Machine learning in Brazilian industry

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Machine learning (ML) is increasingly transforming industries worldwide, including within the Brazilian industrial sector, highlighting key areas where ML drives innovation and efficiency. The Brazilian industrial landscape is actively engaging with machine learning and industry 4.0 technologies, driven by a desire for increased productivity, competitiveness, and innovation. [1] While still navigating challenges such as the high cost of implementation, a skills gap, and the need for robust digital infrastructure and regulatory frameworks, significant progress is being made. [2]

Contents

Machine learning (ML), a subset of artificial intelligence (AI), refers to computational methods that enable systems to learn from data and improve performance over time without being explicitly programmed for each task. These methods are increasingly embedded in everyday technologies, often operating behind the scenes in applications such as recommendation systems, voice assistants, fraud detection, and autonomous vehicles. ML models are capable of identifying patterns, making predictions, and adapting to new data, which makes them especially valuable in dynamic and complex environments. [3]


Machine learning and the digital transformation of brazil's industrial sector

The hierarchy of knowledge fields: artificial intelligence, machine learning, and deep learning. AI hierarchy.svg
The hierarchy of knowledge fields: artificial intelligence, machine learning, and deep learning.

In recent years, ML has become a cornerstone of digital transformation strategies worldwide. Its integration into industrial processes is reshaping how companies operate, enabling predictive maintenance, quality control, supply chain optimization, and real-time decision-making. The ability to process vast amounts of data and extract actionable insights is revolutionizing traditional manufacturing and service models, contributing to increased efficiency, reduced costs, and enhanced competitiveness. [4] [5]

In Brazil, the industrial sector is undergoing significant digital transformation by integrating advanced technologies such as ML into production and management processes. [6] [7] This transformation is part of the broader global movement known as the Fourth Industrial Revolution, or industry 4.0, which emphasizes the convergence of cyber-physical systems, the internet of things (IoT), cloud computing, and AI to create smart, interconnected industrial ecosystems. Industry 4.0 represents a paradigm shift from traditional automation to intelligent systems capable of self-optimization and autonomous decision-making. [8]

Despite its promising potential, Brazil faces structural challenges in fully embracing this new industrial paradigm. A considerable portion of the country’s industrial base still operates at technological levels associated with the Second or early third industrial revolutions, characterized by limited automation and low digital integration. [6] [7] This technological gap is particularly evident among micro and small enterprises (MSEs), which make up the majority of Brazil’s industrial landscape but often lack the resources and expertise to adopt advanced digital solutions. [9]

Nevertheless, there is growing national commitment to modernizing the industrial sector. Public and private initiatives have been launched to foster innovation, support digital infrastructure development, and promote workforce training in emerging technologies. [10] Programs such as the “Plano IA para o Bem de Todos” (AI Plan for the Good of All) and the “Estratégia Brasileira para a Transformação Digital” (Brazilian Digital Transformation Strategy) reflect the government’s strategic vision to position Brazil as a competitive player in the global digital economy.

Furthermore, Brazilian research institutions, development banks, and industry associations play a crucial role in this transformation. Organizations such as BNDES, CGEE, and CNI are actively engaged in funding research, conducting sectoral studies, and developing policy recommendations to accelerate the adoption of industry 4.0 technologies. These efforts are supported by international collaborations and knowledge exchange programs aimed at aligning Brazil’s industrial capabilities with global best practices. [1]

In this context, machine learning emerges not only as a technological tool but also as a strategic enabler of industrial innovation and economic development. Its applications span a wide range of sectors — from agriculture and healthcare to energy and manufacturing [11] — demonstrating its versatility and transformative potential. As Brazil continues to navigate its digital transformation journey, the effective integration of ML will be essential for enhancing productivity, fostering sustainability, and ensuring long-term industrial resilience. [12]

Historical context and industry 4.0 in brazil

The concept of industry 4.0 was first introduced in Germany as a strategic initiative to reinforce the country’s technological leadership and enhance its global industrial competitiveness. [13] This new industrial paradigm is characterized by the integration of digital technologies—such as cyber-physical systems, the internet of things (IoT), and artificial intelligence (AI)—into manufacturing and business operations. Unlike previous industrial revolutions, which focused primarily on mechanization, electrification, and automation, industry 4.0 emphasizes intelligent systems capable of autonomous decision-making, real-time data processing, and seamless communication across the value chain. [13]

The four industrial revolutions until we reach the industry 4.0 phase Industry 4.0.png
The four industrial revolutions until we reach the industry 4.0 phase

