> **来源：[研报客](https://pc.yanbaoke.cn)** Scenarios for the Global Economy Dialogue Series # Four Futures for Jobs in the New Economy: Al and Talent in 2030 WHITE PAPER JANUARY 2026 # Contents Executive summary 3 1 Introduction 5 2 Four futures for jobs in 2030 7 3 Implications for businesses 14 4 How businesses can prepare today for any scenario 16 Contributors 17 Endnotes 19 # Disclaimer This document is published 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 of the World Economic Forum, nor the entirety of its Members, Partners or other stakeholders. © 2026 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. # Executive summary Foresight provides a lens to explore critical uncertainties and shape strategies to navigate and harness transformative shifts. The rapid commercialization of emerging technologies is reshaping workflows, business models and talent pipelines. As artificial intelligence (AI) shifts from experimentation to integration, the pace and trajectory of its advancement deepen the uncertainty about its implications on businesses, workers and the global economy. The views of business executives on the impact of AI vary too: globally, about $54\%$ expect AI to displace existing jobs, and $24\%$ said AI will create new jobs. Nearly $45\%$ also cited an increase in profit margins as a likely impact of AI, and only $12\%$ expect it to lead to higher wages. The future of workplaces and value chains will not be defined by the technologies alone. Human capital strategies and investments prioritized today will determine how well societies and individual businesses can adapt to – and lead in – the new economy. # Turning uncertainty into foresight The World Economic Forum's Scenarios for the Global Economy Dialogue Series uses scenario analysis and cross-industry dialogue to help decision-makers navigate global economic developments and their implications for strategy and investment decisions. This white paper consolidates the views and insights from the members of the Forum's Chief Strategy Officers Community and other experts across the Forum's Industry Communities and Global Foresight Network. This second edition in the series explores how AI advancements and talent trends, and their potential trajectories until 2030, could shape the future of jobs, with varying implications for corporate strategies and investment decisions. # Four scenarios for the future of jobs in 2030 Taken together, AI advancement and talent development vectors generate the following four scenarios for the future of jobs in 2030. 1 Supercharged Progress: Exponential AI breakthroughs reshape industries, business models and workflows. Productivity soars and innovation flourishes. Widespread AI readiness allows people to harness the "agentic leap", adapt to AI-centric economies and partially contain displacement. Many jobs have disappeared, but new occupations emerge and scale fast, in part with humans directing portfolios of capable machines and becoming agent orchestrators. Social safety nets, ethics and governance frameworks struggle to keep up with the pace and scale of change. The Age of Displacement: Exponential AI advancement outpaces the capacity of the workforce to adapt. Businesses race to automate as a stopgap, displacing workers faster than education and reskilling systems can respond. Agentic AI takes over key processes, creating a productivity upsurge, but also new risks. Economies race ahead technologically but fracture socially: unemployment spikes, consumer confidence erodes and governments face mounting societal risks and instability. Co-Pilot Economy: Gradual AI progress and availability of AI-ready skillsets shift the focus towards augmentation rather than mass automation. The AI hype of the 2020s has given way to pragmatic integration: most industries see incremental transformation as human-AI teams reshape value chains. Countries and businesses that invested early in training, mobility, digital infrastructure and AI governance have created conditions to absorb and advance emerging technologies. 4 Stalled Progress: Steady AI progress meets a workforce lacking critical skills. Productivity growth is patchy, and businesses lean on automation to backfill scarce talent. Gains concentrate within businesses and geographies with AI expertise, while others face eroding competitiveness. Displacement hits primarily routine roles, while the value of skilled trades and manual occupations increases. The hope of AI-enabled prosperity fades into frustration, as adoption gaps fuel inequality, create a bifurcated economy and limit growth. # Strategies for the future Drawing on the scenario narratives, analysis of business implications across the alternative futures, and dialogues with chief strategy officers and experts, the following "no-regret" strategies have been identified to help businesses prepare today for any scenario. - Start small, build fast, scale what works Align technology and talent strategies Invest in human-AI collaboration and agentic workflows Invest in data governance and infrastructure - Anticipate talent needs and future-proof value chains - Strengthen organizational culture and trust in emerging technologies Prepare for different implications across occupations, tasks and markets Design multi-generational workflows Leverage strategic partnerships # Introduction The convergence of disruptive emerging technologies and workforce transformation creates new growth opportunities and risks. The convergence of multiple transformative forces is reshaping the global economy. Global macrotrends – such as geoeconomic fragmentation, technological change, the green transition and others – are projected to create around 170 million new jobs by 2030, while displacing about 92 million existing jobs.<sup>1</sup> The rapid acceleration and commercialization of disruptive emerging technologies such as artificial intelligence (AI), autonomous systems and robotics are among the most transformative trends. Over the last few years, AI has moved from experimentation to workflow integration, with the share of businesses using AI in at least one function increasing from $55\%$ in 2022 to $88\%$ in the latest estimates.