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Why SOLIDWORKS Is Leading the AI Revolution in CAD

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Why SOLIDWORKS Is Leading the AI Revolution in CAD

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 SOLIDWORKS and its parent company, Dassault Systems, have been ahead of the competition when it comes to all things AI. SOLIDWORKS started developing AI features, also known as Smart Features, decades ago, giving their software a lead above the competition. While continuing to invest and stay ahead of the pack, all new AI assistants are now directly available within the application, ensuring that integration is seamless.

Follow along in this blog, because I want to show you all the amazing features SOLIDWORKS has already implemented over the year, along with what is in store for the future. By the end, I will have shown how the recent attempts of our competition’s software do not hold a candle to the advances SOLIDWORKS has already made, let alone what is in store for the future.

Past Additions of Machine Learning and Artificial Intelligence

For over a decade, SOLIDWORKS has been continuously adding features that make use of machine learning and artificial intelligence. From features such as Smart Mates or Smart Fasteners to new AI Drawing Creation, SOLIDWORKS has been working to optimize engineer time, and reduce the number of tedious repetitive tasks.

Excelling in time optimization for years, SOLIDWORKS has continued making tools designed with engineering resources in mind. Tools like Fully Defined Sketch and Selection Accelerators have been available for years, helping make the sketching and selection processes faster. Always improving, SOLIDWORKS took the predictive selection accelerator from the Fillet command, and added it into Chamfers in recent years, making seamless group selection even easier than before in both features.

Machine Learning and Artificial Intelligence

Users can go from this underdefined sketch to this fully defined sketch in 3 quick clicks!

Machine Learning and Artificial Intelligence 2

There have even been productivity increasing tools in the assembly environment for just as long! Smart Fasteners and Smart Mates have allowed engineers to snap together parts and fill their holes with fasteners for over a decade. Even before the general public heard about AI and chatbots, SOLIDWORKS has been working to implement AI based features to improve the engineering experience.

Current SOLIDWORKS AI Tool Additions

In 2026, SOLIDWORKS continues this trend of improving the engineering experience through implementing countless new features in the most recent as well as future updates. Some such features include AI Drawing Creation, AI Assembly Creation, Automatic Fastener Recognition, Command Predictor, and Pattern Assistant, to name a few. With these tools, SOLIDWORKS will become even smarter, and can predict an engineer’s next move; whether that move is dropping a nut into place, or needing to add a pattern of bolts in one swift movement. SOLIDWORKS can now even assist engineers in making sure the most efficient patterning methods are being used, as an efficiency check to young engineers.

SOLIDWORKS AI Tool Additions

Tools, like Automatic Fastener Recognition, make use of a database of thousands of fastener files, allowing the SOLIDWORKS AI to determine if a part is a fastener as soon as it is dragged in to your current project. This recognition will allow the system to offer better mate conditions and groupings, for instance pairing a new nut to your existing bolt.

Additionally, features like AI Drawing Creation and AI Assembly Creation take processes out of the engineers hands and begin these processes in the system background before bringing the engineer in for confirmation. From laying out standard views and annotations, to organizing folder structures in assemblies, SOLIDWORKS continues to assist in simplifying and standardizing these initial steps in creation and documentation.

SOLIDWORKS AI Tool Additions

With the use of SOLIDWORKS AI Drawing Creation, a simple conversation with LEO about the desired settings and defaults leads to a drawing created faster than ever before!

SOLIDWORKS AI Tool Additions

Addition of AI assistants in SOLIDWORKS

SOLIDWORKS AI Assistants

The most recent additions of artificial intelligence to SOLIDWORKS include the three all new AI assistants; AURA, LEO, and MARIE. Each serves a unique role throughout the CAD Design process, as described below.

AURA is the starting point of any great project, even before you draw your first sketch. AURA holds the ability to leverage knowledge from both web and enterprise sources, making it your one stop shop for rapid confirmation. For questions regarding basic design rules and suggestions, or even searching your company’s knowledge base, AURA can answer it all.

After the first steps with AURA are completed, LEO takes the reins. LEO can help users effectively solve many complications through the design process, helping validate your design and optimize your processes. Throughout both mechanical design, as well as simulation, LEO can take your prompts to generate assembly structures, as parametric features, run studies, and even help resolve design errors. For both answering questions, and offering solutions, LEO can solve many engineering headaches.

The last assistant in the lineup is MARIE, your scientific research specialist. With expertise in materials science, chemistry and more, your thorough scientific research can be simplified. With this third member of the SOLIDWORKS AI trifecta, you have an assistant in your corner for every part of the engineering design process.

Competitors attempts at replication

Outside of SOLIDWORKS, many competitors have tried their hand in implementing AI for the benefit of users. While many companies have had good feature additions in recent years, it is hard to compare them to the decades of experience and additions seen in SOLIDWORKS. The following sections detail some of these features within the competing software, and shows how SOLIDWORKS has taken the lead in all things AI.

For starters, Autodesk has invested in AI in Fusion 360. However, you will find no such features in Inventor. Looking into these, features like CAM hole recognition have existed in SOLIDWORKS for some time. The drawing AI tool seems to be in the early stages, having very little interaction or flexibility. Fusion can add relationships and dimensions automatically, much like Fully Define Sketch (something that has existed in SOLIDWORKS for nearly 20 years). The main hurdle that Autodesk will have to overcome is that their files don’t talk to each other, unlike the fully associative files found in SOLIDWORKS, making their AI feature development harder.

Other competitors like Siemens have three main enhancements, Magnetic Snap, Automated Drawings, and a design copilot, all things that have existed or do now exist in SOLIDWORKS. Lastly, Onshape has a lot of potential due to their cloud-based nature, however the content released as of now is just in the infancy stage.

The Bottom Line: SOLIDWORKS AI is Changing the Game

After looking at the history of feature development, as well as a brief look at the competition, you can see that SOLIDWORKS continues to be designed with the engineer in mind. From features that increase productivity by decreasing repetition, to tools that give you a head start in the design process, SOLIDWORKS is a lifesaver. Many competitors’ Artificial Intelligence ambitions are just beginning, so SOLIDWORKS is working hard to maintain the lead they already have, while pushing engineering design technology to the next level. Our SOLIDWORKS Technical Team has been ahead of the pack when it comes to learning and using AI, so please contact us with any questions, and find out what makes us the Solidxperts.

Alain

Alain Provost

Senior Technical Sales Executive

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    Resolving issues with part name display in eDrawings compared to SOLIDWORKS

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    Resolving issues with part name display in eDrawings compared to SOLIDWORKS

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    It is quite common for information shared with the workshop through eDrawings not to appear exactly as expected. In fact, part names may seem incorrect, incomplete, or simply different from what the engineering team sees in SOLIDWORKS.

