Imagine you are watching a race in which other countries are not only running faster, but also building the track as they go. Artificial intelligence is becoming a major economic tool because it can support productivity, create new services, reshape work, and open new markets. For countries that delay building local capability, the risk is not simply missing a trend. The risk is becoming a buyer of other nations’ systems, standards, infrastructure, and expertise while domestic firms and workers adapt from the sidelines.
The countries most exposed are those with limited investment in AI research, weak links between universities and industry, insufficient digital infrastructure, uncertain regulation, or a shortage of advanced skills. The IMF’s AI Preparedness Index measures readiness across digital infrastructure, human capital and labour policies, innovation and economic integration, and regulation and ethics. The World Bank also identifies infrastructure, data governance, institutional capacity, and human capital as foundations for using AI effectively. Firms, workers, public agencies, researchers, and students all have a stake because AI adoption is moving from experimentation into everyday business and public-sector use.
The problem appears in workplaces, schools, hospitals, farms, factories, courts, and government services wherever decisions depend on data, software, and specialised knowledge. AI is most useful when it is embedded in real tasks, such as improving customer service, assisting research, analysing documents, supporting logistics, or helping professionals manage complex information. OECD data show that firm-level AI adoption has increased across countries where data are available, rising from 8.7 percent of firms in 2023 to 20.2 percent in 2025. A country that waits too long may find that global competitors have already accumulated better data, stronger supplier networks, deeper technical talent, and more practical experience.
In practice, falling behind works through compounding gaps. Early movers invest in computing capacity, train specialists, test systems, improve regulation, and learn from failure. Later movers must often import tools, compete for scarce talent, and retrofit old institutions under pressure. Like learning a language after everyone else is already negotiating in it, catching up is possible but harder when the conversation has moved on. Public sources do not clearly confirm one single point at which a country becomes permanently late, but they do show that AI readiness depends on multiple foundations that take time to build.
What comes next is a choice between passive consumption and deliberate capability building. McKinsey has warned that AI adoption could widen gaps between countries, companies, and workers, while UNCTAD has called for inclusive AI development so that more countries can shape the technology rather than merely receive it. The clear next step for any country in this position is to map its AI readiness honestly: infrastructure, skills, research, regulation, data access, and industry adoption. Without that baseline, policy becomes rhetorical. With it, leaders can decide where to invest first and how to reduce the cost of starting late.