[2] The adoption of these technologies promises systemic gains in productivity, but also raises critical questions about economic inclusion, values, trust and resilience. According to the World Economic Forum's annual survey of over 10,000 executives globally, about $54\%$ of respondents expect AI to displace existing jobs, and $24\%$ said AI will drive the creation of new jobs. More than four in 10 executives surveyed expect AI to increase profit margins across businesses, and only slightly less said AI will increase accessibility $(37\%)$ and affordability $(30\%)$ of goods and services. Notably, only $12\%$ expect AI to have a positive impact on wages, while $24\%$ predict an increase in industry concentration. FIGURE 1 Perception of the business community about the impact of AI Al will... Source: World Economic Forum. Executive Opinion Survey 2024. In the meantime, global talent dynamics are shifting too. Demographic trends, persistent skills gaps and strained social safety nets create complex – and often compounding – pressures on labour markets. The shortening longevity of skills demands greater agility and foresight from education and training systems. Considering the impact of AI alone, LinkedIn estimates that the demand for AI literacy skills has increased by $70\%$ between 2024 and 2025.<sup>4</sup> Taken together, these shifts – and the diffusion of agentic AI – deepen the uncertainty around the future of the technology and its implications for businesses, workers and the global economy. The transformations that once appeared as distant futures now arrive at speed and scale. Businesses across industries face growing competition to innovate quickly, balance evolving investment priorities and adapt in near real time. Simultaneously, governments are navigating a tightening fiscal space and complex decisions on enabling innovation while supporting jobs and economic inclusion. These trends create new opportunities for industries and individual businesses, but also risks. Looking at business strategies in the next five years, the results of the Forum's latest survey of chief strategy officers highlighted commercialization of AI and emerging technologies (72%), talent shortage and workforce transformation (24%) as some of the most impactful trends. Throughout 2025-2026, the Forum's Scenarios for the Global Economy Dialogue Series uses scenario analysis and peer-level, cross-sectoral dialogue to help decision-makers understand the evolving patterns of the global economy and their implications for strategy, investment decisions and resilience. This second paper in the series explores scenarios for the future of jobs at the intersection of AI advancement and workforce readiness vectors and their potential trajectories until 2030. The AI advancement vector represents the pace and scale of progress in the capability and autonomy of AI technologies. The scenarios consider exponential and incremental trajectories of AI advancement. The workforce readiness vector captures the availability of skills among workers that support their preparedness for an AI-driven economy. The scenarios consider widespread and limited AI readiness. Combining these two vectors generates four plausible futures, each with different implications for corporate strategies, business models, investments and workflows. # 2 Four futures for jobs in 2030 The four scenarios aim to provide stylized narratives from a complex global outlook. # 2.1 | Scenarios at-a-glance The interaction of AI advancement and workforce readiness vectors generates the following four scenarios for the future of jobs in 2030 (Figure 2). Scenario 1: Supercharged Progress. Exponential AI breakthroughs reshape industries, business models and workflows. Productivity soars and innovation flourishes. Widespread AI readiness allows people to harness the "agentic leap", adapt to AI-centric economies and partially contain displacement. Many jobs have disappeared, but new occupations emerge and scale fast, in part with humans directing portfolios of capable machines and becoming agent orchestrators. Social safety nets, ethics and governance frameworks struggle to keep up with the pace and scale of change. Scenario 2: The Age of Displacement. Exponential AI advancement outpaces the capacity of the workforce to adapt. Businesses race to automate as a stopgap, displacing workers faster than education and reskilling systems can respond. Agentic AI takes over key processes, creating a productivity upsurge, but also new risks. Economies race ahead technologically but fracture socially: unemployment spikes, consumer confidence erodes and governments face mounting societal risks and instability. Scenario 3: Co-Pilot Economy. Gradual AI progress and availability of AI-ready skillsets shift the focus towards augmentation rather than mass automation. The AI hype of the 2020s has given way to pragmatic integration: most industries see incremental transformation as human-AI teams reshape value chains. Countries and businesses that invested early in training, mobility, digital infrastructure and AI governance have created conditions to absorb and advance emerging technologies. Scenario 4: Stalled Progress. Steady AI progress meets a workforce lacking critical skills. Productivity growth is patchy, and businesses lean on automation to backfill scarce talent. Gains concentrate within businesses and geographies with AI expertise, while others face eroding competitiveness. Displacement hits primarily routine roles, while the value of skilled trades and manual occupations increases. The hope of AI-enabled prosperity fades into frustration, as adoption gaps fuel inequality, create a bifurcated economy and limit growth. FIGURE 2 Four scenarios for the future of jobs in 2030 Source: World Economic Forum. These futures have different characteristics when it comes to productivity, automation, labour market participation, inequality and other economic data. Table 1 summarizes the potential direction of these characteristics by 2030 relative to today's