You don’t need to be a geek to know that AI is far more than large language models by now. AI-driven solutions have delivered substantial gains in electronics manufacturing for years, particularly in surface-mount technology lines and inspection systems. The tech is now ready to take the next step upward and move into the realm of strategic decision-making.
Leading regional distributors such as MVTech and Danutek Hungary have first-hand experience to validate the shift. For decades, automated optical inspection systems in electronics manufacturing were rule-based. Engineers had to define in advance what constituted a defect: thresholds, geometries and pre-set defect models. These systems functioned as strict “gatekeepers,” and were effective but rigid.
“The traditional, rule-based AOI works like a gatekeeper that has to be told exactly what a defect is,” Ferenc Csizmazia, director of sales for Eastern Europe at MVTech, tells the Budapest Business Journal. “Deep learning, by contrast, can learn from data in a mathematically consistent way, so it handles complex, variable defects and real production deviations much better.”
In practice, this means a fundamental shift from programming to training. Instead of manually assigning algorithms to each new product variant, AI-based systems can automatically identify components according to applicable standards and assign the appropriate inspection logic. Programming time can be reduced by up to 90%.
Deep learning models are particularly powerful in “dirty” real-world environments, where reflections, shadows and high-mix/low-volume production would otherwise trigger false alarms or missed defects. By recognizing subtle patterns in image data, AI systems reduce noise and increase reliability.
One of the pain points of electronics manufacturing is the so-called false call, when inspection systems flag non-existent defects. These false positives require human review, slowing down throughput and increasing labor costs. AI-driven fine-tuning changes the equation.
“With contextual recognition and automatic learning, false calls can be reduced by around 70% without manual intervention,” Csizmazia notes. Thanks to AI, quality control is no longer viewed as a necessary cost.
“AI-based inspection is no longer just a scrap-filtering valve,” Csizmazia says. “It becomes a data source for process optimization and decision support.”
While quality inspection often delivers the fastest ROI, especially during labor shortages, Csizmazia believes the greatest long-term value lies in process optimization.
By correlating inspection data with parameters such as printer profiles, feeder performance or design variants, manufacturers create learning production lines that continuously refine their own settings. “That is what creates a sustainable competitive advantage,” he adds.
From the perspective of Danutek Hungary Kft., a leading distributor and service provider in the SMT manufacturing industry, AI is not a novelty but an evolution.
“For us, this is nothing new,” says Csaba Berta, CEO of Danutek Hungary Kft. “What feels new is its spread toward the general public. In industrial inspection systems, we have been using AI-based decision support for six or seven years.”
Inspection equipment for AOI, SPI and X-ray systems collect massive volumes of data during production. Initially, the machine decides whether a board is good-to-go or defective. But beyond that binary outcome, it stores detailed measurement data.
“When an operator overrides a machine’s defect call several times in the same way, the system learns from it,” Berta explains. “With enough training data, we reach a point where the AI makes the decision on its own and no longer flags it as an error.”
The result is reduced manual intervention and greater consistency. Over time, certain product programs can run with minimal human input, as the AI refines its judgment through accumulated experience. Although Berta was unwilling to share specific numbers, he emphasizes that the financial impact is substantial.
“We are talking about significant sums that can be saved through AI in improved quality, reduced rework and smarter maintenance,” he says.
Predictive maintenance, for instance, can dramatically reduce unplanned downtime by analyzing sensor data and system logs. But beyond maintenance, global cost pressure is a powerful driver. Large multinationals operating across continents are keenly aware that even small process improvements multiply at scale.
“If you optimize a small step in Hungary, you can replicate that saving in Germany, the U.S. or Malaysia,” Berta notes. “On a global level, that becomes a huge cost factor.”
As a distributor, Danutek encourages clients to select equipment that already integrates AI capabilities or at least offers upgrade paths.
“There is no need to reinvent the wheel, but when you choose your equipment, pick one that already has integrated AI,” he says.
According to Berta, most global players, particularly in Asia and the United States, are well ahead in adopting such solutions, while Europe is catching up.
For Hungarian manufacturers, the message is clear: AI is no longer a luxury or an experiment but rather a structural shift in how electronics are produced and controlled. While smaller local players may hesitate to invest in AI-enabled solutions, global clients are already demanding them.
Delvitech is among the fast-growing breakthrough innovative inspection companies delivering truly AI-native AOI electronics solutions. In fact, it closed a Series B investment round of USD 40 million back in October to take their operation to the next level.
As sales manager for CEE András Király explains, by creating synthetic defect scenarios, such as artificial shorts or misalignments, manufacturers can test inspection coverage before production even begins.
This will also usher in a whole new era of engineers, as they increasingly focus on data interpretation, process optimization and AI supervision rather than rule-writing and parameter tuning.
“In five years, quality engineers won’t be AOI programmers. They’ll be AI-assisted process engineers driving continuous improvement from data,” Király notes. “Stop programming inspection, start teaching it. AI turns AOI from a bottleneck into a learning system, and engineers into process experts.”
This article was first published in the Budapest Business Journal print issue of May 8, 2026.
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