Artificial intelligence has long been part of everyday business life across all kinds of industries and other sectors. Many people first think of chatbots or specialized AI-powered software for marketing automation and back-office processes. In reality, however, it is industrial manufacturing and related fields that have experienced a particular AI boom in recent years. According to a survey by Bitkom, as early as 2025, a whopping 42 percent of German industrial companies will be using artificial intelligence in production.
The factors of cost-effectiveness, efficiency, and availability are key drivers here. Today, more than ever, there is a demand for processes that detect deviations early, react quickly, and continuously improve (ideally autonomously). AI makes exactly that possible. When properly integrated, it increases efficiency, productivity, flexibility, and process reliability—not in isolation, but as the result of smarter, data-driven control. In this article, we take a look at the industries where AI will fully demonstrate its strengths by 2026.
The Significance of AI for Industry Today
AI has transformed industry. This transformation did not happen overnight; it has been underway for several years, but is now accelerating noticeably. This is primarily because industrial systems now generate far more data than before, which can be leveraged across the board in conjunction with artificial intelligence. Machines, sensors, warehouse technology, testing systems, and software continuously generate information. AI makes it possible to analyze these data sets in a way that leads to better decisions in day-to-day operations.
For industry, this presents an opportunity for greater dynamism. Traditional software processes data according to fixed rules. Artificial intelligence, on the other hand, recognizes patterns, evaluates deviations, and helps learn from experience. This is particularly useful where processes are complex, many variables come into play, and the situation can change rapidly. In manufacturing, this is almost always the case.
That is why the importance of AI in industry is growing so rapidly. It is not just about making processes a little faster. What is far more interesting is that systems with AI become more robust, adaptable, and, in some respects, more autonomous. For example, a machine will not only report a malfunction but will recognize early on that a problem is looming.
This is relevant for companies for several reasons. On the one hand, demands for efficiency and quality are rising. On the other hand, uncertainties are increasing. Supply chains are under pressure, energy costs are rising, skilled workers are in short supply, and customers still expect fast, reliable results. Anyone operating in this situation solely with fixed technical routines will reach their limits more quickly. AI helps to remain capable of acting under changing conditions.
There is another point that is often underestimated. Industry 4.0 is not just about connectivity. Connectivity alone does not bring progress if no one uses the data effectively. It is AI that transforms connected machines, systems, and processes into a learning ecosystem. It recognizes connections between manufacturing, maintenance, quality, material flow, and planning. As a result, technological decisions are no longer merely reactive but proactive. AI connects many fields. The result is a system that is easier to control overall.
At the same time, new opportunities for products, services, and business models arise. A manufacturer may no longer sell just a machine, but also data-driven maintenance services or intelligent optimization as a service.
The role of humans remains central in all of this. AI does not simply replace personnel across the board in industry. Often, it merely shifts tasks. Employees need to perform routine manual checks less frequently and can focus more on monitoring, evaluation, and improvement. This relieves teams in areas where monotonous or time-critical tasks dominate. Especially in sectors with tight deadlines and limited personnel resources, this is a real game-changer.
Ultimately, it’s not just about technology. It’s about creating competitiveness. Industrial companies must react faster, plan more efficiently, avoid downtime, and make better use of their resources. This is where the great value of artificial intelligence in industry lies. It doesn’t just help with fine-tuning; it transforms how production, control, and value creation are conceived.
In these industries, AI will fully demonstrate its strengths by 2026
In industry today, AI is no longer an abstract topic of the future, but a real tool for very concrete tasks. Below, we present key processes and the areas currently benefiting the most.
It is important to note that almost never does just a single area benefit. In many industries, multiple effects interplay. Better maintenance reduces downtime, more precise quality control reduces scrap, and smarter planning saves materials, energy, and time.
AI-powered robotics in the automotive industry and mechanical engineering
Industrial robotics clearly demonstrates how significantly AI differs from traditional automation. Conventional robots perform predefined movements. AI-powered systems, on the other hand, can analyze images, distinguish between objects, react to changing positions, and adapt processes when conditions change. This makes them significantly more flexible in practice.
This is particularly exciting in the automotive industry. There, high production volumes, tight cycle times, and complex manufacturing steps converge. AI helps robots grasp components more precisely, adapt assembly processes to changing conditions, and detect errors earlier. This also creates an advantage in mechanical engineering, for example with smaller batch sizes or varying product variants, where rigid automation offers little benefit.
The figures from the Bitkom survey mentioned at the beginning once again show that this topic is gaining momentum. According to the survey, 19 percent of the relevant industrial companies already use AI in robotics. Another 46 percent are planning corresponding applications or are at least discussing them.
