Dassault Systèmes has unveiled 3D Universes (styled as 3D UNIV+RSES for branding), a bold step toward reimagining how industries engage with digital and physical realities. This is not just another 3D modeling update. It represents a fundamental shift from static digital twins to an AI-powered, generative intelligence ecosystem. The branding itself—3D UNIVERSES instead of “3D Universes”—signals a new paradigm where virtual and real (V+R) are seamlessly integrated, enabling continuous learning, automation, and adaptability across product lifecycles.

But with this shift comes a set of key challenges: What does this mean for legacy users? How will intellectual property be managed in an AI-driven world? And can Dassault Systèmes convince the market that this is more than just another buzzword-laden evolution?

Virtual + real: more than just digital twins

The concept of V+R (Virtual + Real) is not new to Dassault Systèmes. It has been a central theme in the company’s Virtual Twin Experience, where digital twins are no longer mere representations but are continuously evolving with real-world inputs.

In 3D Universes, this vision is taken further:

• AI-powered models learn from real-world behaviors and adjust accordingly
• Virtual companions provide intelligent assistance in decision-making
• Generative AI and sense computing optimize designs and simulations in real-time

This moves beyond the traditional “digital twin” approach. Rather than acting as a static mirror of the physical world, 3D Universes enables a dynamic, self-improving system that continuously integrates, analyzes, and adapts. The idea is not new. For instance, Siemens and other ‘PLM software’ providers are actively exploring opportunities for AI to add an intelligent layer to the PLM data backbone.

From static to generative intelligence

Dassault Systèmes has long been a leader in 3D modeling, PDM/PLM, and simulation, though 3D Universes marks a significant departure from traditional software functionality. It introduces an AI-driven, generative framework that transforms how products are designed, validated, and maintained.

Key differentiators from this new positioning include:

• AI-assisted workflows that automatically refine and evolve designs.
• Predictive simulations that adapt based on real-world sensor data.
• A “living” knowledge platform that evolves with industry trends and user inputs.

You get the idea. Rather than designing a product in isolation, cross-functional teams, from Product Development, Engineering, Quality, Procurement, and supply chains can now co-create with AI, allowing for an iterative, automated process that reduces risk, enhances efficiency, and accelerates innovation cycles.

Beyond software—a living digital ecosystem

The shift to 3D Universes also seems to represent a move away from traditional licensing-based software models toward a consumption-based, Experience-as-a-Service (XaaS) framework—a similar commercial model per the approach recently described as “AI-as-a-Service” by Microsoft CEO Satya Nadella. This aligns with broader industry trends where companies are transitioning from one-time software purchases to continuous value-driven digital services.

What does this mean in practical terms?

• Customers will consume intelligence rather than static software.
• Real-time virtual twins will become decision-making hubs, constantly updating based on real-world inputs.
• AI-generated designs will automate engineering iterations, dramatically reducing manual effort.

This is a major shift for legacy customers who are accustomed to on-premises, private cloud hosting, and transactional software ownership. Dassault Systèmes will need to provide a clear roadmap to help these organizations transition without disrupting their existing workflows and wider integration landscape.

IP, trust and the generative economy

One of the most critical challenges in this transformation is intellectual property (IP) ownership and data security. In an AI-driven, generative economy, where does human ingenuity end and machine-driven design begin? If AI generates a product variation based on learning from past designs, who owns the output?

Some key concerns include:

• Ensuring IP integrity when AI continuously iterates on existing designs.
• Managing security risks as real-world data feeds into digital models.
• Addressing industry adoption barriers for companies that have built their entire business around traditional IP protection frameworks.

Dassault Systèmes, and other enterprise solution provider in this space, will need to provide strong governance mechanisms to help customers navigate these complexities and build trust in the generative AI-powered design process.

Dassault Systèmes issued a YouTube video presentation as a teaser to outline the core ambitions of 3D Universes, reinforcing its role in shaping a new generative economy—elaborating on key messages:

• Virtual-Plus-Real Integration: A seamless blend of digital and physical data enhances accuracy and applicability in simulations.
• Generative AI Integration: AI-driven processes enable more adaptable and intelligent design iterations.
• Secure Industry Environment: A trusted space for integrating and cross-simulating virtual twins while ensuring IP protection.
• Training Multi-AI Engines: Supports the development of AI models within a unified framework, promoting more sophisticated AI applications.

While the video presents a compelling vision and sets timeline expectations towards an aspirational 15-year journey by 2040, it introduces complex terminology that might not be easily digestible for a broad audience. The use of “Universes” as branding adds an extra layer of abstraction that could benefit from clearer explanations and, in due time, a gradual transition roadmap for legacy users.

Additionally, the practical implementation and real-world applications remain vague, leaving some unanswered questions about industry adoption and integration. How will companies transition to this model? What are the concrete steps beyond the conceptual framework? The challenge will be ensuring that this does not become another overcooked marketing push that confuses rather than inspires potential adopters. Users demand clarity and pragmatism in linking solutions to problem statements and practical value realization.

A bold leap into the future

The potential of 3D Universes is enormous, but its success hinges on several key factors:

• Market Education: Dassault Systèmes must articulate the value proposition beyond buzzwords, demonstrating tangible ROI for both new and legacy users.
• Seamless Transition Strategy: Organizations need a clear pathway to adopt 3D Universes without disrupting their current operations.
• AI Governance & IP Assurance: Addressing industry concerns around AI-generated designs, IP ownership, ethical AI, and data security will be crucial for widespread adoption.

If 3D Universes delivers on its promise, it has the potential to redefine how industries design, simulate, and optimize products across their entire lifecycle. By truly integrating Virtual + Real intelligence, Dassault Systèmes is making a bold statement about the next frontier of digital transformation.

