The ever-evolving business landscape presents one of the most significant challenges in Product Lifecycle Management (PLM). Organizational structures are in constant flux—companies acquire others, merge operations, and strive to integrate disparate systems. In the PLM domain, this transition has traditionally spanned several years.
From a PLM perspective, full integration of an acquired business involves harmonizing processes, tools, and—most critically—product definition data. This includes aligning 3D models, drawings, and item databases at a granular level.
Such integration often demands a painstaking transformation of legacy product data to fit the corporate PLM standard. It requires phasing out existing IT systems, enriching and cleansing data, and converting it into new formats. Beyond the technical hurdles, it also entails significant changes in workflows, practices, and day-to-day operations for individuals within the acquired organization.
This is where AI can be a game-changer. A combined understanding of product data and AI can serve as a powerful platform to harmonize and cleanse information from acquired businesses, compressing timelines from years to mere weeks or days. By training AI to infer conversion logic, even for the most complex cases, companies can accelerate the implementation of corporate systems and dramatically reduce integration overhead.
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