As the manufacturing sector struggles with persistent inefficiencies—including data silos, missed project launch dates, and an over-reliance on administrative tasks that pull engineering teams away from genuine innovation—industry leaders are searching for a new paradigm. Dassault Systèmes is stepping into this space with a bold pivot, moving past traditional Product Lifecycle Management (PLM) toward a future defined by AI-driven virtual twins and connected data foundations.
In an in-depth expert interview conducted by Howie Markson, Stephane Declee, CEO of the ENOVIA brand at Dassault Systèmes, pulled back the curtain on the company’s platform strategy. Centered around a strategic framework known as 3D UNIVERSES, Declee explains how four decades of industrial expertise are being channeled into a platform designed to make generative AI trustworthy, practical, and mission-critical for engineering organizations.
Main Facts: The Evolution Toward 3D UNIVERSES
The manufacturing industry is undergoing a structural shift. Solution providers are no longer judged solely on their ability to manage CAD files or static bills of materials (BOMs). Instead, the competitive edge now belongs to platforms that can safely integrate Artificial Intelligence into the core of product development.
The Strategic Shift: Dassault Systèmes is evolving its flagship 3DEXPERIENCE platform through a strategic direction called 3D UNIVERSES. Rather than an off-the-shelf software product, 3D UNIVERSES is an industry-specific framework built to support the "generative economy"—a space where AI-driven design, simulation, and decision-making become everyday tools.
Virtual Twins vs. Digital Twins: Declee emphasizes a foundational difference between a traditional "digital twin" (a static snapshot reflecting the current state of a product or asset) and a "virtual twin" (a living, dynamic model that simulates geometry, materials, system behavior, and operational constraints continuously).
Intellectual Property Lifecycle Management (IPLM): To support AI-driven outputs, Dassault Systèmes has introduced IPLM. While traditional PLM manages what was built and how it changed, IPLM manages the underlying intellectual property, design rules, regulatory constraints, and simulation models that generated the product.
Targeting the Core Pain Points: 3D UNIVERSES leverages "Virtual Companions" (context-aware AI assistants) and "Generative Experiences" (structured AI workflows) to reduce non-value-added administrative work and combat missed project deadlines.
Chronology: From 40 Years of Industrial Knowledge to the Generative Era
Understanding Dassault Systèmes’ current platform strategy requires looking at the trajectory of manufacturing technology over the past several decades.
1. The Foundation (The Past 40+ Years)
For over four decades, Dassault Systèmes has partnered with the world’s most demanding manufacturers in automotive, aerospace, and industrial machinery. Unlike generic software vendors, the company built its reputation on deep domain knowledge, scientific models, and real-world engineering expertise.
2. The Collaborative Era (The 3DEXPERIENCE Platform)
As product complexity grew, engineering, project management, procurement, and business stakeholders needed a unified environment. ENOVIA emerged as the PLM backbone within the 3DEXPERIENCE platform, breaking down departmental silos and bringing teams together from concept through production.
3. The Current Turning Point: The Generative Economy
Today, manufacturing is hitting an inflection point. While general-purpose AI and natural language interfaces have proliferated, industrial adoption remains in early pilot phases. Companies realize that generic public LLMs cannot be trusted with mission-critical aerospace or automotive designs. This realization prompted Dassault Systèmes to introduce 3D UNIVERSES and IPLM—providing the secure, contextual data foundation required to safely scale industrial AI.
Supporting Data: The Reality of Modern Engineering
Industry research highlights the urgent need for a transformation in how engineering teams operate. Declee and Markson addressed several eye-opening metrics during their discussion:
Missed Deadlines: Industry research indicates that manufacturers miss project launch dates 45% of the time. This chronic delay stems from poor alignment across disconnected systems and downstream supply chain surprises.
Wasted Engineering Talent: Previous research (How to Reduce Non-Value-Added Work in Engineering) reveals that only 54% of an engineer’s time is spent on actual innovation and design work. The remaining 46% is consumed by coordination, administration, and hunting for information.