baseline. TABLE 1 | Economic data trajectory for the four scenarios by 2030 <table><tr><td>Indicator</td><td>Baseline</td><td>Scenario 1 Supercharged Progress</td><td>Scenario 2 The Age of Displacement</td><td>Scenario 3 Co-Pilot Economy</td><td>Scenario 4 Stalled Progress</td></tr><tr><td>AI capability, top five models average MMMU1</td><td>84.2</td><td>↑</td><td>↑</td><td>↑</td><td>↑</td></tr><tr><td>AI literacy and adjacent skills</td><td>N/A2</td><td>↑</td><td>→</td><td>↑</td><td>→</td></tr><tr><td>Labour productivity growth, % annual</td><td>1.5%</td><td>↑</td><td>↑</td><td>↑</td><td>→</td></tr><tr><td>Unemployment rate, %</td><td>5%</td><td>↑</td><td>↑</td><td>→</td><td>↑</td></tr><tr><td>Consumer confidence index</td><td>48.8</td><td>→</td><td>↓</td><td>↑</td><td>↓</td></tr><tr><td>Scaling of agentic AI, % of businesses</td><td>23%</td><td>↑</td><td>↑</td><td>↑</td><td>→</td></tr><tr><td>Wage polarization, D9/D1 earners ratio3</td><td>16.8</td><td>↑</td><td>↑</td><td>↑</td><td>↑</td></tr><tr><td>S&P 500 operating margin, % quarterly</td><td>12.6%</td><td>↑</td><td>↑</td><td>↑</td><td>→</td></tr></table> 1 Massive multi-discipline multimodal understanding (MMMU) 2 No authoritative or standardized global measure of "AI literacy and adjacent skills" currently exists. ${}^{3}$ Ratio between the total hourly earnings of the top 10% and the bottom 10% of the world's wage earners. Note: The arrows denote a directional change in a given scenario characteristic. All values are at the global level unless specified otherwise. The analysis is based on scenario narratives and extrapolations from similar existing research. The directionality is illustrative and for scenario-building purposes only. Sources: Xiang, Y. et al. (2025); International Labour Organization (ILO). (2020-2025 annualized); International Labour Organization (ILO). (2025); Ipsos. (2025); McKinsey. (2025); International Labour Organization (ILO). (2025); S&P. (2025 average). # 2.2 | Scenarios in-depth # Scenario 1: Supercharged Progress Exponential AI advancement, widespread workforce readiness Exponential AI breakthroughs reshape industries, business models and workflows. Productivity soars and innovation flourishes. Widespread AI readiness allows people to harness the "agentic leap", adapt to Alcentric economies and partially contain displacement. Many jobs have disappeared, but new occupations emerge and scale fast, in part with humans directing portfolios of capable machines and becoming agent orchestrators. Social safety nets, ethics and governance frameworks struggle to keep up with the pace and scale of change. AI capability, top five models average MMMU Baseline: 84.2 (Xiang, Y. et al., 2025) AI literacy and adjacent skills Baseline: N/A Labour productivity growth, % annual Baseline: 1.5% (International Labour Organization, 2020-2025 annualized) Unemployment rate, % Baseline: 5% (International Labour Organization, 2025) Consumer confidence index Baseline: 48.8 (Ipsos, 2025) Scaling of agentic AI, % of businesses Baseline: $23\%$ (McKinsey, 2025) Wage polarization, D9/D1 earners ratio Baseline: 16.8 (International Labour Organization, 2025) S&P 500 operating margin, % quarterly Baseline: $12.6\%$ (S&P, 2025 average) In this scenario, an exponential breakthrough in AI capabilities has reshaped economies and created new industries, drastically shortening the timeline to artificial general intelligence (AGI). A radical redesign of education and training systems has enabled the workforce to adapt rapidly. Competition from open-source AI models has helped accelerate the development of AI agents while keeping costs low, enabling broad-based commercialization of AI agents and redefining the approaches to work, learning and value creation. By 2030, many occupations have disappeared entirely, and jobs have shifted from task execution to design and oversight of AI-enabled ecosystems. The productivity return from AI integration has outstripped early projections of a 1.3 percentage point (pp) increase,[5] spurring an AI deployment race across industries. AI capital expenditure (CapEx) has surpassed $1.3 trillion over the 2025-2030 period,[6] with investments into compute and data infrastructure transforming AI from a tool into a core economic actor. A shift to an Al-centric economy has intensified many structural tensions around labour displacement, governance gaps, sustainability strains and rising questions about the role and value of human workers. - Occupations and tasks: Labour markets have restructured around agentic faultlines, with the share of tasks performed by technology spiking from $22\%$ since 2025. Human work becomes modular, fluid and AI-mediated. A significant number of occupations and tasks common in the mid-2020s have disappeared or shrunk in scope. Countries and industries with strong digital readiness and early investment in AI-tailored training have managed to curb mass unemployment, with workers now overseeing hundreds of "digital employees" and a fleet of specialized AI agents or shifting to narrowing human-centric jobs. However, displacement and job quality degradation are common across all sectors and geographies. Economic outlook: An agentic leap has unlocked unprecedented productivity gains, with global GDP (gross domestic product) growth nearing double digits and a significant increase in corporate profit margins. Public and private investments rise rapidly, directed primarily into AI networks, compute, quantum, data infrastructure, talent pipelines and AI-complementary technologies. AI tools make creativity and entrepreneurship accessible to millions who were previously excluded due to geography or education. Innovation and leapfrogging scale rapidly across sectors and geographies, breaking legacy frontiers in energy, materials and health. However, accelerating automation and looming AGI risks weigh on the long-term outlook, investor confidence and sustainability spillovers. - Value chains: The convergence of agentic AI architecture, quantum advancement and blurring of physical and digital ecosystems has allowed industries to restructure around agentic operating layers, reaching an inflection point where AI networks become as central as electricity grids. Digital twins and autonomous end-to-end coordination become standard. - Inequality and polarization: Inequality widens as wage premiums for the AI-ready workforce have nearly doubled from $56\%$ projected in the mid-2020s.8 Workers in human-centric occupations – including in care, hospitality, public and third sectors – see their wages erode slowly as competition for these roles rises. Other workers face declining bargaining power despite overall productivity gains, with wages plummeting across most sectors. Policy and regulatory landscape: Regulatory regimes lag behind the pace and depth of the agentic transformation. Ethical frameworks are slow to adapt, and safety nets struggle to keep up. Some governments experiment with AI dividends, wage insurance and universal basic income models.