Artificial intelligence in the industry is profoundly transforming operations, driving significant advancements in productivity, efficiency, and competitiveness. Not as futuristic as it may seem, the reality of AI is closer than imagined. It refers to the ability of computers or machines to mimic human skills, learning from past experiences to solve problems, make decisions, and interact using language.
This digital transformation has profound impacts on the supply chain and the optimization of production processes. The artificial intelligence technology market reached around $200 billion in 2023, with projections to exceed $1.8 trillion by 2030, according to Statista. Additionally, according to Gartner, by 2025, 70% of organizations will have adopted operationalized AI architectures, driven by the rapid maturation of artificial intelligence implementation initiatives.
The Impact of Artificial Intelligence in Industry
Artificial intelligence in the industry has a profound and transformative impact, playing a fundamental role in both production processes and management. Amanda Santos, Executive Manager of the Meta Hub for Data Automation and Artificial Intelligence, highlights:
“AI helps automate processes, reducing operational costs and repetitive tasks. Additionally, artificial intelligence can enhance decision-making, generating valuable insights from data and personalizing products and services according to customer profiles.”
In the digital transformation era, this tool is essential for process optimization. It enables companies to process and analyze data in real time, allowing for quick and accurate informed decision-making. The continuous analysis of this data facilitates the identification of opportunities for improving production processes, boosting competitiveness and adaptability.
However, the implementation of artificial intelligence faces significant challenges. Amanda points out:
“Cost is a significant factor, especially for generative AI models, although they are becoming more accessible. Another major challenge is data management; many companies still do not have well-structured data, which is essential for working with AI. Workforce qualification is also crucial, as there is a shortage of professionals specialized in AI. Moreover, challenges related to integrating with legacy systems, as well as data security and privacy, must always comply with regulations such as the LGPD (General Data Protection Law).”
This scenario highlights that, despite advancements, the adoption of artificial intelligence requires not only investments in technology but also adequate training and infrastructure to address the contemporary challenges of the digital era.
How Artificial Intelligence Is Used in Industry
In the context of Industry 4.0, artificial intelligence has enabled significant advancements in productivity, efficiency, and competitiveness. AI applications cover various key areas, from predictive maintenance to quality management and process optimization.
“In smart manufacturing, AI can be used for predictive maintenance, production optimization, and quality control. In the supply chain, it can optimize transport routes and inventory management, as well as improve product traceability. In operations, real-time data analysis can assist in critical operational decisions,” explains Amanda.
Predictive Maintenance
One of the major benefits of artificial intelligence in industry is predictive maintenance. Using smart sensors and real-time data analysis, machines are constantly monitored. This technology allows for the prediction of imminent failures in equipment, scheduling repairs before unplanned production stoppages occur.
Inventory Control
AI also revolutionizes inventory management by utilizing advanced algorithms to accurately predict future demand. This facilitates strategic material planning and reduces the risk of stock shortages or excess, optimizing the company’s financial resources.
Generative Design
In the product development phase, AI assists in generative design. Machine learning software explores various design parameter combinations, such as materials and manufacturing methods, to find the most efficient and cost-effective solution, accelerating the innovation process.
Quality Management
To ensure high-quality standards, computer vision systems based on artificial intelligence identify defects and inconsistencies in manufacturing processes. This early detection capability helps maintain product integrity and operational efficiency.
Robotics and Automation
The application of AI in robotics enables automation of repetitive tasks and the execution of complex operations with precision. Robots are used for activities such as assembly, welding, inspection, and material handling, improving efficiency and reducing human errors.
Process Optimization
Smart manufacturing software integrates machine and process data, providing a holistic view of operations. This facilitates the identification of improvement opportunities and automates tasks, freeing up time for strategic and management activities.
Loss Reduction
Finally, AI contributes to loss reduction at different levels. From predicting raw material waste to scheduled equipment maintenance, AI-based solutions minimize operational costs and increase overall production efficiency.
These applications demonstrate how artificial intelligence not only optimizes industrial processes but also drives competitiveness and innovation in the sector.
Which Area of Artificial Intelligence Is Most Used in Industry?
The most commonly used area of artificial intelligence in industry depends on the sector and the specific needs of companies. However, some of the most prevalent applications include:
1. Machine Learning and Predictive Analytics
Machine learning is widely used in predictive maintenance, identifying equipment failures before they happen, reducing operational costs and preventing unplanned downtime. It is also essential in demand forecasting for inventory management and supply chain optimization.
2. Computer Vision
Extensively applied in quality management, computer vision allows for automated product inspections on production lines, detecting defects and ensuring compliance with quality standards.
3. Natural Language Processing (NLP)
Used for automating customer service and technical support, as well as for interpreting reports, analyzing unstructured data, and assisting in machine and equipment operation.
4. Generative Artificial Intelligence
An innovative tool for generative design, AI helps in developing new products by exploring numerous parameter combinations to create more efficient and cost-effective solutions.
5. Robotics and Intelligent Automation
The combination of AI and robotics has transformed industrial automation, allowing robots to perform repetitive tasks such as assembly, welding, and inspection with greater precision and efficiency.
6. Process Optimization and Cyber-Physical Systems
AI is essential in Industry 4.0, integrating cyber-physical systems that connect sensors, machines, and software, enabling real-time data analysis for faster and more accurate operational decisions.
In summary, machine learning, computer vision, and robotics are some of the most widely used AI areas in industry, as they offer significant gains in productivity, efficiency, and cost reduction.
