In November 2024, the Boston Consulting Group presented a global vision of artificial intelligence (AI) maturity across 73 world economies through its new "AI Maturity Matrix." This study not only analyzes which economies are ready to leverage AI, but also identifies six distinct archetypes of AI development and economic potential. The matrix assesses each economy on two fundamental axes: exposure to AI-driven transformations and readiness to manage and utilize them. Let's explore the key findings.
The Leading Economies for AI Maturity
Among the 73 economies examined, only five were identified as "AI Pioneers": Canada, China, Singapore, the United Kingdom, and the United States. These countries have reached a high level of readiness by combining key elements such as investments and infrastructure to turn the potential disruptions caused by AI into a competitive advantage. Advanced AI adoption in these countries is driven by robust technological infrastructure, significant research and innovation capacity, and continuous investment in specialized training. The United States and Singapore stand out for their AI talent pools, which are crucial for driving innovation. China, on the other hand, leads in the production of patents and academic papers related to AI.
The United States demonstrates an excellent ability to attract private investment in AI-related startups. With a total of over 200 unicorns in the tech sector, the United States also leads the global market for AI-focused venture capital, representing about 50% of total investments in the sector. This flow of investment not only supports existing startups but also creates a fertile environment for new companies developing advanced AI solutions. Furthermore, the presence of some of the world's top universities, such as MIT and Stanford, ensures a steady supply of highly qualified talent.
Singapore, despite its relatively small size, has shown remarkable efficiency in implementing a national AI strategy, investing over 743 million dollars in a five-year plan to consolidate its position as a global hub for business and innovation. This investment has been accompanied by government initiatives such as the TechSkills Accelerator, which has trained more than 230,000 people since 2016, increasing the local talent pool and attracting international experts through programs like ONE Pass and Tech@SG. Singapore also established the Centre for Frontier AI Research (CFAR) to support the research and development of advanced AI technologies on a national scale.
Mainland China, on the other hand, has adopted an aggressive approach to AI adoption, focusing on strategic sectors such as consumer electronics, surveillance, and autonomous vehicles. China leads the world in AI-related patents, registering over 150,000 patents in recent years, surpassing the United States and Europe. This commitment has also been supported by massive government investments in technological infrastructure, such as the establishment of world-class supercomputing centers and the enhancement of telecommunications networks. China is also a leader in publishing AI-related academic papers, with output accounting for 27% of all global publications in this field.
The United Kingdom is one of the main AI hubs in Europe, thanks to a combination of favorable policies, access to capital, and a strong network of leading universities such as the University of Cambridge and Imperial College London. The country has developed a national strategy that includes funding centers of excellence for AI, aiming to expand AI use in sectors such as healthcare and finance. Furthermore, the UK government has allocated around 1.2 billion pounds to support AI, also focusing on developing a regulatory framework to ensure the safe and ethical use of technology.
Canada stands out for its inclusive and sustainable approach to AI research and adoption. With the Pan-Canadian AI Strategy, Canada was one of the first countries to outline a national strategic plan for AI, investing 125 million dollars to support development and research in its major tech cities: Toronto, Montreal, and Vancouver. This has led to the creation of a strong research ecosystem, with leading institutes such as the Vector Institute and Mila attracting talent from around the world. Canada has also placed a strong emphasis on ethics and transparency in AI use, contributing to the development of international guidelines for responsible technology adoption.
The analysis shows that these countries are not only investing significantly in research and development but are also establishing specific AI regulatory codes, such as Singapore's "Model AI Governance Framework," which ensures the ethical use of AI. Such an approach allows them to maintain a leadership position, influence the global AI ecosystem, and set international standards.
AI Contenders and Practitioners
Right after the leaders, we find the "AI Contenders" and "AI Practitioners," two categories that include economies like Germany, Japan, India, Malaysia, Saudi Arabia, and Indonesia. The "Contenders" are characterized by high readiness but relatively less exposure to AI compared to the "Pioneers." This means that while they are ready to adopt AI, not all their industries are sufficiently exposed to the benefits of this technology.
Germany is an emblematic example of a "Steady Contender." With strong exposure to the financial services and advanced manufacturing sectors, Germany has benefited from a solid industrial base and a long-term technology innovation strategy. The German government has invested significant resources in developing tech hubs and facilitated collaboration between universities, private companies, and research institutes. The robust telecommunications infrastructure and access to advanced technologies have enabled Germany to position itself among the leading European technological players.