The question now is: Are industries ready to embrace this generative future, or will skepticism slow its adoption? Furthermore, where should organizations start on this journey? Can solution providers be bold enough to share a pragmatic roadmap towards this goal, and keep us posted on their learnings in this space? Will 3D Universes bring us one step closer to the “Industry Renaissance” previously advocated by Dassault Systèmes Chairman Bernard Charles? Time will tell, but one thing is certain—Dassault Systèmes is positioning itself at the forefront of the next industrial/digital revolution.

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Sound familiar?

It does to Nestle. The confectionary giant’s efforts to consolidate its 29 different business units into a standardized ERP was a struggle from the get-go. Misalignment between business processes and ERP capabilities, resistance to change and communication breakdowns were the top causes of delays and frustration among employees.

Employees reporting extensive disruptive change are 67% more likely to have issues with managers. According to the 2024 State of the Global Workplace Report from Gallup, Inc., while difficulties often affect frontline workers, it’s leaders who are experiencing the most change. Despite this, these leaders must be physically and mentally prepared to set the tone for their team to embrace upcoming challenges in their stride. Here are four pearls of wisdom to ensure leaders hit the ground running.

Popularity counts when appointing a change leader

Chaos and uncertainty might be filling your shop floors, but in the boardroom, a different kind of energy must fly. Leaders who are known for their ability to navigate tricky situations with a steady hand and a genuine smile are the right choice to steer this change.

No one can really prepare you for the allies and enemies that might emerge—they often come from the most unlikely places. For example, you might predict resistance from the engineers, muddled about how to access project data, CAD integrations, or track progress. Perhaps, it turns out, they received comprehensive guides, and actually, the pushback is coming from the accounting team. It will be the change leader’s role to clearly articulate the long-term benefits, generate enthusiasm, and establish clear communication channels to ensure these issues don’t slip through the cracks and escalate.

While they will be responsible for ensuring staff are on board and timelines are met, they will work closely with someone who can lead the data side of things. A chief data officer or an ERP vendor who can guide employees through the technical aspects, from data migration to customization.

Of course, implementing a new ERP impacts every aspect of manufacturing companies’ operations. Key individuals will need to support the change leader in reporting any issues with the system or their colleagues. The idea is for these people to become experts in the new ERP and ensure everyone understands their training.

Scoping data migration strategy isn’t a time to be ambitious

The data leaders’ first order of business is ensuring good data. Years of project files, billing records, and financial data lay scattered across various systems, and these need to be mapped and migrated to the new ERP. This isn’t easy. Data leaders must collaborate with change leaders, listening to the business needs and department objectives to meticulously chart a course, prioritizing critical data, identifying potential bottlenecks, deciding which data can go, and bracing themselves for the revamp.

Depending on exactly what manufacturers want to achieve with their new ERP, what modules they will implement, and what processes they will automate, will implicate the project scope. Change leaders must do their homework and reach out to multiple vendors and peers who have undergone similar ERP implementations to establish a realistic timeline and budget. No matter how well they plan, the only guarantee is that delays and unexpected costs will arise at some point.

Most commonly, manufacturers transfer data from old systems to the new ERP in a phased approach. Hershey’s didn’t do this, attempting to implement multiple systems (an ERP, CRM, and SCM system) simultaneously within a 30-month period. The end result was a 19% decline in quarterly profits, a drop in stock price, and a significant loss in market share. Instead of setting themselves up for failure, leaders should unite to agree on priorities, quick wins, the biggest bottlenecks, and the extent of proprietary data customizations.

Once the data roadmap is set, leaders must work with ERP vendors and data teams to ensure proper training programs that are tailored to employees’ roles and responsibilities.

Test, test and test again

With a plan in place, the real work can begin. The installation is the easy part—most likely the chosen ERP partner will ensure the proper configuration of the ERP software. It’s often the complex data transfer that causes issues. 

Roll out the training sessions, make sure everyone understands the “why” behind the change, patiently address concerns, and quell anxieties. During this stage, it helps if the data lead is always available to demystify the technical jargon and guide employees through the intricacies of the new system.

Then, the most important—and time-consuming part—is testing it. Each phased migration must be thoroughly tested to identify and resolve any issues before going live. When the day of the launch arrives, leaders want to feel confident that the initial rollout was carefully phased, allowing teams to acclimate to the new system gradually. The change leaders will feel like they are everywhere at once, troubleshooting glitches, answering questions, and offering encouragement.

The post-implementation cooldown

In the weeks that follow, the factory floor slowly finds a new rhythm. Initial apprehension gives way to a growing sense of familiarity, and leaders start seeing some quick wins impressing the team. If this is the case, leaders should feel extremely proud that they have successfully navigated the company through a very challenging transition.

Adoption by the people who use it every day is where success lies. The best way to ensure teams feel comfortable is for the chosen change leaders to continue checking in with staff about the new processes and using their feedback to identify any areas for improvement.

Likewise, system experts must regularly monitor the ERP’s performance and determine whether objectives are being met and where refinements can be made. Your ERP partner can help you navigate all of these changes post-implementation with regular check-ins and KPI tracking.

Successful ERP integration hinges on user adoption and company-wide engagement. Yes, implementing a new ERP system can entirely disrupt your organization. However, that disruption—when done in a positive way—fuels change and growth. With the right leadership in place, the right ERP partner, an effective communication strategy, and efficient project management, new software built for your industry is often the best thing you can do to scale your business for the years to come.

By breaking your project into digestible phases and following these tips, manufacturers can better coordinate resources, reduce the pain of a new implementation, gain employee buy-in, and position themselves for long-term growth.

John Haddox is the COO of Decision Resources Inc. a Pittsburgh-based ERP consulting and implementation company. Reach him on Linked In.

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I expect effective, enterprise-spanning collaboration to be front and center. Without that, no new product (or service or system) will succeed in the marketplace and/or with its users … or even get to market in the first place.  This points to the criticality of people in digital transformation and why people are cited as a major cause of many digital transformation failures.

