Mechanical And Plant Engineering How AI Agents are Changing the Industry

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Experts anticipate that AI agents—AI-based systems capable of making independent decisions—will be an integral part of industrial processes by 2030. Companies that do not view AI agents as isolated solutions but instead integrate them early and firmly into their operations will secure their competitiveness in the long term.

According to the Deloitte Technology, Media and Telecom Predictions 2025, by 2028 at least 15 percent of decisions in everyday work will be made by AI agents. This outlook shows the enormous potential of agent-based AI for manufacturing and industry. The industry is better suited than others to integrate this technology into everyday work due to its nature. This is because the high complexity of work processes and the dynamic production environment provide ideal conditions for the effective use of AI agents. They can independently evaluate large amounts of data in real-time, reduce errors, and increase production efficiency.

The Benefits of AI Agents for the Industry

AI agents are systems based on artificial intelligence (AI) that are capable of making decisions independently. This allows them to operate without human control or interaction, setting them apart from traditional automation solutions. They make decisions based on a variety of data, which they analyze and evaluate in real-time. They can identify patterns and suggest solutions beyond predefined rules.

While early AI systems mainly served as assistance solutions providing recommendations or analyses, AI agents independently take on certain tasks. The employee's oversight is only needed for final control, while the detailed steps are taken over by the AI. Agent-based AI is thus the natural evolution of traditional AI applications, which no longer just assist humans but actively collaborate with them.

AI Agents in Practice: These are the Use Cases

Generative AI has long been used in the industry for processes such as process optimization. In contrast, agent-based AI could take on an even more comprehensive role in the future and elevate optimization processes to a whole new level. Based on machine data, AI agents independently monitor processes and make subsequent decisions. However, in many production companies, these processes still occur manually: employees detect the disturbance and must identify the source of the error to take appropriate corrective measures. These processes are time-consuming and require specialists.

By using AI agents, these lengthy processes can run much more efficiently. By continuously accessing central sensor data from equipment and machines, AI agents immediately detect deviations from the standard values. This allows them not only to analyze the current status of a machine but also to make predictions about when a failure is likely by evaluating historical data and taking appropriate measures. However, the capabilities of agent-based AI go far beyond mere statistical analyses. While conventional maintenance tools identify warning signals or use statistical models, AI agents can analyze a variety of data sources in real-time, allowing them to recognize patterns indicating future problems.

If entire data networks are made available to AI agents across the entire production, they can analyze inventories, customer orders, or delivery times to avoid bottlenecks, for example, and to seamlessly coordinate the individual steps within the supply chain.

Relieving Humans of Monotonous, Data-Intensive Processes

The goal of using AI agents is to relieve human experts of tedious, monotonous, and data-intensive processes. These processes are particularly error-prone and time-consuming. AI agents remedy this and perform these tasks with greater speed and higher precision than humans. However, this is only possible if large amounts of data can be securely provided to them. Based on this data, AI agents make their action decisions. The larger the data network, the more precisely the artificial intelligence operates.

Through real-time data analysis, the agents also serve as an important control mechanism: Special compliance agents check whether all decisions are traceably recorded, thereby ensuring that all regulatory requirements within the company are met. By meticulously documenting individual actions and decisions, AI agents enable full traceability of processes, which increases transparency in companies.

At the same time, user-friendliness is crucial for the widespread use of these technologies. Platforms like ServiceNow's AI Agent Studio enable companies to design their own AI agents according to the individual needs of each department. This does not require advanced programming skills, as the agents are adapted to specific requirements via chat instructions.

Between Potential And Practice: Challenges in the Industry

The most important prerequisite for the successful use of AI agents in the industry is the availability of comprehensive data. Companies must ensure that they provide the necessary data strategy and infrastructure.

Additionally, when implementing AI systems, it must be considered that acceptance among the workforce needs to be promoted. Employees should be involved in the restructuring process from the start and supported in dealing with AI agents through workshops and training. Transparency and a clear distribution of tasks between humans and machines are crucial factors.

Regulations such as data protection policies, ethical aspects, and compliance rules must also be considered when integrating AI agents into daily work routines. The decision-making powers of these systems can be individually adjusted and should be regulated, especially in safety-critical areas such as pharmaceuticals and medical technology.

Conclusion

Experts anticipate that AI agents will be an integral part of industrial processes by 2030. This will require the ongoing improvement and development of AI systems. The more precise and faster they work, the greater their benefit to companies, which in turn boosts their acceptance among the workforce. Companies that do not view AI agents as isolated solutions but instead integrate them early and firmly into their operations will secure their competitiveness in the long term. Through intelligently networked systems, resources can be used more effectively, and processes can be managed more intelligently. This enables companies to respond directly to market demands and facilitate an optimal synergy between humans and technology.

About the author

Jürgen Schön has more than 30 years of experience in the manufacturing industry—as a consultant, "technology ambassador," and expert in digital transformation.

Since 2021, he has been working as Senior Director Manufacturing Industry at ServiceNow, responsible for the European go-to-market for digital workflow solutions in the industry.

His passion lies in optimizing business processes along the entire value chain and developing new business models through consistent digital transformation.

With in-depth knowledge of current challenges and trends in the manufacturing industry, he combines strategic foresight with a "hands-on" mentality—particularly in the areas of production and supply chain management. Topics such as AI, Generative AI, and Agentic AI broaden his discussion spectrum.

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