    As a result, this is a question we are regularly asked: “Why are part names not the same in eDrawings as in the SOLIDWORKS assembly?”

    So, if you use eDrawings as a viewer for shop floor personnel, this article is for you. Let’s take a few minutes to understand why this happens and more importantly, how to fix it in a sustainable way.

    The Typical Context: eDrawings as a Workshop Support Tool

    In many manufacturing companies, eDrawings is used to:

    • View assemblies without a SOLIDWORKS license;

    • Visualize complete machines on the shop floor;

    • Quickly identify parts to manufacture or assemble;

    • Reduce paper drawings.

    It is an excellent tool as long as the displayed information is clear and consistent.

    However, in some cases, workshop users are faced with:

    • Cryptic file names;

    • Internal references that are not meaningful;

    • Part names different from those used by engineering.

    A Key Point to Understand: eDrawings Does Not Interpret, It Displays

    First of all, it is important thing to clarify: eDrawings does not “guess” anything. It simply displays the information coming from SOLIDWORKS, based on the assembly structure, the properties defined on each part, and the export options used. Therefore, if the display does not meet your expectations, it is almost never an eDrawings bug, but rather a source data or configuration issue.

    The Three Most Common Causes

    In practice, three main causes explain this behavior:

    1. The displayed name is the file name, not the business designation

    By default, eDrawings often displays the part file name (.SLDPRT) instead of:

    • The business designation;

    • The part number;

    • The workshop-oriented description.

    Example: PLT_4587_V3.SLDPRT instead of Conveyor support plate – 10 mm steel

    For the shop floor, the added value is… very limited.

    2. Custom properties are not being leveraged

    Additionally, in SOLIDWORKS, you most likely already have:

    • Description

    • Part Number

    • Internal reference

    • Customer name

    But if these properties are not filled in consistently or eDrawings is not configured to display them, they become useless for the workshop.

     

    3. The eDrawings export process is not standardized

    Finally, an export performed quickly, by different users and without a clear procedure often results in:

    • inconsistent displays;

    • different habits from one project to another.

    As a result, the workshop gradually loses confidence in the tool.

    Recommended Best Practice: Think “Workshop” Directly in SOLIDWORKS

    In reality, the solution is not in eDrawings…it starts in SOLIDWORKS.

    Here is a simple and effective approach:

    Use a workshop-oriented property

    For example:

    • Description

    • or Workshop_Description

    This property should be clear, readable and free of unnecessary CAD jargon.

    Standardize how properties are filled in

    Apply the same logic to all parts:

    • same property name

    • same text convention

    • same language

    Ultimately, this is a small effort on the engineering side…but delivers significant gains on the production side.

     

    Structuring the eDrawings Export for the Workshop

    To ensure consistency, the eDrawings export should:

    • always come from an up-to-date assembly;

    • follow a simple, documented procedure;

    • display useful information, not technical noise.

    This is exactly why a short internal procedure is often an excellent idea.

    eDrawings: An Excellent Tool, When Properly Prepared

    eDrawings is neither a design tool nor a PDM system. It is a technical communication tool.

    In other words, like any communication, quality depends on what is sent, not only on the tool itself.

    As a result, when best practices are in place the workshop gains autonomy, the unnecessary questions decrease, and the interpretation errors are reduced.

    From Confusion to Clarity: Making eDrawings Work for the Workshop

    If part names displayed in eDrawings do not match what you expect, know that you are not alone, it is not inevitable, and it is almost never a bug. More often than not, it is an opportunity to review how information is prepared and transferred to the workshop.

    Very often…a few simple adjustments are enough to turn eDrawings into a true production support tool.

    FAQ

    Why do part names in eDrawings differ from those in SOLIDWORKS?

    eDrawings displays information coming from SOLIDWORKS files, typically the file name or custom properties. If these data are not standardized or workshop-oriented, the display may appear inconsistent.

    Is this an eDrawings bug or limitation?

    No. In most cases, the issue lies in how data is structured upstream in SOLIDWORKS, not in eDrawings itself.

    What is the best practice to display clear part names on the shop floor?

    Use a dedicated, readable SOLIDWORKS property such as Description or Workshop_Description, filled consistently across all parts.

    Is a SOLIDWORKS license required on the shop floor?

    No. eDrawings allows assembly viewing without a SOLIDWORKS license, making it a cost-effective solution for workshop use.

    What is the tangible benefit for the company?

    A clear and standardized eDrawings display helps to:

    • reduce interruptions between engineering and production

    • limit interpretation errors

    • improve overall operational efficiency

    Alain

    Alain Provost

    Senior Technical Sales Executive

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    Whether you’re ready to get started or just have a few more questions, you can contact us toll-free:

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      AI won’t replace you. Someone using AI will.

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      AI won’t replace you. Someone using AI will.

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      AI may not be perfect yet, but it’s precisely why you should start using it today.

      We’ve grown used to talking about artificial intelligence as if the story began in 2022. ChatGPT arrives, the public adopts it, and suddenly AI becomes a topic of casual conversation. But if we want to properly understand what’s happening, we must not confuse media frenzy with historical reality. OpenAI did release ChatGPT publicly (“research preview”) on November 30, 2022, and yes, it was a real social inflection point.

      But AI as a field is much older. Turing formalized the intellectual framework of the “imitation game” as early as 1950, and the Dartmouth Proposal (1955) explicitly announced a summer 1956 project dedicated to “artificial intelligence.” Some early demonstrations also appeared quickly: the Ferranti Mark I ran a limited chess program in 1951 (mate-in-two).

      This reminder is not meant to give you a history lesson. It serves one purpose: AI is not a feature. It is a trajectory.

      And it resembles another well-known human trajectory: that of fire.

      The Fire Analogy: Understanding a Technology We Don’t Yet Understand

      At this point, you’re probably thinking: “What is he talking about?” Stay with me.

      One day, in a cave, one of our ancestors discovered fire. At first, this discovery served very specific purposes: heating, lighting, protection. These were not “industrial innovations”; they were immediate uses. And yet, the full chain – metallurgy, machines, steel industries – that followed from this same discovery reshaped modern history. The human on day one could not imagine the human of today. Not because they were less intelligent, but because they lacked perspective.

      We are at the same stage. Except instead of holding a torch, we are writing prompts. And the typical mistake in 2026 is judging AI based on what it is today, as if it were representative of tomorrow’s trajectory.

