Artificial intelligence in industry – applications, safety, and challenges

Modern industry is undergoing a rapid transformation driven by artificial intelligence (AI). Companies are increasingly deploying AI in industry to optimize production, automate processes, and make better use of data. Machine learning techniques and big data analytics are opening up entirely new ways for manufacturing businesses to streamline operations and boost competitiveness. At the same time, questions are being raised about the safety of such solutions—especially in the context of machine safety and the latest regulations such as the Machinery Regulation 2023/1230. Below, we look at AI applications in the industrial sector, the benefits they deliver, and the challenges and risks that come with this technological revolution.

Applications of artificial intelligence in industry

Artificial intelligence is used across many areas of production and business management. Thanks to it, industrial automation gains a new level of intelligence—systems can learn from data and make decisions in real time. Below are selected areas where AI is already delivering measurable results:

The examples above are only part of what’s possible. More and more companies are investing in AI solutions to increase productivity and production flexibility. For instance, machine learning models can analyze hundreds of process parameters in real time—when they detect an anomaly, the system can automatically adjust machine settings or call for service before a serious failure occurs. Other systems learn from historical data how to optimally configure a production line when the product mix changes.

Benefits of implementing AI in the manufacturing sector

Implementing artificial intelligence in an industrial environment brings numerous business and operational benefits:

• Higher efficiency and less downtime: With AI, machines are less likely to suffer unplanned failures because the system detects early signs of faults. Optimized production and maintenance schedules minimize downtime.
• Improved product quality: Automated inspection and process data analysis make it possible to spot deviations from the norm faster. Fewer defective products mean less material waste and a stronger company reputation.
• Lower operating costs: AI-supported industrial automation reduces the need for manual intervention in routine tasks. Processes become more energy-efficient—AI can, for example, switch off unused equipment or optimize energy consumption.
• Greater production flexibility: AI-based systems adapt quickly to changes—such as fluctuating market demand or modifications to product design. This makes it easier to introduce product customization and shortens response time to trends.
• Better workplace safety: AI can not only create hazards, but also improve safety. One example is intelligent monitoring systems that detect dangerous situations (such as a person approaching a robot) and trigger emergency procedures. In this way, the technology helps protect employees.

All of these benefits translate into a competitive advantage. Companies that use AI can produce faster and at lower cost while maintaining or even improving quality. In addition, effective management of data and processes supports better strategic decision-making. It is worth noting, however, that the scale of the benefits depends on proper implementation—this requires investment not only in hardware and software, but also in staff training and adapting the organization to new ways of working.

Challenges in implementing artificial intelligence in industry

Despite its enormous potential, deploying AI in factories and production plants comes with a number of challenges:

• High costs and return on investment: Developing and training AI models can be expensive, especially when they relate to specific industrial processes. Purchasing sensors, IT infrastructure, and the work of AI specialists are significant costs. For niche or one-off processes, traditional algorithms and automation can be cheaper and more cost-effective than full AI solutions.
• Data access and data quality: Effective AI models require large volumes of sufficiently high-quality data. In plants that are only beginning their digital transformation, data may be incomplete or scattered across systems. Upgrading the machine fleet (adding sensors, SCADA systems) and integrating different information sources is often necessary before IT or engineering teams can prepare data for model training.
• Lack of specialists: Building and maintaining AI systems requires skills at the intersection of IT and engineering. The labor market lacks experienced experts who understand both machine learning and the specifics of industrial processes. Companies need to invest in developing internal capabilities or use external specialists (outsourcing) with the right know-how.
• Integration with existing processes: Implementing AI is not only a technology issue, but also a matter of fitting it into current procedures. An AI system must work effectively with machines and people on the shop floor.
• Technological limitations: Not every industrial task is suitable for AI-based methods. Many processes follow well-defined rules of physics or logic—where traditional control algorithms perform very well. AI gains an advantage mainly where the process is complex, variable, or difficult to model using classical methods. Otherwise, using AI may offer no meaningful advantage over a simpler solution and may introduce unnecessary complexity.
• Acceptance and changes in work culture: Introducing intelligent systems often comes with employees’ concerns about jobs or changes in responsibilities. Proper training and building awareness that AI is a supporting tool—not a threat—are essential. Companies that overlook the human factor may face resistance to the new technology, making it harder to use effectively.

Identifying these challenges early makes it possible to plan the implementation appropriately. It is often recommended to start with a smaller-scale pilot project, draw conclusions, and only then roll out AI more broadly across the organization. This approach helps estimate real costs and benefits more accurately and refine system integration before it becomes critical to core processes.