One of the defining features of industry 4.0 is its holistic impact across all levels of industrial activity. It not only transforms production lines but also reshapes logistics, supply chain management, product development, and customer service. The convergence of physical and digital systems enables companies to create "smart factories" where machines, systems, and humans interact in increasingly sophisticated ways. This transformation is supported by technologies such as cloud computing, big data analytics, robotics, and additive manufacturing, which collectively enable greater flexibility, customization, and efficiency in industrial processes. [14] [15]

In Brazil, the adoption of industry 4.0 has been gradual and uneven. Early assessments revealed a significant gap in digital readiness among industrial firms. A 2016 survey conducted by the Confederação Nacional da Indústria (CNI) found that 42% of Brazilian companies were unaware of the relevance of digital technologies for industrial competitiveness, and 46% either did not use or were unsure about their use. These findings highlighted a widespread lack of awareness and preparedness for digital transformation. [16]

However, more recent data suggest a shift in this landscape. By 2022, the same institution reported a noticeable increase in the adoption of industry 4.0 technologies, although the maturity level of implementation remained relatively low. [16] This indicates that while awareness has improved, many companies are still in the early stages of digital integration, often limited to isolated applications rather than comprehensive transformation strategies. [17]

In the Brazilian context, the term “digital transformation” is often preferred over “industry 4.0” because it encompasses a broader scope of organizational change. It reflects the understanding that digital technologies should not be confined to the factory floor but should permeate all aspects of business operations, including marketing, finance, human resources, and customer engagement. This broader perspective is essential for fostering a culture of innovation and agility in a rapidly evolving global economy. [18]

Several enabling technologies are central to Brazil’s digital transformation agenda. These include:

The advancement of industry 4.0 in Brazil has been supported by key institutions and policy frameworks. The " Banco Nacional de Desenvolvimento Econômico e Social" (BNDES) has played a leading role in financing innovation and publishing strategic studies on advanced manufacturing. [24] The "Centro de Gestão e Estudos Estratégicos" (CGEE) has contributed through research on technological trends and workforce development needs. Meanwhile, the "Confederação Nacional da Indústria" (CNI) has conducted extensive surveys and advocacy to promote digital adoption among Brazilian firmsds. [25]

A major milestone in Brazil’s digital strategy is the launch of the national AI plan, "Plano IA para o Bem de Todos". This initiative outlines 31 strategic actions across sectors such as healthcare, agriculture, education, and industry. It includes substantial investments in AI infrastructure, research centers, and workforce training programs. The plan also emphasizes ethical AI development, data governance, and international cooperation, positioning Brazil as a proactive participant in the global AI ecosystem. [26]

Key sectors embracing machine learning in brazil

The implementation of industry 4.0 technologies, including machine learning, offers substantial benefits across various industrial sectors in Brazil, such as increased productivity, improved product quality, reduced operational costs, enhanced industrial safety, and optimized supply chain management. [6] This is often achieved through the integration of cyber-physical systems that generate large volumes of data, which are then analyzed using AI and ML algorithms to drive decision-making. [27]

Here are examples of ML and digital transformation applications in specific Brazilian industrial sectors:

Chemicals & pharmaceuticals (c&p)

In 2016, the C&P sector ranked 9th and 10th in Brazil for the use of digital technologies in process and development, respectively. A survey conducted by UNICAMP and ABTCP revealed that the cellulose industry, for instance, has a high level of digitalisation and vertical integration, while the paper (integrated with cellulose) and paper (non-integrated with cellulose) sectors show medium levels. industry 4.0 concepts like "Digital Twin" (simulation/mixed reality) are highlighted as key enabling technologies for this sector. [28]

Healthcare

The healthcare sector is crucial for both social development and economic growth, contributing to job creation and technological advancement. [29] Brazil, like many other countries, faces challenges related to an aging population and the increasing prevalence of chronic and non-communicable diseases, which drive up healthcare costs and demand new care models. [30] ML and AI applications in the public healthcare system (SUS) are being actively pursued under the "Plano IA para o Bem de Todos": [26]

  1. "Prontuário Falado no SUS": an AI system to automate the transcription of teleconsultations, aiming to improve efficiency in clinical documentation and quality of care. [31]
  2. "Otimização dos Diagnósticos no SUS": a system to enhance the precision and agility of medical diagnoses for critical conditions like strokes, pneumonia, breast cancer, tuberculosis, and melanoma.
  3. "IA em Saúde Bucal no SUS": technologies to improve the quality of oral healthcare services and the prognosis of oral cancer.
  4. "IA e Big Data para tratamento de câncer": a platform utilizing AI for peritoneal cancer treatment through ultrasound technology for chemotherapeutic aerosolization, aiming to increase treatment response and patient survival.

