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The World Economic Forum's latest report details four scenarios that each reshape how organisations are thinking about corporate strategy, business models, investments and workflows; and how companies may need to adapt in the near future.
Fast-developing technologies such as artificial intelligence (AI), robotics and autonomous systems are redefining how businesses operate, how tasks are performed and what skills are required to stay competitive.
In fact, A World Economic Forum (WEF) report, titled Four Futures for Jobs in the New Economy: AI and Talent in 2030 revealed that more than half (54.3%) of business executives globally expect the technology to displace existing jobs, while 23.5% said AI will create new jobs. In addition, 44.6% also cited an increase in profit margins as a likely impact of AI; far fewer expect it to lead to higher wages.
Despite these technologies promising systemic gains in productivity, its adoption raises critical questions about economic inclusion, values, trust and resilience. In the meantime, global talent dynamics — such as demographic trends, persistent skills gaps and strained social safety nets — are creating complex pressures to the labour markets, therefore demanding greater agility and foresight from education and training systems.
This report, which is the second edition of WEF's Scenarios for the Global Economy Dialogue series, explores four scenarios for the future of jobs at the intersection of AI advancement and workforce readiness vectors, and their potential trajectories until 2030.
Scenario 1: Supercharged progress
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). Education and training systems have been radically redesigned, enabling rapid workforce adaptation. Open‑source competition has driven faster, and more cost-efficient AI agent development, broadening commercialisation and redefining work, learning, and value creation.
The above aside, by 2030, many occupations would have disappeared entirely, with jobs shifting from task execution to designing and overseeing AI ecosystems. Productivity gains far exceed the projected 1.3percentage-point increase, sparking an industry‑wide AI deployment race. Capital expenditure (CapEx) surpasses $1.3tn from 2025-2030, as investments in compute and data infrastructure elevated AI into a core economic actor.
This AI‑centric economy would have also intensified structural tensions around labour displacement, governance gaps, sustainability pressures, and the role of human workers.
Scenario 2: The age of displacement
In this scenario, AI is advancing at breakneck speed, but workforce readiness lags. Traditional education systems fail to produce adaptive talent, fueling a culture of accelerationism and pervasive automation. Governments and businesses automate to offset scarce skills, deepening reliance on technology and displacing workers.
Additionally, rising capital commitments to AI 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.
Lastly, some governments and sectors attempt to regulate mass AI deployment to mitigate societal risks, but competition renders restrictions economically and politically untenable.
Scenario 3: Co-Pilot economy
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.
At the same time, funding of frontier AI ventures has dried up, leading to the recalibration of commercialisation timelines and expectations. The early-stage teams. AI tools have facilitated a reduction in completion time by as much as 80% for certain tasks, with most administrative, standardised and basic analytical tasks being hollowed out. By 2030, more than 40% of skills would have changed, surpassing earlier forecasts.
Finally, 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.
Scenario 4: Stalled progress
In this scenario, AI continues to improve, but capability breakthroughs are rare and costly. The workforce struggles to adapt, leaving applications brittle. A shortage of AI‑ready talent, rising compute costs, and model limitations stall productivity and fuel scepticism.
After the mid‑2020s market correction, regulatory caution grew. Governments and businesses adopt selective, conservative deployment – focused on efficiency gains and offsetting talent shortages, especially in ageing regions.
Overall, progress is visible but far from transformative, with structural constraints hindering growth, resilience, and societal advancement.
How these scenarios will impact businesses
These four scenarios provide a lens for analysing how AI and talent dynamics, as well as its uncertainties underpinning their future trajectory, may reshape industries and corporate strategies. Building on this, each scenario brings various risks and opportunities as well as strategy considerations that may help businesses build resilience and competitiveness within these futures.
Scenario 1: Supercharged progress
Top risks
- 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, fueling rapid obsolescence, a “winner-takes-all” dynamic and weakening control over AI agents, autonomous systems and multiplying proprietary models.
Top opportunities
- Breakthroughs in productivity growth, cost efficiency and innovation.
- Blurring of physical and virtual networks creates interoperable AI-native ecosystems and minimises geographic boundaries in access to talent, markets and critical value chains.
- Leapfrogging progress and human capital development, with the growth of hyper-personalised goods and services, AI-tailored education and healthcare, investments in literacy, longevity and wellbeing.
Strategy considerations
- 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.
Scenario 2: The age of displacement
Top risks
- 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.
Top opportunities
- 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.
Strategy considerations
- 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.
- Institutionalise 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.
Scenario 3: Co-pilot economy
Top risks
- 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.
Top opportunities
- Accelerating innovation cycle and frontier breakthroughs in key sectors.
- Broadening AI adoption equalises 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.
Strategy considerations
- Invest in long-term AI leadership and develop internal governance and integration blueprints.
- Institutionalise 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.
Scenario 4: Stalled progress
Top risks
- 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 polarisation and workforce disengagement.
- Cost pressures and race for short-term returns entrench legacy processes and stall transformation potential.
Top opportunities
- 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.
Strategy considerations
- Strengthen operational and financial buffers and prioritise 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.
How businesses can prepare today for any scenario
The report also shared some strategies businesses can consider to mitigate risks and harness the potential of AI and talent developments in the coming years:
- 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 organisational culture and trust in emerging technologies,
- Prepare for different implications across occupations, tasks and markets,
- Design multi-generational workflows, and
- Leverage strategic partnerships.
READ MORE: Labour markets strong but participation and skills gaps persist in 2025: WEF updates
Infographics / WEF Four Futures for Jobs in the New Economy: AI and Talent in 2030
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