In my work on change management, I’ve been exploring how we can make meaningful improvements to traditional change management processes and implementations. I published four articles back in December where I shared my thoughts about the future of change management transformation and how it connects to existing and future product lifecycle management (PLM) systems and engineering change management services. I also shared my thoughts about how transformation in change management will impact existing and future PLM software. Check these links here:
So far, I’ve focused on ideas like turning the Single Source of Truth (SSOT) into a Single Source of Change (SSOC), using collaborative workspaces, and finding ways to bridge legacy systems with modern solutions.
Originally, I didn’t plan the Part 5 article in this series, but after some time I decided that this series of articles won’t be complete if I won’t talk about product lifecycle, modern PLM software and impact of AI. Change management is deeply intertwined with different disciplines of product lifecycle from simple change requests to optimizing supply chain management, cost, physical parameters of computer aided design (CAD) model, enhanced product quality and many others.
So, today, I want to dive into how new change management concepts I shared about before are connecting to the topic that is everywhere these days – Artificial Intelligence (AI).
AI is everywhere, changing how we think about services and applications. I’ll talk about how AI – especially AI agents and AI co-pilots – fits into the Collaborative Workspace model I’ve been discussing. I’m convinced that bringing AI into this setup can make change management smarter, faster, and more effective.
Will AI Eliminate SaaS and Software as We Know It?
If you’ve been around the software world for a while, you’ve seen some big shifts. I’m sure you remember how so called Web 2.0 came around and web applications started to replace desktop application. Not everyone believed web apps will be everywhere back in those days. By the 2010s, SaaS had taken over from on-premises software. But until 2020 (and even now) I still can hear voices about “PLM on prem needs because cloud is not secured enough”. Now, we’re heading toward another shift: AI-driven apps that might make traditional software models less relevant.
Earlier this week, I’ve been listening to Satya Nadella’s speech about AI making traditional SaaS obsolete. He was describing AI agents and the “death of SaaS.” AI isn’t just an add-on; it’s a game-changer. But not all AI is the same. Let’s focus on two forms AI agents and AI co-pilots:
AI Agents: These are like virtual assistants that can work on their own to handle complex tasks. They’re flexible, learning as they go and adapting to new information. Imagine them taking care of routine, time-consuming processes so you can focus on the bigger picture.
AI Co-Pilots: These are tools designed to help people. They don’t take over; they work alongside you, giving suggestions, insights, and support to make your job easier. For example, they can analyze data, highlight issues, or even help draft reports.
Think about being an engineer trying to decide on a design change. Instead of sorting through mountains of data or manually running cost analyses, an AI agent or co-pilot could give you smart, targeted recommendations almost instantly. That’s where I think we’re heading—making complex tasks feel easy.
How Collaborative Workspace Connects to AI Agents and Co-Pilots
While both AI co-pilots and agents can improve the business process and tasks, in product lifecycle and, especially in engineering change process, the context (product data) is important. It is not simple to send “some data” to the agent or contextualize a specific request or operation.
I see the Collaborative Workspace model as the perfect setup for AI tools. It’s powered by a knowledge graph, which organizes and connects all the product data your team needs. Here’s how this works:
Centralized Data: Collaborative workspaces gather all the information in one place and helps to engineers and other people in the organization to get “on the same page” about planned change. It is like your Google Sheet with the entire data set you need. In such a way a Collaborative Workspace is making it easier for AI to access and analyze the information. Instead of hunting for scattered data, you have a single, organized product data set.
Team Collaboration: These spaces let different teams work together seamlessly. AI thrives in this environment, offering real-time insights that help everyone stay on the same page. Imagine an AI co-pilot helping both engineering and procurement teams align their decisions.
Better Change Management: AI agents can analyze data and suggest changes, while co-pilots guide users through implementing those changes. This makes the process faster and more effective. You don’t just get suggestions—you get a clear path to action.
Examples of What AI Agents and Co-Pilots Can Do
Let’s make some “dreams” to think about how a collaborative workspace with a change management service capturing all changes and getting access to history of the product can become a hub to connect AI agents and AI co-pilots. I put below some practical ways AI could help in a Collaborative Change Management Workspace:
Impact Analysis: An AI agent could check all the products your company has sold to see how a proposed change might affect them – from warranty claims to regulatory compliance. This means you get a full picture of risks and costs before making a move.
Supplier Recommendations: AI can analyze suppliers and suggest better options based on cost, quality, and delivery times. It could even highlight hidden patterns in supplier performance.
Cost Savings: AI might find ways to cut production costs by recommending alternative materials or spotting inefficiencies. Imagine it identifying a material switch that saves 10% without compromising quality.
Weight Reduction: Need to reduce product weight? AI can suggest new materials or design tweaks to meet your goals. For example, it might identify lighter materials or structural changes that maintain strength.
Compliance Checks: An AI co-pilot can flag potential regulatory issues early, saving you time and hassle later. This proactive approach could prevent costly delays.
Predictive Maintenance: By analyzing usage data, AI can predict when components are likely to fail and recommend proactive fixes. Think of it as catching problems before they happen.
I know, these examples are easy to get and they are on the top of mind of every engineering and product development organization. What can be more important than “cost saving” or how ignorant manufacturing can be not thinking about “mitigation of supply chain risks”. However, the purpose I put those examples here can show you how to introduce these new services to the organization. Because any great technology needs to find its way to people and find the adoption path. These examples show how AI can make tasks easier, faster, and more effective. It’s like having an extra pair of super-smart hands on your team, working tirelessly in the context of the product data shared with everyone. AI agents and co-pilot have a seamless access to this information – a medium for change management.
Why Knowledge Graphs Matter
Graph models is a topic I’ve been thinking for a long time as a model to replace traditional “PLM data modelers”. You can easy find my articles about graphs and graph models. Here is one to start with – where I discussed the importance of graph based models for new PLM services.
At the heart of a better data modeling is a graph model and knowledge graphs. Think of it as a digital brain that organizes and connects all your product data. Just like ChatGPT connects to internet to get any information now, AI agents and co-pilots can get access to a knowledge graph with your company products and related data to get the information they need.
With a knowledge graph, AI can pull together insights from different sources, making connections that traditional systems might miss. For example, it could link supplier performance data with production timelines to identify bottlenecks. This kind of intelligence makes AI agents and co-pilots incredibly effective.
What is my conclusion?
For years, traditional change management in PLM has been stuck managing CAD data, check-in/out files and mostly demanded in the engineering silo. The Collaborative Workspace model offers a new approach – one that brings teams together and organizes data in a way that AI can use.
By combining Collaborative Workspaces with AI agents and co-pilots, we can make change management smarter and more collaborative. This setup doesn’t just improve workflows; it helps teams think bigger, solve problems faster, and stay ahead in a competitive market.
To me, the question isn’t whether we’ll embrace this new way of working. It’s how quickly we’ll do it. The technology is here and to bring the data and contextualize AI technology is the strategy I can see will be used by many companies in the next 3-5 years.
Just my thoughts…
Best, Oleg
Disclaimer: I’m the co-founder and CEO of OpenBOM, a digital-thread platform providing cloud-native 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.
