Industrial automation is undergoing one of its most significant transitions since the introduction of programmable logic controllers (PLCs). After decades of evolution—from electromechanical panels to digitally integrated architectures — we have entered a phase where artificial intelligence (AI) and automation engineering increasingly operate in synergy. This shift is redefining the way industrial software is developed, documented, tested and supported.
Over the past few years, the role of the automation engineer has expanded considerably. It is no longer sufficient to focus solely on PLC programming. Modern projects demand proficiency in industrial networks, robotics, machine vision, safety systems, Industrial Internet of Things (IIoT), database integration, manufacturing execution systems/enterprise resource planning (MES/ERP) connectivity and multilingual documentation — while maintaining stability, safety and maintainability throughout the system lifecycle. Within this growing complexity, AI has become a valuable enabler.
AI is already assisting engineers in practical, applicable ways across all stages of industrial software development — from totally integrated automation (TIA) Portal to commissioning—supported by real use cases from recent project environments.
Improving technical translation, message standardization and multilingual software
In global automation projects, it is common to manage human-machine interface (HMI) screens, alarms and system messages in multiple languages — often German, English and Portuguese—with varying levels of consistency. When translation and terminology standards are not aligned, misunderstandings, rework and inconsistencies quickly multiply across cells, stations and entire plants.
A representative example involves translating extensive TIA Portal message lists originally created in German into English and Portuguese, while preserving technical accuracy and adherence to existing machine terminology. Beyond translation, AI supported the identification of terms that should intentionally remain unchanged such as ReleaseNOK, RepeatNOK, MachiningEnable and QData.
With AI, it became possible to translate full datasets while maintaining industrial context, recommend terminology standardization across all equipment, generate multilingual technical glossaries for future expansions and ensure row-by-row alignment for direct use in TIA Portal or Excel.
This process improved efficiency significantly, especially when paired with an Excel macro developed with AI assistance. Tasks that previously required hours of manual work could be completed in minutes with greater consistency and fewer formatting deviations.
Smarter and more accessible software documentation
Documentation is often postponed — not due to lack of importance, but lack of time, particularly during commissioning phases. AI is changing this dynamic. Today, AI can support engineers by generating initial software documentation directly from PLC code; converting TIA Portal blocks into structured descriptive text; creating operation, maintenance and troubleshooting guides; and translating ladder logic into clear step-by-step workflows.
For example, from a structured control language (SCL) inspection control function block (FB), AI can generate documentation in English, Portuguese and German, outlining state transitions, alarms and relevant logic. It can also suggest interface improvements that enhance operator clarity.
With this approach, documentation begins alongside software development rather than being treated as a late-stage task.
Converting manuals and technical documents into an AI knowledge base
A transformative advantage for automation engineers is the ability for AI to learn from the documents it is provided. By securely uploading resources such as Siemens, Beckhoff, Rockwell, Cognex and Keyence manuals; industry standards and technical PDFs; datasheets, input/output (I/O) lists, sequences and diagnostics; and customer specifications and requirement documents, AI becomes a contextualized knowledge base capable of answering specific questions about any chapter; creating summaries, tables or workflow references; providing application-focused examples; generating FAQs for maintenance and operator teams; and reducing time spent searching for information
Examples include:
- “List all mandatory requirements for automatic mode according to the standard.”
- “Explain what @4%6d@ means in TIA Portal supervision messages.”
- “Which Cognex manual chapter describes discrete output latching and how to interface it with the PLC?” This capability transforms large sets of documentation into accessible, actionable knowledge — something that previously demanded extensive reading and cross-checking.
Naturally, this should always comply with internal policies, NDAs and customer confidentiality requirements. When used responsibly, AI becomes a reliable “reference expert” capable of retaining thousands of pages of context.
AI-supported software development and code standardization
Standardizing software across teams remains a major challenge in industrial automation. Differences in programming style often lead to inconsistencies and increase the effort required for support and future modifications.
AI can act as an informed code reviewer by assisting with standardizing naming conventions, UDTs, FBs and interfaces; identifying redundant logic and proposing modularization; comparing code to software standards or templates; and generating commented code with built-in diagnostic support.
Beyond improving clarity, AI reinforces best practices such as edge detection, state validation and diagnostic logging — core elements of robust and maintainable industrial software.
Accelerating testing, simulation, FAT and SAT
Many functional issues are only detected during factory acceptance test (FAT) or, more critically, during site acceptance test (SAT) with the customer present. AI can proactively support these phases by generating structured test lists (state, safety, fault and routine-based); I/O simulation scenarios; state-transition test sequences, including PackML-style flows; and requirement-to-implementation cross-checks.
It can also identify gaps such as “Manual mode does not safely return to Standstill if the E-Stop is released during operation. Condition X is recommended.” This leads to fewer corrections during onsite phases and contributes to more reliable project delivery.
AI for commissioning and onsite support
During commissioning, rapid access to precise information is essential. Engineers often need immediate clarity on parameters, diagnostics or configuration steps. AI can provide direct references to applicable manual chapters, step-by-step instructions, including screenshots, likely root causes and recommended corrective actions and troubleshooting checklists tailored to the equipment. This enables faster diagnostics and contributes to a more efficient and less stressful commissioning experience.
Looking ahead
The industrial automation sector is entering a new maturity phase. Just as PLCs reshaped factory controls in the 1980s and early 1990s, and global software standards elevated the industry in the 2000s, AI is now influencing how industrial software is developed and delivered.
AI is not a replacement for automation engineers. It is a tool that enhances their capabilities. By reducing manual effort, increasing standardization and improving accuracy, AI allows engineers to focus on innovation, reliability, safety and user experience. Professionals and organizations that begin integrating AI into daily workflows — not as a trend, but as a structured practice — will position themselves ahead in this evolving landscape.
AI is becoming a strategic differentiator in industrial automation, and those who learn to apply it effectively will help shape the next generation of industrial projects.
