How businesses can prepare today for any scenario4 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. Four Futures for Jobs in the New Economy: AI and Talent in 2030 16

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