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Artificial intelligence and industry 4.0 in Brazil

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The concept of industry 4.0 has multiple characteristics, one of which is its connection to artificial intelligence, which represents an innovative and transformative force, enabling Brazilian industries to increase productivity and competitiveness. Despite significant initiatives in this field, Brazil still has significant gaps compared to leading nations such as China and the United States.[1][2]

The industrial revolutions over time

The Fourth Industrial Revolution, commonly referred to as Industry 4.0, represents a transformation in industrial processes, characterized by the widespread integration of digital technologies in manufacturing, production, and logistics. Indeed, Industry 4.0 encompasses the presence of several modern technologies that are transforming its reality, including the technical integration of cyber-physical systems (CPS) and the widespread use of the Internet of Things (IoT). This shift in the face of industry enables the development of high-quality products, increased flexibility, and robust production. Among the main enabling technologies of Industry 4.0 are artificial intelligence (AI), machine learning, cloud computing, big data analytics, and augmented reality.[3][4]

For Brazil, an emerging economy, the adoption of Industry 4.0 and AI represents a significant opportunity to open new markets, boost innovation, and increase productivity. However, the country faces considerable challenges in its technological development, raising concerns about its ability to keep pace with the ongoing industrial revolution and fully capitalize on the potential benefits enjoyed by other countries that have invested more and more extensively than Brazil. Understanding this scenario, the Brazilian government has initiated several programs, including the Brazilian Artificial Intelligence Strategy (EBIA), the Brazilian Digital Transformation Strategy (e-Digital), and the National Internet of Things Plan, all designed to guide government actions toward fostering the development and adoption of AI.[1]

Current landscape of AI and industry 4.0 in Brazil

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Scientific production and research ecosystem

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Brazil's position in global AI scientific production, while not at the forefront like China or the United States, has historically ranked among the top 20 countries in the world in academic publications on AI. This position is the result of technical and academic efforts in the field, primarily due to its research institutions. Leading national AI research institutions include the University of São Paulo (USP), the Federal University of Santa Catarina (UFSC)Federal University of Santa Catarina, and the State University of Campinas (UNICAMP). Public funding agencies such as the São Paulo Research Foundation (FAPESP), the Ministry of Science, Technology, and Innovation (MCTI), the Ministry of Communications (MC), the Brazilian Internet Steering Committee (CGI.br), the Coordination for the Improvement of Higher Education Personnel (CAPES), and the National Council for Scientific and Technological Development (CNPq) play a vital role in supporting AI research.[5][6]

However, Brazil's investment in research and development (R&D) remains notably lower than the average for OECD countries. For instance, in 2019, Brazil’s investment in AI startups was a mere USD 1 million, starkly contrasting with the USA’s USD 224 million and China’s USD 45 million. Despite these financial disparities, Brazil possesses valuable data-producing institutions (e.g., the Brazilian Institute of Geography and Statistics (IBGE), Oswaldo Cruz Foundation (Fiocruz), National Institute for Space Research (INPE), and the Brazilian Network Information Centre (NIC.br)), alongside national systems like the Unified Health System (SUS), which generate critical data for AI applications.[6]

To strengthen its AI ecosystem, FAPESP, in collaboration with the MCTI, the MC, and CGI.br, has established 11 engineering research centers/applied research centers (CPE/CPA) in AI. These centers represent a combined public and private investment of approximately R$240 million over a decade. Their areas of focus include healthcare, agribusiness, industry, and smart cities. The main research objectives at these centers include the development of tools for Portuguese language processing, AI applications in healthcare (diagnosis and rehabilitation), agribusiness (food security), and climate forecasting. The challenge for these centers is attracting and retaining top talent, largely due to the competitive salaries offered in the broader Information and Communication Technology (ICT) sector, leading to a "brain drain" of qualified professionals from academia.[5]

Industrial adoption and drivers

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When analyzing the adoption of Industry 4.0 technologies, including artificial intelligence, by Brazilian companies, studies show that it is driven primarily by two critical factors: the existence of a digital security policy and the company's size. Larger companies, especially those with 250 or more employees, are significantly more likely to adopt these technologies, in part because they have greater investment resources and access to better professionals.[3]