Japan, known for its industrial innovation capability, has focused its efforts on applying AI in sectors such as robotics and manufacturing. The Japanese government has launched strategic plans to expand AI use in society, aiming to address challenges like an aging population. Significant investments in digital infrastructure and public-private collaboration have contributed to Japan's growing competitiveness in AI.
India, on the other hand, is classified among the "Rising Contenders." The Indian government has launched several AI-focused initiatives, such as the National AI Strategy, with the goal of integrating AI into key sectors like agriculture and education. India is investing heavily in training a specialized workforce, with education and training programs aimed at increasing the number of AI specialists. Moreover, the Indian startup ecosystem, particularly in sectors like fintech and agritech, is rapidly expanding, supported by growing access to venture capital.
Malaysia represents an interesting case among the Contenders, thanks to strong government support and its National AI Roadmap. This strategic plan includes investments in tech hubs and university programs to train professionals in the AI sector. The Malaysian government's goal is to achieve technological competitiveness comparable to that of high-income economies.
Saudi Arabia and Indonesia are also classified as "Rising Contenders" and are making significant progress in AI adoption. Saudi Arabia, with its Vision 2030, aims to become a global center of excellence for AI in priority sectors like energy, healthcare, and education. This path is supported by substantial investments in digital infrastructure and training programs. Indonesia, for its part, is focusing efforts on education and long-term economic growth, with the National AI Strategy emphasizing education and the adoption of emerging technologies to improve productivity.
Sectoral Exposure. Where AI Has the Greatest Impact
The report also analyzes the level of exposure of different economies to AI based on economic sectors. Six sectors are found to be most exposed to AI-induced changes: information and communication, high-tech goods, financial services, retail, public services, and motor vehicle production. This is due to AI's ability to automate tasks and optimize processes, profoundly transforming how work is done in these sectors. In particular, economies with strong ICT sectors tend to grow in terms of GDP thanks to their ability to produce AI technologies that can be used in other sectors.
In sectors like information and communication, AI can increase productivity through the automation of repetitive tasks and the optimization of business communications. According to the Boston Consulting Group, efficiency in these areas can increase by up to 20% through AI integration. Furthermore, AI technologies are particularly important in the production of high-tech goods, where they can reduce production costs and increase the precision of assembly lines, as demonstrated by sectors like electronics and semiconductors.
Another key sector is financial services. AI adoption enables better risk management, faster decision-making processes, and a more personalized customer experience. For example, many banks use machine learning algorithms to prevent fraud and more accurately analyze customer risk profiles. This approach has allowed operational costs to be reduced and service efficiency to be improved.
In retail, AI is having a significant impact on inventory management and demand forecasting. The use of predictive algorithms helps retailers optimize their inventories, reducing storage costs and improving product availability. AI applications in public services, on the other hand, improve energy efficiency and facilitate resource management through demand forecasting and optimization of distribution networks.
Motor vehicle production is another sector where AI is radically transforming processes. The introduction of AI systems for assembly and quality control has improved production precision and speed, with a direct impact on costs and the quality of produced vehicles. Additionally, the development of autonomous vehicle technologies has further strengthened the role of AI in this sector, creating new opportunities for economic growth.
On the other hand, countries with a sectoral composition more oriented towards agriculture and construction, such as India, Indonesia, and Ethiopia, show less exposure to potential disruptions caused by AI. However, the use of AI can still bring indirect benefits, improving efficiency in the agricultural sector and modernizing adjacent sectors like transportation. For example, the use of AI in precision agriculture allows optimization of production through monitoring weather and soil conditions, reducing resource use and increasing agricultural yields.
In general, AI is contributing to differentiation between sectors that rapidly adopt the technology and sectors that lag behind, creating uneven impacts on the overall economy. However, sectors that effectively integrate AI see significant increases in productivity and competitiveness, as highlighted by the Boston Consulting Group, which estimates a revenue increase of up to 2.5 times for companies that adopt AI compared to those that do not.
The ASPIRE Index. Assessing AI Readiness
To assess each economy's readiness, the matrix uses the ASPIRE index, which consists of six dimensions: Ambition, Skills, Policy and Regulation, Investments, Research and Innovation, and Ecosystem. Only five economies out of 73 have surpassed 50% in all these dimensions, demonstrating a high degree of maturity in AI adoption.
The ASPIRE index considers several key metrics to assess an economy's overall readiness for AI adoption. Among these metrics are the existence of a national AI strategy and the presence of a specialized government entity for implementation, which are key indicators of a country's ambition. Additionally, the index evaluates the concentration of AI specialists through indicators such as the number of professionals registered on platforms like LinkedIn and public contributions on GitHub, highlighting a country's ability to train and retain talent.
