7 Manufacturing Trends for 2026: What Is Changing Factories This Year?

Among the manufacturing trends having the greatest impact in 2026, many share a common element: data used to make decisions on the shop floor. AI, robotics, data spaces, and predictive maintenance can all be effective solutions when they address a clearly identified process problem and their impact on business results can be measured.

A production director should therefore look beyond familiarity with the technology itself and determine what the change can improve, what it requires, and when it makes sense to allocate budget to it.

According to Statistics Poland (GUS), in July 2026, sold industrial production in Poland was 5.1% higher than a year earlier, while in January–July it increased by 3.6% year over year. At the same time, 35% of participants in the ABB Trends 2026/2027 survey said they planned to increase capital expenditures in 2026–2027. The CAWI survey was conducted by IQS on behalf of ABB in April and May 2026 among 145 representatives of the industrial sector in Poland.

In a roadmap published on July 3, 2026, NIST identifies AI and machine learning applications in smart manufacturing related to areas such as industrial data analytics, autonomous systems, robotics, digital twins, logistics, and supply chains. The same document also identifies barriers, including managing large industrial datasets, integrating different measurement and control systems, and meeting requirements related to trustworthiness, explainability, and operational reliability.

An AI project should have a clearly defined, measurable objective before a tool is selected. If a model is expected to predict quality issues, process parameters need to be connected with quality inspection results. If it is intended to support maintenance, historical machine signals must be combined with information about when and under what conditions a failure occurred.

Without this context, it is difficult to assess model quality and its impact on the process. That is why a mature AI project starts with data that can be linked to a specific event, outcome, or decision.

A PLC records machine behavior. SCADA stores alarms and trends. MES can connect an event with a production order, operation, product, or downtime cause, while ERP adds business context. In many plants, future analytics projects will therefore depend on whether this information can be combined into a consistent view of the process.

The importance of access to industrial data is also increasing because of the EU Data Act, which has applied since September 12, 2025. The regulation covers, among other things, the right of a user who owns, rents, or leases a connected product to access certain data generated while using that product or a related service, as well as the ability to make that data available to a third party of their choice.

This applies to raw and pre-processed data that is readily available to the data holder. It does not automatically mean access to all data generated around a device, particularly inferred or derived data created through additional processing. The European Commission lists industrial machinery as an example of products covered by this area of regulation.

A good test of integration maturity is whether you can reconstruct the course of a specific production order, downtime event, or quality issue without manually combining information from several systems and spreadsheets. If that is still necessary, future analytics or AI projects will first require the data flow to be organized.

According to the International Federation of Robotics, 542,000 industrial robots were installed worldwide in 2024. That is more than twice as many as ten years earlier. Europe accounted for 16% of new installations, while the number of new deployments in the region fell by 8% year over year to approximately 85,000.

For your plant, however, process parameters matter more than global statistics. Before deciding to invest in robotics, evaluate primarily:

A robot can improve a well-prepared process, but it can also reinforce its weaknesses. That is why an analysis of operation times, sources of losses, and process variability should come before selecting the scope of automation.

In predictive maintenance, it is easy to focus on sensors and algorithms, even though for a maintenance manager it is more important to first determine which failures can be identified in advance and for which assets that information creates value.

A system can monitor parameters such as vibration, temperature, rotational speed, or electrical characteristics. Prediction becomes useful only when a change in one of these signals actually correlates with the condition of the machine.

In one of our customer projects for an automotive components manufacturer, sensors monitor process pumps, collecting vibration data every 50 ms together with motor temperature and rotational speed. When an anomaly is detected, the information can be transferred to the CMMS.

Focus especially on implementations where failure of a selected asset has a major impact on the process and where a measurable change in condition occurs before the failure. In those cases, monitoring has a chance to provide maintenance teams with information they can act on.

MES-to-ERP integration primarily concerns the flow of information between systems within an enterprise. Data spaces address a broader need: controlled data exchange and reuse between independent organizations based on shared infrastructure and governance principles.

The European Commission is developing common European data spaces across several strategic sectors, including manufacturing. In the manufacturing sector, it currently lists initiatives such as Data Space 4.0, SM4RTENANCE, and UNDERPIN.

One example use case could involve machine maintenance. The equipment manufacturer has technical information, the plant has operating data, and the service provider needs selected diagnostic data. A data space can provide a framework for sharing selected information among these participants according to predefined rules.

This creates a broader perspective on data architecture. What matters is no longer only MES-to-ERP communication, but also the ability to define what data a company can share with partners, under what conditions, and what information it will need from them in return.

This is particularly relevant for plants that are already organizing data related to quality, traceability, energy, or machine condition. The way this information is structured may later affect whether it can be used beyond a single system or a single organization.

The ABB Trends 2026/2027 survey shows that energy remains an important area of investment for Polish industrial companies. In the CAWI survey conducted by IQS on behalf of ABB in April and May 2026 among 145 representatives of the industrial sector in Poland, 38% of respondents said they had implemented solutions for real-time energy consumption monitoring. Among investment priorities, 40% identified improving energy efficiency and optimizing costs.

A total energy consumption reading alone does not explain why the result changed. Much more insight comes from combining energy data with production context: a specific machine, line, product, shift, or operation.

This level of detail makes it possible to determine whether a difference is related to production volume, process parameters, equipment condition, or the way work is organized. Energy becomes a useful manufacturing KPI when its consumption can be tied to a specific process or output.

The more machines, systems, and data sources a plant connects, the harder it becomes to run a project within a single department. According to ABB, 90% of surveyed representatives of Polish industry experienced difficulties, to varying degrees, in recruiting and retaining qualified technical staff, while 46% considered this a major or critical challenge.

During implementations, it quickly becomes clear why this matters. Automation engineers understand signal sources and control systems, production teams understand the process, and IT is responsible for areas such as infrastructure and communications. Analytics projects also require ownership of data quality and interpretation.

If these areas are not connected organizationally, even a well-prepared technical project may stall after the first stage. That is why the process owner, data sources, and responsibility for maintaining the solution should be defined before implementation begins.

How can you assess which manufacturing trend applies to your plant?

There is no need to include all seven areas in a single investment plan. A better filter is to use five elements that allow you to quickly assess project readiness:

If any of these elements remains unclear, your project still needs preparation before a technology is selected. In plants where data is distributed across machines, PLCs, SCADA, MES, and ERP systems, it may be better to start with an analysis of the process and available data sources.

FAQ

What are the most important manufacturing industry trends in 2026?

Key manufacturing industry trends include AI in manufacturing processes, IT/OT data integration, robotics, predictive maintenance, data spaces, energy consumption analytics, and technical skills that connect production, automation, and IT. The importance of each trend depends on the process, available data, and the specific problem a plant needs to solve.

How is a data space different from standard systems integration?

Systems integration connects specific applications or data sources. A data space includes infrastructure and governance principles that enable access to and exchange of data among participants, including participants from different organizations. The European Commission is developing this concept through common European data spaces.

Which manufacturing technologies are worth investing in in 2026?

The choice should be driven by the plant’s problem. If failures of critical assets are a major source of losses, condition monitoring may be a good candidate. If production data is fragmented, IT/OT integration may need to come first. AI projects require data that can be connected with a process outcome.

Does every factory need AI?

No. AI requires a task that is suitable for data-driven methods, appropriate input data, and a way to evaluate the outcome. NIST identifies data management, systems integration, and operational trustworthiness as important challenges in implementing AI in smart manufacturing.