For decades, programmable logic controllers (PLCs) have been the foundation of industrial automation: orchestrating assembly lines, chemical plants, and energy systems. But while industrial systems have evolved, PLC programming has largely stayed the same – manual, time-intensive, and heavily reliant on expert knowledge. As a result, engineering teams face mounting backlogs and growing pressure to deliver more with fewer resources.
At the same time, workforce realities are creating a scalability crisis. Veteran engineers are retiring, onboarding new hires takes too long, and even minor updates require reverse-engineering years of legacy logic. In many OEM environments, as much as 80% of a machine application stays the same from project to project. Yet engineers still spend most of their time deciphering existing code rather than implementing the 20% that actually changes. This model can’t keep pace with modern demands.
To move faster, we need a new approach. One that reduces dependency on legacy knowledge, accelerates customization, and makes PLC development more repeatable and intelligent. With the rapid advancement of automation and AI, we can reimagine how control logic is created, understood, and delivered.
Addressing the scalability crisis in PLC engineering
So how do we address these scalability pain points? Instead of hand-coding every sequence, engineers can now use AI copilots: tools that turn a simple description of intent into a working system. Then, AI-assisted engineering and intelligent automation can shoulder the heavy lifting to generate the underlying logic. This new approach bridges skill gaps, reduces boilerplate development, and gives experts the ability to review, validate, and deploy in a fraction of the time.
However, PLC automation has a unique requirement: trust. A generic AI model cannot meet that bar. Early experiments with generic code-generation tools proved that LLMs can generate code, but the output lacked PLC hardware context and reliability safeguards, producing generic boilerplate code that’s unusable in real industrial environments.
Real-time PLC systems demand predictable, auditable logic and cannot rely on probabilistic inference alone. As a result, AI is only viable in automation when it is grounded in domain-specific knowledge, safety, and control standards.
At Schneider Electric, our approach embeds multiple layers of guardrails – validated PLC libraries, coding best practices, hardware-specific constraints, and even customer-level application knowledge – to limit hallucinations and ensure the highest-quality output.
One of the most impactful breakthroughs enabled by AI is not only in code generation itself, but comprehension. By visually mapping program workflows, engineers can quickly understand how legacy logic behaves, identify where customization is required, and modify only what matters – reducing engineering hours and onboarding time for new team members.
Additionally, we apply a Generate → Validate → Train feedback loop. Here, human experts review AI-generated logic, validate references, and feed corrections back into the repository to continuously increase quality over time.
While these advancements slash time and energy, human-in-the-loop supervision remains essential. These are the three principles we use to define and build trustworthy industrial AI:
- Determinism over probability
- Guardrails over open-ended generation
- Oversight over automation
The outcome is not autonomous AI coding. It’s an AI-assisted workflow that preserves human control.
Introducing the PLC Code Generation Copilot
Schneider Electric’s PLC Code Generation Copilot applies these principles in practice. Built inside the EcoStruxure Automation Expert Platform, the copilot generates, explains, and tests PLC code using Schneider’s trusted libraries and multi-layer guardrails. It can reduce development time by up to 30–50% by helping engineers:
- Automatically generate structured, hardware-relevant PLC code
- Understand and document legacy applications
- Create intelligent test cases for validation
- Map and visualize program workflows
- Accelerate onboarding for new team members
Because it’s integrated into EcoStruxure, the copilot can support end-to-end workflows – from logic to HMI to testing – within a unified engineering environment.
The PLC Code Generation Copilot marks a turning point for industrial automation. It proves that generative AI, when grounded in domain expertise and rigorous safety standards, can become an everyday engineering tool. As the industry continues to grapple with a skills gap, the copilot provides a clear path forward with faster development cycles, greater code quality, and a scalable way to transfer knowledge across teams.
If you’re interested in learning more, tune into the AI At Scale episode with Malini Nambiar on how AI is changing industrial engineering.
Add a comment