In terms of adoption rates for specific technologies that comprise Industry 4.0, cloud computing leads among Brazilian companies, although its adoption rate (below 30%) is below the OECD average (45%). IoT and AI adoption show similar magnitudes, while big data analytics is less prevalent. When analyzing AI adoption rates in Brazil, they are generally comparable to those observed in European countries. When analyzing this framework by sector, the information and communication technology sector has a higher adoption rate for IoT and AI compared to the traditional industrial sector. On the other hand, the growing use of industrial robots in the Brazilian manufacturing industry is noteworthy. The main motivations driving companies to adopt new technologies, including IoT, smart sensors, cloud computing, big data, and AI, are the search for greater competitiveness, cost reduction, and increased productivity.[1][3]

The Brazilian automotive industry offers concrete examples of Industry 4.0 applications. The use of RFID (Radio Frequency Identification) tags captures production data throughout the vehicle manufacturing process, facilitating real-time decision-making by robots and devices. Augmented reality (AR) applications are used to digitize process sheets, improving assembly comprehension and reducing rework. Big data analysis aids production and quality management by identifying, measuring, and eliminating waste. Furthermore, automated guided vehicles (AGVs) are used to transport materials in companies' internal logistics, aiming to increase agility on production lines without human intervention.[7]

Key enablers of AI and technologies in industry 4.0

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The fundamental concepts and technologies driving Industry 4.0 are:

  • Artificial intelligence (AI): AI involves the development of machines capable of performing tasks that traditionally require human intelligence. This broad field includes machine learning (ML), robotics, natural language processing, speech recognition, and image recognition. Rapid advances in AI are largely due to significant increases in computing power and the availability of vast data sets for training algorithms. AI applications are diverse, ranging from complex medical diagnoses to autonomous vehicles and sophisticated intelligent games..[6]
  • Internet of Things (IoT): IoT refers to a network of devices interconnected via the internet and corporate intranets that have the ability to self-organize, share information, data, and resources, and react to environmental changes. It transforms the way humans interact with everyday objects, enabling them to collect and process information from their surroundings.[1]
  • Cloud computing: allows users to access large files and applications from anywhere globally, requiring only an internet connection to the "cloud". This technology significantly reduces expenses associated with maintenance and software licenses, enabling the efficient use of high-processing resources.[8]
  • Big data analytics: involves the collection and analysis of immense amounts of data generated by industrial systems. It encompasses a wide range of statistical and computational techniques and tools designed to extract valuable insights for decision-making.[7]
  • Cyber-physical systems (CPS): CPS integrates the physical and virtual worlds, allowing industries to create virtual representations of physical processes. This enables real-time decision-making, simulations, and predictive analysis through networks connecting communication, computation, and physical control. The implementation of CPS can lead to substantial gains in competitiveness, productivity, reduced failures, improved processes, and waste minimisation.[7]
  • Augmented reality (AR): AR is a technology that overlays virtual information onto a user's real-world view, enabling real-time interactive experiences. In industrial settings, AR can improve processes and decision-making, bringing benefits in areas such as operator training, equipment maintenance, and remote production monitoring and control.[7]

Challenges and opportunities for Brazil

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Workforce and education

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A critical challenge for Brazil is the shortage of a skilled workforce capable of navigating the demands of digital transformation and AI. Digital literacy and specific digital skills are increasingly becoming essential requirements for employment, serving as a foundation for developing other crucial competencies.[6]

The advent of AI poses substantial impacts on the labour market, including job displacement and a pronounced need for widespread reskilling. Projections suggest that a significant portion of the global workforce will need to adapt, with some estimates indicating that a third of all workers will need to "reinvent themselves" to retain their jobs, and nearly half of existing jobs in some economies could be automated within two decades. In Brazil, approximately 12% of jobs face a high risk of automation in the short term. However, AI is also expected to foster the creation of new roles, particularly those focused on supervising, maintaining, and enhancing emerging technologies. Professions requiring human creativity, complex problem-solving, social interaction, and care are considered less susceptible to automation.[5]