These failures, estimated at three-fourths of all digital transformation projects, are inevitable.  As long as users and managers don’t understand Digital Transformation, they will not accept it and may even actively resist it. In what I see as a corollary to Murphy’s Law (the idea that “anything that can go wrong will”) when digital transformation efforts fail too often, they will stoke all-too-real fears that lead people to thwart further efforts.

There is nothing mysterious about digital transformation failures.  They are caused by the same factors and lack of oversight that often leads to every other technology implementation failure.  Any list of such failures will suffice, so I see no point in reiterating them here. 

Assuring the successful implementation and adoption of any new technology (or system or process, for that matter) requires that users get comfortable with it and see how its use in their everyday jobs provides value.  It’s that simple … and that complex!

The myriad changes and disruptions surging through the global economy make successful digital transformations increasingly urgent, CIMdata’s clients tell us.  As the leading authority on Product Lifecycle Management (PLM) and its digital transformation, CIMdata provides research, education, and strategic consulting to clients around the world.  This requires us to have our ears to the ground.

Before we get into what I see as being 2025’s key digital transformation drivers, we must be clear: What is Digital Transformation?  While there are many definitions of digital transformation, CIMdata prefers Gartner’s: “…the use of digital technologies to change a business model and provide new revenue and value-producing opportunities; it is the process of moving to a digital business.”

At the end of this article, we will take a more comprehensive look at digital transformation and its impacts.

Now, on to 2025’s drivers.

The ongoing maturation of PLM technologies is Driver No. 1.  By continually expanding capabilities for product design, development, analysis, and connectivity, PLM solution providers are helping pull operations technology (OT) and engineering technology (ET) closer together with powerful new tools that support and enhance an organization’s overall information technology (IT) landscape.

Enhancing PLM’s new capabilities are artificial intelligence (AI) as both Generative AI (GenAI) and Retrieval Augmented Generative (RAG AI) running on the powerful new computers they require.  AI and this hardware are rapidly spreading through the global economy, and every day brings new announcements.

The growth of PLM and the new surge of interest in digital transformation have the same drivers.  Every organization in the industrial and commercial world is under growing pressure to create products, develop services, and enhance and/or build/rebuild these systems on which success depends. 

CIMdata clients, among the world’s largest companies, tell us they are subject to the same drivers.  This is especially true of the huge aerospace, defense, and transportation corporations, and the builders of the production systems on which they depend.  They pioneered the use of PLM-enabling technology in the mid-to-late 1980s to support complex products and systems with service lives measured in decades.

Industrial consolidations and acquisitions are Driver No. 2.  They combine multiple PLM implementations that differ widely in source, age, and extent of use.  They must be made to work together (i.e., they must be “harmonized”), usually with extensive modifications, many of which result in PLM implementation revisions and enhancements that are part of its maturation.

Accompanying and often driving these consolidations and acquisitions are staffing shortages, adding to the pressure to simplify, speed up, and broaden access to data.  This access fosters more effective collaboration, which is essential for developing a steady stream of competitive and profitable offerings in every marketplace.

In addition, digital transformation “upskills” every job and task it touches.  The need for new skills must be clearly understood and acted upon.

Industrial consolidations and expansions may accelerate yet again as the U.S. Department of Defense continues to drive its Digital Engineering initiatives, adding pressure for digital transformation throughout its vast supply chains.  Additionally, new domestic policies may step up the “reshoring” of manufacturing, which is already underway in many other countries.  As a result, companies that have failed with digital transformation may be incentivized to try again.

Fortunately, as PLM matures, the time lags between advances in PLM capabilities and their comprehension and adoption are shrinking.  However, while this is Driver No. 3, I still don’t see comprehension and adoption growing fast enough to keep up with Driver No. 2.  

Still needed is a better understanding of processes as they are actually used, a deeper appreciation of the benefits of newly available technologies, and redoubled efforts to get “people” to look up from their day-to-day tasks and see what is happening all around them.  Ultimately, they must recognize the growing knowledge gaps in their part of the business.

Knowledge gaps mean people: users, implementers, managers, and leadership. Digital transformation will fail again unless “people” are:

•  Relieved of fears of retribution if they point out difficulties

•  Eased out of any temptation to disrupt the transformation

•  Made to see that user buy-in is essential, not just for success but for survival.

This employee buy-in and its corresponding user empowerment are also strategic to Digital Transformation.  Hopefully, this surprises no one.

Talk to the “people” singly and in groups about what they like and dislike about progress and personal expectations.  Find out why they feel the way they do, then help them to see how one more failure, even a small one, will hurt everyone. 

For 2025 and Beyond

From my perspective, it seems that everything in the digital world has become “strategic,” i.e., every change is critical and fosters more change.  Looking at this objectively, I see that:

•  Digital transformation is strategic for organizations to strive and then prosper.

•  PLM is increasingly viewed as a core enabler of Digital Transformation for most

   companies that design and deliver products and/or services to the market.

•  A solid grasp of People, Processes, and Technologies is strategic to PLM.

Digital transformation presents a dramatic informational vision of an industrial and economic future that is accelerating right in front of us.  Powerful new technologies, including enhanced PLM, are raising expectations across the entire organization.  Common sense tells us these expectations should be consolidated into Key Performance Indicators (KPIs) and measurable return on investment (ROI) calculations that align with the organization’s business processes and its most promising expectations.

To conclude, the three drivers described above and mentioned below are the most prominent positive developments:

1. The ongoing maturation of PLM’s capabilities
2. Industrial consolidations and acquisitions with the resulting harmonization of multiple digital implementations
3. Shrinkage of the digital knowledge gap

For 2025, taken together, these offer—and require—better insights into the new technologies being evaluated, providing a better grasp of new processes being implemented and making it easier for people to trust in the long-term benefits of Digital Transformation … ultimately with collaboration front and center.