Early AI Maturity: Despite widespread excitement, the actual maturity level of AI in product development remains early. While large OEMs are running pilots, very few have deployed industrial AI at scale because the data prerequisites—such as structured enterprise ontologies—are exceptionally difficult to build.
Official Responses: Insights from Stephane Declee
During the interview, Stephane Declee addressed several critical misconceptions about modern PLM, data silos, and the practical application of AI.
On the True Meaning of a Virtual Twin
When asked why Dassault Systèmes avoids the term "digital twin," Declee explained that a digital twin is inherently descriptive, telling you what exists at a point in time. A virtual twin, however, is dynamic and predictive.
"A virtual twin is a living model. It connects every dimension of your product… inside a single platform. It doesn’t just reflect reality; it simulates it. You can test a design change, predict how a system will perform under stress, or anticipate a production bottleneck before anything is physically built."
On Solving the Data Silo Problem for AI
Companies frequently struggle with data scattered across disparate CAD systems, PLM databases, and purchasing tools. Declee explained that technology alone cannot fix this; it requires an ontology.
"The first step isn’t technology, it’s ontology. You need a structured model that recognizes your data across systems, aligns definitions, and establishes that a component record in one system is the same entity as a reference in another. Without that, the AI is working with noise."
On Introducing IPLM (Intellectual Property Lifecycle Management)
Addressing how AI-generated designs must be tracked against evolving regulations, Declee clarified the role of IPLM:
"With IPLM, you can trace every generative output back to the IP that produced it, manage the lifecycle of that IP as regulations evolve, and ensure that the knowledge your teams build today remains a trusted, compounding asset and not an unchecked output you can’t stand behind."
On Busting the "All-or-Nothing" Misconception
Addressing what manufacturers misunderstand most about 3D UNIVERSES, Declee stressed that it is not a monolithic product that requires a risky "big bang" migration.
"Manufacturers sometimes hear about virtual twins, industrial AI, generative experiences, and IPLM in the same conversation and conclude that this must be a massive, multi-year transformation… You start where your pain is greatest. If your engineers are spending too much time on administrative work, you start with virtual companions. If your project teams are missing launch dates, you start with AI-assisted project management."
Implications: What 3D UNIVERSES Means for the Future of Manufacturing
The shift toward 3D UNIVERSES and contextual industrial AI carries profound implications for the global manufacturing landscape.
1. Reclaiming Engineering Productivity
By embedding Virtual Companions directly into the 3DEXPERIENCE platform, engineers can query past designs, simulation results, and institutional knowledge naturally. By automating repetitive administrative tasks and streamlining project risk assessments through Generative Experiences, platforms can help engineers reclaim the 46% of their time currently lost to non-value-added work.
2. Enterprise-Wide and Organizational Digital Twins
The virtual twin paradigm is no longer restricted to physical hardware. Forward-thinking manufacturers are now modeling their entire organizations—including processes, workflows, and decision-making rituals—virtually. As demonstrated by small industrial machinery builders like Automation Express, organizations can simulate process changes, scale engineering-to-order operations, and even deliver a machine’s virtual twin directly to the customer as an entirely new value stream.
3. Trust as the Ultimate Prerequisite for Industrial AI
Ultimately, the success of generative AI in engineering hinges on trust. Without a connected semantic layer and a robust IPLM framework, AI is merely a capable chatbot that creates regulatory liabilities. By grounding AI in an enterprise’s proprietary data ontology and historical context, Dassault Systèmes is establishing a blueprint where AI acts as a genuine, reliable engineering partner rather than a source of computational noise.
This article is based on an expert interview conducted by Howie Markson of Tech-Clarity with Stephane Declee, CEO of the ENOVIA brand at Dassault Systèmes, with interview coordination arranged by Kyle Herring.