      The Real Signal: Speed of Evolution

      What matters is not only what AI does today. What matters is how fast it improves. To make that speed tangible, a cultural artifact has emerged: the “Will Smith Eating Spaghetti test,” now documented as an informal benchmark.

      Case Study: The “Spaghetti Test”

      In its 2023 version, human motion is unstable: faces and hands deform, physics is not believable. In the 2026 version, the result becomes coherent enough that the difference is obvious: we are no longer looking at a “grotesque meme,” but at a rendering that requires a critical eye to detect AI involvement.

      What matters here is the underlying learning dynamic. The progression observed between 2023 and 2026 cannot be attributed solely to model improvements. It is also the result of user adoption.

      Early uses produced low-quality, unstable, and difficult-to-use outputs. However, these experiments helped gradually identify model limitations, refine interaction methods (prompts, iterations, post-processing), and structure more robust practices.

      In other words, the improvement in outputs in 2026 is inseparable from the learning accumulated by users over time. Current performance is not only technological; it is also cognitive and methodological.

      This is how the concept of cumulative advantage should be understood: it does not rely solely on access to technology, but on the experience gained by using it under imperfect conditions.

      From Internet Culture to the Engineering Office: Why SOLIDWORKS Is Concerned

      The transition from “spaghetti → SOLIDWORKS” is not arbitrary. It is the same mechanism applied in a different context. A general-purpose technology crosses a threshold, then infiltrates products, becomes invisible, and ultimately reshapes practices.

      We’ve already seen this in the 2010s: AI did not “look like ChatGPT,” but it was already embedded in everyday life. Google Maps, for example, deployed models (including graph neural networks) at scale for ETA (Estimate Time of Arrival) and traffic prediction. The result: you use AI without thinking about it. The advantage rarely comes from an “AI button,” but from the routines that evolve around your activity.

      SOLIDWORKS 2026: The AI Shift Is Underway

      This is exactly the same dynamic in SOLIDWORKS.

      SOLIDWORKS 2026 already integrates AI into areas where real time is lost: drawings, assemblies, and access to knowledge. Dassault Systèmes presents SOLIDWORKS 2026 as an “AI-powered” portfolio (design, collaboration, data management).

      A clear example: Auto-Generate Drawings (BETA). The “What’s New in SOLIDWORKS 2026” documentation explicitly describes automatic drawing generation, including section views and hole callouts.

      The same logic applies to assemblies: SOLIDWORKS documents AI-based fastener recognition to automatically create SmartMates, with explicitly listed limitations. This level of detail is precisely what makes the promise credible (and reminds us that this is not “magic,” but engineering with constraints).

      Rather than listing every available feature, it is more relevant to focus on the direction: Dassault introduces “Virtual Companions” (AURA, LEO, MARIE), with AURA and LEO already available and MARIE announced soon. SOLIDWORKS also highlights “AI-guided” features in FD01 (guided analysis, guided creation).

      What matters here is not proving that everything is ready. It is recognizing that AI has entered the tool, meaning the learning process has begun, whether you like it or not. And it is moving fast.

      Waiting for Maturity: A Strategic Mistake

      Let’s be clear: in 2026, all of this is still imperfect. And that is normal. We are at the “spaghetti 2023” stage of AI-assisted CAD: promising, functional in certain areas, but not yet obvious everywhere.

      The instinctive reaction for many teams is: “we’ll wait until it’s mature.”

      This reaction is human. But strategically, it is a serious mistake.

      In 2025, we clearly entered a phase of mass adoption. Nearly 88% of organizations report using AI in at least one function, compared to 78% the previous year. This adoption is accelerating and follows an exponential curve.

      From an economic perspective, the signals are just as clear. The generative AI market reached nearly $60 billion in 2025 and could exceed $400 billion by 2031.

      In industry, the shift is already visible: nearly 76% of manufacturing companies are using AI in 2026.

      But the most interesting point is not adoption. It is the gap between adoption and impact. Despite massive investments, only about 5% of companies currently manage to generate significant value from AI. In most cases, projects remain stuck at the experimental stage, and the majority of initiatives never reach production.

      In other words: everyone has access to AI, but very few truly know how to use it. So “waiting” does not mean being cautious. It means allowing a capability gap to form. Because knowing how to use AI is a skill. And it must be learned.

      What Research Says About Gains (and Their Limits)

      To address the assumption “we’ll wait until AI is ready,” it is important to understand a key nuance: AI does not deliver uniform gains, and that is precisely why early learning matters.

      The operational conclusion is simple: early adoption is not a blind bet; it is a mapping phase. It helps you understand when AI works, when it fails, and most importantly how to control it.

      What It Really Changes: Redefining Engineering Performance

      This is where the thesis becomes concrete: AI will not replace you. A competitor who masters it will.

      And I mean mastery in the strict sense. Asking ChatGPT for a carbonara recipe does not count. We are talking about work practices, standards, quality control, understanding when AI accelerates a task and when it introduces risk, knowing where to integrate AI in a project without breaking traceability, and knowing how to train teams without creating blind dependency.

      In other words, mastery is not built when the tool becomes “perfect.” It is built while it is imperfect, because that is when you establish your standards, your checklists, your controls, and your best practices.

      Ultimately, the value of an engineer will not only be their technical skill. It will be their ability to amplify that skill with properly framed AI.

      Conclusion: From Intention to Action

      The question is no longer whether you are using AI. It is already present in your tools, your processes, and your competitive environment.

      The real question is whether you are learning to use it properly.

      Like all major technological transformations, the advantage does not go to those who wait for everything to stabilize. It goes to those who start while it is still imperfect, who experiment, who structure, and who gradually build solid methods.

      AI does not replace engineering. It redefines its standards.

      And this transition does not happen alone.

      At solidxperts, our teams are already working with these tools on a daily basis. We support companies in implementing practical AI use cases in SOLIDWORKS: identifying relevant use cases, integrating them into existing processes, training teams, and establishing reliable standards.

      If you want to understand concretely what AI can bring to your environment, we offer demos and working sessions tailored to your reality.

      The simplest next step is to start the conversation.

      Max Laramée

      Max Laramée

      Marketing Director

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        7 Myths About AI: Demystifying Bias and Technological Limits

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        7 Myths About AI: Demystifying Bias and Technological Limits

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        Every wave of innovation in artificial intelligence (AI) brings real technological progress, along with a dramatic rise in hype. With every breakthrough, new narratives emerge: AI is portrayed as “magical,” endowed with its own will, on the verge of becoming superhuman, or conversely as something completely uncontrollable by law.