Gap between Brazil and the international scenario

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The current global landscape of artificial intelligence reveals a widening technological divide, with advanced nations concentrating expertise, infrastructure, and innovation capacity. This concentration is driven by the demand for highly specialized professionals and robust research ecosystems, which many developing countries struggle to match. In such contexts, the lack of widespread access to quality education and limited investment in science and technology hinder the ability to produce and apply AI solutions independently. As a result, there is a growing risk of technological dependency, where countries become mere consumers of foreign innovations, compromising their economic competitiveness and digital sovereignty.[2]

In this scenario, the challenges are compounded by a shortage of qualified professionals, low patent activity, and the migration of talent to more favorable environments abroad. Despite some academic achievements, the absence of critical mass in research and development, combined with insufficient infrastructure and incentives, limits the potential for meaningful progress. Without strategic and sustained public policies, including investment in education, research, and innovation, the technological gap is expected to grow, potentially leading to long-term setbacks in economic development, social equity, and national autonomy in the digital age.[2]

Policy and regulation

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The Brazilian artificial intelligence strategy (EBIA) serves as the primary framework for guiding the nation's AI development, promoting research, innovation, and ethical deployment. Recognised as a "living document" the EBIA necessitates continuous monitoring and adaptation to keep pace with the exponential evolution of AI technologies.[6]

Despite various governmental initiatives, such as the e-Digital strategy, and ongoing legislative debates, Brazil currently lacks specific, comprehensive AI regulation. This contrasts with the European Union's proactive approach, exemplified by its AI Act, which employs a risk-based regulatory framework. The EU's legislation prohibits "unacceptable risks" (e.g., cognitive behavioural manipulation, real-time biometric identification in public spaces) and mandates prior analysis for "high-risk" applications, while also incorporating regulatory sandboxes to encourage innovation. In contrast, the United States relies on executive orders and legislation that outline principles focusing on user safety, non-discrimination, data transparency, and the necessity of human oversight in AI systems.[9]

Brazil faces the complex task of crafting regulations that strike a balance between fostering technological innovation and safeguarding individual rights, privacy, and cybersecurity. Ethical considerations, including preventing algorithmic bias, ensuring transparency, and establishing accountability mechanisms, are paramount. Brazil's General Data Protection Law (LGPD) already grants individuals the right to request reviews of automated decisions and requires clear information about the criteria used, highlighting the critical role of human intervention in high-risk scenarios. The term "Responsible AI" is already used within some sectors, as an approach to designing, implementing, and managing artificial intelligence systems that prioritize human well-being, align with ethical principles, and promote trust, upholding fundamental human values.[10]

Economic Aspects and Investment

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When it comes to SMEs, the high costs of implementing digital technologies represent a significant obstacle. They often face difficulties accessing adequate financing, which often leads to a "vicious cycle" in which their inability to provide sufficient information and guarantees results in unfavorable credit conditions.[11]

When their difficulty in establishing significant investments in the technological sphere is recognized, concerns arise about their ability to effectively compete in terms of productivity and quality on a global scale with other players. Thus, there is a real risk that Brazil will be marginalized if it does not keep pace with global industrial advances.[12]

Therefore, government support is crucial to mitigating these challenges. This includes providing incentive programs, improving digital infrastructure, and formulating policies that ensure a more equitable distribution of digital benefits throughout the economy. Initiatives such as "Brasil Mais" are specifically designed to boost productivity and competitiveness among SMEs through management improvements and the implementation of digital solutions. Strategic sectors identified for AI application in Brazil, such as healthcare, energy, finance, biodiversity, and agriculture, are poised to contribute significantly to national economic growth and improve the quality of life of its citizens. For example, AI can optimize energy resource management, aid in medical diagnoses, identify patterns of financial fraud, and improve climate forecasting and agricultural planning.[3][2]