Predictive Maintenance in Chemicals, Metals, Energy, and Manufacturing
A breakdown rarely costs just a few minutes in industry, and almost never just a few euros. Often, an entire chain of events depends on it. Machines come to a standstill, delivery deadlines come under pressure, staff wait around, materials pile up, and costs rise rapidly. That is why predictive maintenance—i.e., the data-driven prediction of potential equipment failures or corresponding proactive maintenance—is one of the most important fields of AI application.
The principle is easy to explain: sensors provide data on a machine’s temperature, vibration, pressure, runtime, and/or power consumption. The AI analyzes this information and detects whether a defect is developing. This allows you to schedule maintenance not only after something breaks, but to intervene in advance and, if necessary, on a regular basis.
This is particularly relevant in the chemical industry, metal processing, energy supply, and traditional manufacturing facilities with (often) expensive and/or heavily utilized equipment. In these sectors, any unplanned downtime has immediate consequences. In practice, AI is already being used relatively frequently for such analytical applications. As Bitkom found, 32 percent of German industrial companies use such automated monitoring systems. Another 42 percent are in the planning stages for this.
Quality Control in the Electronics, Pharmaceutical, and Food Industries
For a long time, quality assurance was heavily reliant on random sampling, visual inspections, and adherence to fixed control rules. AI is transforming this field dramatically. Today, camera systems and sensors scan surfaces, dimensions, shapes, colors, or movement patterns completely automatically at high speed. AI detects relevant defects that the human eye easily overlooks—such as minimal deviations or unusual patterns—and directly identifies potential process issues.
This is particularly valuable for the electronics industry, where defects are often very small but can quickly have major consequences. The same applies to the pharmaceutical industry, where documentation and reproducibility are also key. In the food industry, on the other hand, the focus is on consistent quality and minimizing waste. AI supports these efforts not only during final inspection but right in the middle of the ongoing process—around the clock. This is crucial because it makes problems visible as soon as they arise, rather than only at the end when they may already be extremely costly.
Optimizing Resource and Material Usage in the Raw Materials, Metal, and Plastics Industries
Rising raw material prices, material shortages, and high cost pressure make the efficient use of resources a key issue. AI can help here because it identifies consumption patterns and reveals optimization potential that often remains hidden in large-scale production environments. However, it is not just about using fewer materials, but also about better sequencing, less waste, and more sensible use of existing capacities.
This is particularly relevant in the raw materials industry, metal processing, and the plastics sector. In these sectors, even a small efficiency gain can often translate into significant savings. For example, when AI adjusts production parameters to minimize material loss or reduce waste, it directly impacts profitability. Benefits also arise in areas such as formulations, mixing ratios, or raw material usage, provided sufficient quality data is available.
For many companies, this represents very tangible added value, because it is felt not only in the process itself, but also in purchasing, cost calculation, and margins.
Digital Twins in Plant Engineering, Aviation, and Complex Manufacturing
In industry, a digital twin is usually a digital representation of a machine, a plant, or a process. It enables simulations, analyses, and monitoring to optimize the performance of its real-world counterpart. Such models are continuously fed with real-world data. AI then helps to identify correlations, run through scenarios, and better evaluate changes.
This is particularly exciting in plant engineering, aviation, and complex manufacturing environments. In these fields, processes are often very expensive, technically demanding, and difficult to test during ongoing operations. With digital twins, you can first test planned adjustments virtually before implementing them in real-world production. This reduces risks and makes decisions more robust.
This also provides an advantage in maintenance, capacity planning, or process improvement. Instead of relying solely on historical data, various developments can be simulated. This gives companies greater confidence when it comes to making changes to complex systems.
Supply Chain Management and Autonomous Intralogistics in Retail, Consumer Goods Production, and Industrial Manufacturing
Supply chains are prone to disruptions, and warehouse processes often need to operate at high speeds. AI can help in both areas because it consolidates data from demand, inventory, transportation, production planning, and order status. This allows potential bottlenecks to be identified earlier, inventory to be better managed, and internal transport to be organized more intelligently.
In supply chain management, this is particularly relevant for consumer goods manufacturers, industrial SMEs, and networked production facilities. Those who need to coordinate many parts, suppliers, and fluctuating demand situations benefit significantly from better forecasts and faster responses.
In intralogistics, the focus is on autonomous or semi-autonomous systems in warehouses and production. Driverless transport systems, intelligent warehouse technology, or AI-supported route planning accelerate material flows and reduce the workload on employees. The benefits are greatest where goods movements are tightly scheduled and delays immediately affect other areas.
