At an inter-ministerial meeting of the World Health Organization (WHO) on eHealth more than two decades ago, I had made a simple but urgent point: Bureaucrats and ministers must undergo a crash course in eHealth. At that time, digital health itself was still emerging. Today, that suggestion has become even more relevant in the age of Artificial Intelligence.AIHealth care systems across the world are standing at a defining moment in history. Artificial Intelligence (AI) is no longer a futuristic concept; it is rapidly becoming the operating system of modern health care. From diagnostics and clinical decision support to drug discovery, disease surveillance, hospital operations, and personalised medicine, AI is transforming every layer of health care delivery.Yet beneath this transformation lies an uncomfortable truth: The people writing the rules, approving the budgets, framing regulations, and signing procurement contracts are often the least equipped to evaluate what they are deciding.The deficit is not in technology. The deficit is in AI literacy among policymakers.Over the past decade, nations have invested heavily in training clinicians, engineers, data scientists, and entrepreneurs. Very little attention, however, has been paid to training policymakers who must decide whether an AI system is safe enough to triage patients, fair enough to allocate health care resources, or trustworthy enough to influence clinical decisions. This is the missing link in health care transformation.Health care systems are ultimately shaped not by algorithms alone, but by policy decisions; regulations, reimbursement frameworks, procurement rules, ethical standards, educational reforms, and public investments. If policymakers do not adequately understand AI, nations risk either over-regulating innovation or blindly adopting technologies without understanding their long-term implications. Both outcomes are dangerous.The global AI race is often portrayed as a technology competition. In reality, it is a policy ecosystem, governance and execution competition.The US became an AI leader not merely because of its technological innovation, but because policymakers allowed business leaders, researchers, venture capital ecosystems, and universities to shape implementation at scale. Companies like OpenAI, Google, NVIDIA, Microsoft, and Amazon were not treated as isolated technology firms; they became strategic national assets supported by enabling policy frameworks, capital access, research ecosystems, and aggressive deployment culture.China followed a different but equally decisive path. It aligned state policy, industrial strategy, infrastructure, academia, and private sector execution under a national mission. AI was not treated as a sectoral initiative but as a geopolitical and economic priority. Massive investments in compute infrastructure, data ecosystems, semiconductor capabilities, surveillance systems, robotics, and healthcare AI allowed China to accelerate implementation faster than most democracies.Both nations understood a critical principle: Leadership in AI does not come from discussing AI. It comes from implementing AI at scale. And implementation happens when policymakers understand enough about technology to create enabling ecosystems rather than bureaucratic bottlenecks. The literate policymaker learns to interrogate the foundation before admiring the architecture. This is especially important for developing economies. Many countries are rushing toward AI adoption while basic digitization itself remains incomplete. Deploying AI without trusted digital public infrastructure risks amplifying inefficiencies, inaccuracies, and inequities at machine speed.Not all AI is the same. One of the biggest misconceptions in health care policy is treating AI as a single technology. In reality, AI evolves through stages of maturity. I developed the Functional AI Pyramid framework, AI progression can be understood across five levels, each level carries different levels of autonomy, risk, and governance complexity.A chatbot scheduling appointments does not pose the same ethical challenge as an autonomous AI system making treatment recommendations inside an ICU. Yet many governments continue regulating all AI systems uniformly.Policy must, therefore, become capability-sensitive rather than technology-generic.Health care AI is not merely a technology issue. It is a governance issue, an economic issue, a workforce issue, and ultimately a national competitiveness issue.Countries that succeed in AI-driven health care transformation will not necessarily be those with the best algorithms. They will be the ones with AI-literate leadership capable of making informed decisions at speed, and that will decide the future of nations. (The views expressed are personal)This article is authored by Rajendra Pratap Gupta, chairman, Academy of Digital Health Sciences.