Artificial intelligence is superb at corralling volumes of data but still lacks the flexibility needed for many engineering tasks.

AI is transforming electronics design and manufacturing, making significant strides in everything from procurement to PCB design, and even defect detection. But while there’s a lot AI can do today, there’s still a whole lot it can’t (yet). Here’s what’s really happening on the ground according to experts in the field.

Celus’ André Reggiani says AI shines at retrieving datasheet information.

The easy consensus among those we spoke with is that AI automates the repetitive stuff. At Celus, André Reggiani, AI product manager, highlights that AI is really shining when it comes to “retrieving tons of information from datasheets” and helping engineers “find the right components for their circuits.” No more scouring datasheets, crossing fingers that you’re not missing something important. It’s like having an incredibly efficient assistant, but with fewer coffee breaks.

Zachary Feuerstein of Breadboard promotes AI’s cleanup capabilities.

Similarly, Zachary Feuerstein from Breadboard points to AI’s prowess in automating data extraction – think BoMs, RFQs, POs and supplier communications. “It can clean up unstructured data, extract part numbers and even handle back-and-forth emails with suppliers,” he says. This doesn’t just save time; it slashes the annoying admin work that no one signed up for in the first place.

AI can add structure to scattered information, Luminovo’s Timon Ruban says.

Timon Ruban, cofounder and managing director of Luminovo adds that AI is currently best used for “turning unstructured into structured data.” He explains: “Think ‘extracting a BoM from a PDF,’ ‘extracting the PCB specification from a PDF’ or ‘extracting technical parameters from a datasheet.’ AI is taking that chaotic, scattered information and turning it into something engineers can actually use.” This is a time-saver for engineers buried under mountains of unstructured data.

Problem Predictor

Forget waiting for things to break, AI’s got a crystal ball. According to Arch Systems cofounder and CEO Andrew Scheuermann, AI’s predictive powers are a game-changer. From anticipating when materials will run out to flagging potential downtime-causing machines, AI’s shifting the focus from reactive to proactive. This not only keeps things running smoothly, but it also keeps engineers ahead of the game, sparing them from panic mode.

It’s all about staying ahead of the curve. “AI’s now able to surface real-time, actionable guidance,” says Scheuermann. So, no more endlessly scrolling through dashboards. AI serves up the next best action, whether it’s pinpointing the nozzle causing scrap or identifying early signs of quality issues based on historical patterns.

AI’s Role in PLM – Streamlining Complexity

Duro’s Michael Corr believes AI’s strength is assessing bespoke datasets.

When I sat down with Michael Corr from Duro, we dove into why AI is such a strong fit for product lifecycle management (PLM). As he put it, “AI is great at handling bespoke datasets and making sense of them,” which is a step up from older machine learning tools that required everything to be perfectly formatted. This flexibility is why AI excels in the PLM space, where a mix of CAD content, manufacturing instructions, test data and more all come together. Instead of forcing all that info into a single format, AI can work with it as-is and still deliver meaningful insights.

Corr also highlighted how AI can cut down on redundancy across teams. “It’s easy for an engineer to recreate a part because they couldn’t find the original,” leading to duplicates and inefficiencies. AI can spot these issues by flagging identical or nearly identical parts, suggesting they be merged or removed. This streamlining of data helps strengthen the digital thread, making the entire process more accessible and collaborative. While we’re not there yet, Corr is excited about AI’s potential to not just organize data but also analyze changes over time and flag potential future issues before they arise.

Breaking barriers – literally. AI’s ability to break down language and cultural barriers is another unexpected perk. Scheuermann points out that AI tools are bridging global divides by translating across languages and dialects, allowing teams in different plants to communicate seamlessly. This means that once a solution is discovered in one factory, it can be applied to another without the hassle of language barriers. Talk about team spirit!

Alek Tyszka from Instrumental agrees, adding that AI can detect defects in real-time using computer vision. This means no more waiting to catch issues during the testing phase – AI spots them as they happen. But what’s next? Full integration from CAD to automated assembly and testing. The holy grail? A fully automated digital thread. But that’s still a few steps down the line.

The Current AI Struggle: Fully Autonomous Procurement

Now, let’s talk about the stuff AI still can’t do, no matter how many improvements we see. First up, Breadboard’s Feuerstein points to AI’s inability to act as a fully autonomous trading agent in electronics procurement. “AI still can’t dynamically buy components in real-time,” he explains. While it can forecast stock availability and help with price predictions, it’s not yet ready to pull the trigger on purchasing decisions without a human steering the ship.

