en.Wedoany.com Reported - Jon Wilde, Vice President of Product Management at US-based SimScale, pointed out that the key factor enabling engineering organizations to scale AI beyond experiments and achieve results lies in infrastructure. Wilde stated: "Plastics product development involves complex simulation requirements, including material behavior, thermal performance, and mold flow. The ability to iterate quickly and explore more design options early on directly impacts cost and time."

Data shows that organizations with mature AI programs view cloud-native platforms (75%), governance and compliance frameworks (48%), and software interoperability (42%) as key enablers. Engineering teams using AI-driven processes report a daily iteration rate of 88%, while traditional methods require an average simulation cycle of 17 hours. AI-empowered workflows reduce turnaround time from 17 hours to 6 hours, with the number of design variants evaluated per project being more than three times that of traditional methods.

Wilde noted that 65% of respondents believe clearly defining the responsibility for moving pilot projects into production is a critical factor in building a mature AI program. Chris Kuntz, Vice President of Strategic Operations at Augmentir, stated that AI is not replacing workers but empowering them to take on more strategic roles, shifting work from reactive and manual to proactive and data-driven.

AI applications such as predictive maintenance, process optimization, and defect detection provide manufacturers with immediate return on investment. By investing in infrastructure like cloud-native platforms and software interoperability, plastics manufacturers can transition from artificial intelligence pilots to full-scale production, compressing simulation cycles from 17 hours to 6 hours, managing more design options, and accelerating material behavior modeling.

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