Why CAD Isn’t Enough Anymore: The Rise Of Engineering Intelligence

What Is the Real Bottleneck in Electrical Engineering?

Ask most people where engineering slows down and they will point to the software. Zein disagrees. In his view, the limiting factor is not CAD or any other tool, but the number of experienced engineers available to do the work. “The bottleneck is engineering capacity and every machine builder I speak with tells me the same thing, we don’t have enough experienced engineers,” he says.
Everything else can be scaled with a purchase order. “You can buy another CAD licence, you can buy more computing power, it’s all no problem to do that, but you cannot buy experienced engineering talent overnight.” This, Zein explains, is why AI matters so much to electrical engineers: “It doesn’t just make software smarter, it has the potential to multiply engineering capacity itself.”

Why an AI Copilot Is Only the First Step

Are AI copilots not already doing exactly this? Only partly, says Zein. He describes them as a first step in the right direction, but one with a clear limit: “Many co-pilots help you operate the CAD software.” They draw on help files and answer questions about how to perform a task in the program.
WSCAD knows this territory first-hand. Two years ago, the company introduced what Zein calls the world’s first AI-powered electrical CAD software, with a copilot that helps engineers find information, automate repetitive tasks and work significantly faster. Yet building it revealed something important. “The engineer was still doing all the engineering and the co-pilot was making engineering faster but it wasn’t creating any engineering capacity.” That realisation, Zein says, was the moment WSCAD decided it had to change its thinking.

The Principle That Has Not Changed in 40 Years

According to Zein, one principle has shaped electrical engineering software for the past 40 years: “the engineer designs in their head and the CAD software documents.” The industry has optimised CAD systems and documentation accordingly, from drawings and numbering to lists and reports.
“But documentation was never the real bottleneck. The real bottleneck was always the human capacity to engineer.” Once WSCAD understood this, Zein says, a completely different question emerged: “What if software could actually participate in engineering itself?”

What Is Engineering Intelligence?

Zein defines engineering intelligence as a new software category. Instead of asking the engineer to create every connection, every circuit or every geometry manually, the engineer specifies the intent: the requirements, the design constraints, applicable standards and customer preferences. The system then performs a significant part of the engineering work itself and finally generates all the documentation automatically inside the CAD system.
That, Zein stresses, is a fundamentally different approach from an AI copilot. “Because here the AI is no longer helping you, the engineer, operate the software. It is helping you engineer.”

What Does Engineering Intelligence Look Like in Practice?

To make the idea tangible, Zein uses the example of a packaging machine. Today, an engineer starts with an empty project or a general template. Tomorrow, he believes, the engineer will simply describe what is needed:

• 250 bottles per minute
• Allen-Bradley hardware
• UL standard
• Redundant safety circuits
• A maximum cabinet width of 50 inches
• Additional customer requirements, supplied as a PDF or Word document

The engineering intelligence software understands those requirements, proposes an architecture, selects components, generates schematics, creates the cabinet layout and the bill of material, and finally prepares the documentation in the CAD system.
Responsibility stays where it belongs. “The engineer, still in charge, evaluates the results, modifies it where necessary, approves it or not.” What changes is the nature of the work: “instead of drawing everything manually, the job somehow moves towards orchestrating the engineering process itself. And the result is a 10X improvement in engineering productivity.”

Will AI Replace Electrical Engineers?

The fear of AI as a job killer is familiar. Zein’s answer is unequivocal: “Absolutely not. Actually, I believe quite the opposite, because engineering is becoming more and more complex every year, literally. We don’t need fewer engineers, we need more engineering.”
The real problem, he says, is that experienced engineers are becoming increasingly difficult to find. Engineering intelligence allows one engineer to accomplish much more: “instead of replacing expertise, it amplifies it by making knowledge scalable throughout the company through hundreds of projects.”
In Zein’s view, this also changes the job for the better. Engineers move from drawing individual wires to defining architectures, and from routine work in the CAD system to evaluating solutions and making engineering decisions. “In the end, that’s why you became an engineer.”

What Happens to CAD?

If engineering intelligence takes on part of the engineering, what becomes of CAD? Zein does not see it disappearing. Instead, its role shifts. “Today’s CAD systems are the central hub, usually of engineering departments, because this is where products are documented.” The product lives in the engineer’s head and, once documented, in the CAD system.
Engineering intelligence, Zein explains, becomes a layer above the CAD system. It helps the engineer design the product and then triggers the CAD system to produce the documentation. “So the CAD still remains an essential part of the workflow. But is no longer the place where engineering happens.”