So we ask again, what is digital transformation?  Every firm in any digital business has its own definition.  Here are three:

•  Siemens Digital Industrial Software: “digital transformation is taking advantage of high-end computer systems and software to revolutionize the way products are developed, produced and optimized.  Foundational to such a transformation are digital twins, virtual models that let engineers test and evaluate products and systems before building them, and digital threads, virtual connections between tasks and processes throughout the product lifecycle.”

•  IBM: “Digital Transformation is a business strategy initiative that incorporates digital technology across all areas of an organization.  It evaluates and modernizes an organization’s processes, products, operations and technology stack to enable continual, rapid, customer-driven innovation.”

•  McKinsey & Co.: “Digital Transformation is the rewiring of an organization, with the goal of creating value by continuously deploying tech at scale. A clear digital transformation strategy focused on specific domains and enabled by a set of specific capabilities is critical for organizations to not only compete but survive. Digital transformation is not a one-and-done project; most executives will be on this journey for the rest of their careers.”

At the end of the day, I am convinced that every aspect of these definitions will accelerate in 2025 and the coming years. 

The post PLM and Digital Transformation Trends for 2025 appeared first on Engineering.com.

Deloitte has collaborated with SimplrOps for years to deliver differentiated services that help simplify and automate cloud ERP and HCM operations and implementations.

“As we fully integrate SimplrOps’ technology into our offerings, we are poised to deliver improved performance that helps our clients maximize ROI in their technology investments,” said Simona Spelman, U.S. Human Capital leader and principal, Deloitte Consulting LLP. “SimplrOps has helped us automate complex processes for our clients, allowing teams more time to focus on key business priorities. This acquisition will accelerate our ability to bring the technology to the market, unlocking tremendous potential for future growth.”

The platform also allows Deloitte to embed functional leading practices and conduct real-time checks, reducing data discovery time from weeks to minutes. SimplrOps’ technology clarifies the release management process by enabling teams to quickly understand which parts of a cloud provider’s release are relevant to their solution and offers guidance on testing and feature adoption.

“The acquisition of SimplrOps’ business is a pivotal moment for Deloitte’s Human Capital practice, enabling us to offer our clients one of our first standalone SaaS products,” said Marty Marchetti, HR Cloud Operate offering leader and managing director, Deloitte Consulting LLP.

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New Zealand-based Miraka, the world’s first dairy processor to get its power from renewable geothermal energy, has chosen smart manufacturing software Plex from Rockwell Automation to become even more efficient and sustainable.

Miraka will use Plex to integrate its enterprise resource planning (ERP) systems. ERP is a software system that helps organizations streamline and automate their core business processes—including financial management, human resources, supply chain, sales, and customer relations—across the entire enterprise.

Miraka says its use of geothermal energy helps it “emit 92% less manufacturing carbon emissions than traditional coal-fired factories, giving Miraka one of industry’s lowest global carbon footprints.”

The dairy company will use Plex to connect, automate, track and analyze its operations—from the pasture to the factory floor— to take its core values of excellence and innovation to the next level.

Robert Bell, Miraka CFO, calls Plex a “single source of truth,” with intuitive tools that will help Miraka optimize their business and operational performance by increasing efficiencies.

Plex supports Miraka’s goals to become even more resilient, agile, and sustainable by offering a holistic view across the enterprise so Miraka quickly can respond to market demands and customer changes without interrupting production. The Plex software was built around the pillars of smart manufacturing, helping companies not only streamline their operations, but making it easier for them to follow industry standards and grow their business.

“Plex is a modular system, so it can grow and adapt as needs change in the future, allowing companies like us to remain agile and stay ahead of the competition,” added Bell.

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The push for digital transformation in product lifecycle management (PLM) comes from our everyday experiences, where nearly every aspect of our personal and professional lives is mediated through digital tools.

For businesses, this shift toward digitalization raises a crucial question: How can all these tools, applications and processes coordinate seamlessly to deliver value and innovation? Just a few years ago this was limited to focusing on specific tasks or processes, but now the demand is to have up-to-date information on every tool or system you use.

We live in a hyper-connected era where systems must communicate and adapt in real time. Businesses face the challenge of managing increasingly complex ecosystems of applications, each with their own data and processes. This complexity demands a shift from traditional, siloed thinking to a more integrated, data-driven approach.

Navigating this transformation requires rethinking how data and applications interact, enabling businesses to achieve greater flexibility, efficiency and innovation.

Enterprise Applications as Process Enablers

Historically, business systems and enterprise applications were built to support specific functions and processes within a business. These tools were designed to operate within narrowly defined parameters, focusing on individual data sets, including:

• CAD Applications focused on creating and managing design data, enabling engineers to develop and iterate on product designs.
• MRP Systems (Material Requirements Planning) supported resource allocation and production planning to ensure efficient manufacturing processes.
• CRM Tools (Customer Relationship Management) managed customer interactions, sales pipelines, and support workflows.

While effective for their intended purposes, these applications were largely self-contained with minimal connectivity to other systems. Think of a PLM solution, supply chain, document management or production process. Although companies demanded interoperability, most of these projects were about how to “sync” data from design and engineering to material planning/ ERP rather than setting up a collaborative environment.

Data was treated as a byproduct of the process rather than a core asset. Over time, this siloed approach led to fragmented data landscapes, making it difficult to achieve the integration and real-time insights needed for modern business operations.

The result? Legacy systems often struggle to adapt to the demands of a digital-first world, where agility, collaboration and innovation are paramount. As businesses scale and evolve, the limitations of process-centric architectures become increasingly apparent.

