[Edaily Reporter Shin Young-bin] #LG Electronics is embarking on a transformation of its development process to reduce repetitive research and development (R&D) tasks using artificial intelligence (AI) by 2029. The plan is to automate repetitive development tasks, such as requirements analysis and circuit board design, using AI, allowing engineers to focus on core design and decision-making.

Kim Yong-yeon, a research fellow at LG Electronics, delivered a presentation titled “Next Engineering: Transition to AI-Native” at Dassault Systèmes’ “Simulia User Day 2026,” held on the 11th at The Westin Seoul Parnas in Gangnam-gu, Seoul. Kim noted that the pace of change in the manufacturing industry is accelerating.

He explained that while product development cycles are shortening and customer demands are becoming more complex, companies are under pressure to develop high-quality products faster and at lower costs.

Research Fellow Kim stated, “It is crucial to redesign the entire engineering workflow,” adding, “It is difficult to bring about significant change by simply applying Artificial Intelligence Transformation (AX) or Digital Transformation (DX) technologies while keeping existing work methods intact.” He further emphasized, “End-to-end R&D automation is necessary for workflow redesign.”

Commissioner Kim highlighted inefficiencies in the R&D environment. He explained that an analysis of research institute operations revealed a heavy burden of non-value-added tasks—such as administrative work, reporting, and paperwork—compared to core R&D activities.

“When I examined the tasks performed alongside engineers one by one, I found that the time spent on administrative tasks far exceeded the time spent on actual R&D work,” he said. “This revealed insights into where AI could be applied to allow engineers to focus on their core responsibilities.”

Documentation tasks, in particular, were cited as a prime example of what hampers R&D productivity. Commissioner Kim noted, “Excessive documentation tasks account for 34% of our workload,” adding, “Streamlining this alone would yield significant results.”

The underlying principle is that repetitive and administrative tasks must be reduced through AI so that R&D personnel can devote more time to essential tasks such as design, verification, and problem-solving.

However, LG Electronics did not view the adoption of AI as merely the automation of individual tasks. The company determined that if data related to product requirements, design, printed circuit boards (PCBs), product lifecycle management (PLM), and computer-aided engineering (CAE) remained disconnected, the effectiveness of AI would be limited.

Research Fellow Kim noted that tasks and data across development stages are not yet organically connected, stating, “Let’s redesign our existing workflows.”

LG Electronics also outlined a plan to automate repetitive development tasks—such as requirements analysis and circuit board design—using AI, and to gradually integrate these into the product development process by 2029.

Data governance was also cited as a key requirement. For AI to deliver meaningful results in manufacturing R&D, a system is needed to connect data scattered across departments and stages and manage it under consistent standards. Research Fellow Kim identified end-to-end R&D automation, physics AI, and data governance systems as key priorities.

The AI-native engineering envisioned by LG Electronics is a structure that connects the entire development cycle, from requirements analysis and design automation to simulation, verification, and data management. It enables engineers to reduce repetitive tasks and focus on core design and decision-making, leading to shorter development times and improved quality. The center of gravity for Manufacturing AX is shifting beyond the production floor to the entire R&D process.