01 AI Is Moving PLM from Data Storage Toward Intelligent Engineering Support The PLM in Discrete Manufacturing Market is shifting toward AI-assisted lifecycle management. Modern PLM platforms are embedding generative and agentic AI to help engineers search technical documents, understand product records, identify related issues, summarize information, and automate routine workflows. This reduces time spent manually searching large engineering databases and helps manufacturers reuse existing knowledge. AI is also expanding into manufacturing planning, quality management, and change processes, turning PLM into a more active decision-support environment. For discrete manufacturers managing thousands of components and revisions, this development can improve engineering productivity, reduce duplicated work, and strengthen coordination between design, manufacturing, quality, and service teams. In April 2026, PTC launched Windchill AI Assistant, enabling manufacturing teams to use natural-language queries to find and summarize information stored in PLM product documents.
01 Growing Product Complexity Requires One Connected Digital Backbone Discrete products increasingly combine mechanical parts, electronics, embedded software, batteries, sensors, and other interconnected systems. Managing these elements through disconnected engineering tools makes configuration control and product changes more difficult. PLM provides a common backbone for bills of materials, CAD information, revisions, quality records, requirements, and manufacturing data. Connecting this information helps engineering and production teams understand how a design change affects components and downstream processes. This complexity is especially visible in automotive manufacturing as vehicles become more software- and electronics-intensive. Consequently, manufacturers are investing in PLM to strengthen traceability, configuration management, cross-domain collaboration, and connections between engineering, MES, ERP, and quality systems.
01 Legacy Data and Customizations Make PLM Migration Expensive and Complex Migration remains a major restraint because established discrete manufacturers may have decades of CAD files, bills of materials, product revisions, custom workflows, integrations, and engineering records stored in older systems. Moving this information to modern cloud PLM requires more than transferring files. Companies must assess data quality, map structures, validate migrated information, modify unsupported customizations, test integrations, and manage operational cutovers without disrupting ongoing engineering programs. PTC notes that extensive PLM data migration can be a substantial process and recommends implementation partners when datasets are large. These requirements increase project cost, implementation time, and organizational risk, causing some manufacturers to delay modernization despite potential long-term benefits.
01 Digital Product Passports Create New Lifecycle Traceability Demand Digital Product Passports create an important opportunity for PLM providers because manufacturers will increasingly need structured product information extending beyond engineering and production. DPP systems can contain data on materials, sustainability, repairability, recyclability, environmental impacts, maintenance, and compliance throughout a product's lifecycle. PLM already manages many product structures and lifecycle records, positioning it as a logical foundation for collecting and maintaining this information. The opportunity is particularly relevant for manufacturers of batteries, electronics, machinery, vehicles, and other regulated products. Vendors can expand PLM with sustainability data, material traceability, compliance workflows, APIs, and DPP connectivity, creating a broader digital thread from design through use and end-of-life.
01 Poor Parts Data Can Reduce the Value of Enterprise PLM Maintaining accurate and standardized product data remains a major challenge as discrete manufacturers scale PLM across departments and locations. Different engineering teams may create duplicate components, inconsistent naming conventions, incomplete metadata, or separate versions of similar parts. These problems increase inventory, complicate sourcing, create unnecessary engineering work, and weaken the reliability of automated workflows. The issue becomes more important as companies introduce AI because intelligent systems depend on trusted lifecycle data to produce useful results. Manufacturers therefore need continuous data governance, classification, duplicate detection, ownership rules, and standardized product structures. Successful PLM transformation depends not only on installing software but also on maintaining a reliable enterprise product-data foundation.
In January 2026, Siemens acquired ASTER Technologies to strengthen its PLM ecosystem with advanced design-for-test and electronics lifecycle management capabilities within the Siemens Xcelerator platform.
In September 2025, SAP announced a partnership with OpenAI to integrate generative AI capabilities into enterprise software systems, supporting product lifecycle management workflows through AI-assisted data processing and automation.
In August 2025, Siemens was named a Leader in the Forrester Wave for PLM in discrete manufacturing, reinforcing its partner ecosystem strength across PLM, CAD, and manufacturing software integration networks.