A similar gap is also seen in the universe of AI. AI talent is scarce and unequally distributed across industries, sectors, and nations. More than half of the population in the developing world lack basic digital skills. In the age of AI, this digital skills divide is broadening, with a few countries progressing quickly while most of the developing world is lagging. AI policies and pro- grammes should work to minimise negative outcomes and increase access to AI for those left behind.
The GTCI’s longitudinal analyses highlight that—even if it is the exception rather than the rule—some developing countries (e.g., China, Costa Rica, and Malaysia) can become talent champions in their respective regions, while others (e.g., Ghana and India) have significantly improved their capacity to enable, attract, grow, and retain talent over the past few years, and hence can be labelled talent movers. As India did in the late 1990s (becoming a global offshoring base for IT services), AI may provide opportunities for other countries/regions (e.g., Latin America) to become ‘global delivery centres’ for AI applications.
AI can play a key role in providing solutions to help humanity achieve the United Nations Sustainable Development Goals (SDGs): Education (with customised online programmes) and health (with personalised remote diagnosis and follow-up, as well as big data analysis to track and reduce endemic diseases and epidemics) are two of the most immediate examples. This, however, will require multi-stakeholder cooperation. The two sides (supply and demand) of the AI/talent equation deserve concurrent attention: (1) Build the skills necessary to ensure optimal human/machine cooperation and (2) create the conditions to maximise the social value and long-term sustainability of such co- operation. It is also fundamentally important that AI be designed within universally accepted guiding principles respecting the rule of law, fundamental human rights, inclusion, and diversity.
At all levels of qualifications, workers will need training on adaptability, social intelligence, communication, and problem-solving. Life-long learning will increasingly play a key role in developing skills to foster empathy, creativity, imagination, judgement, and leadership, which are likely to continue to be human-only activities. Re-skilling will also be necessary to develop fusion skills in order to allow humans and machines to effectively and efficiently interact in hybrid activities.
It seems critical to create a narrative about AI and the future of jobs that emphasises its many possibilities instead of just instilling more fear. It is essential, however, that the broader workforce (including the youth, women, and older people) should have the opportunities, skills, and interest (and feel empowered) to fulfill the millions of new jobs that will be created by AI, directly or indirectly. It should be emphasised that AI will augment human capabilities directly and that human-AI teams could be more productive than either AI or workers alone. AI-based automation also offers opportunities to re-humanise time (e.g., through a better work-life balance for humans) and to offer more intellectually stimulating jobs. Policymakers and regulators will have key roles to play—including through the provision of social safety nets—to ensure smooth job transitions. Since AI-induced changes will be fast and broad-ranging, it will be important for educators and leaders to realise that new generations will continue to attach key importance to values and seek jobs that offer them opportunities to contribute in a meaningful way to society.
Such efforts translate to different initiatives and strategies (curricula in local universities and schools and aggressive policies to detect, attract, and retain AI talents, for example). In many respects, such efforts coincide with cities’ strategies to become smart cities, as AI becomes a core engine of the local transformation of transportation networks, energy grids, and other fundamental components of urban strategies. Currently—and increasingly in the future—cities continue to be the main testbeds for new AI-based tools such as facial recognition, tele-surveillance, and self-driven vehicles. Experience shows that perceptions of the value of such technologies vary greatly from one city to another, which is a phenomenon worth watching before these tools can be sustainably deployed.