BCG moves Japan and South Korea into its top tier for AI readiness

Publishing a national AI strategy no longer sets an economy apart, according to Boston Consulting Group’s AI Maturity Matrix 2026, released October 20. Japan and South Korea advanced into its highest readiness tier. BCG says the distinction increasingly comes from how quickly governments turn their ambitions into coordinated action across the economy.

Two measures, not one AI league table

The matrix assesses more than 75 economies across 33 indicators. It separates AI exposure—how much the technology could automate or reshape important industries and jobs—from readiness, meaning an economy’s preparation to capture the opportunities. An economy’s exposure therefore reflects the makeup of its industries and workforce, not simply how advanced its AI companies are.

Readiness spans six areas: ambition, skills, policy and regulation, investment, research and innovation, and the wider ecosystem. BCG calls this its ASPIRE framework. This year’s assessment adds three indicators covering computing capacity, domestic model development and regulation, bringing those parts of national AI capability explicitly into the analysis.

Nearly every economy assessed now has a published AI strategy, BCG says. Among those making the most progress, the differentiator is the scale and speed of building usable capability. The assessment combines its indicators with strategic analysis of government actions, rather than treating a statement of intent as sufficient preparation.

Japan and South Korea took different routes

Eight economies moved up a readiness tier since the first matrix in 2024: Chile, the Czech Republic, Ecuador, Greece, Japan, Lithuania, Nigeria and South Korea. Japan and South Korea entered the AI Pioneer tier, joining established members including Canada, Mainland China, Singapore, the United Kingdom and the United States.

• Japan attracted partners through an AI-friendly regulatory framework, securing large commitments from major cloud providers and strengthening its ecosystem. BCG highlights partnerships and outside investment as important elements of its advance.
• South Korea emphasized domestic model development while directing large-scale public investment into computing capacity and its broader AI ecosystem. Its route placed more emphasis on developing homegrown model capability.

The partnership question extends beyond those two economies. In its release accompanying the report, BCG divides AI sovereignty into chips, computing and cloud infrastructure, models, and applications. Owning all four is exceedingly rare, BCG says; governments must decide which dependencies to retain and which capabilities to secure through partnerships.

Deployment runs into physical and social limits

For infrastructure, the report identifies practical constraints: electricity availability, permitting speed and the ability to connect facilities to power networks. Chips and servers alone are not enough. BCG treats the ecosystem measure as a test of institutions’ ability to act, as well as a count of infrastructure already installed.

Regulation presents another execution test. BCG finds countries using policy to attract investment as well as limit risk. It describes both lighter-touch and oversight-based approaches, but says the distinction is fading: transparency, ways to seek remedies and human oversight in high-stakes uses are becoming conditions for sustained adoption across the spectrum.

The effects on work also depend on deployment choices. BCG distinguishes between redesigning jobs so people collaborate with AI and substituting AI for tasks people previously performed. Current exposure is concentrated in knowledge work, particularly language and code. The report expects manufacturing, logistics and agriculture to become more exposed as AI operating robots and machines advances.

That leaves a challenge beyond climbing a readiness ranking. Report coauthor Christian Schwaerzler warns that if businesses and work change faster than economies adapt, tax bases, education systems and safety nets could face pressure simultaneously. Fellow coauthor Steven Mills says governments must manage workforce disruption, trust and resource demands while continuing to accelerate AI adoption.