        As a result, this fog of myths makes AI opaque to the public, complicates decision-making for organizations, and distracts attention from the real technical and societal challenges.

        In this article, we aim to clarify two key questions:

        • What are the main myths currently surrounding AI?

        • And what technical, physical, and social realities help dismantle them?

        The Major Myths Shaping Our View of AI

        Several myths structure today’s collective imagination about artificial intelligence.

        “AI has agency.”
        The idea that AI systems act on their own initiative, with intentions, goals, or desires.

        “Superintelligence is imminent.”
        The belief that we are only a few years, or even months, away from a general intelligence far surpassing human capabilities.

        “AI can be objective or impartial.”
        The assumption that algorithms are inherently neutral because they rely on computation.

        “AI has a clear definition.”
        As if AI referred to a single, clearly defined technology, when in reality no universal definition exists.

        “Ethical guidelines are enough to protect us.”
        The perception that voluntary ethical charters are sufficient safeguards against harmful AI uses.

        “AI cannot be regulated.”
        The claim that technological innovation moves too fast for legal systems to keep up.

        “AI can solve any problem.”
        The idea that AI is a universal solution applicable to any technical, economic, or social challenge.

        In reality, these myths stem from a mixture of marketing, science fiction, and technical misunderstanding. To move beyond them, we need to return to what AI actually is today.

        1. Agency and Consciousness: AI as a “Stochastic Parrot”

        One of the most common misconceptions is attributing intention to AI. We often talk about what AI “wants,” “decides,” or “thinks.” Yet modern systems, especially large language models (LLMs), function much more simply.

        Models That Predict, Not Understand

        An LLM does not interpret your sentences in the human sense. Technically, it:

        • receives a sequence of tokens (pieces of words) as input

        • computes a probability distribution over the next token using a trained neural network

        • selects or samples the next token according to this distribution

        • repeats the process until a complete response is produced

        This mechanism relies on massive statistical correlations learned during training. At no point does the system possess:

        • semantic understanding of concepts

        • an internal model of the world comparable to a human’s

        • independent intentions or goals

        In other words, what researchers sometimes call a “stochastic parrot”: a machine that reproduces learned language structures in sophisticated probabilistic combinations.

        Anthropomorphism as a Persistent Bias

        If these systems appear to “think,” it is largely because humans naturally anthropomorphize systems that display seemingly intelligent behavior. This cognitive bias is central to many misunderstandings about AI today.

        2. Superintelligence and the Resource Wall

        Another dominant narrative suggests that we are on the verge of general superintelligence, held back only by corporate caution. However, the actual infrastructure behind AI tells a different story.

        The Data Wall: A Finite Resource

        Today’s large models rely on enormous volumes of high-quality human-generated data: text, conversations, code, and multimedia content. But this resource is not infinite.

        Estimates suggest that high-quality training data suitable for ever-larger models could be largely exhausted between 2026 and 2032. Beyond that point:

        • existing datasets would be reused repeatedly, yielding limited improvements

        • or synthetic data would be used, introducing new risks and feedback loops

        Physical Constraints and Diminishing Returns

        The idea of unlimited growth in model power faces several practical limits.

        Energy and cooling constraints
        The computing density required for training and deploying the largest models pushes data centers toward limits in:

        • electrical grid capacity

        • cooling infrastructure needed to dissipate heat

        Hardware limits
        GPUs and other accelerators are approaching physical limits in terms of performance per watt and cost efficiency.

        Diminishing returns
        Scaling models by increasing parameters, data, or compute still improves performance, but each additional gain becomes smaller relative to the resources invested.

        These “resource walls” do not prevent progress, but they challenge the idea of a straightforward path toward limitless superintelligence.

        3. Objectivity and Impartiality: AI as a Mirror of Human Bias

        AI is often presented as a way to eliminate human bias. In reality, AI systems frequently inherit and sometimes amplify existing inequalities.

        Data Bias: Who Is Represented?

        Models can only generalize effectively if training data represent a sufficiently diverse set of situations and populations.

        When datasets are imbalanced, performance degrades unevenly. Studies have shown, for instance, that some facial recognition systems exhibit error rates up to 35% higher for darker-skinned women than for white men.

        This is not an isolated bug. It reflects underlying representation biases in the data.

        Design Bias: Optimization Choices Matter

        Even with balanced datasets, models reflect the priorities of their designers:

        • How is overall accuracy balanced against fairness between groups?

        • Which metrics are optimized during training and deployment?

        • What trade-offs are accepted between false positives and false negatives?

        These decisions directly shape who benefits from an AI system and who may be harmed. Claims of algorithmic objectivity often overlook these design choices.

        4. The Plural Architecture of AI

        Contrary to popular belief, “artificial intelligence” does not describe a single unified technology. Instead, it is an umbrella term covering a broad and heterogeneous set of methods, theories, and applications.

        A Hierarchy of Often-Confused Concepts

        Many people use AI, Machine Learning, and Deep Learning interchangeably, although they represent different levels of abstraction.

        Artificial Intelligence (AI)
        The broader field of computer science focused on creating systems capable of performing tasks that require human-like cognitive abilities.

        Machine Learning (ML)
        A subset of AI in which systems learn patterns from data rather than relying solely on explicit programming.

        Deep Learning (DL)
        A specialized ML approach using multi-layer neural networks to process complex data such as images, speech, or language.

        Divergent Definitions

        The meaning of AI changes depending on perspective.

        • Scientific definition: a research discipline exploring computational models of cognition.

        • Technological definition: systems capable of perceiving their environment and taking actions accordingly.

        • Popular definition: a largely anthropomorphic vision attributing awareness or autonomy to machines.

        A Fragmented Ecosystem

        AI is not monolithic. It includes multiple research traditions and technical approaches.

        Two historical families illustrate this diversity:

        Symbolic AI
        Systems based on logical rules and expert knowledge.

        Connectionist AI
        Statistical approaches based on large datasets and neural networks, including modern language models.

        Narrow AI vs General AI

        Today’s systems belong entirely to narrow AI, designed to perform specific tasks such as:

        • playing chess

        • recognizing objects in images

        • detecting fraud

        • generating text

        Artificial General Intelligence (AGI), capable of learning any intellectual task a human can perform, remains a speculative concept.

        5. Ethics, Marketing, and the Need for Regulation

        In response to AI risks, many organizations have adopted ethical charters and voluntary guidelines. While useful, these tools have clear limitations.