The Shift to Data-First Thinking

Digital transformation fundamentally changes the relationship between processes and data. In the traditional model, processes dictated how data was structured, stored and accessed. Digital transformation flips this paradigm, prioritizing data as the foundation of modern business operations. Companies shift their attention on how to trust data, because data lives longer than applications and business tools. The design history goes on for years, but a company can switch CAD and PDM applications. Data records are more important, and here are some key reasons:

• Real-Time Insights: Data provides the foundation for real-time analytics, enabling businesses to make informed decisions quickly and respond to changing conditions.
• Flexibility and Adaptability: Processes are inherently static and limited to predefined scenarios, while data enables dynamic, context-aware responses.
• Collaboration Across Ecosystems: By connecting data through a digital thread, businesses can ensure seamless collaboration across design, manufacturing, and operations.
• Long-Term Value: Unlike processes, which can be reengineered relatively quickly, data accumulates value over time. A robust data foundation supports innovation, automation, and strategic decision-making.

In this new paradigm, data is not merely a byproduct of processes, it’s the central asset that drives innovation, efficiency, and competitive advantage. Businesses that prioritize data management are better equipped to navigate the complexities of digital transformation and achieve sustainable success.

Rethinking the Status Quo

For decades, enterprise applications like PLM (Product Lifecycle Management) systems have been designed to organize engineering processes within organizations. These systems emphasized creating a ‘single source of truth,’ focusing on managing product data records (e.g., design files, engineering documents) and enforcing processes such as data approval, versioning and updates.

PLM was an effective approach when the demand was to store data and gatekeep access. Today, this misalignment of the traditional PLM approach is becoming obvious. PLM tools should become a source of data shared with everyone.

The foundation of the switch is how PLM software adapts, becoming an “agent” that performs specific tasks on the data, such as engineering change order approval. This shift from a process-centric to a data-centric approach requires rethinking foundational concepts such as the single source of truth and adopting new strategies that prioritize collaboration, flexibility and adaptability.

A Single Source of TRUTH Change

In the context of digital transformation, the traditional single source of truth is evolving into a single source of change. This new approach emphasizes:

• Dynamic Data Organization: Data is no longer confined to rigid hierarchies or processes. Instead, it is modeled and organized to reflect real-world relationships and dependencies.
• Collaborative Workspaces: Data becomes a shared resource that enables cross-functional collaboration, regardless of where it originates or is maintained.
• Context-Aware Data Management: Systems must understand not only what the data is but also how it is connected, who can change it, and under what circumstances.

Instead of treating data as a secondary consideration, businesses must prioritize creating flexible, adaptable data models that reflect the complexity and interconnectivity of modern operations.

Practical Strategies for Disconnecting Data from Applications

Switching to a data-first approach can be challenging, especially for companies with old systems deeply tied to specific software. Some companies have data from a dozen ERP systems and several PLM applications including legacy databases. What can those companies do? Here are a few practical approaches:

Rethink Data Models

Traditional data models are often built around specific applications, which makes it hard to use data across different systems. Most traditional data systems use relational SQL databases with inflexible schemas. To solve this, companies should use modern flexible data models that don’t rely on fixed structures and can change as needed.

Graph databases are a great option because they are good at handling complex connections between data. It’s also important to organize data in a way that lets systems understand its meaning and context, making it easier to work with.

Decouple Data from Applications

Data shouldn’t depend on specific software—it should stand alone as a valuable resource. To achieve this, companies can combine data from various systems into one central place for easier access. Using tools like APIs and integration layers helps different applications share data seamlessly. Another option is data federation, which keeps data in its original systems but allows centralized access and visibility.

Knowledge Graph and AI models

This is a modern data modeling approach which is growing. A product knowledge graph organizes all product-related data—design, engineering, and manufacturing details—in one connected system. This ensures data is consistent and accurate across all areas. It also supports better decision-making with advanced analysis tools and makes it easier for different teams to work together by sharing the same information.

With a huge spike in GPT and LLM models, we can see how these models can consume data in a more holistic way disconnected from the applications where the data was originally created. They can also provide quick insights and help find the information you need from large data sets quickly and accurately, to support various chatbots and future analytics and AI agents.

Collaborative Data Management

In a data-first setup, teamwork is key. Companies should enable real-time data sharing so everyone has up-to-date information. Teams across different departments need tools to work together effectively, and clear processes for managing changes are crucial to ensure everyone stays on the same page and changes are tracked.

By following these strategies, businesses can move away from outdated, application-tied data systems and unlock new possibilities for efficiency, innovation, and growth.

Embracing the Future of Data-Driven Manufacturing

We are living through a period of profound transformation in manufacturing and PLM. The traditional model of application-defined data is giving way to a new paradigm of data-defined action. This shift represents a fundamental rethinking of how businesses approach data, applications, and processes.

By prioritizing data as the central asset of their operations, businesses can:

• Achieve greater flexibility and adaptability.
• Drive innovation and efficiency through real-time insights.
• Build resilient, future-ready systems that support long-term success.

The transition to a data-first approach is not without its challenges, but the rewards are immense. By embracing modern data management strategies and technologies, businesses can position themselves at the forefront of digital transformation, unlocking new opportunities for growth, innovation, and competitive advantage.

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Many executives foresee AI revolutionizing Software-as-a-Service (SaaS) by transitioning from static tools to dynamic, personalized, and intelligent capabilities. AI-as-a-Service (AIaaS) offers businesses unprecedented opportunities to innovate and scale. The promise is a future powered by AI agents and Copilot-like systems that streamline infrastructure, connect enterprise data, and reduce reliance on traditional configuration and system integration.

In a recent BG2 podcast, Satya Nadella shared his vision for AI’s role in reshaping technology and business. He stated, “The opportunities far outweigh the risks, but success requires deliberate action.” These opportunities extend beyond industry giants to startups and mid-sized enterprises, enabling them to adopt AI and leapfrog traditional barriers. Smaller enterprises, in particular, stand to gain by avoiding the pitfalls of complex digital transformations, taking advantage of AI to innovate faster and scale effectively.

