and deployed globally. When workforce skills align with market demand, we can reduce mismatches, boost productivity and build more inclusive and resilient economies.

This vision is at the heart of the World Economic Forum’s Reskilling Revolution and Future of Jobs Initiative. These two programmes bring together businesses, governments and civil society to address the growing need to transform education, skills and learning, and to promote access to good jobs for all.

The future of work depends on unlocking human potential. Technology will be an important driver of this transformation.