Agrifood

The swine farming industry, for example, faces environmental challenges related to high water consumption and waste generation, where technological solutions are being explored for mitigation. Genetic improvement has significantly advanced swine farming in Brazil, reducing the time to reach slaughter weight and decreasing fat content, enhancing meat quality. [32] IoT and precision agriculture technologies are being deployed to collect vast amounts of data on soil quality, irrigation levels, climate, and pest presence. This data enables predictive maintenance and improved production, harvesting practices, and tool usage, potentially leading to significant productivity gains and reduced consumption of inputs like fertilizers, herbicides, and fuel. [32]

Blockchain technology offers potential for improved food traceability and detection of low-quality products within food supply chains, creating a competitive advantage for users. AI is also being used to calculate bovine weight via 3D cameras, aiming to reduce animal stress during handling and improve meat quality, while providing agile business management data for rural producers. [32]

Automotive

The global automotive industry is undergoing a profound transformation, requiring a redesign of business models to accommodate digital technologies and data-centric strategies. Brazilian automakers are exploring connectivity and data analytics as platforms for new business models, seeking to derive new revenue streams from vehicle, consumer, and geolocation data. The large volume of data generated by increased vehicle connectivity presents a challenge to transform into valuable information for customer perception and brand reputation analysis. This shift demands robust data governance and advanced analytics capabilities. Moreover, the rapid pace of data sharing in this ecosystem necessitates the development of robust cybersecurity resources to protect against cyberattacks and invasions, a growing concern as vehicles become increasingly connected and autonomous. [33]

Oil & gas

The oil and gas sector is making significant investments in digital technologies, with Big Data and analytics being particularly crucial due to the immense volume of data generated by sensors. For instance, modern offshore drilling platforms can have up to 80,000 sensors generating 15 petabytes of data over an asset's lifespan. Digitalisation initiatives in this sector focus on digital asset lifecycle management, collaborative ecosystems, creating new services "beyond the barrel," and supporting new energy sources. Machine learning and artificial intelligence are also central to forecasting in the oil and gas industry, enabling predictive maintenance, reservoir modeling, and operational optimization. [34]

Aerospace and defense

This sector is traditionally a significant developer of new technologies and makes extensive use of advanced analytics, AI, and Big Data for threat identification and advanced manufacturing. [35] Cybersecurity is a primary concern for Aerospace and Defense, given the complex demands of national security.

Native forests

Brazil faces a substantial environmental restoration challenge, with the New Forest Code (Lei 12.651/2012) estimating a need to restore 10.3 million hectares of native vegetation by 2030, particularly in the Mata Atlântica (2.7 million hectares) and Amazon (3.5 million hectares) biomes. This requires massive investments in expanding native seedling production capacity, ranging from R$ 160 million to R$ 540 million nationally, depending on the restoration techniques and scale. The need for hundreds of new nurseries, especially in the Amazon region, is evident due to the high demand and limited existing capacity. While not explicitly stated in the sources, the immense scale and data complexity of managing such an extensive restoration effort, from seedling production to monitoring, present a clear opportunity for ML applications in optimization, logistics, and predictive modeling. [36]

Software development

Machine learning for software defect prediction (ML SDP) is a promising area in software engineering, with increasing validation in industrial settings. [37] [38] Common ML classifiers used in SDP include Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and various neural networks. Features for SDP typically involve process metrics, source code characteristics, and historical defect data, though their industrial availability can be challenging. Popular frameworks for building ML SDP models include WEKA, Scikit-Learn, Keras, and TensorFlow. [39] Industrial datasets are crucial for validating ML SDP solutions, but the lack of public access to proprietary data hinders further research and adoption. Studies on the cost-effectiveness of ML SDP are rare, yet some evidence shows significant benefits, such as a 30% reduction in quality assurance costs and a remarkable 7300% Return on Investment (ROI) in a simulated industrial project. Key learnings for practitioners include the importance of gathering structured feedback, comparing multiple methods and features, validating solutions on diverse datasets (research, open-source, and industry), and conducting ROI calculations.

Micro, small and medium enterprises

MSMEs constitute 98% of Brazil's industrial park, yet most operate at levels closer to the Second Industrial Revolution, with low digital maturity. [40] This limits their participation in digitalized markets and data-driven business models [41] Key challenges for MSMEs include a lack of awareness about digital technologies, slow adoption of management digitalization tools like Enterprise Resource Planning (ERP) systems, the need for customisation in advanced automation, and challenges in developing and training their workforce for new digital paradigms. Larger companies generally exhibit higher adoption rates of industry 4.0 technologies. [41]

Despite these hurdles, MSMEs have significant opportunities:

Research centres and initiatives in brazil

While the new technologies of industry 4.0 offer immense potential for boosting productivity and competitiveness, their implementation in Brazil comes with significant adjustments and challenges. [6] [14]