Saving Energy Through AI in All Energy-Intensive Industries
Energy is no longer just a minor expense. In many industries, it is increasingly becoming a strategic factor. This is particularly true for chemicals, metals, glass, paper, and other energy-intensive sectors. AI helps here by better analyzing consumption, identifying peak loads, and controlling systems so that energy is used more efficiently.
The particular value lies in the fact that smart systems do not just look at individual measurement values, but recognize patterns over longer periods of time. This allows loads to be shifted, inefficient conditions to be uncovered, and consumption and production requirements to be better aligned.
This field is still in the early stages of development in many German industrial companies. According to Bitkom, only 7 percent currently use AI in energy management. At the same time, there is great potential here for many. Around 64 percent are planning corresponding measures.
Workplace safety and strategy and planning support in many industries
Not every benefit of AI is immediately measurable in terms of output or scrap. A less obvious but all the more important area lies in workplace safety. Here, intelligent image processing and assistance systems can make risky situations visible earlier, detect unusual movements, or provide support during safety-critical processes. This is particularly relevant in industries with heavy machinery, high traffic volumes, or complex and high-risk work environments.
When it comes to benefits that tend to operate in the background, we must not forget strategic and operational planning. AI helps companies adjust production plans, assess bottlenecks more realistically, and make decisions based on a broad data foundation. This isn’t just relevant for large corporations. Mid-sized industrial companies also gain greater visibility when demand, staff availability, material status, and machine utilization are constantly changing. Feel free to read our article on common mistakes in AI projects at mid-sized companies.
The real added value lies in the fact that planning becomes less rigid. Companies can react sooner and subsequently manage their processes (again with AI support) with greater clarity.
Conclusion
AI and industry will be inseparably linked by 2026 and will likely continue to grow closer together. This is evident not only in individual pilot projects but also in the way modern production is conceived today. Intelligent systems can combine data from machines, planning, quality assurance, maintenance, warehouse management, and energy consumption into a larger whole. This results in processes that react faster, waste fewer resources, support one another, and function more reliably overall.
Almost every industry can benefit from this. Some sectors do so primarily through improved quality controls. Others derive the greatest benefit from predictive maintenance, intelligent robotics, or optimized intralogistics. However, the integration of multiple applications is particularly exciting. For example, a plant can use AI to monitor machines, plan maintenance proactively, better control material flows in the warehouse, and simultaneously adjust energy consumption to actual capacity utilization. The result is fewer downtimes, less waste, better planning, and lower costs.
These possibilities ensure that artificial intelligence is more than just another digital trend in industry. It forms a key link for sustainable processes in Industry 4.0. For further reading, we recommend the article “Introducing AI in the Enterprise”.
FAQ
Where is AI used in industry?
By 2026, artificial intelligence will be used particularly frequently in production workflows, maintenance procedures, quality control, resource optimization, warehouse technology, and planning. The more data-intensive and complex a process is, the more valuable the integration of such smart tools becomes. It is by no means only traditional manufacturing that benefits from this. The chemical, pharmaceutical, and food industries, as well as mechanical engineering, automotive production, and logistics, are already using AI for very specific tasks.
What advantages does AI offer in industry?
The biggest advantage is that companies can react more quickly and precisely to real-world conditions. AI recognizes patterns in data, detects deviations early, and helps to better control processes. This often reduces downtime, scrap, and unnecessary resource consumption. Added to this are greater efficiency, better quality, higher equipment availability, and more flexibility in production.
Is AI important for industry?
Yes, and not just at the process level. AI is gaining importance for industry because it helps companies better align their entire business structure. Those who can plan faster, deliver more reliably, and use scarce resources more effectively strengthen their market position. There is also a second point to consider. Many industrial companies are under pressure because demands for speed, adaptability, and cost-effectiveness are rising. AI helps ensure that these factors are not viewed in isolation, but rather that production, logistics, quality, energy, and planning are more closely integrated. This makes it a key component for future viability, innovative strength, and resilient business models in the long term.
Posted by:
Jens is the co-founder of bakedwith, a boutique consultancy firm specialising in smart automation and AI. He helps medium-sized companies and corporations to optimise processes, reduce manual work and achieve growth through efficient workflows. Previously, he was Growth Lead at OMR and worked as a freelancer, helping numerous companies optimise their CRM and automation systems. Jens is passionate about combining growth and efficiency to enable teams to focus on what matters most.
