Drivers of skill disruption This section discusses how each of the five identified macrotrends driving labour-market transformation – technological change, geoeconomic fragmentation, green transition, demographic shifts and economic uncertainty – are expected to influence skill evolution by 2030. Technological change Technological advances are expected to drive skills change more than any other trend over the next five years. The increasing importance of AI and big data, networks and cybersecurity, and technological literacy is driven by the expansion of digital access and the integration of AI and information processing technologies. These trends are not only seen as responsible for the growth of these three fastest-growing skills but also for the rising importance of analytical thinking and systems thinking. These shifts highlight the increasing complexity of decision-making and the need for critical problem solving in a data-driven world. Beyond the top 10 fastest-growing skills, design and user experience, along with marketing and media skills, are also expected to see growth driven by technological advancements. These skills are closely linked to digital transformation, reflecting the rising importance of delivering seamless digital experiences and understanding the impact of consumer behaviour. Robots and autonomous systems are also seen as a key driver of skills change, contributing to the increased demand for not only the three top-growing skills, but also programming and systems thinking – skills essential for managing and optimizing interactions with autonomous technology. As noted in Chapter 2, robots and autonomous systems are also among the primary drivers behind the fastest-growing jobs. Coupled with the rising demand for the three top growing skills, and programming, this trend underscores the importance of technological expertise and systems thinking as core skills in technical fields. These capabilities are crucial for enabling employees to adapt to, and collaborate effectively with, automated systems across a range of industries. While technology fuels demand for certain skills, it also accelerates the decline of others. Skills such as manual dexterity, endurance, precision, and reading, writing, and mathematics are expected to diminish in relevance as digital access, AI and information processing, and robotics increasingly automate these tasks. Interestingly, whereas programming remains stable as an in-demand skill, both respondents expecting growth in its use and those expecting decline consistently point to technological change as the primary driver behind this change. As discussed in more depth in Chapter 2, this highlights the dual effect of technology, underscoring how the same technological forces that drive job creation may also contribute to job displacement. Additionally, as also discussed in Chapter 2, the primary impact of technologies such as GenAI on skills may lie in their potential for “augmenting” human skills through human-machine collaboration, rather than in outright replacement, particularly given the continued importance of human-centred skills (Box 3.1). These findings underscore an urgent need for appropriate reskilling and upskilling strategies to bridge emerging divides. Such strategies will be essential in helping workers transition to roles that blend technical expertise with human-centred capabilities, supporting a more adaptable workforce in an increasingly technology-driven landscape.3.2 Generative AI and human-centred skills BOX 3.1 In collaboration with Indeed The release of ChatGPT 3.5 in November 2022 marked an inflection point in public awareness of GenAI technologies, which sparked both excitement and apprehension regarding their potential impact on the workforce.40 In this context, research conducted by Indeed for this report highlights the continued importance of human-centred skills in an age of GenAI. Figure B3.1 illustrates the capacity of GenAI to substitute a human in executing specific skills, based on an assessment by GPT-4o of its own ability to utilize skills across three areas: its ability to provide theoretical knowledge about a given skill, its problem-solving abilities related to that skill, and the need for physical presence or manual actions in performing that skill.41 The chart categorizes more than 2,800 granular skills into the World Economic Forum’s Global Skills Taxonomy and evaluates their capacity of substitution by GenAI according to five categories: very low capacity, low capacity, moderate capacity, high capacity, and very high capacity. Zero of the more than 2,800 skills assessed were determined to exhibit “very high capacity” to be replaced by the current generation of GenAI Future of Jobs Report 2025 43