Manufacturing

The position of the industrial sector

Poland is a significant player in European manufacturing. Eurostat data consistently shows that the industrial sector generates a substantial share of Polish GDP and employs a significant proportion of the workforce. The top Polish plants are, on one side, subsidiaries of international groups and, on the other, Polish capital groups such as KGHM, Grupa Azoty and Synthos.

Most plants deployed ERP systems from SAP, Oracle or the Polish vendor Comarch between 2018 and 2022. MES (Manufacturing Execution System) and SCADA systems are standard on production lines. The technology stack exists. The bottleneck is the integration between the OT layer (operations technology on the floor) and the IT layer (business systems).

Five use cases with documented ROI potential

A synthesis of McKinsey's "The State of AI," BCG's "AI in the Factory of the Future" and the WEF Lighthouse Network.

Predictive maintenance. AI trained on sensor data (vibration, temperature, current, noise) from critical production machines predicts failures before they occur. McKinsey and BCG independently cite this category as the most mature in manufacturing. It requires sensors on the line and a minimum of a dozen-plus months of historical data to train the model.

Quality control based on computer vision. Cameras on the production line plus models that detect defects with accuracy higher than a human inspector. A use case with quick ROI in industries with a high cost of defects (automotive, electronics, pharmaceuticals). It requires a labeled training base with a large number of defect examples.

Energy consumption optimization. Models that optimize the operation of furnaces, compressors and refrigeration in response to dynamic tariffs and line load. A strategically important use case after the rises in Polish energy tariffs in 2023-2024. The metallurgy and chemical industries have the most room here.

Demand forecasting. AI that forecasts demand with accuracy higher than classic statistical methods. It allows inventory to be reduced and the production plan to be optimized. It requires a quality sales history of at least two years plus data on promotions, seasonality and macro factors.

Generative AI for technical documentation. LLMs that generate first drafts of operating manuals, safety data sheets and procedure documentation. Quick ROI in R&D and quality departments. A low barrier to entry.

Lighthouse factories, a global benchmark

The World Economic Forum Global Lighthouse Network is an initiative that identifies the world's highest-maturity Industry 4.0 factories. The list is publicly available and updated annually. Most Polish plants in the network are subsidiaries of international groups. Polish capital groups are only on their way.

This shows the balance of power: a significant share of Polish plants have several technology generations to catch up on. AI deployments in this period should be primarily quick wins with measurable ROI in 6 to 12 months, not systemic transformations spread over three years.

Regulatory specifics

The AI Act of February 2026 classifies safety systems in production (for example, AI controlling a hydraulic press, or AI in the motion-control systems of heavy machinery) as high-risk. It requires risk assessment documentation, human oversight and log retention for a minimum of six months.

The CSRD (Corporate Sustainability Reporting Directive), introduced by the EU, requires large enterprises to report CO2 emissions, water consumption and waste generation. AI in energy monitoring is a natural way to ease compliance.

Three barriers typical of Polish plants

First, the separation of the OT layer (operations technology on the floor) and the IT layer (business systems). AI models need data from both layers, and their integration is often the largest cost of the deployment.

Second, limited retention of historical data. SCADA typically stores operational data for a few months to a year, while training predictive maintenance requires 12 to 24 months. That often means a year of waiting while the infrastructure collects data.

Third, the absence of data engineering as a competence in Polish manufacturing companies. IT focuses on maintaining systems. Analytics and ML are an absent organizational function for most plants. The first AI project often requires hiring new people or contracting external competence.

What yesfor.ai brings that is specific

A yesfor.ai manufacturing audit takes four to six weeks. The standard scope: an assessment of the current state of OT/IT integration, identification of use cases with measurable ROI, an assessment of data readiness (quality, retention, structure), and a roadmap.

The first recommendation often is: you start not with predictive maintenance, but with putting data integration in order. Otherwise every future AI deployment will keep returning to the same bottleneck.