See also

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Further readings

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  • Castro, G. H. L. D., Azzoni, C. R., & Chagas, A. L. S. (2022). Convergência de habilidades na indústria: um abordagem de regionalização via machine learning. Anais.
  • Stradowski, S., & Madeyski, L. (2023). Industrial applications of software defect prediction using machine learning: A business-driven systematic literature review. Information and Software Technology, 159, 107192. DOI: 10.1016/j.infsof.2023.107192
  • Alves, A. P. S., Kalinowski, M., Mendez, D., Villamizar, H., Azevedo, K., Escovedo, T., & Lopes, H. (2024). Industrial Practices of Requirements Engineering for ML-Enabled Systems in Brazil. arXiv preprint arXiv:2407.18977.
  • Cardoso, L. A. S., Mesquita, B. D. R. de, & Farias, P. R. S. (2025). Use of machine learning algorithms in the context of sugarcane in Brazil: a review. Iran Journal of Computer Science. https://doi.org/10.1007/s42044-025-00250-y.
  • Leite Coelho da Silva, F., da Costa, K., Canas Rodrigues, P., Salas, R., & López-Gonzales, J. L. (2022). Statistical and artificial neural networks models for electricity consumption forecasting in the Brazilian industrial sector. Energies, 15(2), 588.
  • Scaliante Wiese, I., Souza, J., Fernandes, L., Lucas Correia, J., Barboza, E., Torres Pinto, R.,& Ribeiro, M. Designing Data Architecture for industry 4.0Applications: Lessons Learned from a Brazilian Multinational Industry. Available at SSRN 5280043.

References

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  1. ^ a b c d Rosa, M. B.; Kubota, L. C. (2025). "Artificial intelligence: where does Brazil stand in global scientific production and what are the main technical determinants of adoption by Brazilian companies". Economia. doi:10.1108/ECON-01-2025-0010.
  2. ^ a b c d GT-IA da Academia Brasileira de Ciências (2023). Recomendações para o avanço da inteligência artificial no Brasil [Recommendations for the advancement of artificial intelligence in Brazil] (in Portuguese). Academia Brasileira de Ciências. ISBN 978-65-981763-0-3.
  3. ^ a b c d Kubota, L. C.; Rosa, M. B. (2024). "Capítulo 3: Adoção de tecnologias da Indústria 4.0 por empresas brasileiras". In Kubota, L. C. (ed.). Digitalização e tecnologias da informação e comunicação: oportunidades e desafios para o Brasil. Vol. 1. Instituto de Pesquisa Econômica Aplicada (Ipea). doi:10.38116/9786556350660cap3.
  4. ^ Gimenez, D. M.; dos Santos, A. L. (2019). Indústria 4.0, manufatura avançada e seus impactos sobre o trabalho (Report). Instituto de Economia, UNICAMP.
  5. ^ a b c Brandão, R. (2024). "O cenário atual de desenvolvimento da Inteligência Artificial no Brasil: Mapeamento dos centros de Inteligência Artificial no Brasil: iniciativas, ações e projetos". Panorama Setorial da Internet. 1 (16).
  6. ^ a b c d e Ministério da Ciência, Tecnologia e Inovações (2021). Estratégia Brasileira de Inteligência Artificial – EBIA (PDF). Governo Federal do Brasil.
  7. ^ a b c d Cimino, C. P; Nascimento, D. R. (2025). "Revisão bibliográfica sobre a Indústria 4.0: Impactos das tecnologias contemporâneas nas indústrias automotivas brasileiras e suas implicações para otimização de processos". REVISTA ESPACIOS. 46 (03): 16. doi:10.48082/espacios-a25v46n03p16.
  8. ^ Almeida, E. S.; Pinheiro, R. R. G. (2022). "A relevância da indústria 4.0 para desenvolvimento do polo industrial brasileiro frente aos desafios". Brazilian Journal of Development. 8 (9). doi:10.34117/bjdv8n9-289.
  9. ^ ABES (2025). "O uso de IA no Brasil e na União Europeia: hora de acelerar". Think Tank ABES. ABES.
  10. ^ Pedro, E. D. A.; Panizzon, M.; Weber, C. G. (2023). OHS Professionals AI Adoption: A UTAUT Research in Brazilian Industry. 2023 15th IEEE International Conference on Industry Applications (INDUSCON). IEEE. pp. 850–857.{{cite conference}}: CS1 maint: multiple names: authors list (link)
  11. ^ Aniceto, D. K. (2025). "The Role of Artificial Intelligence (AI) and Machine Learning (MI) in the Oil and Gas Industry". Journal of Technology and Systems. 7 (1). Journal of Technology and Systems: 6–27.
  12. ^ Stradioto, L.; Frazzon, E. M. (2023). "Digital transformation in Brazilian industry: bridging theory and practice". Production. 33. ABEPRO: e20220076. doi:10.1590/0103-6513.20220076.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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