AI in Polish Industry: Where It Delivers Results and Where It Is Just Hype

AI in Polish Industry – Is It Already a Necessity?

AI in industry looks impressive in presentations. On the production floor, however, it quickly becomes clear whether a company has a real problem to solve, reliable data and people who are ready to change the process. Without these elements, artificial intelligence remains little more than a fashionable buzzword. With them, it can reduce defects, predict failures, improve planning and support production decisions.

In Poland, AI is not yet a widespread standard. According to Statistics Poland (GUS), 8.7% of enterprises used artificial intelligence technologies in 2025. By comparison, Eurostat reports that 19.95% of companies across the European Union used AI in the same year. This suggests that Polish industry is still at the stage of selective implementation rather than a full-scale revolution.

This does not mean that the subject can be ignored. Statistics Poland reports that in 2023, 44.9% of industrial enterprises used at least one advanced technology, such as cloud computing, the Internet of Things, Big Data or AI. Among large industrial enterprises, the figure had already reached 94.2%. AI is therefore increasingly becoming another layer of Industry 4.0, but it does not replace its foundations.

When AI in Industry Actually Works

Artificial intelligence delivers results in manufacturing when it solves a specific problem. The goal is not simply to implement a new tool, but to improve performance: fewer defects, fewer complaints, shorter downtime, a more stable process, lower energy consumption or better machine utilization.

A good AI project therefore begins with one question: what currently costs the plant the most? If a company does not know the cost of downtime, its scrap rate, the number of complaints or the sources of delays, it will be difficult to calculate the return on investment honestly. AI does not create a strategy for the company. It can support one when the data and processes are already sufficiently organized.

The most valuable implementations are usually those closest to the production floor. When a model analyzes camera images, machine data, failure histories, process parameters or production schedules, its actual value is easier to see. It also becomes clear more quickly whether the project works only as a pilot or genuinely improves daily operations.

Quality Control and Machine Vision

Quality control is one of the most practical applications of AI. A system based on cameras and image-recognition models can detect surface defects, assembly errors, damaged packaging, incorrect labels, missing components or dimensional differences. For a manufacturing plant, this is not an abstract innovation. It is a way to reduce complaints, sorting work and quality-related losses.

AI performs particularly well where people must repeat the same assessment hundreds or thousands of times. The operator is still needed, but the model can take over part of the monotonous inspection work and perform it consistently. The condition is simple: the system must be trained using good examples of both conforming and non-conforming products.

The best pilot project does not cover the entire factory from the start. It is better to begin with one production line, one type of defect and a carefully calculated cost of error. If the system reduces the number of undetected defects or limits the need for manual inspection, the company can then consider scaling it.

Predictive Maintenance and Maintenance Operations

Another strong area is predictive maintenance. AI can analyze vibration, temperature, power consumption, pressure, cycle time and other machine operating parameters. If the model identifies a change in the equipment’s behavior, it can provide an early warning of a potential failure before it would be detected through a traditional maintenance schedule.

This makes the most sense where downtime is expensive. Examples include continuous production lines, critical machines, compressors, pumps, presses, furnaces, robots, conveyor systems and packaging lines. If a single failure stops several stages of production, even a small improvement in equipment availability can have a significant impact on financial performance.

However, companies should be cautious about promises unsupported by data. Predictive maintenance will not work effectively if the company has no failure history, does not record the causes of downtime or cannot distinguish between planned and unplanned stoppages. A model can detect anomalies, but someone still needs to determine which anomalies genuinely require action.

Planning, Scheduling and Inventory

AI can support production planning, but the requirements are higher in this area. A schedule depends on numerous variables, including orders, materials, production capacity, changeovers, inventory levels, employee absences, data quality and delivery dates. If this information is fragmented, the model will work with an incomplete picture.

The greatest value emerges when AI helps predict risks. It can identify which orders are at risk of delay, where a bottleneck may develop or which material may run out sooner than the plan suggests. It can also analyze the history of completed production orders and recommend more realistic deadlines.

However, AI is not a magical production planner. If the ERP system contains outdated inventory information and production data reaches the system with a delay, AI will merely optimize incorrect assumptions. For this reason, implementation often begins not with a model, but with improving the flow of information between sales, warehousing, purchasing and production.

Process and Cost Optimization

AI can also support production process optimization. This is particularly relevant to processes in which quality or efficiency depends on many parameters at the same time. These may include temperature, pressure, humidity, line speed, mixing time, formulation, raw-material supplier or machine settings.

People often recognize the most obvious relationships. A model can detect less intuitive ones. It may show that an increase in defects occurs only with a specific combination of parameters. It may also indicate that a small adjustment to the settings reduces energy consumption without compromising quality.

Caution is required in this area. An AI recommendation should not automatically change critical process parameters without supervision. At first, it is better to treat the system as support for a process engineer, automation engineer or production manager. Deeper automation of decisions should only be considered after the results have been verified.

When AI Is Just Hype

AI becomes hype when a company starts with the tool rather than the problem. Management hears that competitors are implementing artificial intelligence. A supplier presents an impressive demonstration. A pilot project, dashboard and presentation are created. After several months, however, nobody can say whether the number of defects has fallen, downtime has been reduced or on-time production has improved.

The most common mistake is asking: “Let us see what AI can do for us.” A better question is: “Which production problem costs us the most?” Only then can the company assess whether AI is the right tool.

The second problem is data. On the production floor, information is often distributed across numerous systems and locations: ERP, MES, SCADA, CMMS, spreadsheets and local notes. Data alone is not enough. It must be current, properly described and connected to a decision. If a company does not know where a particular value comes from or who is responsible for it, the model will be built on weak foundations.

