The World Economic Forum’s Future of Jobs Report 2025 presents a nuanced portrait of AI’s impact on the global labor market: while automation and artificial intelligence are expected to reduce a substantial number of roles, they are also projected to generate a large wave of new opportunities, accelerating skill demands and reshaping how work gets done across industries. The net effect, according to the report, is a sizable positive swing—an estimated 78 million more jobs worldwide by 2030, even as 92 million roles are anticipated to disappear and 170 million positions are created anew. This duality highlights the complex dynamic between displacement and growth, underscoring the importance of proactive workforce strategies for both employers and workers.
Overview and key findings from the Future of Jobs Report 2025
The Future of Jobs Report 2025 emphasizes that the advent of AI and related technologies is prompting substantial strategic shifts within firms. At the center of these shifts is the recognition that AI can drive efficiency, open new revenue pathways, and enable new business models. While headlines may spotlight significant workforce reductions, the broader analysis points to a net increase in employment opportunities as organizations reconfigure tasks, departments, and value chains to harness AI’s capabilities. The report underscores that AI’s influence is not merely about replacing human labor; it is also about augmenting human capabilities and expanding the scope of what work can entail.
A key disclosure from the study is that 170 million new jobs could be created globally by 2030, contrasted with a potential loss of 92 million positions. The resulting net gain of 78 million jobs signals a substantial opportunity for labor markets if policymakers, business leaders, and workers align their efforts to capture the upside. The finding reframes the narrative around automation—from a binary plot of job loss to a more dynamic picture of job design, task reallocation, and skill evolution. The repurposing of roles and the emergence of new occupations are central to this outlook.
The report also reveals that roughly half of employers intend to reorient their business models in response to AI. This reorientation may involve restructuring teams, pivoting product lines, or adopting new operating models that rely more heavily on intelligent automation. In tandem, about two-thirds of employers plan to hire workers with specific AI skills, indicating a strong demand signal for education and training in AI-related domains. Conversely, around 40 percent anticipate reducing their workforce in areas where AI can automate tasks, signaling continued, albeit selective, displacement in certain job categories. Taken together, these figures depict a labor market in transition, where the balance between automation-driven efficiency and new opportunity hinges on strategic workforce planning.
The report’s data set provides a broad lens on global employment dynamics. It draws on input from 1,000 companies that collectively employ roughly 14 million workers around the world. This cross-section is used to map near-term hiring intentions, skill requirements, and the anticipated evolution of task structures over the 2025–2030 horizon. The World Economic Forum conducts this kind of employment analysis on a two-year cycle to help policymakers, business leaders, and workers make informed decisions about hiring, training, and career planning. The aim is to translate macro trends into actionable insights for ecosystems spanning education, industry, and governance.
In terms of transformative potential, the report identifies AI as the leading driver among new technologies for job creation. A striking 86 percent of companies surveyed expect AI to transform their operations by 2030, underscoring the scale of AI’s expected impact on daily workflows, productivity paradigms, and organizational design. This confidence about AI’s reach reinforces the case for targeted investments in AI literacy, data handling capabilities, and cross-functional collaboration. The emphasis is not merely on adopting tools but on embedding AI into strategic decision-making, product development, and customer engagement.
This section of the report also situates AI within a broader ecosystem of digital capabilities. While AI stands out as a principal catalyst for new job creation, its success is closely tied to complementary competencies such as data analytics, cybersecurity, and the ability to leverage networks and cloud-based platforms. Firms that marry AI deployment with robust data governance, secure architectures, and scalable infrastructure are more likely to realize durable gains in efficiency and new opportunities in product and service delivery. The findings emphasize the necessity of an integrated approach to digital skills that spans technical, analytical, and security domains.
Because the World Economic Forum’s analysis aims to inform long-range planning, the report also articulates how adoption trajectories may unfold across sectors. Certain industries are positioned to experience steeper adoption curves due to the nature of their processes, data readiness, and customer dynamics. Others may encounter barriers such as regulatory constraints, skill gaps, or capital intensity that could moderate the pace of AI-enabled growth. The overall message is that AI’s employment impact will be highly contingent on context, with proactive policy and corporate strategies playing a crucial role in shaping outcomes.