[9] Others grapple with the trade-offs between rapidly tightening fiscal space and growing societal strain. # Scenario 2: The Age of Displacement Exponential AI advancement, limited workforce readiness Exponential AI advancement outpaces the capacity of the workforce to adapt. Businesses race to automate as a stopgap, displacing workers faster than education and reskilling systems can respond. Agentic AI takes over key processes, creating a productivity upsurge, but also new risks. Economies race ahead technologically but fracture socially: unemployment spikes, consumer confidence erodes and governments face mounting societal risks and instability. AI capability, top five models average MMMU Baseline: 84.2 (Xiang, Y. et al., 2025) AI literacy and adjacent skills Baseline: N/A Labour productivity growth, % annual Baseline: 1.5% (International Labour Organization, 2020-2025 annualized) Unemployment rate, % Baseline: $5\%$ (International Labour Organization, 2025) Consumer confidence index Baseline: 48.8 (Ipsos, 2025) Scaling of agentic AI, % of businesses Baseline: $23\%$ (McKinsey, 2025) Wage polarization, D9/D1 earners ratio Baseline: 16.8 (International Labour Organization, 2025) S&P 500 operating margin, % quarterly Baseline: $12.6\%$ (S&P, 2025 average) 1 No authoritative or standardized global measure of "AI literacy and adjacent skills" currently exists. Notes: The arrows denote a directional change in a given scenario characteristic. All values are at the global level, unless specified otherwise. The analysis is based on scenario narratives and extrapolations from similar existing research. The directionality is illustrative and for scenario-building purposes only. In this scenario, AI advances at a breakneck speed, but workforce readiness remains limited. Education and training systems, entangled in traditional approaches, have failed to produce the adaptive and cross-functional talent needed for an AI-native economy. A culture of accelerationism takes hold, and automation becomes pervasive. Governments and businesses race to automate as a stopgap to scarce talent, deepening dependence on technology and displacing workers at scale. Rising capital commitments to Al CapEx in the late 2020s have drastically lowered barriers to AI deployment across industries. Competing models, AI agents and fully autonomous systems scale fast. Automation has become significantly cheaper than mass upskilling and reskilling of workers. Some governments and sectors attempt to regulate mass AI deployment to mitigate societal risks, but competition renders restrictions economically and politically untenable. - Occupations and tasks: Labour markets have been severely disrupted by mass displacement and tightening employment pathways across all skill levels. AI and end-to-end automation are taking over key tasks across sectors. Workforce displacement is no longer transitory. In many sectors, entire occupational families have shrunk or disappeared. New AI-complementary occupations have emerged, but the workforce has struggled to reskill and adapt to capture emerging opportunities. The share of tasks absorbed by technology has surpassed $50\%$ , approaching $90\%$ in high-exposure sectors. Talent shortages have pushed some businesses to fully outsource decision-making to AI agents with limited to no oversight from human workers. Labour mobility has nearly dried out by 2030. Human-centric jobs and gig work have attracted displaced talent, but cannot absorb all displaced workers. Additionally, these roles are not immune to automation as AI acceleration absorbs new tasks, and there are further advancements in robotics. - Economic outlook: Although AI breakthroughs have delivered immense productivity gains, the global economy has been strained by the depth of disruption. Volatility and uncertainty have increased. Corporate profit margins increase, driven by a handful of state-like companies controlling foundational models, compute and proprietary datasets. They gain unchecked influence in the global economy, determine the cost of automation and control access to – and governance of – AI. Consumer confidence ebbs globally, falling below the historical low of 44.10 The scale of AI deployment and data centre workloads grows faster than grid capacity, intensifying energy and the environmental impact of automation. - Value chains: Workflows become more algorithmic. Business models are redesigned around heavily self-optimizing, automated and Al-native architectures, with opportunities for human-Al complementarity drying out by 2030. Many sectors localize digital infrastructure and adopt sovereign AI stacks, but ultimately lack talent to balance resilience and efficiency at scale. - Inequality and polarization: Wages decline globally, but the pace varies across regions and sectors. Income inequality and poverty reach a historic level. Workers in human-centric roles have been relatively more immune to the initial disruption, but are ultimately squeezed by oversaturated labour markets. Trust in institutions declines amid rising misinformation and a surge of AI-generated content that overtakes traditional media sources. Social safety nets stretch beyond capacity and polarization