Who will determine the future of industrial control: algorithms or engineers?

In discussions about industrial automation, artificial intelligence (AI) is often portrayed as a disruptive force replacing traditional engineering roles. PLC engineers, in particular, are often the focus of this discussion. However, this view is overly simplistic and ignores the realities of factories and engineering teams.

The reality is not a role replacement, but a redistribution of responsibilities. While AI is changing how industrial control systems are developed, engineers remain responsible for determining the operating principles and effectiveness of these systems.

Compressing Roles in Industrial Automation

Historically, industrial control has had a clear division of labor: PLCs execute deterministic logic, engineers design and maintain that logic, and software tools function solely as development aids. This model worked well during periods of stable production environments and slow change in system complexity.

Today, industrial systems face frequent configuration changes, stricter efficiency targets, and increasing demands for data integration. In this context, engineering workflows, not hardware, are the primary constraint. The challenge is no longer whether the PLC can control the machine, but whether the engineering team can keep up with the system's evolution.

AI has emerged to address precisely this pressure, not to replace expertise.

Engineering input is no longer proportional to engineering value.

In many automation projects, a significant portion of engineering time is spent on necessary but low-intelligence tasks. Creating repetitive logic, adjusting standard templates, verifying signal mapping, and updating documentation all consume significant resources.

These tasks are structured, pattern-based, and repeatable, making them well suited to AI assistance. By allowing AI tools to generate preliminary control structures or identify inconsistencies early, engineers save time without losing control.

The shift in value is clear: less time is spent building logic, and more time is spent evaluating the system.

AI changes the entry point, not the exit point.

A major benefit of AI-assisted PLC development is accelerating onboarding speed. Even junior engineers can now perform tasks that previously required deep knowledge of vendor-specific environments. However, this does not mean that senior engineers are indispensable; rather, it changes how their expertise is applied.

Senior engineers are increasingly taking on roles such as reviewers, system architects, and risk managers. Their role is shifting from writing code line by line to verifying system behavior in real-world environments. AI accelerates execution, but judgment still requires a human element.

Supplier strategies are guiding change.

The integration of artificial intelligence (AI) into industrial control platforms reflects broader strategic considerations. With PLC hardware differentiation declining, reliability is no longer a competitive focus. Suppliers are therefore focusing on the engineering environment, software ecosystems, and long-term user engagement.

AI strengthens these ecosystems by incorporating accumulated engineering knowledge into tools that can be applied across a variety of projects. Siemens' Industrial Copilot is a prime example, not because of its novelty, but because it formally established the trend of transforming engineering knowledge into reusable software. This shift not only benefits both suppliers and users, but also reframes expectations of experts.

Increased efficiency doesn't mean abdicating responsibility.

AI tools can accelerate engineering workflows, but they don't assume responsibility for the system's outcomes. Industrial automation operates in physical environments, and failures have tangible consequences, such as equipment damage, downtime, and safety hazards.

AI isn't responsible for debugging equipment, responding to on-site anomalies, or making final operational decisions. Engineers remain responsible for every aspect of the system's operation. This fundamental responsibility cannot be replaced by automation.

Productivity Shifts to Systems Thinking

Organizations that have adopted AI-assisted engineering tools report a significant reallocation of work priorities. Teams spend less time on configuration and more time on performance analysis, process improvement, and lifecycle optimization.

Engineers no longer ask, "How can I write this logic faster?" but rather, "How should this system function in the long term?" This change enhances, rather than diminishes, the role of engineers.

A New Competitive Edge

The most valuable automation professionals are no longer defined solely by their programming skills. Today's competitive advantage lies in combining specialized knowledge with the critical application of AI tools.

These engineers understand how to validate AI-generated output, how to apply it to specific industry environments, and how to ensure compliance with standards and safety requirements. AI complements these capabilities, not replaces them.

The Position of AI

Positioning AI and PLC engineers as opposing forces creates a misleading narrative. Industrial automation has always evolved through better tools paired with skilled practitioners.

AI accelerates engineering tasks. Engineers ensure systems function reliably in the real world. One without the other is insufficient.

Control Remains a Human Responsibility

The future of industrial control will not be decided by algorithms alone, nor preserved by tradition. It will be shaped by engineers who understand how to integrate AI without surrendering responsibility.

In this future, AI handles repetition and pattern recognition. Engineers handle judgment, accountability, and innovation. The true protagonist of industrial automation is not a technology or a profession—but the collaboration between them.