As the discussion advances toward the practical implications of these projections, the report reiterates that the future of work will not be a monolithic trend but a mosaic of scenarios. Some tasks will be fully automated, some will be redesigned for higher skill levels, and new roles will emerge that did not previously exist. The ongoing interplay between automation and upskilling will determine how quickly workers can transition to tasks that harness AI responsibly and effectively. The emphasis on adaptability and continuous learning remains a core takeaway for the road ahead.
Overall, the Future of Jobs Report 2025 presents a framework for understanding AI-driven labor market shifts that is richer than a simple displacement narrative. By highlighting both net growth and restructuring pressures, the report invites stakeholders to treat AI adoption as a strategic enterprise—one that requires careful alignment of training, hiring, and career pathways with the evolving needs of the economy. The findings set the tone for subsequent discussions about skill-building priorities, sector-specific impact, and the design of social and economic policies that support workers through the transition.
Skills in demand and declining roles
The report maps a nuanced skills landscape that accompanies the broad shifts in job design and task allocation driven by AI and related technologies. At the apex of the in-demand skill sets are capabilities directly tied to AI deployment, data analytics, and digital infrastructure, as well as the human competencies needed to operate, integrate, and secure AI-enabled systems. Companies identify AI and big data expertise, networks and cybersecurity, and technological literacy as the three most sought-after skill sets shaping hiring decisions through 2030. The emphasis on these areas reflects a convergence of advanced analytics, secure digital ecosystems, and the capacity to reason about and interact with intelligent automation in real-world contexts.
AI and big data expertise form the centerpiece of talent demand. Roles connected with building, training, validating, and refining AI models are likely to proliferate, including machine learning engineers, data scientists, and AI product specialists. These positions require the ability to work with complex datasets, design experiments, interpret results, and translate insights into actionable business outcomes. The demand for AI-related capabilities also extends to product development, customer experience optimization, and operations where AI can drive efficiency, accuracy, and speed. As AI systems become more integrated into core processes, the need for professionals who can steward these systems, ensure alignment with business goals, and monitor performance will intensify.
Networks and cybersecurity emerge as critical underpinnings of AI integration. The rapid expansion of connected systems, cloud platforms, and AI-powered tools heightens exposure to cyber risks, privacy concerns, and data governance challenges. Employers look for talent that can design resilient network architectures, safeguard data integrity, and implement robust security protocols. The ability to orchestrate secure communications, manage threat landscapes, and ensure compliance with evolving regulations will be central to sustaining AI-enabled growth across industries. The emphasis on cybersecurity signals that AI adoption does not exist in a vacuum; it relies on protections that enable trust and continuity in digital operations.
Technological literacy—defined as comfort with digital tools, platforms, and the fundamentals of how technology shapes business processes—also ranks highly. This category encompasses not only specialists but a broad base of employees who can engage with AI tools, interpret automated outputs, and contribute to decision-making in data-informed ways. As AI systems become more embedded across departments, even roles not traditionally associated with advanced tech will benefit from foundational digital literacy. Firms indicate that a baseline of technological proficiency across the workforce will facilitate smoother collaboration between humans and machines, reducing frictions and enabling more rapid scaling of AI-driven initiatives.
Beyond these top three areas, the report highlights a broader spectrum of digital and analytical competencies that will be valuable in a landscape dominated by automation. Skills related to data governance, experimentation, and the ability to translate technical results into business-case language will increasingly define who gets hired and promoted. The hiring landscape is shifting toward roles that bridge technical depth with strategic, customer, and organizational outcomes. As a result, employees across multiple disciplines are encouraged to elevate their digital fluency and to cultivate a mindset oriented toward continuous learning and adaptation.
The decline in certain job categories is another essential dimension of the report’s skill narrative. The data points to specific roles facing headwinds as AI and automation mature. Postal service clerks, executive secretaries, and payroll staff top the list of shrinking positions, with changes driven by automation and process optimization. For the first time in the report’s history, graphic designers and legal secretaries appear among the fastest-declining positions, suggesting that generative AI capabilities are extending into creative and administrative work in ways that compress conventional tasks and redefine job boundaries. These declines are not uniform across all geographies or industries, but they illustrate where the competitive pressure from automation is likely to be most pronounced.
The generative AI trend, in particular, seems to be reshaping expectations for creative and administrative support functions. While AI can streamline design iterations, generate text, and automate routine editorial tasks, it also opens opportunities for new forms of creative collaboration. Designers and editors may shift from executing routine tasks to curating AI outputs, directing AI-based workflows, and applying higher-level creative and strategic judgment. Similarly, legal secretaries may transition toward roles emphasizing data synthesis, contract analysis, and compliance monitoring—areas where AI accelerates routine data processing but human oversight remains critical for nuance, interpretation, and risk assessment. The net effect is a reallocation of labor toward roles that leverage human judgment alongside machine-generated outputs.