erodes informal networks. Countries that fail to adapt welfare systems to the Al-centric economy see persistent unemployment and rising political polarization. - Policy and regulatory landscape: Tax bases shrink, and governments face rising fiscal burdens from mass unemployment, retraining needs and the supply of basic services. Some governments seek a new balance through taxation of AI technologies and business applications. Efforts to harmonize AI safety standards, agentic governance and data frameworks face deadlock. This dependency introduces systemic vulnerabilities. Outages, geopolitical restrictions on compute and access to AI networks drive economy-wide instability. Many governments grapple with the question of democratic oversight in a world where key decisions are outsourced to autonomous systems. # Scenario 3: Co-Pilot Economy Incremental AI advancement, widespread workforce readiness Gradual AI progress and availability of AI-ready skillsets shift the focus towards augmentation rather than mass automation. The AI hype of the 2020s has given way to pragmatic integration: most industries see incremental transformation as human-AI teams reshape value chains. Countries and businesses that invested early in training, mobility, digital infrastructure and AI governance have created conditions to absorb and advance emerging technologies. AI capability, top five models average MMMU Baseline: 84.2 (Xiang,Y.et al.,2025) AI literacy and adjacent skills Baseline: N/A Labour productivity growth, % annual Baseline: $1.5\%$ (International Labour Organization, 2020-2025 annualized) Unemployment rate, % Baseline: $5\%$ (International Labour Organization, 2025) Consumer confidence index Baseline: 48.8 (lpsos, 2025) Scaling of agentic AI, % of businesses Baseline: $23\%$ (McKinsey, 2025) Wage polarization, D9/D1 earners ratio Baseline: 16.8 (International Labour Organization, 2025) S&P 500 operating margin, % quarterly Baseline: $12.6\%$ (S&P, 2025 average) 1 No authoritative or standardized global measure of "AI literacy and adjacent skills" currently exists. Notes: The arrows denote a directional change in a given scenario characteristic. All values are at the global level, unless specified otherwise. The analysis is based on scenario narratives and extrapolations from similar existing research. The directionality is illustrative and for scenario-building purposes only. In this scenario, steady AI progress and high AI readiness among workers allow businesses to embrace human-AI complementarity. AI deployment widens but remains shallow. Most industries undergo gradual transformation, shaped by tailored and task-specific AI integration rather than the structural redesign of workflows. Following a wave of capital commitments and ballooning valuations of AI-related stocks, the hopes of productivity gains from AI integration have faltered, and the "AI bubble" burst in the mid-2020s. Funding of frontier AI ventures has dried up, leading to the recalibration of commercialization timelines and expectations. The early-stage displacement of workers has driven investments into readiness – from AI literacy initiatives and prompt design to the transformation of reskilling, upskilling and training systems. AI skills have become as common as digital literacy skills in the early 2020s. Although displacement and job churn have risen, governments, businesses and workers increasingly view AI as an opportunity rather than a threat, and focus on pragmatic integration and absorption of emerging technologies. - Occupations and tasks: Workers have leaned into AI tools to augment routine tasks, and managers have adjusted to leading human-AI teams. AI tools have facilitated a reduction in completion time by as much as $80\%^{11}$ for certain tasks, with most administrative, standardized and basic analytical tasks being hollowed out. By 2030, more than $40\%$ of skills have changed, surpassing earlier forecasts. $^{12}$ Labour markets show higher mobility and job fluidity, with stronger demand for problem-solving, social, managerial and uniquely human skills. The hybrid roles that combine AI knowledge and narrow domain expertise have expanded. The share of gig workers and entrepreneurs has also risen, with broadening access to AI and societal readiness spurring experimentation. However, job quality varies widely: improving where workers lead AI, and deteriorating where AI shrinks human creativity and agency. Economic outlook: Human-AI complementarity has succeeded in breaking the tepid GDP growth patterns of the preceding decades. Without an automation shock, productivity gains accumulate steadily, with annual labour productivity growth spiking above $1.5\%$ recorded in the early 2020s.[13] The uptick in dynamism and augmentation-accelerated innovation across sectors strengthens consumer and investor confidence. Higher margins and lower cost pressures have somewhat stabilized inflation, but intensifying competition and a tight labour market keep volatility risks high. Businesses and geographies with transparent AI adoption frameworks enjoy lower risk premiums, trust and investment inflows. Differences in AI infrastructure maturity and energy costs fuel divergence between Al-ready economies and the rest of the world. - Value chains: Businesses reorganize around more modular, digitized and AI-augmented workflows, with human workers in core loops and decision-making roles. Automation density has also risen, primarily affecting highly standardized processes. - Inequality and polarization: Inequality widens between workers who can adapt dynamically to the evolving AI landscape and those locked out by lower access to education, digital infrastructure or supportive employers. However, with AI tools lifting skills floors, wage gaps narrow slightly among mid- and high-skilled workers. The rise of remote, flexible work and entrepreneurship has also widened economic opportunities across peripheral regional and marginalized communities. With AI-generated content overtaking human content,[14] polarization and misinformation concerns grow. Trust shifts towards curated sources, verified