        Ethical Marketing

        Without enforcement mechanisms, many ethical charters function more as reputation tools:

        • they reassure stakeholders

        • they improve brand image

        • but they rarely prevent high-risk systems from being deployed

        Toward Enforceable Regulation: The EU AI Act

        Contrary to the myth that AI cannot be governed, regulatory frameworks are emerging.

        The European Union’s AI Act proposes a risk-based approach:

        • Unacceptable risk systems are banned

        • High-risk systems must comply with strict requirements including transparency, traceability, documentation, conformity assessments, and human oversight

        • Minimal risk systems face limited regulation

        The goal is not to slow innovation, but to ensure that AI systems remain accountable within existing legal frameworks.

        6. AI Is Not a Magic Wand

        Perhaps the most persistent myth is that AI can solve any problem.

        In reality, successful AI systems are:

        • specialized, designed for specific tasks such as image recognition, text summarization, fraud detection, or code generation

        • limited in common sense, often failing when faced with situations outside their training distribution

        • highly context-dependent, relying on data quality, system integration, and human oversight

        The same model may perform extremely well in a well-defined environment yet fail dramatically when conditions change or when real-world usage diverges from intended scenarios.

        AI as a Component, Not a Strategy

        For organizations, AI should be viewed as:

        • a technical component within a larger system architecture

        • integrated into a broader strategy involving governance, metrics, risk management, and human supervision

        The wrong question is:

        “How can we add AI everywhere?”

        The better question is:

        “On which well-defined problems does AI provide a real advantage compared to existing solutions?”

        Moving Beyond the Myths

        Today’s AI is neither a conscious entity, nor an imminent superintelligence, nor a universal solution.

        It is a set of powerful techniques deeply grounded in real-world constraints. These systems are limited by physical infrastructure such as energy, cooling, and hardware, as well as by the availability of data and computational resources. They are also shaped by the social structures and human biases embedded in the data and objectives guiding their development.

        By dismantling the myths surrounding AI, autonomous agency, imminent superintelligence, perfect objectivity, legal ungovernability, or universal applicability, we can ask better technical questions, design safer systems, and build more effective regulatory frameworks.

        Ultimately, understanding these realities allows us to treat AI for what it truly is: a powerful but specialized tool that must be used with rigor, transparency, and human oversight.

        If you have questions about AI and its practical applications, our experts are here to help. Contact us to start the conversation.

        Benoit Bilodeau

        Senior Solutions Architect

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          SWOOD and Material Management: From Design to Wood Manufacturing

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          SWOOD and Material Management: From Design to Wood Manufacturing

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          Material is More Than Just a Visual Appearance

          In the furniture, cabinetry, and commercial millwork industries, material selection plays a critical role. It impacts not only product aesthetics, but also manufacturability, cost control, quality, and production repeatability. Yet, in many organizations, material management is still treated as a secondary concern, often limited to a visual texture or a late-stage production note.

          As a result, this approach frequently leads to well-known issues. Designers and production teams may face inconsistencies between design and the shop floor, incorrect panel selection, edge banding errors, material waste, and costly rework. In addition, standardizing internal processes becomes much more difficult.

          At a time when companies are striving to improve operational efficiency and production reliability, these issues can quickly turn into costly bottlenecks.

          This is where the combination of SOLIDWORKS and SWOOD makes a real difference. By integrating intelligent material management directly into the design phase, SWOOD transforms materials into structured, manufacturing-ready data. As a result, this information remains consistent throughout the entire digital workflow.

          The Limitations of Material Management in SOLIDWORKS

          SOLIDWORKS is a powerful and flexible CAD platform, widely recognized for its robustness and parametric capabilities. In addition, it offers advanced material handling for mechanical design, including physical properties, mass calculations, and rendering. However, when applied to wood-based design, certain limitations quickly emerge.

          In fact, native SOLIDWORKS materials are primarily intended for mechanical applications. As a result, they do not fully address the realities of wood manufacturing, such as:

          • engineered wood panels,

          • commercial panel thicknesses,

          • wood grain direction,

          • supplier-specific decors,

          • edge banding compatibility,

          • or CNC manufacturing constraints.

          As a result, designers often rely on generic materials and manual adjustments. This information remains disconnected from manufacturing processes, forcing production teams to reinterpret design intent. The lack of continuity increases error risks and severely limits automation.

          Why Material Management Is Critical in Wood Design?

          In wood design, materials are never neutral. A panel is not simply a thickness and a color. Instead, it represents a supplier, a finish, compatible edge banding, machining rules, and cost implications.

          Without proper material definition, several issues can arise. For example, poor material management can lead to:

          • incorrect panel usage in production,

          • edge banding mismatches,

          • nesting inefficiencies,

          • inaccurate material cost estimates,

          • and inconsistencies across similar projects.

          On the other hand, structured material management allows companies to:

          • ensure design-to-production consistency,

          • reduce manual data entry,

          • improve communication between departments,

          • and secure manufacturing outcomes early in the design process.

          In this context, materials become a strategic data asset, just as critical as dimensions or tolerances.

          How SWOOD Structures Material Management?

          Material Libraries Designed for the Wood Industry

          SWOOD introduces material libraries specifically developed for cabinetry, furniture, and millwork professionals. Unlike generic CAD materials, these libraries are designed to reflect real manufacturing requirements. As a result, SWOOD materials include production-relevant parameters such as:

          • actual panel thickness,

          • material type (MDF, melamine, plywood, solid wood, etc.),

          • grain direction,

          • tolerances,

          • and attributes required for bills of materials and cut lists.

          These libraries can be standardized company-wide, ensuring consistent practices across all projects and designers.

          Direct Link Between Materials and CNC Manufacturing

          One of SWOOD’s key strengths is the direct connection between materials and manufacturing processes. Because of this, materials are no longer used only for visualization. Instead, they actively drive CNC machining behavior.

          Based on the selected material, SWOOD can:

          • adapt machining strategies,

          • select appropriate tools,

          • control cutting depths,

          • and automatically prepare data for production.

          This significantly reduces manual adjustments on the shop floor and improves manufacturing reliability, even for highly customized projects.

                    

          Edge Banding and Decor Management

          Edge banding is a critical aspect of wood manufacturing. SWOOD enables intelligent associations between panels and compatible edge banding materials.

          Decors are not used solely for visualization. They are also embedded into:

          • bills of materials,

          • cut lists,

          • nesting data,

          • and shop floor documentation.

          By automating these relationships, SWOOD minimizes human error and ensures consistent data from design through production.

          From Design to Manufacturing: A Controlled Digital Continuity

          SWOOD is built around the concept of digital continuity. Data defined during design is the same data used for manufacturing, without re-entry or reinterpretation.

