Japan and South Korea join top tier of AI-ready economies

A shift in the global AI race

Artificial intelligence is transitioning from a future promise of productivity into a primary force reshaping global labor markets and industrial economies. According to the AI Maturity Matrix 2026 report by Boston Consulting Group (BCG), the decisive factor in this race is no longer just the ambition of a country's strategy. Instead, the focus has shifted to how quickly nations can turn those plans into tangible economic outcomes.

The report assesses 79 economies based on their exposure to AI and their readiness to capture its benefits. While nearly every economy studied has made progress in at least one area, the speed and scale of capability-building are creating a clear divide between leaders and laggards. This distinction is defined by a country's ability to mobilize capital, attract talent, and implement policy quickly.

Enterprise investment in AI doubled in 2026 compared to the previous year. This surge in spending is backed by results, as nearly nine in 10 CEOs surveyed by BCG reported seeing cost or revenue benefits from their investments. This suggests that the technology is beginning to deliver measurable business value at scale.

New leaders in AI readiness

One of the most significant findings in the BCG report is that Japan and South Korea have moved into the highest category of AI-ready nations. They now join established pioneers such as Canada, mainland China, Singapore, the United Kingdom, and the United States. This move reflects a broader shift in how global leadership in AI is being defined and achieved.

Japan reached this status by combining an AI-friendly regulatory framework with heavy investments in data centers and computing infrastructure. The country has also focused on strengthening its domestic ecosystem through partnerships with major technology companies. BCG noted that large hyperscaler commitments and the opening of Anthropic's first Asian office in Japan are key examples of its ability to attract investment.

South Korea took a different but equally effective path by prioritizing domestic AI model development and public investment in computing capacity. The country's efforts have been highly successful, as it ranked among the top five globally for notable AI models in the 2026 AI Index by Stanford University's Institute for Human-Centered AI. Eight other economies, including Greece, Chile, and Nigeria, also moved into more advanced categories since 2024.

Measuring readiness and exposure

BCG evaluates economies using a matrix that balances AI exposure with AI readiness. Exposure measures the potential for AI to automate tasks and reshape sectors based on an economy's structure. Readiness, on the other hand, assesses whether a country actually possesses the institutions and capabilities needed to take advantage of those changes.

The report uses the ASPIRE framework to measure readiness across six specific dimensions. These include Ambition (national strategies), Skills (talent availability), Policy and Regulation (governance and legislation), Investment (funding for companies and infrastructure), Research and Innovation (patents, model development), and Ecosystem (access to data centers, cloud services, and electricity). The 2026 assessment uses 33 indicators to group economies into six archetypes.

These archetypes, which include AI Pioneers and Steady Contenders, represent various mixes of readiness and exposure instead of a simple ranking of investment dollars. This framework helps identify which countries are positioned to convert AI potential into actual economic value. It highlights that having a national strategy is no longer enough to be considered a leader in the space.

From white-collar to physical AI

The nature of AI disruption is evolving from knowledge-intensive sectors toward the physical economy. Currently, AI impact is concentrated in industries like financial services, high-tech goods, and business services because these fields involve language, coding, and information processing. However, the rise of agentic AI—systems that can carry out multiple steps in a task rather than just responding to single prompts—is expanding this potential.

BCG's modeling suggests that roughly half of US jobs could be meaningfully redesigned over the next two to three years. Over a longer period, about one in 10 of those jobs could face displacement. These estimates are specific to the US labor market and are not intended as a universal forecast for every economy.

The next phase of disruption is expected to hit manufacturing, logistics, and agriculture as robotics and industrial automation improve. Japan and China are already positioning themselves for this transition by leveraging their industrial strengths. China is focusing on humanoid robotics, while Japan is prioritizing industrial robotics and physical AI, which rewards countries with established supply chains and manufacturing expertise.

Infrastructure as a competitive edge

Readiness increasingly depends on physical infrastructure rather than just software or research. Data centers, advanced chips, cloud capacity, and reliable connectivity are becoming critical components of national competitiveness. BCG identifies five signals to help governments see if their goals are becoming actual capabilities: the pace of regulatory approvals, the use of public funds, the activation of hyperscaler investments, the availability of cheap electricity, and clear rules for regulation.

The availability of power is particularly vital as the computing requirements for AI continue to rise. A country might secure investment commitments, but delays in electricity connections or infrastructure development can stall actual deployment. Governments must also decide which parts of the technology stack they want to control domestically and which they will access through international partnerships.

For countries like India, the challenge is ensuring AI adoption improves productivity across the entire economy. India is working to widen access to skills through initiatives like Yuva AI for ALL, which aims to upskill 10 million citizens. The goal is to match AI strategies to existing economic structures, ensuring that benefits reach sectors like agriculture and public services alongside information technology.