Regarding regulation, the ASPIRE index includes measures of policy quality, government effectiveness, and data management, as well as the alignment of democratic values with AI development. In terms of investments, the index takes into account the value of AI startups, the market capitalization of tech companies, and the availability of venture capital, elements that indicate how well an economy can financially support AI adoption and growth.
The "Research and Innovation" dimension is represented by the number of scientific publications on AI, patents registered, and the number of AI startups, factors that reflect a country's ability to innovate and contribute to the global development of technology. The maturity level of a digital ecosystem, on the other hand, is measured through indicators such as the quality of telecommunications infrastructure, average download speed, and public cloud spending per employee, aspects that directly influence the ability to implement AI technologies on a large scale.
The United States and Singapore lead in the skills dimension, with highly developed talent pools. Specifically, the United States leads in investments, thanks to sophisticated capital markets and the presence of numerous unicorns in the AI field. Mainland China, in contrast, excels in research and development, being a leader in both patents registered and the number of academic AI publications. Countries like Japan and Germany perform well in the field of infrastructure and digital ecosystem but often lack adequate levels of investment in research and development, which could limit their long-term competitiveness.
The global reality of AI adoption clearly shows significant disparities. More than 70% of the economies analyzed scored below half in the dimensions of ecosystem participation, skills, and R&D. This indicates that many countries still need to work significantly to achieve satisfactory readiness for AI adoption. Governments and the private sector must collaborate to improve infrastructure and promote policies that foster education and technological innovation. The ASPIRE index not only serves as a measure of the current level of AI maturity but also provides a practical guide to identifying priority areas for action to accelerate AI adoption in a balanced and sustainable manner.
Italy. A Case of Potential and Challenges
Italy ranks among the "AI Practitioners," with a moderate level of exposure and readiness. The country has begun to take significant steps towards AI adoption, but several obstacles still need to be overcome to reach the level of global leaders.
One of the crucial aspects characterizing the Italian situation is the lack of adequate technological infrastructure. In particular, the availability of supercomputing centers and advanced data centers is lower compared to many other European economies. This technological limitation affects the country's ability to support large-scale AI projects and reduces attractiveness for foreign investments in high-tech sectors. The number of data centers in Italy is significantly lower than the European average, limiting data storage and processing capacity, an essential aspect for implementing complex AI solutions.
In the manufacturing sector, which is one of the pillars of the Italian economy, AI adoption can lead to significant improvements in efficiency and automation. However, only a fraction of companies has started adopting these technologies extensively. The report indicates that about 30% of Italian manufacturing companies have implemented advanced automation solutions, compared to an average of 50% observed in major European countries like Germany and France. Automation of production processes and the introduction of AI technologies for predictive maintenance are two areas of particular interest but require targeted investments and coordinated action by the government and the private sector for effective implementation.
Regarding agriculture, the potential for adopting AI technologies is high, especially in precision agriculture, which could significantly improve efficiency in the use of natural resources. However, large-scale adoption of such technologies is hampered by the fragmentation of the agricultural sector and the lack of access to dedicated funding. According to report data, less than 20% of Italian farms have access to the advanced technologies needed for precision agriculture, while countries like Spain and the Netherlands exceed 35%. The implementation of specialized training programs and concessional financing could help overcome these obstacles and facilitate the transition to more modern and sustainable agriculture.
Another key element for improving Italy's position in the AI ecosystem concerns skills development. Currently, the number of AI specialists per million inhabitants is much lower than the European average. Only 15 specialists per million inhabitants are dedicated to AI, compared to a European average of 40. Creating regional tech hubs and incentivizing university and post-university programs specifically focused on AI are crucial to bridging this gap. Moreover, integrating AI training courses into high school curricula and collaboration between universities and companies could significantly help expand the available skills pool.
Investments in research and development (R&D) are another critical point for Italy. Currently, Italy invests less than 1.5% of GDP in R&D, a value well below the European average of 2.5% and far from the levels of leaders like Germany and France, which invest over 3%. This low level of investment translates into a reduced capacity to innovate and develop advanced technologies. The report suggests that to improve competitiveness, Italy should increase research funding and encourage greater collaborations between the public and private sectors, particularly in AI application areas such as healthcare, mobility, and energy.