Machine safety and AI – regulations and good practices

Introducing artificial intelligence into machines and production equipment requires particular attention to safety. Traditional machines operate according to predefined, programmed routines, whereas AI systems can make autonomous decisions based on data. This raises the question: are AI-controlled machines as safe as conventional ones? Legislators have recognized this issue, as evidenced by Regulation (EU) 2023/1230 on machinery (the so-called new Machinery Regulation).

The new rules, which from 2027 will replace the current Machinery Directive 2006/42/EC, address, among other things, risks arising from digitalization and the use of AI systems. Machines equipped with artificial intelligence components must meet additional safety and conformity assessment requirements to ensure user protection. In practice, this means that the manufacturer of such an “intelligent” machine must carry out an extended risk analysis, taking into account atypical scenarios of algorithm behavior. It is also necessary to ensure that the AI system cannot independently change the machine’s function in a way that endangers operators or the surrounding environment.

Regulation 2023/1230 also introduces the concept of high-risk machinery—categories of equipment that, due to potential hazards (e.g., arising from autonomous operation), require additional certification by an independent body. This includes, among others, autonomous mobile machines and collaborative industrial robots. In such cases, the manufacturer’s own declaration of conformity is no longer sufficient; involvement of an external notified body is required, along with testing to confirm compliance with tightened requirements. It is also worth noting that the definition of a so-called “safety component” has been expanded—it now covers not only physical devices (e.g., emergency stop devices), but also digital elements and software. This means that, for example, an AI-based safety-zone monitoring system has the same status as a mechanical guard or a light curtain.

AI adoption in industry therefore does not take place in a legal vacuum—on the contrary, it is being closely watched by regulators. The aim is to ensure that innovation goes hand in hand with the safety of workers and consumers. The good news is that when a company meets stringent safety requirements, it also builds trust in its products and services. CE certification for machinery with advanced control systems becomes proof that even in the AI era, a high level of safety can be achieved.

Risks associated with using AI in industry

In addition to implementation challenges, there are also certain risks associated with using artificial intelligence in industrial environments. These need to be identified and monitored so they can be addressed in time:

• Unpredictable system behavior: AI is driven by data and statistics, which means that in unusual situations it may make unexpected decisions. An error in the algorithm or in the input data can result in unsafe machine behavior (e.g., an incorrect robot movement). That is why it is important for every AI-enabled machine to have traditional safeguards—such as an emergency stop device or manual control mechanisms—allowing the operator to stop the equipment immediately.
• Software defects and updates: AI software requires regular updates and patches. A new version of an algorithm may introduce unintended side effects. If software quality-control procedures fail, an updated machine may behave undesirably even though the previous version worked correctly. Hence the recommendation to run tests that simulate real operating conditions before deploying updates.
• Attacks via data manipulation: AI can be “fooled” by feeding it manipulated data. In real industrial settings, a genuine threat is the deliberate disruption of sensor readings or providing the algorithm with false information, which can trigger unwanted responses (e.g., stopping production or failing to react to a real hazard). Therefore, when designing AI systems, anomaly detection mechanisms and input-data filtering should be included.
• Dependence on technology vendors: Many companies rely on external providers of AI systems or cloud platforms for data analysis. This creates the risk of dependence on “black box” technology whose operation is not fully understood. If the vendor stops supporting the product or imposes unfavorable terms, the business may face difficulties. Key processes should therefore be built on transparent solutions, and knowledge of them should be progressively brought in-house.
• Impact on employees: AI-driven automation can lead to headcount reductions or changes in job profiles. This is a social risk—if a company does not invest in reskilling, AI will be seen solely as a threat to jobs, which negatively affects morale and the workforce’s willingness to cooperate in rolling out new technologies.

All of the above risks make it essential to apply a principle of limited trust in AI. This means that even if a system operates autonomously, the organization should put oversight in place. Regular risk analysis and verification of decisions made by AI (especially in critical applications) help detect potential irregularities early. In practice, a “human-in-the-loop” approach is often used—keeping a person in control of key decisions until AI has proven its reliability over the long term.