Sergiy Nesterenko of Quilter.ai says AI falls short at managing design tradeoffs.

Sergiy Nesterenko from Quilter.ai also offers a sobering view of AI’s current limitations in PCB design. While AI is, to a degree, fantastic for automating routing and placement, getting a board ready in hours instead of days, he points out that the real challenge comes in designing something as complex as a motherboard. When it comes to “balancing signal integrity, power integrity, routing density and thermal relief,” AI isn’t quite at the stage where it can manage those trade-offs like an experienced human engineer. It’s like a juggler trying to keep too many balls in the air – something’s bound to fall.

Rui Calsaverini of Celus awaits AI’s improved interaction with engineers.

Rui Calsaverini, VP of R&D at Celus, echoes that sentiment: “Probabilistic systems like AI cannot generate an exact design on their own (yet),” he says. “The evolution in AI becomes more about how well it interacts with the engineer and the engineer’s intent.”

David Wiens says Siemens is focused on improving engineers’ productivity, not replacing them.

David Wiens of Siemens reinforces this design-focused view, noting that while AI excels in specific areas today, the leap to full design autonomy remains a major hurdle. “We’ve leveraged AI for things like natural language processing using large models, predictive analytics across the supply chain and rapid component selection based on datasheet information,” he explains.

These tools already help engineers identify part availability, streamline BoM creation and even predict optimal signal configurations much faster than traditional simulation. However, Wiens is realistic about what AI can’t yet do: generate high-complexity schematics and layouts. “Most of the tools you see today focus on simpler designs,” he notes, due to the lack of high-quality training data and the sheer complexity of real-world boards. “We’re exploring these technologies, but we’re focused on improving engineers’ productivity, not replacing them.”

Celus cofounder André Alcade adds engineers must still intercede.

André Alcade, Celus cofounder, adds: “These tools are producing higher-quality output over time, but even as the quality gets better, you can’t turn them into something for public use without an engineer’s guiding hand.”

From Design to Production – A Bridge Still Missing

At Celus, there’s a similar gripe about AI’s failure to bridge the gap between design and production in real-time. Alcade explains that while AI is excellent at interpreting block diagrams and datasheets, it still struggles to provide actionable insights that cut across both design and production. Imagine AI suggesting a design tweak based on real-world production data. Sounds like a dream, right? But we’re not there yet.

Scheuermann from Arch Systems agrees, emphasizing that unlocking real-time end-to-end intelligence is the big frontier for AI in electronics. “Once AI can bridge that gap, like surfacing how a layout decision impacts yield, we’ll see a massive leap forward,” he says. Right now, design and production still live in their own silos. AI can’t yet connect the dots in a way that can predict or fix issues on the fly.

Zuken’s Kyle Miller asserts AI can reshape PCB design.

Zuken’s Kyle Miller has laid out a compelling case for how AI is reshaping PCB design in ways that traditional rule-based automation never could. “It doesn’t do it like I would do it,” he wrote, citing a common frustration among engineers who feel that rigid automation systems don’t mirror their judgment or design style. His point is that past automation tools failed because they were brittle – too inflexible, too difficult to set up and too unaware of when breaking the rules was actually the best decision. AI, by contrast, “offers a fundamentally different approach, providing adaptive intelligence rather than rigid rule-following.”

Miller explains that modern AI systems can balance competing design priorities, customize to individual preferences and reuse design intelligence from past projects. He outlines key areas where AI is already helping – from intelligent constraint management during setup to adapting layouts based on previous successful designs. “By allowing the designer to interact with the PCB at a much higher level,” Miller argues, “it speeds up time to market but still keeps overall control with the designer.” Zuken’s own tools, he notes, already leverage AI for routing and decap placement, with more adaptive features planned for future releases.

The Road Ahead

While AI is undoubtedly making waves in electronics manufacturing, it’s clear that it’s not ready to take the reins. From making predictions before things go wrong to automating tedious tasks, it offers some serious time savings.

But there’s still a long road ahead, whether it’s perfecting procurement, mastering design trade-offs or integrating the entire design-to-production process, AI’s journey is only just beginning. As Nesterenko of Quilter.ai puts it, “AI’s real challenge is learning to make the same kind of tradeoffs as an experienced engineer.”

Stay tuned for more updates on how AI continues to shape the world of electronics. Who knows what it’ll do next?

Ryann Howard is managing editor of PCD&F/Circuits Assembly; This email address is being protected from spambots. You need JavaScript enabled to view it.. Prior to joining PCEA, she helped train the AI for a major US-based company.