In tandem with these trends, the report underscores the centrality of human-machine collaboration as a defining feature of the modern workplace. Rather than perceiving AI as a wholesale replacement for human labor, many firms anticipate a future in which workers partner with intelligent systems to achieve outcomes that neither could reach alone. Toward this end, a large share of organizations plan to embed retraining and upskilling into their strategic agendas, recognizing that employees must acquire new capabilities to design, supervise, and optimize AI-enabled processes. The projection that 77 percent of surveyed firms will launch retraining programs between 2025 and 2030 reinforces the view that capacity building is indispensable to realizing AI’s potential while mitigating displacement.
The skill dynamics highlighted in the report have important implications for education and professional development. Educators and training providers are urged to align curricula with the competencies that matter most for the AI era. This includes strong emphasis on data literacy, cybersecurity concepts, and the practical application of AI tools in business contexts. Institutions may also need to expand partnerships with industry to deliver hands-on experiences that mirror real-world AI deployments, enabling learners to transition more smoothly into roles that involve designing, deploying, and managing AI systems. The emphasis on compatibility between education and industry expectations is a core feature of the report’s guidance for preparing the workforce of the future.
Finally, the report makes clear that the job market’s shift will be uneven across regions and sectors, with geographic and sectoral nuances shaping outcomes. Some economies may experience rapid diffusion of AI capabilities, accelerating job creation and skill upgrades. Others may face slower adoption and a longer horizon for realizing benefits, which could prolong transition challenges for workers in those regions. Policymakers and business leaders are thus encouraged to tailor interventions to local contexts, prioritizing investments in training, job placement services, and infrastructure that support AI-enabled growth while addressing disparities that could widen between different communities.
Declining and evolving roles; the creative and administrative frontier
In this section, the report presents a candid look at the job categories most exposed to decline as AI and automation advance. It names specific roles that are experiencing contraction and explains the factors driving these shifts. Postal service clerks, executive secretaries, and payroll staff are identified as among the most affected positions, with automation systems and process optimization reducing demand for routine tasks traditionally performed by these workers. The impact on these roles underscores how automation can erode demand where repetitive, rule-based tasks are prevalent, even in fields that historically relied on human labor for reliability and personal touch.
The analysis also highlights the emergence of rapid declines in sectors related to creative and administrative work. For the first time in the report’s history, graphic designers and legal secretaries appear among the fastest-declining occupations. This development is linked to the expanding capabilities of generative AI, which can perform many routine or semi-creative tasks with increasing sophistication. The implication is not that creative professions will disappear outright but that a broad swath of routine and administrative components of these roles can be automated, prompting a redefinition of duties, the integration of AI-assisted workflows, and a shift toward higher-level creative oversight and strategic planning.
The generative AI phenomenon is central to understanding these shifts. As AI models become more capable of producing visuals, text, and administrative outputs, the boundary between tools and talent becomes more porous. Creative workflows, once dominated by human-only labor for ideation and execution, are increasingly augmented by AI-generated drafts that require human curation, refinement, and governance. This dynamic pushes professionals in design and administrative support to focus on tasks where human discretion, ethical judgment, and nuanced interpretation are essential, while leveraging AI to handle routine or data-heavy components.
The decline in certain roles also raises questions about the geographic and sectoral distribution of risk. Jobs tied to centralized administrative functions or legacy process models may see more pronounced reductions in regions or companies where legacy systems remain prominent. Conversely, sectors that move quickly to adopt AI-enabled solutions, automate repetitive processes, and integrate advanced analytics may experience a more favorable employment trajectory, with displaced workers finding pathways into adjacent roles that require higher cognitive, creative, or complex problem-solving capabilities. The report’s emphasis on these shifts invites a proactive response from education and workforce development ecosystems to prepare for transitions that minimize disruption.
Despite the looming reductions in specific occupations, the report emphasizes that the overall trajectory is not a simple tale of widespread replacement. The same data indicating declines in particular roles sits alongside evidence of robust opportunities elsewhere, driven by AI-enabled innovation, new product lines, and expanded service capabilities. The key takeaway is that job destruction and job creation will occur differentially across occupations, with some workers experiencing displacement while others gain access to new positions demanding advanced competencies. This nuanced picture reinforces the importance of targeted retraining and strategic task redesign to ensure a smoother transition for workers who might otherwise be left behind.
