The Art of PLM Software Testing and What Companies Miss in PLM Implementations

The Race for Developing New Features

In many organizations today, the excitement of creating or adding new features to PLM systems often takes priority over everything else. The IT department wants to innovate quickly and show progress to end users about how cool the PLM systems can be. But in the drive to move faster, the they overlook the very safeguard that protects their success: TESTING.

Proper end-to-end testing becomes something that most of the time, the team thinks they can “do later” or handle with minimal effort. But this approach almost always leads to problems. When testing is not planned properly, issues appear much later, sometimes disrupting everyday workflows across teams. At that point, fixing them becomes more expensive, more time-consuming, and far more disruptive than testing thoroughly in the first place.

Why PLM Testing Is Complex and Often Ignored

Testing PLM systems in an Agile environment becomes even more challenging because every sprint introduces new changes, configurations, or integrations that need validation. PLM workflows are long and interconnected, so even small updates require deep end-to-end testing. This takes time, but many companies skip or shorten it to keep up with sprint deadlines. As a result, issues pile up quietly and only appear much later when the system is already heavily used.

PLM systems also integrate with CAD tools, ERP systems, MES platforms, configuration systems, and supplier portals. Any change whether an upgrade, a customization, or even a small configuration adjustment can break something somewhere else. This makes manual testing extremely difficult, especially when teams try to repeat dozens or hundreds of workflows every time the system changes. Many companies simply do not have the time or resources to test everything manually, and that is when problems begin to appear in production.

Traditionally, many organizations tested their PLM systems using manual testing or UI automation frameworks like Selenium and API-based scripts. While these methods worked for smaller applications, they were never a perfect fit for complex PLM environments. Selenium tests often break whenever the UI changes, and maintaining large script libraries becomes slow and expensive.

API frameworks require strong technical skills, and they still cannot cover end-to-end PLM workflows that involve multiple modules, CAD integrations, and long business processes.

Manual testing is even more challenging because testers must repeat lengthy workflows every time a change is made, which becomes impossible to scale in Agile. As a result, companies spend too much time maintaining tests, miss critical scenarios, and struggle to keep up with the frequent updates that PLM systems require.

How AI and Modern Tools Transform PLM Testing

The good news is that the testing landscape is changing, and modern AI-powered testing tools are helping organizations overcome these challenges. Today’s AI systems can understand user actions, read test scenarios, and create test steps automatically. They can learn patterns from past test runs, detect failures faster, and even update broken test scripts without human help. This is especially valuable in PLM environments, where workflows change often and involve many business rules.

Modern low code testing tools make this even more accessible by allowing teams to write tests in simple English instead of writing complex code. These tools use AI to generate tests, heal broken scripts, suggest test coverage improvements, and execute tests across web, mobile, API, and desktop applications.

For PLM teams, this means they can cover more workflows, test more frequently, and maintain their test suites with much less effort. When upgrades or customizations are delivered, the team can validate the entire system faster, ensuring stable performance for engineering and manufacturing users.

Testing as a Strategic Investment

When companies treat testing as a strategic process rather than an afterthought, they gain stability, smoother operations, and stronger user trust. But when they rush development and delay testing, they create technical debt, production issues, and unhappy users.

For PLM implementations, this difference is even more significant because PLM is the backbone of product development.

A small error can influence everything from design decisions to manufacturing delays. By embracing structured testing, involving domain experts, and adopting AI-powered tools, organizations can ensure that their PLM systems deliver value without creating hidden risks.

In the end, the real art of testing is not just about catching bugs, it is about building confidence.