platforms and human intermediaries. - Policy and regulatory landscape: AI standards and data privacy regulations multiply but vary widely across jurisdictions. Some geographies and sectors invest in harmonization and interoperability of AI and data systems. Others focus on restrictive approaches and nationalization of AI tools, networks and infrastructure. Talent mobility frameworks also oscillate between borderless digital talent pipelines and talent protectionism. # Scenario 4: Stalled Progress Incremental AI advancement, limited workforce readiness Steady AI progress meets a workforce lacking critical skills. Productivity growth is patchy, and businesses lean on automation to backfill scarce talent. Gains concentrate within businesses and geographies with AI expertise, while others face eroding competitiveness. Displacement hits primarily routine roles, while the value of skilled trades and manual occupations increases. The hope of AI-enabled prosperity fades into frustration, as adoption gaps fuel inequality, create a bifurcated economy and limit growth. AI capability, top five models average MMMU Baseline: 84.2 (Xiang, Y. et al., 2025) AI literacy and adjacent skills Baseline: N/A Labour productivity growth, % annual Baseline: $1.5\%$ (International Labour Organization, 2020-2025 annualized) Unemployment rate, % Baseline: $5\%$ (International Labour Organization, 2025) Consumer confidence index Baseline: 48.8 (Ipsos, 2025) Scaling of agentic AI, % of businesses Baseline: $23\%$ (McKinsey, 2025) Wage polarization, D9/D1 earners ratio Baseline: 16.8 (International Labour Organization, 2025) S&P 500 operating margin, % quarterly Baseline: $12.6\%$ (S&P, 2025 average) In this scenario, AI continues to improve, but capability breakthroughs are rare and costly. In the meantime, the workforce has not adapted to incremental AI advancements, leaving real-world AI applications brittle. A chronic shortage of AI-ready talent, rising compute costs and exposed limitations of the existing models lead to unrealized productivity gains and growing scepticism towards AI tools. Regulatory caution has heightened following the spillovers from the mid-2020s market correction of AI companies' valuations. Governments and businesses shift to selective and conservative AI deployment, prioritizing efficiency increments within the existing workflows and offsetting talent shortages, particularly in rapidly ageing geographies. Technological progress is visible, but it is far from transformative, with structural constraints stalling growth, resilience and societal progress. - Occupations and tasks: Workforce displacement has risen, with businesses turning to automation to backfill talent gaps. However, most occupations have been hollowed out rather than fully eliminated, with AI infrastructure remaining patchy. Many displaced workers shift into lower-productivity, lower-skill and less protected jobs – such as services and gig work. Early-career jobs and administrative tasks are particularly exposed to AI-driven replacement, with labour market entry pathways narrowing. Workforce mobility, redeployment and augmentation have been constrained by talent gaps and outdated training frameworks. Economic outlook: Global growth is patchy. A handful of frontier sectors and businesses that have harnessed early investments into AI and digitalized workflows see productivity and innovation increase at the margins. Others struggle to unlock broad-based AI gains. Investors have grown more cautious, with entrenched uncertainty and a weak macroeconomic outlook thinning out profit margins across sectors and inflating the cost of capital. Consumer confidence also declines amid rising costs of living, eroding wages and weak social benefits. - Value chains: AI use cases grow, but remain shallow and task-specific. Businesses have partially digitized legacy processes but have failed to restructure workflows. Most sectors use AI to automate routine tasks, prioritizing general-purpose and generative AI tools or outsourcing parts of value chains to AI-enabled service providers. End-to-end agentic networks do not take off outside of a small number of frontier companies. The use of highly customized and proprietary models is marginalized by cost pressures and uncertain returns on investments. - Inequality and polarization: Income inequality has widened within and across countries. Wages have been squeezed by AI adoption and displacement. However, many high-skilled workers benefit from growing bargaining power in a world of talent shortages and rising complexity of not-yet-automated tasks. Chronic job insecurity, eroding opportunities and safety nets fuel polarization and declining trust. Societal frustration intensifies globally amid broken prosperity promises. - Policy and regulatory landscape: Many regulators have tightened guardrails and standards around AI. However, global harmonization and integration of AI infrastructure remain limited. Divergences in AI oversight capacity, technology protection, censorship and data privacy continue to grow. Labour market policies expand in an attempt to capture growing platform work, informal occupations, eroding safety nets and cross-border talent reallocation. Execution is stalled by exhausted fiscal space and limited trust in institutions. # 3 # Implications for businesses These scenarios are designed to support dialogue anchored in structured foresight around business risks, opportunities and strategies. The narratives of these four exploratory scenarios provide a lens for analysing how AI and talent dynamics – and the uncertainties underpinning their future trajectory – may reshape industries and corporate strategies. Building on the analysis in previous chapters and a series of workshops and consultations with chief strategy officers and subject matter experts, this chapter explores key risks and opportunities associated with the four scenarios as well as strategy considerations that may help businesses strengthen resilience and competitiveness within these futures. Scenario 1: Supercharged Progress <table><tr><td>Top