With this understanding of declining roles in mind, the report explores how the workforce can adapt to these pressures. It highlights the central role of upskilling and retraining as a bridge between current capabilities and future demands. The emphasis is on practical, job-relevant programs that equip workers with the skills needed to design, supervise, and optimize AI-enabled processes. The focus on retraining aligns with broader policy and corporate strategies that aim to preserve employment levels while enabling workers to grow into roles that leverage human strengths in partnership with machines. The ultimate objective is to cultivate a labor force that remains agile, capable of learning, and prepared to take on responsibilities that complement AI technologies.
In summary, the declining roles section of the report does not present automation as a uniform threat but rather as a dynamic channel through which task structures evolve. It underscores the inevitability of some job reductions while simultaneously signaling where growth, transformation, and opportunity will arise. By recognizing which occupations face pressure and which new avenues are likely to emerge, organizations and workers can focus their efforts on areas with the greatest potential for durable employment and meaningful career progression in an AI-enhanced world.
Workforce retraining and human-AI collaboration
A core theme flowing through the report is the emphasis on retraining as a practical necessity to navigate the AI-enabled transition. The data indicate that about 77 percent of surveyed firms intend to launch retraining programs to help current workers collaborate with AI systems between 2025 and 2030. This commitment to retraining signals a broad recognition that human-AI collaboration will define the next era of work, rather than worker replacement alone. The focus is on equipping employees with the capabilities to supervise, troubleshoot, and meaningfully contribute to AI-driven workflows, ensuring that technology augments human expertise rather than eroding it.
Retraining is not limited to basic upskilling; it encompasses a spectrum of approaches designed to embed AI literacy into broader competencies. Organizations may implement on-the-job training, formal courses, micro-credentials, and partnerships with educational institutions to build pipelines of AI-savvy professionals. The aim is to create a workforce that can translate AI outputs into actionable business decisions, maintain accountability for automated processes, and adapt to evolving tools as models and platforms advance. This emphasis on practical, job-focused education is central to realizing the productivity gains and innovation potential that AI promises.
Alongside retraining, the report highlights a parallel investment in talent capable of shaping AI tools themselves. About 70 percent of firms plan to hire specialists who can design AI tools, while 62 percent seek employees skilled at working alongside these systems. This signals a shift in hiring priorities toward roles that blend technical prowess with collaborative competencies. The objective is to assemble teams capable of driving AI integration from both the development and operations perspectives, ensuring that AI solutions align with business goals, risk management standards, and user needs.
The emphasis on human-machine collaboration is reinforced by the broader implications for personnel strategy. Companies recognize that AI adoption alters job design, performance metrics, and day-to-day workflows. Rather than viewing AI as a standalone engine of change, many organizations are integrating AI into teams, processes, and decision-making pathways to maximize synergy. This approach requires careful consideration of governance, ethics, and accountability to ensure that AI-driven decisions reflect organizational values and comply with regulations, data privacy standards, and customer expectations.
The retraining imperative extends into policy considerations as well. Public and private sector stakeholders are urged to create supportive environments for continuous learning. This includes expanding access to affordable education, certifying AI-related competencies, and providing safety nets that ease transitions for workers who may need time to retool or shift to new industries. The goal is to cultivate resilience at the individual level while maintaining competitiveness at the national and regional levels. As organizations scale their AI capabilities, the importance of scalable training models, adaptable curricula, and robust career services becomes increasingly apparent.
The Davos forum and similar global gatherings serve as important forums for discussing the practical implications of these findings. Leaders from business, government, and civil society gather to share best practices, align on standards, and coordinate policies that support workforce transitions. In this context, the report’s emphasis on retraining and collaboration takes on added significance: it provides a roadmap for harmonizing corporate strategies with public policy and social protection mechanisms, aiming to minimize disruption while maximizing opportunity.
In terms of implementation, firms are encouraged to design retraining programs that are iterative, outcome-driven, and closely tied to business priorities. Programs should be tailored to different career stages, from early-career professionals seeking to enter AI-enabled fields to experienced workers transitioning to more advanced roles. By aligning training with concrete career pathways and clear performance milestones, organizations can measure impact, optimize investments, and sustain momentum across changing skill needs. The practical takeaway is that retraining must be embedded in organizational strategy, not treated as a one-off initiative or an afterthought.