Why Engineering Intelligence Needs a New Architecture

WSCAD’s first instinct, Zein admits, was a familiar one: “initially we thought we’d just add more AI features to an existing CAD system.” That might make documentation faster, but it would not touch the fundamental problem. Addressing engineering capacity required a different architecture.
“What we chose is an AI native layer because you need a software that understands engineering intent.” Such a layer understands what a packaging machine means in technical terms, what requirements mean and where in the generation of the product they should apply and be checked. It understands constraints, standards and company knowledge, and can learn from previous projects. To do so, it orchestrates multiple AI models and agents, accesses enterprise knowledge and continuously reasons about engineering decisions.
Zein points to one particularly demanding aspect: a decision affects everything that follows it, but “an engineering decision might also affect a previously made decision. And that’s a tricky part.” The conclusion was clear. “So we realised we were not just building another CAD release. We’re building an entirely new engineering platform and you need a totally different concept for that.”
That platform is ELECTRIX OS. “So ELECTRIX OS is exactly the product that we’ve engineered to perform engineering intelligence,” Zein says. “We didn’t start with the idea of building an operating system.” The starting point was the conviction that tomorrow’s software must participate in engineering, at least partially. ELECTRIX OS is the engineering intelligence layer that understands intent and reasons about engineering decisions. “It sits above the CAD system, interfaces with the CAD system,” because the CAD system remains the place where the finished product is documented.

Why Engineering Decisions Need to Be Traceable

One capability Zein highlights is that ELECTRIX OS documents its reasoning. The reason is practical: “you might come to a point where you say, oh, the machine’s broken down, who’s at fault, who made that decision? So you need to go back and be able to track why a specific decision was made.”
This also addresses a problem that exists in engineering departments today. Ask an engineer about a project they completed five years ago, Zein notes, and they often have no idea why a particular component was chosen, even though the documentation exists. A system that records the reasoning behind each decision keeps that knowledge available.

How Should Companies Get Started with AI in Engineering?

For companies preparing for this shift, Zein’s advice is pragmatic: “My general advice on dealing with AI is start small.” Trying to replace an entire engineering process in one step will not work, he warns.
Instead, he recommends a gradual path:

• Identify repetitive engineering tasks and automate them
• Let engineers gain experience with AI tools and see the productivity gains for themselves
• Then move step by step towards higher value engineering automation

The payoff, in Zein’s view, is strategic: “And I think companies that learn how to combine human expertise with AI are generally the ones who create competitive advantage.”

What Did the Industry Report Reveal?

WSCAD’s industry report The State of Electrical Engineering 2026 surveyed more than 1,200 electrical engineers. What surprised Zein most was how universal the problem is. Whether in Europe, the United States or Asia, whether at a machine builder or a panel builder, respondents described the same challenges: “Too much repetitive work, too little engineering capacity, too little time for innovation.”
“It’s an industry where people have tremendous time pressure. We don’t have time to innovate because we’re busy being busy.” For Zein, the findings confirmed WSCAD’s belief that “the industry doesn’t simply need faster CAD software,” but a fundamentally new engineering paradigm. He sees the report’s findings as applying to electrical engineers worldwide.

What Will Electrical Engineering Look Like in Five to Ten Years?

Looking five to ten years ahead, Zein expects an engineering intelligence layer to become standard, and “engineers won’t be busy fiddling with CAD systems anymore.” They will start with intent: describing the machine, specifying performance standards, setting constraints and requirements. The engineering intelligence layer will then generate the first complete engineering solution.
The engineer becomes the architect and the reviewer, challenging the system, making and changing decisions, “so much like an orchestra conductor who emerged from a one-man band.” Engineering, in Zein’s words, shifts “from a manual creation to you could call it an intelligent orchestration.”
He places this development in a longer historical line. Every industrial revolution has multiplied human capability: the steam engine multiplied human power, automation multiplied manufacturing productivity, and CAD multiplied engineering productivity. “And I think engineering intelligence will multiply engineering capacity. And I believe this is the next major chapter in industrial engineering. And we are only at the very beginning.”