Revolutionizing Experiences and Integration

AI is (or will be) fundamentally changing how users interact with SaaS platforms. Traditional SaaS tools are often said to be rigid, offering one-size-fits-all interfaces that require users to adapt. In contrast, AI brings opportunities to disrupt this model by analyzing user behavior in real-time to offer personalized workflows, predictive suggestions, and proactive solutions. Nadella emphasized this transformation, saying, “The next 10x function of ChatGPT is having persistent memory combined with the ability to take action on our behalf.”

This aligns with the emergence of Copilot systems, where AI acts as a collaborative partner rather than a mere self-contained tool. Imagine a SaaS platform that not only remembers user preferences but actively anticipates needs, offering intelligent guidance and dynamic adjustments to workflows. Such personalization fosters deeper engagement and loyalty while transforming the management of business rules and system infrastructure.

Empowering Smaller Enterprises

The promise of AI extends not only to large enterprises but also to smaller businesses, particularly those in manufacturing and traditionally underserved sectors. For example, a small manufacturer could adopt AI-driven tools to optimize supply chain management, automate repetitive tasks, and deliver personalized customer experiences—all without the complexity of traditional ERP systems.

To ensure successful adoption, businesses must:

• Identify high-impact areas: Focus on processes that benefit most from automation and predictive analytics, such as customer service, supply chain management, or marketing optimization.
• Leverage scalable solutions: Choose AI platforms that align with current needs but can scale as the business grows.
• Build internal expertise: Invest in upskilling employees to work alongside AI tools, ensuring alignment between human and machine capabilities.
• Partner strategically: Collaborate with AI vendors that prioritize interoperability and ethical standards to avoid vendor lock-in and compliance risks.

Redefining Value: Pricing Models and Proactive Solutions

AI is not only transforming technical capabilities but also redefining pricing models for SaaS platforms. Traditional subscription fees are being replaced by real-time, usage-based pricing, powered by AI algorithms that align revenue with the value delivered. Nadella warned, “Do not bet against scaling laws,” underscoring AI’s potential to adapt and optimize at scale. For instance, AI can analyze customer usage patterns to calculate fair, dynamic pricing, ensuring customers pay for the outcomes that matter most.

This shift to value-based pricing can help SaaS companies differentiate themselves in competitive markets, reinforcing their commitment to customer success. Additionally, as AI drives data integration, traditional software vendors (ERP, CRM, PLM, MES, etc.) will need to adapt their business models. With AI, vendor lock-in could become obsolete, or at least redefined, as businesses migrate data seamlessly across platforms, fueled by open standards and interconnected data assets.

Overcoming Adoption Challenges

While the promise of AIaaS is immense, transitioning from traditional SaaS is not without its hurdles. Businesses must address:

• Cost barriers: AI solutions can require significant upfront investment, especially for smaller firms. Clear ROI metrics and phased implementation plans can mitigate this challenge.
• Technical expertise gaps: The lack of in-house AI expertise can slow adoption. Partnering with AI-savvy consultants or platforms can bridge this gap.
• Resistance to change: Shifting from static tools to dynamic AI-driven systems requires cultural change. Leadership must communicate the benefits clearly and provide training to ease transitions.

Responsible AI: Trust, Compliance, and the Road Ahead

The rise of AI-powered SaaS platforms presents both immense opportunity and significant responsibility. As these platforms analyze vast datasets, safeguarding user privacy and ensuring compliance with regulatory standards will be non-negotiable. Nadella’s remark that “Innovation must go hand in hand with ethical considerations” underscores the need to balance technological advancement with accountability.

To build trust and ensure accountability, businesses must prioritize:

• Transparent data policies: Clearly communicate how user data is collected, stored, and used.
• Robust security measures: Safeguards against data breaches are critical for maintaining trust.
• User-centric governance: Empower users with control over their data while ensuring compliance with global regulations.

Final Thoughts…

Looking ahead, adaptive AI systems and large language models will continue to redefine how SaaS platforms deliver value, addressing evolving customer needs with precision and speed. Nadella’s vision for AIaaS is inspiring, but businesses must remain grounded. To lead in this new era, organizations must tackle critical questions:

• How can they balance AI’s immense potential with the risks of misuse or ethical lapses?
• What steps are necessary to ensure AI enhances—not replaces—human decision-making?
• How can smaller enterprises leapfrog traditional barriers to scale with AI?
• Can persistent memory systems foster meaningful personalization without sacrificing user trust?
• What role will regulatory frameworks play in ensuring accountable innovation?

By addressing these questions and embracing the opportunities AI presents, SaaS providers can chart a path toward sustained success. The question is not whether AI will transform SaaS, but how organizations will adapt to lead in this new digital era

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Deloitte’s recent report, The Intelligent Core: AI Changes Everything for Core Modernization, highlights a critical shift in the role of core systems due to the rise of AI: “For years, core and enterprise resource planning systems have been the single source of truth for enterprises’ systems of records. AI is fundamentally challenging that model.” AI is moving core systems away from static, rigid structures, offering systems that are adaptive and predictive, transforming how businesses operate.

For smaller manufacturers, this shift underscores the importance of moving beyond static systems. By adopting modular, cloud-based ERP solutions, they can introduce intelligence incrementally without overhauling their entire infrastructure. Scalable platforms allow small and medium manufacturers to integrate AI gradually, starting with targeted applications like inventory management or demand forecasting.

Converging technologies for strategic growth

Deloitte emphasizes the convergence of AI with technologies like IoT and robotics as key drivers of transformation: “In an increasingly convergent world, enterprises would do well to explore intentional industry and technology intersections that propel innovation across boundaries.” While core technologies and enterprise systems may seem exclusive to large enterprises, smaller manufacturers can strategically adopt them to address their unique challenges.