Overall, the retraining and collaboration narrative from the Future of Jobs Report 2025 presents a practical blueprint for turning disruption into opportunity. It emphasizes that the future of work hinges on the capacity to develop new capabilities within the workforce, to reimagine job roles around AI-enabled workflows, and to cultivate cultures that prize continuous learning. By combining retraining with deliberate design of human-AI collaboration, firms can unlock productivity gains, improve outcomes for customers, and create pathways for workers to advance in an increasingly automated economy. This integrated approach, when scaled effectively, supports both business resilience and individual empowerment in a rapidly evolving labor landscape.
Longer-term outlook and policy implications
Beyond the near-term shifts, the report acknowledges that it is too early to declare definitively whether AI will yield a net positive or negative effect on employment over the long horizon. The outlook remains contingent on a constellation of factors, including the rate of AI capability advancement, the pace of adoption across sectors, the effectiveness of retraining programs, and the design of social and economic policies that cushion transitions for workers. The overarching expectation is a continued recalibration of work, with knowledge-based roles evolving in ways that reflect AI-enabled capabilities. This evolution could lead to a more fluid definition of tasks, job titles, and career ladders, with workers moving more readily across functions as tasks shift from routine to more complex and creative areas.
The report situates AI as a catalyst that could reshape the nature of knowledge work. While some tasks may be automated, others will be augmented, enabling knowledge workers to think more strategically, analyze larger data sets, and apply insights in faster, more iterative cycles. The shift may be gradual, but the potential for meaningful changes in how professionals approach problem solving, decision making, and collaboration is substantial. This perspective aligns with broader industry commentary that emphasizes the complementarity between human expertise and machine intelligence, rather than a simple replacement dynamic.
A centerpiece of the long-range discussion is the ongoing exploration of policy approaches that could address the distributional effects of automation. One well-known concept often discussed in public discourse is universal basic income (UBI), popularized in part by OpenAI’s leadership in the public conversation about economic safety nets amid automation. Proponents argue that a baseline income could provide stability as workers transition between jobs or pursue retraining, while critics raise concerns about feasibility, incentives, and long-term sustainability. The report notes that discussions around UBI have included extensive trials and theoretical modeling, underscoring the importance of evidence-based policy exploration as AI capabilities continue to advance.
The broader conversation about UBI and related social supports reflects a recognition that the labor market’s evolution may require more than technical training alone. Social protections, wage insurance, and targeted subsidies could play a role in stabilizing households during periods of adjustment, particularly for workers in regions or sectors most exposed to automation. Policymakers may also explore proactive labor market policies, including public-private partnerships to fund continuing education, apprenticeship programs, and transitional employment schemes that connect workers to emerging opportunities in AI-enabled industries.
From an industry perspective, the long-term outlook emphasizes strategic workforce planning, investment in AI literacy across organizations, and careful governance of AI deployment. Firms that align workforce development with AI adoption plans—integrating retraining, hiring for AI-skill roles, and designing roles that exploit human-AI collaboration—stand a higher chance of realizing sustained growth and resilience. This approach requires leadership commitment to talent development, clear roadmaps for skill acquisition, and metrics to track progress over time. The goal is to create an adaptive labor market that can respond to technology’s pace while maintaining social and economic stability.
The Davos context underscores the importance of global coordination and shared best practices in navigating AI’s impact on jobs. Leaders from around the world convene to discuss how to harmonize regulation, standards for AI safety and ethics, and approaches to workforce development. The emphasis is on creating ecosystems that support responsible AI deployment, protect workers’ interests, and accelerate innovation in ways that contribute to broad-based economic growth. The conversations at these forums illuminate a common understanding that AI’s influence on employment is not a localized issue but a global challenge that benefits from collaborative solutions and cross-border learning.
In the longer-term horizon, the report invites ongoing monitoring and iterative strategy refinement. The trajectory of AI adoption will shape not only which jobs exist but also how those jobs are structured, how people acquire skills, and how workplaces operate. The potential for a substantial net gain in employment hinges on successful navigation of displacement risks, timely retraining, and the ability to align AI capabilities with human strengths. The policy implications are clear: invest in education and training, design social protections that support transitions, and promote inclusive access to AI-enabled opportunities so that the benefits of automation are broadly shared.
