A long time ago, I came across a simple formula for building enterprise software: find a stakeholder in a company, figure out their processes, identify the data they manage, and create a database to match. Add “Management” to the stakeholder’s title – Supply Chain Management, Product Management, you name it – and voilà, you have an enterprise application.
The approach should be simple with product lifecycle management – check what product data management includes, focus on product data, describe entire product lifecycle and don’t forget document management workflow and project management and integrate with other business systems. Why it doesn’t work?
Enterprise Software Seems Simple – But It’s Not
At first glance, the formula I described above makes sense. Every enterprise system is built around the same basic structure: databases that describe business relationships, and CRUD (Create, Read, Update, Delete) actions to manage them.
Recently, Satya Nadella, Microsoft’s CEO, made a fascinating prediction that AI will eventually replace traditional enterprise software altogether. He called this “the end of SaaS” because AI can replicate the same logic of all those systems that basically not different than CRUD logic on business data.
Check my earlier article – End of SaaS and AI Agents. It’s an interesting idea, but the devil is in the details, in my view. Even with today’s systems, getting them to work isn’t as simple as ticking off boxes on a checklist.
Why PLM Implementation Checkbox Mentality Doesn’t Work?
When it comes to IT projects like PLM (Product Lifecycle Management), the path seems clear: define what you need, find CxO sponsor, make a plan, implement it, test it, and repeat. On paper, it looks foolproof.
It is very easy to find a typical “5-Step PLM Implementation” plan online. An average plan will promise if you follow this “checkbox plan”. It sounds great, right? Just follow the steps, pick a successful PLM software and you’re done.
Unfortunately, it doesn’t work that way. Despite the countless guides and checklists, many companies still struggle with Product Lifecycle Management (PLM) implementations. So, what’s going wrong? Why isn’t following the steps enough?
Three Reasons Companies Fail
Let me share three harsh realities I’ve learned over the years:
Business Processes Are Messy
No company has a perfect, clear set of processes waiting to be digitized. What you find instead are years of messy workflows, outdated practices, and ad hoc solutions that people built as they went along. Processes evolve, not always logically, and they often depend on key people who “know how things work.”
When you try to turn this chaos into a clean PLM system, you quickly realize the problem: it’s not just about capturing the process—it’s about untangling it first.
Every Company Is Unique, One-Size-Fits-All Doesn’t Work
One company’s approach to PLM might work brilliantly for them but fail miserably for another. Why? Because industries, company cultures, and even the way people think and work are different. What works in a high-tech startup might be unworkable in a traditional manufacturing company.
Here’s the kicker: even when two companies implement the same PLM solution, they can end up with completely different outcomes. On paper, it’s the same system. In practice, the differences in how it’s implemented, configured, and adopted can make or break the project.
The Real Problem: It’s Not “What,” It’s “How”
When companies fail at PLM implementation, it’s usually not because they didn’t know what they needed—like managing bills of materials, handling change requests, or improving collaboration. The real challenge is how to make it all work.
PLM systems are meant to manage complex data, enable collaboration, and streamline processes. But implementing these systems means understanding the messy reality of your company and adapting the tools to fit—not just ticking boxes on a checklist.
Stop Relying on Polished Slide Decks and PLM Consulting Decks
Here’s a trap many companies fall into: relying on a big vendor name or a fancy presentation to guide their PLM decisions. A great demo or a long list of features might look impressive, but it doesn’t mean the system will work for you.
In today’s world, you need more than promises. You need to see how a system captures the data you have, provide an experience you need, and can support KPIs of your organization that important for you. You need to see the system in action—working with your data, your processes, and your team.
The Modern SaaS Advantage: Flexible Data Models + Instant Trial
This is where modern SaaS platforms really shine. Unlike traditional systems that either sell you an OOTB model that will force you to change everything you do or will demand months of setup before you can see results, SaaS platforms often let you try them out immediately and some of them are capable to automatically bring your existing data because of flexibility of their data model. You can upload your data, experiment with configurations, and see how the system works in your unique environment.
Look for SaaS platforms that are capable to capture your data using flexible knowledge graph data models and help you plan your PLM strategy with a meaningful consulting using real-time data captured from your company. It’s like getting a head start without committing to a full implementation right away.
Don’t Trust Checkboxes – You Need To Experience You Data
The biggest lesson I’ve learned is this: don’t settle for a checklist. Sure, checkboxes are helpful, but they’re just the starting point. If you’re serious about PLM, demand more. You need to experience your data with the system and processes. See the software in action, with your own data. Test it. Break it. See how PLM vendor will fix it. Make sure it fits your needs before you commit.
What is my conclusion?
PLM implementation isn’t about ticking boxes—it’s about solving real problems in a way that works for your company. Success comes from understanding your messy, unique processes and finding tools that can adapt to them.
Modern SaaS platforms are making it easier to do this by offering instant trials and intelligent configuration. Don’t rely on promises. Insist on proof. When it comes to PLM, seeing is believing—and that’s the only checkbox that really matters.
Just my thoughts…
Best, Oleg
Disclaimer: I’m the co-founder and CEO of OpenBOM, a digital-thread platform providing cloud-native collaborative services including PDM, PLM, and ERP capabilities. With extensive experience in federated CAD-PDM and PLM architecture, I’m advocates for agile, open product models and cloud technologies in manufacturing. My opinion can be unintentionally biased
