Four Futures for Jobs in the New Economy AI and Talent in 2030 2025

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 –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 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 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 Strategy considerations –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 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 polarization 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 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