risks</td><td>- Overconfidence, regulatory lag and complacency amid accelerated progress - Strained energy grids, price spikes in critical materials and rising environmental externalities in the absence of a breakthrough in the green transition - An exponential increase in complexity and AI capabilities outpaces the capacity of businesses and governments to adapt, fuelling rapid obsolescence, a “winner-takes-all” dynamic and weakening control over AI agents, autonomous systems and multiplying proprietary models</td></tr><tr><td>Top opportunities</td><td>- Breakthroughs in productivity growth, cost efficiency and innovation - Blurring of physical and virtual networks creates interoperable AI-native ecosystems and minimizes geographic boundaries in access to talent, markets and critical value chains - Leapfrogging progress and human capital development, with the growth of hyperpersonalized goods and services, AI-tailored education and healthcare, investments in literacy, longevity and wellbeing</td></tr><tr><td>Strategy considerations</td><td>- Redesign business models around agentic networks, high-autonomy processes and AI-complementary talent pipelines - Scale data infrastructure, grid efficiency, value chains integration and resilience - Invest in agility, ecosystem and AI governance leadership - Align talent and AI strategies, and involve workers, governments and key industry stakeholders in AI deployment processes</td></tr></table> Scenario 2: The Age of Displacement <table><tr><td>Top risks</td><td>-Decision-making blind spots, over-reliance on agentic AI systems and lack of oversight increase systemic risks and cognitive manipulation -Talent shortages in critical roles and in AI design, architecture and oversight functions -Concentration of power in a handful of technology platforms and governments distorts markets and regulatory frameworks -Breakdown of societal and economic foundations following mass unemployment, collapse of social safety nets, growing environmental externalities and AI-driven disinformation</td></tr><tr><td>Top opportunities</td><td>-Expansion of ultra-lean and AI-native processes, business models and R&D cycles -Transparent and responsible AI deployment and data governance become key sources of trust, reputation and competitive differentiation -Structural redesign of approaches to work, education, value creation and redistribution</td></tr><tr><td>Strategy considerations</td><td>-Strengthen resilience, develop adaptive demand and investment planning to navigate tightening consumption and macroeconomic space -Strengthen data standards, and diversify AI tools and infrastructure to reduce dependency on any single model or provider -Institutionalize human-centric roles and decision-making frameworks to ensure oversight and control over critical processes amid tightening talent pool -Engage regulators and key stakeholders in automation and workflow redesign</td></tr></table> Scenario 3: Co-Pilot Economy <table><tr><td>Top risks</td><td>- Systemic over-reliance on AI-enabled process reduces human judgement, increasing risk of model weakness, biases and governance gaps - Tightening financial landscape and weak investor confidence following “AI bubble” burst - Operational divergence, with sectors and geographies that overregulate or underinvest falling behind - Escalating strategic rivalry around AI capability, talent advantage and control of critical value chains</td></tr><tr><td>Top opportunities</td><td>- Accelerating innovation cycle and frontier breakthroughs in key sectors - Broadening AI adoption equalizes opportunities, multiplies human ingenuity and allow workers to focus on complex problem-solving and high value tasks - Heightened resilience of critical value chains and interoperability of physical and digital ecosystems</td></tr><tr><td>Strategy considerations</td><td>- Invest in long-term AI leadership, and develop internal governance and integration blueprints - Institutionalize human-AI collaboration, define uniquely human processes, and redesign legacy workflows and tasks for augmentation - Scale training, reskilling and upskilling ecosystems to elevate human expertise and increase internal mobility</td></tr></table> Scenario 4: Stalled Progress <table><tr><td>Top risks</td><td>- Overextension of AI and technology commitments amid fragmented progress and diminishing returns on AI investments - Rising talent protectionism and talent mobility restrictions - Economic stagnation, tightening fiscal space and eroding social safety nets drive polarization and workforce disengagement - Cost pressures and race for short-term returns entrench legacy processes and stall transformation potential</td></tr><tr><td>Top opportunities</td><td>- Technological sobriety, with slower AI progress creating space for global coordination on AI governance and standards before broad-based deployment - Rise in domain-specific AI solutions, localized innovation and talent pipelines - Lower-risk experimentation and piloting landscape</td></tr><tr><td>Strategy considerations</td><td>- Strengthen operational and financial buffers and prioritize core markets - Strengthen workforce readiness though job-tailored and dynamic training curricula, AI-complementary skills and mobility frameworks - Invest in AI architecture and data infrastructure to unlock efficiency gains and augment human-AI workflows - Harness partnerships and industry alliances to mitigate structural capability gaps and expand innovation synergies</td></tr></table> 4 # How businesses can prepare today for any scenario Strategies and investments prioritized today will define how businesses and industries adapt to – and lead in – the new economy. Drawing on a series of workshops and consultations with chief strategy officers and experts, the following strategies have emerged as potential "no-regret" moves that could help businesses prepare today for any scenario. Whether AI advancement accelerates or slows down, and whether the workforce adapts quickly enough or lags behind, these strategy considerations aim to help businesses mitigate risks and harness the potential of AI and talent