The Future of Work: How to Align Talent with Tomorrow’s Opportunities
The advancement of emerging technologies and human intelligence will be key elements in ensuring that workers find roles that best match their skills. The question is: how can talent be prepared for the opportunities of the future? By 2030, more than 20% of jobs worldwide will undergo significant changes due to major labor market transformations driven by technology, demographic shifts, and ecological transitions.
Image: Sandtech
Opportunities and Challenges on the Horizon
The technological revolution, the green transition, demographic changes, and geo-economic fragmentation could create around 170 million new jobs. However, these same forces may also lead to the disappearance of approximately 92 million jobs. The challenge, therefore, is not just about creating new opportunities but ensuring that professionals have access to them. For this, the combination of humans and technology plays a fundamental role in aligning talent with the opportunities of the future.
A Roadmap for the Future of Employment
To help policymakers and companies navigate this changing landscape, the World Economic Forum has developed a practical guide called “Matching Talent to the Jobs of Tomorrow”. This guide presents a five-step model for using technology wisely and connecting the right talent with the right roles.
5 STEPS TO ALIGN TALENT WITH FUTURE PROFESSIONS
1 – Collect Dynamic Data on Labor Market Trends
The first step in aligning talent with the job market is gathering real-time data on hiring trends, job demand, and skill gaps. Instead of relying on static reports, decision-makers should adopt technological tools that provide dynamic and predictive insights.
How can technology help?
- APIs integrated into job platforms provide updated data on recruitment trends.
- AI algorithms analyze shifts in the most in-demand skills.
- Automated resume screening systems help identify suitable candidates
Case Study: The Moving Forward program in Guatemala is already using data-driven solutions to improve the matching of talent and jobs. The next phase will involve adopting predictive analytics with AI to make processes more efficient and precise.
2 – Create a Common Terminology for Jobs
One of the biggest obstacles in recruitment is the lack of consistency in job descriptions and required skills. Companies, industries, and countries use different terminologies, creating misalignment between candidates and opportunities.
How can this issue be resolved?
- Develop a standardized framework to define job titles and competencies.
- Use taxonomy tools to ensure uniform classification of jobs.
- Apply natural language processing to structure data and reduce language barriers.
Case Study: In Singapore, the SkillsFuture program developed a national framework that harmonizes terminology used by the private sector and government institutions.
3 – Ensure Reliable Data
Trust in recruitment systems is essential for both employers and candidates. To achieve this, it is crucial to ensure that the data presented is secure, verifiable, and tamper-proof.
How can technology help?
- Blockchain can be used to validate certifications and professional records.
- Skill assessment platforms allow candidates to take tests and prove their capabilities.
Case Study: In Nigeria, the National Talent Export Programme uses AI to verify skills and professional records, optimizing the talent-job matching process. The goal is to facilitate the employment of 1 million workers over five years.
4 – Use AI to Expand Access to Skills
Many workers fear that their skills will become obsolete. The challenge is not only finding a job but also preparing for new opportunities.
How can technology help?
- AI-based learning platforms create personalized training pathways.
- Integration with training systems facilitates access to reskilling programs.
Case Study: In Sweden, the public employment service Arbetsförmedlingen offers specialized training and internships to help vulnerable groups integrate into the job market.
5 – Utilize Advanced Language Models (LLMs)
The final step involves combining all collected data to create the best possible match between candidates and job openings. Here, artificial intelligence can play a crucial role.
How can technology help?
- Machine learning analyzes candidate information and historical data to predict optimal matches.
- Advanced language models (LLMs) contextualize a candidate’s suitability for a given role, considering not only technical skills but also motivations and preferences.
Case Study: In France, the France Travail service combines AI, semantic analysis, and generative models to create personalized recommendations and guide candidates through detailed career paths.
Image: World Economic Forum
The Key to Success: Cross-Sector Collaboration
For this model to work, it is essential that governments, businesses, and educational institutions work together to create solutions that use AI and automation to enhance, rather than replace, human talent. Promote teams with generational, cognitive, and cultural diversity, ensuring that different needs are met. Develop strategies adapted to the dynamics of the labor market in each region.
By placing humans at the center of transformation, it is possible to create a more resilient, fair, and future-ready job market.
Is your organization ready for this change?
One way or another, no one can escape the future of work, and the revolution has already begun!
Article by Sérgio Almeida, in partnership with Vida Económica.