Referring to “core transformation” implies more than digital transformation; AI is poised to disrupt what is, or should be, in the core because it drives new, accessible, capabilities. This is certainly the beginning of some sort of “data democratization” across functions, leveraging both structured and unstructured data sets. The notion of digital core is perhaps more than a merely data repository or functional vault. It is about intellectual property and pan-enterprise dynamic insights—while maintaining appropriate levels of consistency, traceability, and security of the relevant data assets.

Collaborations with technology providers or local academic institutions can help small and medium manufacturers access cutting-edge solutions tailored to their needs without heavy upfront investments. Intentional adoption of converging technologies ensures immediate and sustained value. Among other things, AI can elevate IT from a support function to a strategic enabler, allowing smaller manufacturers to use AI selectively to drive measurable outcomes.

AI-powered tools, such as shop-floor predictive maintenance, can analyze machine data to predict failures, reducing downtime and costs. Similarly, AI-driven production scheduling can optimize workflows, helping manufacturers meet tight deadlines. These high-impact, low-barrier applications of AI can deliver substantial value for small and medium businesses.

Sustainability and scalability as core principles

Deloitte also highlights the importance of balancing sustainability with technological modernization: “The AI revolution will demand heavy energy and hardware resources—making enterprise infrastructure a strategic differentiator once again.” For smaller manufacturers, this presents an opportunity to make strategic decisions that combine scalability with environmental responsibility.

Furthermore, a cloud-first strategy can help small and medium manufacturers reduce costs while enhancing scalability. Cloud services allow businesses to pay for only what they use, easing the financial burden of infrastructure investment. By investing into energy-efficient hardware and renewable energy sources, businesses can align their modernization efforts with sustainability goals.

This intersection of scalability and sustainability also extends to supply chain practices. For instance, AI-powered just-in-time inventory management can contribute to minimize waste and the environmental impact of overproduction. IoT-enabled sensors can track goods in real time, improving logistics efficiency and reducing emissions. These innovations provide operational savings and enhance a manufacturer’s environmental credentials, strengthening their position in the marketplace.

AI-enabled ways of working

The convergence of AI and enterprise digital technologies offers smaller manufacturers the ability to rethink their entire system of operations. By adopting AI-enabled ways of working, businesses can unlock new levels of scalability and agility. AI maximizes resource utilization, reduces inefficiencies, and enables faster, more accurate decision-making. As such, AI-powered analytics uncover hidden patterns, driving innovation in product design and service delivery, which gives manufacturers a competitive edge.

AI also shifts operations from reactive to proactive. For example, integrating AI into CRM systems allows manufacturers to anticipate customer needs and adjust production schedules dynamically. AI-powered chatbots and virtual assistants enhance customer interactions, providing instant support and fostering stronger relationships. This can drive significant value to end-users, such as:

• Improving knowledge management, and in turn, reducing errors and duplication.
• Minimizing essential non-value-added activities, without complex data and digital transformation investment.
• Learning from new insights (and enabling new technologies), embedding lessons into continuous improvement opportunities.
• Driving continuous efficiencies and time-to-market optimization.

The vision described by Deloitte is about an AI-enabled core aligning with what the business is doing, rather than the reverse: “In the truly agentic future, we expect to see more of these kinds of bots that work autonomously and across various systems. Then, maintaining core systems becomes about overseeing a fleet of AI agents.” McKinsey reinforces this perspective in its latest quarterly insights publication, stating: “Companies are rethinking their digital strategies, moving away from massive transformations to more modular approaches that focus on areas of greatest impact.” This modularity ensures that smaller manufacturers can scale AI capabilities incrementally, avoid the risks of large-scale overhauls, and achieve meaningful progress.

Strategic growth through AI

Smaller manufacturers can achieve long-term scalability by focusing on creating ecosystems that support seamless data exchange and collaboration. AI-driven simulations, such as digital twins, can refine processes before implementation, reducing risks and maximizing efficiency. These ecosystems improve productivity while preparing businesses for future technological advancements. Starting with high-impact, low-barrier AI initiatives like predictive maintenance and optimized production scheduling allows manufacturers to achieve immediate benefits. These small-scale efforts can pave the way for broader digital transformation, leading to sustained growth.

As ERP systems and other core technologies transform into intelligent platforms, leveraging AI to provide dynamic, real-time insights instead of relying on static records, PDM and wider PLM systems are poised to embrace similar advancements. The adoption of AI-driven PLM systems is already underway in some forward-thinking organizations, and the wider industry is quickly following suit. While transitioning from legacy systems can be complex, the promise of intelligent, predictive PLM systems is worth the effort. As AI technology matures and platforms become increasingly interconnected, enterprise platforms will evolve into dynamic, proactive solutions that enable manufacturers to make smarter, data-driven decisions and unlock new opportunities for growth and innovation.

Digital transformation and AI certainly offer smaller manufacturers a clear path toward scalability and competitiveness—pending they are not afraid of experimenting. By strategically adopting converging technologies, prioritizing sustainability, and gradually integrating AI into operations, small and medium manufacturers can modernize their processes without overstretching resources. This incremental approach might foster resilience and agility, ensuring that businesses can evolve alongside the technological advancements that will define the future of manufacturing.

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Don brings over 25 years of experience in the product development industry and PLM market, with expertise in navigating direct and indirect channels. His background includes building and nurturing enablement organizations, guiding enterprise software sales teams to deliver outstanding results, and implementing effective sales processes.

“I am extremely excited to be joining Aras to continue my journey helping customers and partners adopt and deploy PLM – and realize the value from a digital thread,” said Don Cooper. “Aras is uniquely positioned to deliver innovative solutions that drive long-term value, and I look forward to collaborating with the team to make a lasting impact for our customers.”