          A typical workflow includes:

          1. Designing furniture or millwork in SOLIDWORKS with SWOOD Design.

          2. Applying structured, manufacturing-ready materials.

          3. Transferring data directly to SWOOD CAM and SWOOD Nesting.

          4. CNC production driven by consistent and reliable information.

          This approach improves traceability, reduces lead times, and increases overall production confidence.

          The Impact on Costs and Industrial Performance

          Effective material management directly impacts business performance. By integrating materials early in the design phase, companies can:

          • improve material cost estimation accuracy,

          • reduce waste and scrap,

          • optimize panel nesting,

          • standardize internal workflows,

          • and accelerate onboarding of new employees.

          These benefits are especially valuable for growing organizations that need scalable and repeatable processes.

          Which Companies Benefit Most from SWOOD Material Management?

          SWOOD material management is particularly valuable for:

          • furniture manufacturers,

          • commercial millwork companies,

          • industrial cabinet makers,

          • CNC woodworking shops,

          • and organizations seeking to structure or automate their design-to-production workflows.

          Regardless of company size, this approach increases reliability, productivity, and competitiveness.

          Why SWOOD Is the Best Solution for Wood Design in SOLIDWORKS

          SWOOD does not replace SOLIDWORKS, it enhances it. It adds a critical industry-specific layer tailored to wood manufacturing requirements. By combining SOLIDWORKS’ parametric power with SWOOD’s manufacturing intelligence, companies gain a coherent, scalable, and production-oriented environment.

          This integration unlocks the full potential of the digital manufacturing chain, from design through CNC production.

          Material as a Core Element of the Digital Wood Workflow

          In modern wood manufacturing, materials can no longer be treated as simple visual properties. Instead, they must be managed as essential design and manufacturing data that supports the entire production process.

          When material management is structured properly, companies gain much better control over their operations. With SWOOD, wood manufacturers can reduce errors, better control material costs, and improve overall production reliability.

          Ultimately, integrating materials early in the design phase helps create a more consistent and efficient workflow from design to manufacturing.

          Looking to improve your material management and secure your digital workflow from design to production? Solidxperts helps wood manufacturing companies implement SWOOD, train their teams, and optimize their design-to-production processes.

          FAQ

          What are the financial benefits of materials management with SWOOD?

          Materials management with SWOOD reduces manufacturing errors, rework, and material waste. By standardizing materials from the design stage, companies improve the accuracy of cost estimates, optimize nesting, and reduce scrap, generating a measurable return on investment from the very first projects.

          How does SWOOD contribute to reducing production errors?

          SWOOD eliminates information gaps between the design office and the shop floor. Materials defined during the design phase are used directly in CNC manufacturing, without re-entry. This digital continuity significantly reduces errors related to incorrect panels, incompatible edges, or incorrect machining parameters.

          Does SWOOD improve the productivity of the design office?

          Yes. By using standardized material libraries, designers spend less time checking or correcting material information. Projects are faster to design, more consistent, and easier to reuse, improving overall engineering productivity.

          What is the impact of SWOOD on time to market?

          By reducing manual approvals and last-minute adjustments, SWOOD accelerates the transition from design to manufacturing. With reliable data from the design stage, time to market is shortened and bottlenecks between departments are reduced.

          Does managing materials with SWOOD facilitate company growth?

          Yes. SWOOD helps structure internal processes, which is essential for supporting growth. Standardized practices, reduced reliance on key experts, and faster onboarding of new employees allow the company to grow without a proportional increase in operational risks.

          How can the ROI be concretely measured after implementing SWOOD?

          ROI can be measured through several indicators: reduced scrap, shorter design time, fewer production errors, improved panel utilization, and shorter delivery times. These indicators are easily observable before and after implementation.

          Is SWOOD profitable for a wood industry SME?

          SWOOD is particularly well-suited to SMEs. The gains from reduced errors, optimized material usage, and improved productivity quickly offset the initial investment. Many SMEs see a return on investment within a few months, especially when producing diverse or custom projects.

          Does SWOOD help secure internal knowledge and standards?

          Yes. SWOOD’s material libraries and design rules allow for the formalization of company standards. This reduces reliance on individual knowledge and safeguards expertise, even in the event of staff turnover.

          Alain

          Alain Provost

          Senior Technical Sales Executive

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            Artificial Intelligence in Engineering: Automation Without Losing the Human Touch

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            Artificial Intelligence in Engineering: Automation Without Losing the Human Touch

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            Artificial intelligence (AI) is playing an increasingly important role in engineering processes, particularly when it comes to automating repetitive tasks and accelerating the production of technical documentation. However, its role remains fundamentally complementary to that of engineers. Creativity, domain expertise, and decision-making responsibility remain human.

            In this article, we explore:

            • what AI concretely brings to engineering

            • which tasks remain (and will remain) human

            • how to organize an effective human–machine collaboration

            • and what this means for the engineering profession

            1. What AI concretely brings to engineering

            1.1 Automating repetitive, low-value tasks

            The daily work of engineering teams is filled with essential but repetitive tasks that consume a great deal of time without fully leveraging engineers’ expertise. This is precisely where AI excels.

            A typical example is generating technical drawings from 3D models.

            Traditionally, producing technical drawings involves:

            • manually creating the different views (front, section, detail views)

            • applying dimensioning and tolerancing standards

            • reusing elements from previous projects, often manually

            • performing successive checks for consistency and compliance

            With AI, a large portion of this work can be:

            • automated: generating technical drawings directly from 3D designs

            • contextualized: taking into account company history, internal standards, and previously validated models

            The result: fewer repetitive clicks and more time for analysis and improvement.

            1.2 Measurable efficiency gains

            The operational impact is far from marginal.

            Where dozens of people were previously needed to produce, adjust, and verify detailed drawings, organizations can now concentrate human work within a smaller team of reviewers responsible for:

            • correcting the remaining inconsistencies

            • validating compliance

            • managing special cases not covered by the models

            AI handles the repetitive heavy lifting. Humans focus on quality, reliability, and exception management.

            2. Tasks that remain (and will remain) human

            Despite these gains, certain activities remain difficult to automate and may remain so in the short and medium term.

            2.1 Creative design and early project phases

            The early stages of a project, when the architecture of a product and the major technical choices are defined, rely on:

            • creativity

            • accumulated domain expertise

            • the ability to integrate sometimes ambiguous constraints (real-world usage, environment, maintenance, ergonomics)

            • complex decision-making that affects overall product performance

            These activities require systemic understanding, multi-criteria trade-offs, and a form of intuition that current AI models cannot replicate.