The technological startup ecosystem in Italy is still weak compared to other advanced economies. The number of AI startups remains low, with fewer than 200 active startups compared to over 500 in comparable economies like Spain. The reasons for this lag include poor availability of venture capital and a high perceived risk associated with investments in emerging technologies. To address this issue, more aggressive tax incentives and dedicated acceleration programs are needed, capable of attracting national and international investments and creating an environment conducive to the birth and growth of new companies in the AI sector.
In summary, Italy has the potential to improve its position in the AI field but requires a structural commitment and a long-term vision involving both the public and private sectors. Collaboration between universities, industries, and the government will be crucial to accelerating progress and achieving greater AI adoption maturity. A concerted effort is needed to develop technological infrastructure, increase specialist skills, and create an environment conducive to innovation and entrepreneurship in artificial intelligence.
Strategic Next Steps for Countries
The report proposes a set of initiatives for each archetype to promote AI adoption. For "AI Emergents," the economies still in the early stages, it is recommended to build national AI strategies and invest in basic digital infrastructure. This includes adopting measures to develop basic digital skills in the population, such as digital literacy programs and AI-focused training courses. It is also crucial to create research and development centers in partnership with international players to improve access to advanced technologies.
For "AI Contenders" and "AI Practitioners," the focus should be on accelerating AI adoption. A key recommendation is to focus investments on applied research projects that can generate tangible results in the short term. These countries should also encourage collaboration between industrial sectors to promote the sharing of best practices and the implementation of AI solutions in the most promising sectors, such as manufacturing and financial services. Infrastructure enhancements, such as the expansion of the data center network and improvements to telecommunications networks, are essential to support greater AI adoption on a national scale.
The "AI Pioneers" are called to play a global leadership role. To further expand their competitive advantage, these countries must promote flexible regulatory policies that encourage innovation while ensuring the safety and ethics of AI use. Furthermore, the Pioneers should create testing environments (sandboxes) for the development of advanced AI technologies, involving international players to share knowledge and promote the harmonization of global standards. It is also important to invest in continuous workforce training, ensuring that AI skills keep pace with technological advances.
An example of a sectoral strategy is India, which aims to use AI to optimize the entire agricultural supply chain, improving yields and logistics through the use of data and predictive technologies. Similarly, Malaysia is promoting the development of specialized tech hubs and tax incentives to attract innovative startups, fostering the creation of a solid ecosystem for AI growth.
To conclude, each archetype of country has a set of specific steps to take to advance its AI maturity journey. The strategies suggested in the Boston Consulting Group report aim to provide practical guidance to policymakers on how to navigate the evolving landscape of artificial intelligence and harness its potential to strengthen economies and improve overall social welfare.
Conclusions
The Boston Consulting Group's analysis clearly highlights how artificial intelligence is becoming a strategic variable for the competitive advantage of global economies. However, the true value of this matrix lies not only in the current snapshot but also in the systemic implications it has for the future of economies and businesses. The emerging reflection is that investing in AI is not enough: it is essential to understand how this technology redefines the economic rules of the game, reshaping strategic priorities.
One of the key points is the need for a collaborative ecosystem between public and private sectors. Global leaders such as the United States and Singapore show that government policies are not merely technological "enablers" but tools for co-creation with the private sector. This is a new paradigm, where public investments in training, infrastructure, and regulation are designed not only to stimulate adoption but to foster the birth of entire economic ecosystems. Companies must therefore consider governments as strategic partners, not just regulators.
Another crucial aspect is the asymmetry between sectors. Knowledge- and technology-intensive sectors, such as ICT and financial services, are already capitalizing on AI's advantages, while traditional sectors like agriculture and construction are lagging behind. However, this polarization can represent an opportunity. Companies operating in traditional sectors now have a unique window to position themselves as local AI pioneers, turning the delay into a strategic advantage. It is evident that AI adoption is not just a technological issue but a cultural and organizational choice that requires visionary leadership.
Competition is shifting towards skills and the ability to retain qualified talent. Countries like Singapore and Canada, which combine strategic immigration policies and investments in advanced training, show that human capital is the core of innovation. For companies, this means that investing in internal training and attracting global talent is not a cost but a competitive imperative.
Finally, a fundamental reflection emerges on ethics and governance. AI leaders are setting international standards not only from a technical point of view but also an ethical one. Companies that integrate ethical principles into their AI applications from the outset not only avoid reputational risks but also create a competitive advantage in gaining the trust of consumers and institutions. In an increasingly interconnected world, compliance with global regulations and transparency will become distinguishing elements.
In summary, AI is no longer just an emerging technology but an accelerator that forces companies and nations to rethink their business models and governance.
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