“Don’s ability to align strategic business initiatives with GTM execution has made him a trusted leader in the SaaS sales landscape,” added Roque Martin, CEO of Aras. “Don will amplify our Build with Aras initiative by activating our entire community to unlock greater innovation and expand the capabilities of Aras Innovator.”

For more information, visit aras.com.

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In an era of rapid technological change, businesses are under increasing pressure to stay competitive. Managing complex product development processes while ensuring seamless communication and real-time data flow is one of the most significant challenges. Traditional, isolated systems are no longer sufficient. Instead, organizations need integrated solutions that connect every facet of their operations — from product development and procurement to sales and customer service — into one cohesive ecosystem.

Seamlessly integrating product lifecycle management (PLM) and enterprise resource planning (ERP) solutions delivers on the mission critical requirement to manage product data across the enterprise. By breaking down silos and enabling smooth data flow between systems, companies can simplify operations, promote innovation and foster better collaboration.

Let’s dive into why integrated PLM and ERP systems matter, and how they can drive your business forward.

Why Seamless Integrations Matter

You might be asking yourself: Why is integration such a critical factor for business success? The answer lies in the tangible benefits that connected systems bring to your product development and operations. Let’s break down those key advantages:

1. Data consistency across platforms

Inconsistent data is a costly problem. When information is scattered across different systems, errors and delays are almost inevitable. Companies need to ensure that product data is synchronized across all platforms — whether it’s design, procurement, manufacturing or sales. Bi-directional data exchanges mean that changes made in one system are automatically reflected in others, ensuring everyone, from engineers to suppliers, works with the same accurate, up-to-date information. Real-time data consistency helps minimize errors, accelerate decision-making and keep your processes running smoothly.

2. Streamlined collaboration

Effective collaboration can make or break your product development process. Internal and external teams need to be connected with seamless communication that provides access to real-time data. With bi-directional data exchanges, teams can confidently share information knowing it remains consistent across all platforms. No more long email chains, fragmented conversations, or delayed approvals. Teams can collaborate more efficiently, speeding up development cycles and reducing time-to-market. Integrated platforms also make it easier to work with suppliers, partners and customers — creating a more connected and nimble business ecosystem.

3. Enhanced product experience

Today’s customers expect more than just quality products; they demand engaging product experiences. Businesses want to leverage interactive 3D models, designs and detailed visualizations. Thanks to bi-directional data exchanges, the most current product data is always available, ensuring that designs and models reflect the latest specifications. This enhances customer understanding, engagement and helps them make informed purchasing decisions. The result? Higher customer satisfaction, stronger loyalty and a competitive edge in the marketplace.

4. Automation and efficiency

Automation is a powerful tool for boosting productivity. Organizations require the automation of key processes, such as: approvals, order processing and workflow management. Bi-directional data flow between systems also means that these automated processes are always working with the latest information. With real-time visibility into tasks and operations, you can quickly identify bottlenecks and inefficiencies. This proactive approach helps streamline workflows, reduce manual effort and ensure that your teams stay focused on high-value activities.

Three high-value scenarios for effective PLM integrations

When PLM solutions integrate with other core business tools, companies can better manage product data, collaborate efficiently and make faster, more informed decisions. Here are three high-value examples where integration can drive significant business innovation:

1. Connect your data: integrating PLM with ERP systems

When PLM systems integrate with ERP platforms, they enable real-time synchronization of product and business data. For example, manufacturing teams can access the most up-to-date bills of material (BOMs) directly from the PLM system, ensuring accurate production. Service teams can use design data, like 3D models, to troubleshoot and maintain products effectively. Bi-directional data ensures consistent and reliable information throughout the product lifecycle, resulting in products that meet quality standards from design to delivery.

2. Connect to platforms: integrating third-party PLM systems

Businesses often rely on specialized third-party PLM systems, such as Siemens Teamcenter, Dassault Systèmes 3DEXPERIENCE or Autodesk Vault, to coordinate various aspects of product development. Integrating these tools with enterprise systems ensures that product data flows seamlessly fostering collaboration and maintaining data integrity.

This integration also allows design teams to work in their preferred tools while ensuring the data is consistently available and accurate. For instance, product designs created in a third-party PLM system can be transferred into ERP for direct use in downstream supply chain processes, ensuring a smooth handoff of information and reducing the risk of miscommunication.

3. Connect your ecosystem: integrating PLM with supplier networks

Close collaboration with suppliers during sourcing and procurement can significantly increase business success. Having an integrated digital platform where the latest engineering, development and manufacturing information is securely shared with suppliers, enables companies to eliminate the need for endless email exchanges, phone calls and manual updates.

For example, material specifications, quality standards and design changes can be exchanged with suppliers in real-time, ensuring everyone is on the same page. Companies can onboard suppliers faster, improve compliance and speed up the procurement processes. The outcome? Smoother supplier collaboration, faster time-to-market and improved product quality.

Embrace integration for operational excellence

Integration isn’t just a technical convenience — it’s a strategic necessity. Seamlessly connected systems empower businesses to streamline product lifecycle processes, improve collaboration and make data-driven decisions with confidence. In a world where complexity is the norm, integration provides the clarity and flexibility needed to stay ahead of the curve.

When product data flows across design, engineering, procurement and production, the entire organization benefits. Teams can collaborate in real time, minimize errors, manual tasks and respond faster to market demands. Suppliers and partners stay aligned, ensuring smooth handoffs and consistent communication. And with automated workflows, bottlenecks and inefficiencies can be identified and resolved before they impact productivity. Integrated systems also allow you to deliver quality products and exceptional experiences that build loyalty.

Forward-thinking businesses recognize that tight integration is the key to unlocking future growth and resilience. To learn more about how SAP can support your product development processes, we invite you to explore our other articles in this series, visit our website or check out our newest website for PLM Systems Integration.

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