            2.2 Safety, compliance, and responsibility

            A clear example is the design of powerful machinery.

            Engineers must:

            • integrate safety factors to protect users

            • sometimes introduce additional margins based on experience or real-world conditions that are difficult to simulate

            These decisions directly affect safety, regulatory compliance, and legal responsibility.

            Today, these types of decisions cannot be delegated to AI.
            Decision-making responsibility remains with humans, not algorithms.

            3. Toward intelligent human–machine collaboration

            The key question is therefore not whether AI will replace engineers, but how to organize an effective collaboration between the two.

            3.1 AI as a copilot during design

            During the design process, AI can act as a copilot or technical assistant. For example, it can:

            • propose lighter materials that still meet strength requirements

            • suggest geometric variations to reduce weight or improve rigidity

            • quickly analyze the impact of small design changes on overall performance

            In practice, engineers can ask AI questions such as:

            • “Which materials meet these strength and weight constraints?”

            • “What geometric alternatives could reduce the mass by 10 percent?”

            However, final validation, trade-off decisions, and system integration remain the responsibility of the engineer.

            3.2 AI as an analyst for standardized tasks

            For more standardized analytical tasks, AI becomes a particularly useful engineering assistant. It can support:

            • the processing and structuring of large volumes of data

            • the automatic generation of variants for comparative studies

            • consistency checks across large sets of technical documentation

            This allows teams to explore more possibilities in less time, without removing the engineer from the decision-making process.

            4. Should engineers fear being replaced by AI?

            The fear of being replaced by machines is real and understandable, especially in technical professions.

            4.1 Vulnerable jobs vs resilient jobs

            A job is more exposed to automation when its tasks are:

            • repetitive

            • highly standardized

            • not very creative

            • associated with limited decision-making

            In contrast, a job is more resilient when it involves:

            • significant creativity

            • a global understanding of complex systems

            • multi-criteria trade-offs (cost, performance, risk, environmental impact)

            • strong responsibility for safety, compliance, or performance

            In engineering, activities such as:

            • defining a product’s overall architecture

            • breakthrough innovation

            • high-impact technical decisions

            • field responsibility

            remain firmly within the human domain.

            4.2 A change in role rather than disappearance

            Consider the example of technical documentation.

            Yes, AI can generate documents based on validated models or historical data.

            No, it does not replace engineers when it comes to:

            • critical decision-making

            • technical trade-offs

            • creative innovation

            What changes most is how time is allocated:

            • less manual and repetitive production work

            • more design, analysis, validation, and innovation

            Toward augmented engineering, not automated engineering

            Artificial intelligence brings real value to engineering by:

            • automating repetitive, low-value tasks

            • accelerating the generation of drawings and technical documentation

            • assisting engineers in exploring design alternatives and performing analysis

            However, creativity, domain expertise, and responsibility remain central to the engineer’s role.

            The goal is not to replace humans, but to build intelligent collaboration:

            • letting AI handle what it does best (speed, repetition, scale)

            • preserving what defines engineering expertise: inventing, evaluating trade-offs, and taking responsibility for decisions

            The future of engineering will not be “human or AI,” but clearly human + AI: augmented engineering that is more efficient, safer, and more focused on innovation.

            Benoit Bilodeau

            Senior Solutions Architect

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              3DEXPERIENCE World 2026: Big Ideas, Real Innovation, and a Community That Inspires

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              3DEXPERIENCE World 2026: Big Ideas, Real Innovation, and a Community That Inspires

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              This year’s 3DEXPERIENCE World 2026 in Houston brought together the global SOLIDWORKS community. Designers, engineers, students, educators, makers, and executives from all over the world gathered for three days of inspiration, learning, and connection. From powerful keynotes to cutting-edge tech announcements, here’s a highlight reel you’ll want to read.

              Day One: Vision, AI, and the Future of Engineering

              Day One kicked off with a high-energy General Session that set the tone for the entire event: innovation powered by people and amplified by the right tools. Thousands of attendees gathered to hear leadership from the industry share bold perspectives on where product development is heading.

              A major theme throughout the day was artificial intelligence (AI). SOLIDWORKS leadership made it clear that AI isn’t a gimmick, it’s already reshaping workflows and helping teams accelerate insight, design, and validation. We saw a live demo of new AI assistants: Aura, Leo, and Marie. Three virtual companions that work together to streamline everything from knowledge context and engineering reasoning to scientific rigor.

              • Aura orchestrates requirements, projects, and changes.

              • Leo brings engineering reasoning to life (mechanics, motion, simulation), think Leonardo da Vinci.

              • Marie applies scientific and regulatory insight, think Marie Curie.

              These assistants are designed as companions, not autopilots. They’re tools that let engineers stay in control and design with confidence rather than guesswork.

              3DXWorld 2026 - Day 1

              Later, attendees heard from Pascal Daloz and Gian Paolo Bassi on how a combination of human creativity and connected technology fuels progress faster than ever. One standout message? “Success shouldn’t be judged by speed alone, but by time to value”, meaning design outcomes that are reliable, meaningful, and innovative.

              Day One wrapped with a keynote from futurist Pablos Holman, reminding us that the real power of technology lies in solving real world problems, from healthcare to space exploration. His message was forward-looking, inspiring, and grounded in practical impact.

              Day Two: Engineering Partnerships and AI in Action

              Day Two turned the spotlight to how industry partnerships and technology collaboration are reshaping the way we work. The session opened with Dassault Systèmes CEO Pascal Daloz discussing how a strong engineering community combined with strategic technology partnerships creates innovation that scales.

              We heard from Jensen Huang, Founder and CEO of NVIDIA, about the long-standing collaboration between NVIDIA and Dassault Systèmes. Their work emphasizes science-driven AI and virtual twins, paired with high-performance computing to give engineers tools that can handle truly complex systems in simulation and design at scale.

              3DXWorld 2026 - Day 2

              SOLIDWORKS CEO Manish Kumar also joined the stage to outline how AI is being embedded into real design workflows, not as a theory, but as a practical productivity boost. This includes contextual AI tools that help reduce repetitive tasks, free up time for innovation, and bring deeper insight into design decisions.

              One of the most exciting themes of Day Two was this: “AI isn’t here to replace engineers; it exists to empower them.” By putting smart tools into your hands, you get faster iteration cycles, fewer errors, and a stronger connection between your design intent and your final product.

              Day Three: Community Celebration and the Next Generation

              The final day of 3DEXPERIENCE World 2026 was all about community, the people who make this ecosystem special. Whether you’re a seasoned pro, an educator, a student, or a maker, this day celebrated the connections that make innovation possible.

