AI and Machine Learning Open New Possibilities for Industrial Automation
Artificial intelligence (AI) and machine learning (ML) are changing how engineers design, operate, and maintain industrial automation systems. On October 20, 2026, Mouser Electronics highlighted its AI and ML resource center, which brings together technical articles, engineering guides, and industry insights to help professionals understand these emerging technologies.
As manufacturing systems become more connected and production requirements continue to change, traditional automation methods face new challenges. AI and ML offer additional ways to improve operational flexibility, reduce repetitive engineering tasks, and support faster decision-making. However, their practical value depends on how effectively engineers integrate these technologies with existing control architectures and production processes.
AI-Assisted Engineering Simplifies System Configuration
Industrial automation projects often require engineers to configure robot cells, adjust control logic, and validate system behavior whenever production requirements change. AI-assisted engineering can reduce some of this manual work by helping teams adapt configurations and explore different system designs more efficiently.
Tools such as OpenUSD also support the development and validation of industrial environments before physical deployment. By creating digital representations of equipment and production systems, engineers can evaluate layouts, test interactions, and identify potential issues earlier in the design process.
From an engineering perspective, the greatest benefit is not simply faster configuration. It is the opportunity to identify design problems before they affect commissioning schedules or production operations. Nevertheless, engineers must still verify system behavior against real equipment constraints, safety requirements, and established control standards.
Intelligent Machine Learning Improves Automation Adaptability
Machine learning enables automation systems to identify patterns in operational data and respond to changing conditions. These capabilities can support equipment monitoring, process optimization, anomaly detection, and selected reconfiguration tasks.
For example, an intelligent system may analyze operating data to identify changes in machine behavior or help determine when a production line requires adjustment. Such capabilities can reduce the time engineers spend reviewing large volumes of information and help maintenance teams investigate potential problems earlier.
However, adaptive behavior must remain within clearly defined operating limits. In industrial environments, a system that changes its configuration without adequate validation can introduce new risks. Engineers should therefore establish approved operating boundaries, verification procedures, and human oversight before deploying AI-driven adjustments.
Large Language Models Support PLC Engineering Workflows
Large language models (LLMs) are creating new opportunities for engineers who work with programmable logic controllers (PLCs), industrial documentation, and complex technical datasets. These tools can help explain unfamiliar code, summarize technical manuals, draft program structures, and assist with troubleshooting.
For engineering teams, the main advantage is improved access to technical information. Engineers can spend less time searching through extensive documentation and more time evaluating system behavior and solving operational problems.
Nevertheless, AI-generated PLC code should never be treated as production-ready simply because it appears logically correct. Incorrect instructions, overlooked interlocks, and misunderstandings of equipment behavior can lead to serious operational consequences. Every proposed change should undergo code review, simulation where appropriate, functional testing, and the required commissioning checks before deployment.
Physical AI Brings Intelligence Into Machines
Physical AI extends artificial intelligence beyond software applications by integrating intelligent perception and decision-making into robots and other physical machines. Through sensors, computing hardware, and AI models, these systems can interpret environmental information and adjust selected actions in response to changing conditions.
This approach is particularly relevant to autonomous mobile robots (AMRs), automated guided vehicles (AGVs), machine vision systems, and flexible manufacturing cells. Unlike conventional automation that relies heavily on predefined sequences, physical AI can help machines handle certain variations in their surroundings without requiring engineers to program every possible scenario.
Even so, greater adaptability does not eliminate the need for predictable control behavior. Industrial deployments must account for response times, sensor reliability, environmental disturbances, and safe operating limits. AI perception should complement established safety functions rather than replace independently validated protective mechanisms.
Industrial Hardware Supports AI and ML Applications
The growing adoption of industrial AI depends on suitable computing platforms, controllers, imaging devices, and communication interfaces. Mouser highlights several product categories that illustrate how hardware suppliers are supporting these applications.
RevPi Connect 5 industrial box PCs by KUNBUS
Based on the Raspberry Pi Compute Module 5, the RevPi Connect 5 combines industrial computing capabilities with a modular design. Its listed configuration includes 32 GB of eMMC storage, 4 GB or 8 GB of RAM, and optional WLAN and Bluetooth connectivity. These features make it a candidate for edge applications such as predictive maintenance and visual inspection, subject to the requirements of the specific installation.
Opta micro PLC by Arduino
The Opta combines PLC functionality with the flexibility of the Arduino development ecosystem. Its dual-core Arm Cortex processor supports real-time control and monitoring, while connectivity, security, and remote management features support industrial and building automation applications. Engineers should evaluate its I/O requirements, communication compatibility, and environmental ratings before selecting it for a particular control task.
Acuros CQD SWIR cameras by onsemi
These shortwave infrared cameras support imaging across the visible, near-infrared, and shortwave infrared regions. High frame rates, global shutter operation, and dynamic range of up to 70 dB make the devices relevant to demanding machine vision and industrial inspection applications. Potential uses include fill-level monitoring, automotive systems, and specialized inspection tasks where conventional visible-light imaging may not provide sufficient information.
AFE-R360DVK vision AI robotics development kits by Advantech
Powered by an Intel Core Ultra 125H processor, these development kits deliver up to 32 TOPS of AI performance. Their listed interfaces include three LAN ports, seven USB ports, four high-speed COM ports, and two CAN FD interfaces. This combination supports development work involving autonomous mobile robots, automated guided vehicles, and sensor-intensive robotic systems.
These examples illustrate an important engineering principle: AI performance depends on more than the processor or AI model alone. Memory capacity, sensor quality, network latency, software compatibility, thermal management, and long-term hardware availability can all influence the success of an industrial deployment.
Engineering Considerations Before Adopting Industrial AI
Although AI and ML offer significant opportunities, successful implementation requires a clear understanding of the application and its operating environment. Engineers should assess several factors before introducing intelligent functions into an existing automation system.
- Define measurable objectives: Identify whether the project aims to reduce downtime, improve inspection accuracy, shorten engineering time, or increase production flexibility.
- Check system compatibility: Confirm that computing hardware, PLCs, sensors, industrial networks, and software interfaces can exchange the required data.
- Protect control and safety functions: Keep safety-critical operations within appropriately validated architectures and ensure AI cannot bypass established protective measures.
- Evaluate data quality: Incomplete, inconsistent, or unrepresentative data can undermine model performance and produce misleading results.
- Plan maintenance and updates: Establish procedures for software updates, model evaluation, cybersecurity, fault recovery, and hardware replacement.
- Measure results in real operating conditions: Validate performance through controlled trials before expanding the application across a production facility.
In my view, industrial AI should be introduced where it solves a clearly defined engineering problem rather than added simply because the technology is available. A focused predictive maintenance project or machine vision application can provide a more useful starting point than a large-scale transformation without measurable targets.
The Future of Industrial Automation Will Combine AI With Engineering Expertise
AI and ML are expanding the capabilities of industrial automation, from engineering assistance and intelligent monitoring to adaptive robotics and edge computing. Resource centers that bring together technical guidance and hardware information can help engineers evaluate these developments and identify practical applications.
However, the long-term success of industrial AI will depend on more than increasingly powerful models. Integration with existing control systems, dependable data, cybersecurity, functional safety, and maintainability will remain central to deployment decisions.
The most effective approach is to combine AI's ability to process information and recognize patterns with engineers' understanding of equipment, processes, and operational risks. This balance can help manufacturers build automation systems that are more adaptable while maintaining the control, predictability, and accountability required in industrial environments.