developments in the coming years. Start small, build fast, scale what works. Run small, controlled experiments starting with the lowest-risk operational and digitalization challenges. Learn from failure at low cost, understand different technology use cases across industries and companies, and scale AI integration carefully. Align technology and talent strategies. As the pace of transformation accelerates, ensuring that technology and talent evolve in tandem is critical for unlocking broader productivity gains and systemic resilience within value chains. AI learning must be integrated into the flow of work to allow for continuous, personalised and domain-specific talent development. Invest in human-AI collaboration and agentic workflows. Designing workflows that thrive on human-AI collaboration will be critical to increase trust, productivity, adoption and resilience. Prioritize investments in augmentation, integration of agentic workflows and development of AI-ready lifelong learning systems, contextual judgement and core workforce skills. Invest in data governance and infrastructure. AI models are only as good as the data they are trained on. Reliable data will be a critical source of corporate value, reputation and trust. Businesses that invest systematically in data infrastructure, standards and governance will emerge more resilient. Anticipate talent needs and future-proof value chains. Use foresight and AI-enabled predictive analytics to scope emerging talent and capability gaps. Invest in dynamic talent pipelines and partnerships with education providers and governments. Develop in-house training capacity, intra- and inter-industry talent mobility frameworks to help workers transition across occupations and tasks, and develop cross-functional and complementary skills. Strengthen organizational culture and trust in emerging technologies. Curiosity, agility and experimentation will be as critical as AI literacy in building trust in technologies and supporting business transformation and competitiveness. Engage key stakeholders, implement ethical guardrails and ensure transparency in technology development and deployment to address biases, build accountability and trust. Prepare for different implications across occupations, tasks and markets. The pace and scale of impact from AI advancement will vary widely across occupations, tasks, geographies and sectors. Although many routine, administrative and basic analytical tasks may face the highest early-stage displacement, others may face rising exposure with the acceleration of AI capabilities. Industries such as financial services, logistics and others may advance rapidly, while construction and energy may face slower transformation. Meanwhile, convergence of AI and robotics is a critical uncertainty that may affect both blue- and white-collar workers. Design multi-generational workflows. Older workers should learn from younger cohorts, who are generally better acquainted with AI. Building multi-generation learning teams can help accelerate adoption and reduce culture gaps. Leverage strategic partnerships. Working with partners - industry peers, universities, start-ups, software vendors and investors - will be critical to draw on external expertise, build information flows and continuously surface use cases and learning. # Contributors # Centre for the New Economy and Society Filippo Crotta Lead, Strategy and Transformation Attilio Di Battista Head, Economic Growth and Transformation Mark Elsner Head, Global Risks Initiative Sam Grayling Insight Lead, Work, Wages and Job Creation Kateryna Karunska Insight Lead, Economic Growth and Transformation Saadia Zahidi Managing Director, World Economic Forum; Head, Centre for the New Economy and Society # Global Industries Team Francisco Betti Head, Global Industries Team We are grateful to our colleagues in the Centre for the New Economy and Society, Global Industries Team and Strategic Intelligence team who have provided invaluable insights and support over the course of this project. At the Centre for the New Economy and Society: Neil Allison, Aengus Collins, Sriram Gutta, Amalya Khachatryan, Till Leopold, Tanya Milberg and Eoin O Cathasaigh. At the Global Industries Team: Andre Belelieu, Stefan Koch, Maciej Kolaczkowski, Witold Kopytynski, Isidora Kosta, Eli Pipic, Giacomo Ruscelloni, Margi Van Gogh, Renee van Heusden. At Strategic Intelligence: Bryonie Guthrie. # Acknowledgements The World Economic Forum would like to thank the Network of the Global Future Councils, in particular the members of the following Global Future Councils, for contributing their perspectives and insights, as well as Leandro Loss, Martina Szabo and Lea Weibel for their support over the course of this project. Global Future Council on Advanced Manufacturing and Value Chains Global Future Council on Business of Economic Growth Global Future Council on Decentralized Finance Global Future Council on Equitable Transition Global Future Council on Energy Nexus Global Future Council on Experience and Lifestyle Economy Global Future Council on Geopolitics Global Future Council on Human Capital Development Global Future Council on International Trade and Investment Global Future Council on Investing in Gender Parity Global Future Council on Jobs and Frontier Technologies Global Future Council on Leadership The authors would also like to express their gratitude and appreciation to the organizations – from a wide range of geographies and industries – experts and members of the World Economic Forum's Chief Strategy Officers Community that generously contributed their thought leadership and perspectives through a set of workshops and consultations. The paper does not necessarily reflect the views of these individuals and/or their organizations. The opinions are purely consultative in nature and do not imply any association with the takeaways or conclusions presented within this paper. # Gunter Beitinger Siemens # Dirk Bullmann CLS Bank International # Gero Corman Volkswagen # Nirupam Das Tamer Group # Antonio de la Torre Repsol # Ana Maria Dieppa Promigas # Dante Disparate C