The third problem is the lack of a process owner on the production side. AI cannot be exclusively an IT project. If the system generates an alert, someone must know what to do next. Should the machine be stopped? Should it be inspected during the next planned stoppage? Should the batch be rejected? Should a process parameter be changed? Without clearly assigned responsibility, AI becomes another source of notifications that people eventually stop responding to.

How to Calculate Whether an AI Implementation Makes Sense

A discussion about AI should begin with the cost of the problem, not the price of the software licence. If one hour of production-line downtime costs tens of thousands of zlotys, a system that provides an early warning of failure may quickly justify its cost. If a product defect results in a complaint and the need to sort an entire batch, machine vision may have a strong business case.

The simplest calculation is based on three questions. How often does the problem occur? How much does one incident cost? What proportion of that cost can realistically be reduced? Only then should the company consider the cost of the pilot project, integration, model maintenance, sensors, cameras, infrastructure and employee time.

In practice, it is worth measuring several indicators: OEE, defect rate, scrap, complaints, downtime, the proportion of unplanned downtime, MTBF, MTTR, energy consumption per unit of product, changeover time and on-time order completion. Not every project requires all of these metrics. What matters is that the indicators are defined before the pilot begins.

A successful AI pilot does not always result in full implementation. Sometimes its value lies in discovering that the company must first organize its data, reporting standards or maintenance operations. This may be less impressive than implementing a model, but it is often more necessary.

The Biggest Barriers to AI in Polish Industry

Technology is not always the biggest barrier. The number of available tools is increasing, and many of them can be tested more quickly than they could several years ago. The more difficult challenges involve daily operations: data integration, competencies, accountability and the trust of production-floor employees.

The Polish Economic Institute has estimated that between approximately 6% and 16% of companies in Poland use AI, depending on the research methodology. At the same time, 77% of business owners who do not use AI have no plans to implement it until doing so becomes necessary. This clearly demonstrates the gap between interest in the technology and readiness to introduce real change.

Companies that are further advanced in their digital transformation have an easier starting point. If a plant already uses MES systems, machine data, quality-control systems and properly documented production orders, AI has information it can work with. If most information is still manually corrected in spreadsheets, the first step should focus on data rather than the model.

Financing also matters. In many companies, AI implementation competes with investments in machines, automation, robotics and infrastructure modernization. It is therefore worth checking programmes offering grants for industrial digitalization, but only when the project already has a clearly defined business objective.

How to Start Implementing AI on the Production Floor

The safest starting point is small and specific. Instead of declaring that “we are implementing AI across the company,” the goal should be to solve one problem on one production line, machine or process. Such a project is easier to calculate, quicker to verify and simpler to stop if it fails to deliver results.

A good starting process looks like this:

• select one process with a measurable problem,
• calculate the cost of the current loss,
• check what data is already available,
• assess whether the data is complete and reliable,
• appoint a project owner on the production side,
• select the success metrics,
• plan what will happen after an AI alert or recommendation,
• define the conditions for scaling or closing the pilot project.

This approach is well suited to companies developing automation in Polish factories but unwilling to implement technology purely for its image-building value. AI should strengthen a process rather than conceal its weaknesses.

AI in Industry: Investment or Hype?

The simplest test is highly practical. An AI project makes sense if the company can answer several questions:

• what production problem are we solving?
• how much does this problem cost per month or per year?
• what data do we already have?
• who is responsible for the process on the production floor?
• which indicator is expected to improve?
• what will we do when the system generates an alert?
• how will we verify that AI has actually helped?

If the answers are specific, AI may be a worthwhile investment. If they remain general, the project is probably premature. In that case, it is better to begin with data, system integration and working standards rather than another tool.

Summary

AI in industry is neither a miraculous solution to every problem nor an empty trend. Its value depends on where it is applied. It works best when the problem is measurable, the data is reliable and the model’s recommendation leads to a specific decision.

In Polish manufacturing plants, the greatest potential can be seen in quality control, predictive maintenance, planning, process optimization and production-data analysis. The greatest risk arises when a company implements AI without a defined objective, a process owner or performance indicators.

A dashboard is enough for a presentation. On the production floor, the company must prove that the process works better than it did before. This difference determines whether AI in industry is an investment or merely another fashionable buzzword.

AI in Polish Industry – FAQ

Is AI in industry cost-effective for small and medium-sized companies?

Yes, but not necessarily as a large system implemented across the entire company from the start. Small and medium-sized enterprises should begin with a narrowly defined problem, such as quality control, downtime analysis, reporting or one critical process.

Where should a company start when implementing AI in manufacturing?

With the problem and the data. First, the company must determine what it wants to improve, how much the current loss costs and what data is available. Only then should it select a tool.

Will AI replace operators on the production floor?

In most cases, AI supports operators rather than replacing them. It can detect anomalies, recommend decisions and reduce repetitive inspection work. People remain responsible for assessing the context and responding to unusual situations. This issue is closely connected to the broader question of whether robotics and artificial intelligence will take jobs in Polish industry.

How is AI different from conventional automation?

Automation performs programmed actions. AI analyzes data, detects patterns, classifies images, predicts failures and recommends decisions. In practice, the two approaches often complement each other.

When is AI in a company just hype?

AI is just hype when there is no measurable problem, reliable data, process owner, KPI or action plan for responding to the system’s recommendations. If the project’s success ends with a presentation rather than an improvement in production results, AI is merely a fashionable addition.