How the first AI-generated control cabinet was created

Control cabinet engineering is one of the most demanding disciplines in electrical design. Design, layout, wiring, labeling, and documentation are closely interlinked. In addition, manufacturing data must be generated for wires and harnesses, mounting plates, and cabinet enclosures. In practice, however, these steps are often handled in separate workflows.
The use of artificial intelligence now makes it possible to systematically integrate these steps and, based on existing digital schematics, generate a production-ready control cabinet layout. To achieve this, the AI analyzes existing schematic data and uses it as a foundation to automate and interlink downstream design steps. For the first time, control cabinet design can be automated in a structured way – delivering tangible benefits.

The role of the electrical designer

Even with a high degree of automation, the electrical designer remains a central authority. The AI does not assume engineering responsibility; instead, it supports the execution of defined design logic. The designer defines the framework within which the AI operates and reviews the generated results.

Human expertise remains indispensable, particularly for project-specific requirements, custom solutions, or customer-specific standards. Normative requirements, internal company rules, and individual preferences cannot be fully automated. The designer reviews the AI-generated proposals, makes targeted adjustments, and releases the final design.

Decisions, rules, and standards are stored within the system and made available to the AI for future projects. This ensures that experiential knowledge is reproducible, consistently applied, and no longer dependent on individual experts.

The quality of AI-assisted design depends largely on how well company-specific and customer-specific knowledge is integrated. Users can incorporate their own documents – such as specifications, internal design guidelines, interpretations of standards, or manufacturer approvals – into the AI Copilot. This information is available across projects and can be queried for questions such as permissible technical design or the selection of approved components.

The AI’s responses are based exclusively on these stored documents, ensuring that knowledge is applied consistently and permanently embedded in the system, while the electrical designer retains full professional responsibility. This shift also impacts collaboration within organizations. Design logic is no longer tied to individuals but systematically documented. At the same time, the expertise of experienced designers remains a decisive factor in achieving high-quality and precise design results.

Major advantages of AI in electrical engineering

1. Speed – Design steps that were previously sequential and manual can now be executed in parallel and automated. This significantly reduces engineering lead times while increasing overall consistency. Inconsistencies between schematic, layout, wiring, and labeling are systematically avoided.
2. Consistent data availability for downstream processes – Material lists, terminal plans, wiring information, and labeling data are generated directly from the engineering model. These data can be transferred to manufacturing and documentation processes without manual re-entry. Media breaks are eliminated, and coordination effort is reduced.
3. Automatic translation of project contents – Individual texts or complete documentation can be translated directly within the system into any language. Electrical engineering terminology and project-specific terms are taken into account. This significantly reduces the effort required for multilingual documentation in internationally deployed systems. Translations that previously required external service providers and extensive coordination can now be created, updated, and kept consistent directly within the engineering workflow.
4. Mitigating skilled labor shortages – Less experienced users benefit from structured guidance, while experienced designers can scale their expertise more effectively. As a result, design quality becomes less dependent on individual resources.

Limitations of AI

• Despite its advantages, AI is not a substitute for engineering responsibility. The quality of the results depends heavily on the quality of the input data. Incomplete or inconsistent information directly affects design outcomes, making a clean data foundation essential.
• AI also reaches its limits when project-specific decisions, exceptions, or interpretations of standards are required. Standards allow for degrees of technical judgment that cannot be fully automated.
• In addition, AI operates probabilistically. It produces technically plausible and consistent suggestions that must always be reviewed. Without clear guidelines, defined standards, and responsible approvals, there is a risk of generating solutions that are formally correct but not optimal in practice. Experience shows that AI delivers the greatest value when embedded in a clearly structured design workflow – supporting, accelerating, and structuring processes without making independent decisions.

How the AI-generated control cabinet is shaping the future of electrical engineering

The world’s first AI-generated control cabinet, presented by WSCAD and its partners at SPS 2025, marks a fundamental shift in electrical engineering. Artificial intelligence enables the integration of complex design processes, consistent use of data, and targeted relief for electrical designers. At the same time, professional responsibility clearly remains with humans. The true potential of AI-assisted electrical engineering lies in the combination of automated support and engineering expertise.