Cybersecurity and artificial intelligence in industry

The growing use of AI also brings new challenges in the area of cybersecurity. Industrial plants are becoming increasingly digitized and network-connected—machines, IoT sensors, SCADA systems, and AI platforms exchange data within the factory and often also with the cloud. This expands the potential attack surface for cybercriminals. Threats include, among others:

• Ransomware attacks and production sabotage: Criminals may try to infect the plant network with malware to take control of control systems or encrypt them. When AI manages critical processes, the impact of a successful attack can be severe—from downtime to equipment damage or the mass production of defective products. That’s why it is so important to follow proven cybersecurity standards in industrial automation (e.g., standards from the IEC 62443 family) and to segment the OT (Operational Technology) network to isolate production systems from the Internet.
• Theft of data and intellectual property: AI systems learn from production data, which may contain sensitive information about technological processes or recipes. Stolen data can be used by competitors or for industrial espionage. Implementing strong protection mechanisms (encrypted communications, multi-level access authentication, network monitoring) is a necessity in the era of Industry 4.0.
• Manipulation of AI models: A more advanced attack vector is an attempt to influence the AI model itself—for example, by “poisoning” it during training with incorrect data. This can cause the model to learn false patterns and start making wrong decisions. This type of threat requires a highly deliberate approach to the model training process: training data must be protected and its integrity verified.
• Attackers using AI: Artificial intelligence is a double-edged sword—just as it helps defend systems, it is also used by hackers. For example, AI makes it easier to create convincing phishing messages or to search for security vulnerabilities. The defense here is to use equally advanced tools on the defenders’ side, such as machine-learning-based intrusion detection systems that analyze network traffic for anomalies.

For industrial companies, this means the need to integrate an AI strategy with a cybersecurity strategy. Every new AI-based component should undergo an IT security assessment. It is also worth ensuring that OT teams work closely with IT security departments—a secure development culture (DevSecOps) should also cover AI projects. Only then will innovation not weaken, but strengthen, the security of the entire enterprise.

Questions to ask before deploying AI in the production process

Before you sign a contract for your first pilot project, answer—both for yourself and for the team—at least five key questions. If even one of them does not have a clear, positive answer, hold off on the investment and close the readiness gap.

1. Do I have enough good data to train the models?
   Data must be consistent, complete, and properly described. A lack of measurement history or frequent gaps in logs means the model will not learn useful rules.
2. What specific business goals and KPIs do I want to achieve with AI?
   Define a measurable outcome—for example, reducing downtime by 20% or increasing the OEE by 5 pp. Without a clear goal, it is difficult to assess project success and calculate ROI.
3. Will my machines and lines meet the requirements of Regulation 2023/1230 after integrating AI?
   Make sure that safety systems—both hardware and software—remain aligned with future standards and that you have a certification plan for CE marking for machines after modifications.
4. How will I minimize the risk of cyberattacks on AI solutions?
   Plan network segmentation, multi-level authentication, and anomaly monitoring. Remember that attackers may try to “poison” models with false data.
5. Is the organization culturally ready for change?
   Check whether operators, maintenance, and management understand AI’s role, know how to work with it, and are not afraid of losing their jobs. Without this, even the best technology will not deliver the expected benefits.

“Yes” answers to all questions mean a green light to start the pilot project. If any of the topics still raises doubts, plan to fill the gaps—before the algorithm takes the controls of your production line.

The future: AI as an industry standard?

Despite the challenges and risks listed above, the direction of industrial development seems clear—artificial intelligence in industry will, over time, become as common as automation or robotics. Even now, some factories referred to as “smart factories” base most decisions on data analysis and intelligent algorithms. In the future, we can expect further refinement of these technologies: AI models will become more specialized (e.g., dedicated to very narrow technological operations) while also becoming easier to deploy thanks to tools such as AutoML and ready-made cloud services.

A next step may be the wider adoption of digital twins—high-fidelity digital models of production lines or entire factories, continuously fed with real-time data. AI working on such a “twin” will be able to test different optimisation scenarios without putting real production at risk. This approach could revolutionise machine design and construction, shortening the time from concept to commissioning through simulation and the automated identification of the best design solutions.

We also cannot overlook the human factor. The concept of Industry 5.0 assumes harmonious collaboration between people, robots, and AI—so that technology enhances human creativity and capability rather than merely replacing it. In this vision of the future, the machine operator will become a kind of “pilot” overseeing a fleet of intelligent devices, while routine and hazardous tasks will be carried out mainly by autonomous systems. People will be able to focus more on supervision, planning, and continuous process improvement.

In summary, artificial intelligence has enormous potential to further reshape industry—boosting efficiency, improving safety, and supporting sustainable development. The key, however, is a conscious and responsible approach to implementation. Companies that invest in AI now and take the safety aspect seriously (both physical and digital) gain an advantage and prepare more effectively for what lies ahead. Artificial intelligence in industry is not a passing trend, but the foundation of the next industrial era—so it is worth getting to know it better and learning how to work with it safely.