The report finds that nearly every economy assessed has made progress in at least one area of AI readiness. However, the speed and scale of capability-building are separating leaders from countries that are still developing their AI ecosystems.
Enterprise investment in AI doubled in 2026 from the previous year, according to BCG. Nearly nine in 10 CEOs surveyed by the consulting firm reported cost or revenue benefits from their AI investments, suggesting that the technology is beginning to deliver measurable business value.
Japan, South Korea join the AI pioneers
One of the report's most significant findings is the elevation of Japan and South Korea to the top tier of AI-ready economies, joining established pioneers Canada, mainland China, Singapore, the United Kingdom and the United States.
Japan has combined an AI-friendly regulatory framework with investments in data centres and computing infrastructure, partnerships with major technology companies, and efforts to strengthen its domestic ecosystem. BCG points to large hyperscaler commitments and Anthropic's first Asian office as examples of the country's ability to attract investment and accelerate deployment.
South Korea, meanwhile, has prioritised domestic AI model development and public investment in computing capacity and the wider AI ecosystem. The country ranked among the top five globally for notable AI models in Stanford University's Institute for Human-Centered AI's 2026 AI Index, the report notes.
Eight economies moved to a more advanced AI-readiness category compared with BCG's 2024 assessment: Japan, South Korea, Greece, the Czech Republic, Chile, Lithuania, Ecuador and Nigeria.
The findings highlight a broader shift in the global AI race. Publishing a national strategy is no longer enough. Countries that can mobilise capital, develop infrastructure, attract talent and implement policies quickly are better positioned to convert AI potential into economic value.
How BCG measures AI readiness
BCG's matrix evaluates economies across two broad dimensions: AI exposure and AI readiness.
AI exposure measures the potential for artificial intelligence to automate tasks, transform jobs and reshape key sectors, based partly on the structure of an economy. Readiness assesses whether a country has the capabilities and institutions needed to take advantage of those changes.
The report uses the ASPIRE framework to assess readiness across six dimensions:
- Ambition: Whether a country has a national AI strategy and dedicated government institutions.
- Skills: The availability of AI specialists and the broader talent base.
- Policy and regulation: The quality of governance, data policies and AI-related legislation.
- Investment: Funding for AI companies, technology businesses and related infrastructure.
- Research and innovation: AI research, patents, startups and domestic model development.
- Ecosystem: Access to computing infrastructure, data centres, cloud services, connectivity and affordable electricity.
The 2026 assessment covers more than 75 economies and uses 33 indicators, including new measures of computing capacity, domestic AI model development and regulation.
The matrix groups economies into six archetypes: AI Pioneers, Steady Contenders, Rising Contenders, Exposed Practitioners, Gradual Practitioners and AI Emergents. These categories reflect different combinations of readiness and exposure rather than a simple ranking of countries by AI investment.
AI's impact is moving beyond white-collar jobs
The report also highlights how the nature of AI disruption is evolving. Its impact is currently concentrated in knowledge-intensive sectors, including information and communications services, financial services, high-tech goods and business services.
These industries are particularly exposed because AI can already perform or assist with tasks involving language, coding, information processing and analysis.
The emergence of agentic AI is expanding that potential. Unlike systems that respond to individual prompts, agentic systems can carry out multiple steps in a task, potentially changing how entire workflows are organised.
BCG's modelling suggests that roughly half of US jobs could be meaningfully redesigned over the next two to three years, while around one in 10 could face displacement over a longer period. These estimates relate to the US labour market and should not be interpreted as a forecast for every economy.
The next phase of disruption could extend more deeply into the physical economy. As robotics, industrial automation and other forms of embodied AI improve, manufacturing, logistics and agriculture are expected to face greater exposure.
Japan and China are positioning themselves for this transition by building on their industrial strengths and investing in robotics. China is seeking to leverage its large share of global robot installations to expand humanoid robotics, while Japan has focused on industrial robotics and physical AI.
This shift could change the competitive landscape. Leadership in digital AI depends heavily on model development and research, while physical AI also rewards countries with manufacturing capacity, industrial expertise and established supply chains.
India's challenge: Turning AI talent into wider economic gains
For India, the challenge extends beyond developing AI models or attracting technology investment. It is also about ensuring that AI adoption improves productivity across the economy while preparing workers for changing job requirements.
BCG identifies India among the economies in its AI Maturity Matrix and discusses the country's efforts to widen access to AI skills. Initiatives include Yuva AI for ALL, a free foundational AI course intended to upskill 10 million citizens, and IndiaAI FuturesSkills, which aims to establish AI labs in smaller cities and expand access to professional certifications.
The report also notes India's access to skilled professionals, including AI talent returning from countries such as the US, the UK, the UAE and Canada.
However, building a broad talent pipeline requires more than training a limited pool of specialists. As AI becomes embedded in business operations, workers across industries will need opportunities to acquire new skills and adapt to changing roles.
Companies will also face a choice between using AI to augment employees' capabilities and deploying it to replace tasks previously performed by people. The balance between these approaches will influence employment, wages and the distribution of productivity gains.
For India, the opportunity lies in applying AI across established strengths such as information technology, financial services and business services, while extending its benefits to manufacturing, logistics, agriculture and public services.
The report's framework suggests that countries need to match their AI strategies to their existing economic structures and the sectors where AI can deliver the greatest gains.
Infrastructure, investment will determine who moves ahead
AI readiness increasingly depends on physical infrastructure, not just software and research. Data centres, advanced chips, cloud capacity, reliable connectivity and access to electricity are becoming critical components of national AI competitiveness.
BCG identifies five signals that can help governments assess whether their AI ambitions are translating into real capabilities: the speed of regulatory approvals, deployment of public capital, activation of hyperscaler investments, access to affordable electricity and clarity over the regulatory framework.
The availability of power is particularly important as AI computing requirements rise. A country may attract investment commitments, but delays in approvals, electricity connections or infrastructure development can slow deployment.
Governments also need to decide which parts of the AI technology stack they want to develop and control domestically and which they can access through international partnerships. Such decisions affect investment requirements, strategic autonomy and the ability to participate in the AI economy.
The report identifies several possible approaches, including public investment, partnerships with international technology companies and collaboration with other countries to develop AI capabilities.