Key challenges

Key opportunities

The integration of advanced technologies such as machine learning, automation, and real-time data systems is driving significant transformations within Brazil’s industrial sector. These innovations are not only modernizing traditional processes but also generating a wide range of strategic benefits that extend across the entire value chain. From operational efficiency to global competitiveness, the adoption of digital tools is enabling Brazilian industries to overcome longstanding limitations and align with international best practices. [46] The key impacts of this transformation can be observed in several critical areas:

Policy and regulation of AI

The debate around regulating AI in Brazil, particularly the proposed bill PL 2.338/2023, highlights significant concerns from industry stakeholders. The "Confederação Nacional da Indústria" (CNI) which stands for National Confederation of Industry, for instance, worries that the bill's excessively broad scope - regulating not only AI use but also its conception and development - could stifle innovation and research by applying to even low- and medium-risk AI systems. Other concerns include the fixation of user rights over clear company obligations, potential regulatory overlap, disproportionate governance requirements for smaller companies, and risks to intellectual property (trade secrets) due to external auditing provisions. [7]

Recommendations to improve ai regulation and foster growth

The ai plan for the good of all

Brazil's government vision about AI plan 2024-2028 What is AI for the good of all.png
Brazil's government vision about AI plan 2024-2028

The "Plano IA para o Bem de Todos" reflects this commitment, allocating substantial resources for: [26]

Digital security’s role

The presence of a digital security policy within a company is a highly significant determinant for the adoption of industry 4.0 technologies in Brazil. It influences the likelihood of adopting cloud computing, big data analytics, IoT, and AI. This impact is particularly pronounced for larger companies, suggesting that their ability to implement robust security policies facilitates broader digital transformation efforts. Experts consider cybersecurity analysts as an emerging profession, and knowledge in digital security is a crucial skill for industry 4.0 professionals. [58]

Challenges and limitations

Despite the promising potential of machine learning in Brazilian industry, several challenges and limitations hinder its widespread adoption and full impact. [6]

Unequal access to technology

There is a significant disparity in the level of digitalisation among Brazilian companies, [59] with a large portion, especially micro and small enterprises (MSEs), still operating at levels characteristic of the Second Industrial Revolution. This limits their integration into digital markets and data-driven business models. Compared to OECD countries, Brazil lags in the adoption of technologies like cloud computing, IoT, and AI. [41]

Lack of structured data

The effective application of AI and ML heavily relies on the availability of large, structured, and quality datasets. There are concerns regarding the challenges in managing and governing data, including aspects of privacy and intellectual property. [60] The need to create and enhance national databases for AI training is a recognised challenge in Brazil’s digital transformation agenda.

Scarcity of specialised professionals

A critical barrier is the lack of qualified labour capable of implementing and managing digital transformation processes. This includes a need for workforce requalification and alignment of educational programmes with industry 4.0 demands. The perception of a "low" or "medium" availability of industry 4.0 specialists in the market highlights the necessity for significant professional training.

Cultural and financial barriers

Regulatory uncertainty

The regulatory environment for AI in Brazil, particularly the bill 2.338/2023, has raised concerns within the industry regarding potential over-regulation that could stifle innovation and deter investments. This contrasts with more flexible approaches in other leading countries. The current proposal is considered by some to be the most restrictive globally, regulating the technology from conception to adoption, rather than just its high-risk applications. [57]

Lack of solutions for smes (small and medium-sized enterprises)

Technology providers often focus their resources on larger corporations, leading to a "lack of tailored solutions for SMEs" and exacerbating their specialisation gap. This limits the ability of smaller firms to benefit from the efficiencies and innovations offered by ML and other industry 4.0 technologies. [41]

Future perspectives

The integration of machine learning is seen as a pivotal element for the future growth and competitiveness of Brazilian industry, deeply intertwined with national industrial policy. [62]

Adoption of ml in industrial policy

Brazil is actively developing and implementing national strategies to accelerate digital transformation and industry 4.0. The Brazilian Digital Transformation Strategy (e-Digital) and the National IoT Plan are key initiatives aiming to stimulate informatisation, dynamism, productivity, and competitiveness. [62] These plans propose actions such as fostering the development and implementation of IoT platforms, promoting open IoT platforms, and supporting Robot as a Service (RaaS) models.

A significant initiative is the "Plano IA para o Bem de Todos" (AI for the Good of All Plan), which outlines a comprehensive strategy for 2024–2028. This plan aims to: [26]

The plan allocates a projected US$ 54.5 billion in investments from various sources, including public funds, private sector contributions, and state-owned companies, to achieve these goals. It aligns with the "Nova Indústria Brasil" (New Brazil's industry) policy, which seeks to increase the technological and economic complexity of national industry, making AI a fundamental part of this trajectory. [63]

Future trends in ML applications in Brazilian industry align with global industry 4.0 advancements: [64]

See also

Further readings

References

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