Just 5–7 years ago, artificial intelligence in industry was seen as an expensive experiment or a “technology for the future,” hardly applicable to complex, real-world production environments.
Today, AI is a practical working tool for manufacturing plants, energy companies, extractive industries, and state-owned enterprises. Let’s take a closer look at where AI is already being implemented, what problems it solves, and why this trend will only accelerate. According to McKinsey, more than 55% of large industrial companies already use AI in at least one mission-critical process.
1. Equipment maintenance tracking and predictive monitoring
One of the most costly challenges in industry is accidents and unplanned downtime.
AI analyzes data from sensors - vibration, temperature, pressure, electrical currents, and noise - to detect anomalies and predict equipment failures long before a critical breakdown occurs.
Examples:
- Siemens uses AI models to monitor turbines, reducing unplanned downtime by approximately 40%.
- In energy and nuclear projects, AI is applied to predict failures of critical infrastructure components.
Result: fewer accidents, lower repair costs, and a higher level of operational safety.
2. AI-based quality control
Computer vision has become a standard on modern production lines.
AI systems:
- detect micro-defects,
- identify geometric deviations,
- spot assembly errors in real time.
3. Energy consumption and environmental compliance
AI is actively used to monitor and meet environmental and sustainability requirements:
- energy consumption and thermal process optimization,
- optimization of furnaces, compressors, and boilers,
- control of water treatment and filtration systems.
Examples:
- AI-driven building and engineering systems reduce energy consumption by 10–20%.
- In metallurgy and chemical industries, ML models reduce emissions and fuel usage without lowering productivity.
AI enables compliance with environmental regulations without halting production or sacrificing margins.
Examples:
- BMW uses AI to inspect weld seams and paint coatings.
- In metallurgy, AI identifies surface defects that are impossible to detect consistently through visual inspection.
Result: stable product quality, reduced waste, and lower costs associated with defective output.
Key AI integration scenarios in industry
4. Supply chain management
In supply chains, AI is used for:
- demand forecasting,
- optimization of raw material and component inventories,
- prevention of supply disruptions.
Examples:
- Large industrial groups reduce excess inventory levels by 10–15%.
- AI models account for seasonality, logistics constraints, and market risks.
Result: less capital tied up in stock and more resilient supply chains.
5. Workplace Safety and Labor Protection
AI is increasingly used to prevent industrial incidents.
Computer vision systems:
- detect safety violations,
- identify hazardous zones,
- prevent accidents before they occur.
Result: reduced injury rates, fewer production stoppages, and compliance with regulatory requirements.
Today, AI adoption is incremental rather than a single large-scale rollout, with initial results often achieved within 3–6 months. ROI in industrial environments is frequently higher than in traditional digital products due to the scale and criticality of industrial processes.
AI does not replace engineers or managers - it significantly amplifies their decision-making and operational capabilities.
Why AI is no longer a “Costly Toy” for industry
- Start with a clearly defined business problem.
- Ensure data quality and availability.
- Integrate AI into existing workflows rather than creating parallel systems.
- Work with teams and actively manage organizational change.
- Design solutions with scalability in mind from day one.
What to consider when implementing AI
AI in industry is no longer an experiment or a passing trend - it is a proven tool for operational efficiency. It reduces costs, improves safety, supports environmental compliance, and makes large enterprises and state-owned organizations more resilient.
Companies that delay AI adoption will soon find themselves competing with organizations already managing production through data-driven decision-making - and they are likely to lose ground in that competition.
Conclusion
If you are considering AI implementation at a factory, industrial holding, or state-owned enterprise, it’s crucial to start with the right architecture and clearly defined objectives.
We help organizations identify the highest-impact AI use cases, design solutions aligned with real operational processes, and implement AI technologies without disrupting ongoing business operations. Let’s discuss where AI can deliver the greatest value for your organization.
08/01/2026
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