Context, methodology, and scope
The Future of Jobs Report 2025 draws on a structured survey methodology designed to capture diverse employer perspectives across industries and regions. The data set encompasses responses from 1,000 companies that collectively employ around 14 million workers worldwide. The findings reflect hiring intentions, skill demand, role evolution, and the planned use of retraining programs within a two- to three-year horizon. The report’s methodology emphasizes triangulation across company size, sector, and geographic location to generate a holistic picture of how AI influences labor markets in the near term and beyond.
A key aspect of the methodology is its emphasis on forward-looking indicators. Rather than focusing solely on current employment levels, the report tracks planned changes in hiring, retraining, and the structural organization of work. This forward-looking lens helps policymakers and business leaders anticipate where investments in talent development and infrastructure will be required and how to prioritize efforts to maximize positive outcomes from AI adoption. The two-year analysis cadence also enables the monitoring of evolving trends, capturing shifts as organizations move from pilot projects to broader scale deployments of AI systems.
The report acknowledges certain limitations inherent in its approach. The sample, though broad, represents a subset of global firms and may not capture the full diversity of labor market dynamics, particularly in informal economies or regions with limited data collection infrastructure. Additionally, the projections depend on multiple assumptions about AI capability growth, regulatory environments, and macroeconomic conditions that could change over time. Readers are reminded that forecasts carry uncertainty and should be interpreted as directional guidance rather than precision predictions. The WEF frames its findings as a structured forecast intended to inform strategic decision-making in both the public and private sectors.
To support robust interpretation, the report couples quantitative projections with qualitative insights drawn from employer narratives and expert assessments. This combination helps illustrate how the numbers translate into concrete workplace changes, such as the redefinition of roles, the emergence of AI-focused positions, and the integration of retraining plans into workforce strategies. The qualitative components provide depth to the numerical findings, offering context about how firms plan to navigate challenges, invest in talent, and cultivate cultures that embrace change. The result is a more complete understanding of what the AI era may mean for workers, managers, educators, and policymakers.
The scope of the analysis is intentionally global, reflecting the interconnected nature of modern labor markets. While regional variations will persist, AI-related transformation is expected to cross borders, shaping industry practices and talent pipelines in multiple countries. This global lens reinforces the importance of cross-country collaboration, knowledge sharing, and coordinated policy responses to ensure that AI-driven growth translates into inclusive employment gains. The report invites stakeholders to consider not only the immediate implications but also the long-range strategic implications for education, infrastructure, and social protection systems that will support workers as the labor landscape adjusts over the next decade.
In summarizing the methodological context, the Future of Jobs Report 2025 provides a robust framework for understanding AI’s impact on employment. It combines a broad survey base with careful interpretation of skill demands, role evolution, and organizational strategies. While the exact outcomes will depend on ongoing developments in technology and policy, the core message is clear: AI represents both disruption and opportunity, and proactive, coordinated efforts by governments, businesses, and workers will determine how the benefits are distributed across society.
Conclusion
The Future of Jobs Report 2025 offers a detailed, forward-looking map of how AI and related technologies are set to reshape employment by 2030. It presents a balanced narrative in which significant job creation coexists with meaningful disruption, underscoring the need for strategic investment in retraining, AI literacy, and adaptable workforce design. The evidence points to a global net gain in jobs, driven by new opportunities and the expansion of AI-enabled capabilities, even as certain occupations decline and some work processes become automated.
Key takeaways emphasize the centrality of human-AI collaboration, with a substantial share of firms planning retraining initiatives to prepare workers for AI-enabled roles. The report also highlights the demand for AI-specific skills, data analytics, cybersecurity, and broad technological literacy as critical drivers of hiring and career growth through 2030. The changing job landscape will require educators, policymakers, and employers to align curricula, infrastructure, and social protections with the evolving needs of the AI era, ensuring that workers can not only survive the transition but thrive within it.
Looking ahead, the long-term outlook remains cautiously optimistic but contingent on deliberate policy choices and effective execution. While AI could, in theory, replace a portion of the labor force, it also promises to liberate workers to tackle higher-skilled, more creative, and strategically important tasks. The key to turning the potential into reality lies in comprehensive retraining programs, robust talent pipelines, and policies that support workers as they adapt to new roles and responsibilities. Collaboration among governments, businesses, educators, and civil society will be essential to maximize the positive impact of AI on employment, while mitigating risk and ensuring an inclusive transition for workers around the world.