              Suchit Jain, VP of Strategy and Business Development, kicked things off by highlighting how collaboration across industry, education, and local innovation hubs builds the workforce of tomorrow. There were strong messages about supporting emerging talent, integrating real-world problem solving into education, and making sure SOLIDWORKS continues to be accessible to innovators of all backgrounds.

              3DXWorld 2026 - Day 3

              Day Three also spotlighted how regional and global communities are using SOLIDWORKS tools to solve real problems, whether that’s in manufacturing, healthcare, education, or startup growth. It was a reminder that technology only reaches its potential when it’s put to work by passionate people.

              The final sessions included inspiring competition recaps, community-driven breakout sessions, and previews of what’s coming next, including early looks at SOLIDWORKS 2027 features that continue the theme of smarter workflows and tighter collaboration between design, simulation, and data.

              What This Means for You

              Across all three days of 3DEXPERIENCE World 2026, a few themes stood out loud and clear:

              1. Innovation grows when humans and tools work together.
                AI companions are here, but they’re companions. They help you work smarter, not replace your expertise. AND the best part, they come with SOLIDWORKS with Cloud Services.

              2. Connected ecosystems :  partnerships + community to accelerate progress.
                Whether it’s NVIDIA, startups, educators, or global manufacturers, connection drives insight at scale.

              3. The next generation of designers is in focus.
                Programs, hubs, and community efforts are investing in future creators, ensuring SOLIDWORKS tools remain integral to how engineering gets done in the years ahead.

              3DEXPERIENCE World 2026 wasn’t just a conference, it was a reminder of why we design, why we connect, and why we build communities around shared purpose, problem solving, and progress.

              We’re already looking forward to 3DEXPERIENCE World 2027!

              3DX World 2027 - Save the date

              If you want to know more about how SOLIDWORKS users can begin to leverage AI in 2026, call us, or visit at 2650 Avenue Marie-Curie, QC.

              Michael Habrich

              3DEXPERIENCE Specialist

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                What’s New in SOLIDWORKS 2026? Part 2 – Collaboration and Data Management

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                What’s New in SOLIDWORKS 2026? Part 2 – Collaboration and Data Management

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                SOLIDWORKS 2026 delivers a wave of powerful enhancements designed to accelerate collaboration, streamline data management, and strengthen connectivity across design teams. From AI-driven support to enterprise-grade approval workflows, every improvement empowers engineers, designers, and manufacturers to work faster, smarter, and more seamlessly together.

                Top 10 Enhancements You Need to Know

                1. AURA Integration in 3DSwym

                AI meets collaboration. With AURA integrated directly into 3DSwym, users can instantly summarize posts, wikis, and Q&As or pull in insights from entire discussions to drive smarter conversations.

                What's new in SOLIDWORKS? P. 2.1
                These enhancements deliver faster insights and more informed collaboration with less manual searching.

                2. Collaboration Directly Inside SOLIDWORKS

                Work together without ever leaving your design space. Collaborate through the SOLIDWORKS User Forum, share files via 3DDrive and 3DSwym, and eliminate the need for conversions or third-party tools.

                What's new in SOLIDWORKS? P. 2.2
                These enhancements deliver smoother teamwork and more efficient communication with less switching between tools.

                3. Drawing Stamping with Maturity Status

                Simplify change management with automatic maturity status stamps. User names and emails are embedded in drawings for full traceability and accountability throughout the design lifecycle.

                What's new in SOLIDWORKS? P. 2.3
                These enhancements deliver clearer documentation and more reliable approvals with less manual tracking.

                4. Cut List Management on the 3DEXPERIENCE Platform

                Cut list data can now be fully managed on the platform, enabling tighter integration between EBOM and MBOM.

                What's new in SOLIDWORKS? P. 2.4
                These enhancements deliver better alignment between design and manufacturing with less data re-entry.

                5. Recent Files by Active Tenant

                For teams managing multiple tenants, SOLIDWORKS 2026 now filters your recent files to show only those from the active tenant.

                What's new in SOLIDWORKS? P. 2.5
                These enhancements deliver cleaner organization and faster access with less confusion.

                6. Excel File Management for Design Tables

                Design table management is now more reliable thanks to Excel file support on the 3DEXPERIENCE platform. This ensures version consistency, better automation, and stronger data integrity.

                What's new in SOLIDWORKS? P. 2.6
                These enhancements deliver greater consistency and more reliable design automation with less manual version control.

                7. Support for Deformable Components

                Easily switch between rigid and deformed part states in assemblies for improved accuracy and flexibility.

                What's new in SOLIDWORKS? P. 2.7
                These enhancements deliver faster detailing and more accurate assemblies with less manual effort.

                8. Automatic Bookmark Content Updates

                Bookmarks now update automatically each session, ensuring you always access the latest design data, no manual refresh needed.

                What's new in SOLIDWORKS? P. 2.8
                These enhancements deliver up-to-date project information with less time spent managing data.

                9. Enhanced Bookmark Editor

                A redesigned Bookmark Editor delivers faster navigation and smarter selection when multiple bookmarks exist.

                What's new in SOLIDWORKS? P. 2.9
                These enhancements deliver clearer navigation and more accurate project organization with less manual searching.

                10. Enterprise Document Maturity Routing & Approval

                Large organizations gain new governance power with automated approval workflows tied to document maturity. Predefined routes trigger automatically for consistent, traceable, and compliant approvals.

                What's new in SOLIDWORKS? P. 2.10
                These enhancements deliver stronger governance and more reliable approvals with less administrative overhead.

                A New Standard for Collaboration and Data Management

                SOLIDWORKS 2026 builds on its legacy of innovation with a sharper focus on collaboration, automation, and traceability. These ten enhancements don’t just make design faster, they make teamwork more intelligent, connected, and efficient across every stage of product development.

                Join our official SOLIDWORKS 2026 launch to see these new features in action and get your questions answered by our experts!

                Michael Habrich

                3DEXPERIENCE Specialist

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                  What’s New in SOLIDWORKS 2026? Part 1 – Design

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                  What’s New in SOLIDWORKS 2026? Part 1 – Design

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                  SOLIDWORKS 2026 continues the trend of making design faster, smarter, and more connected. With AI-powered tools, streamlined workflows, and deeper collaboration features, this release is all about helping engineers and designers work more efficiently while maintaining accuracy and flexibility.

                  Here are the Top 10 Enhancements you need to know:

                  1. AI-Powered Design and Detailing

                  Artificial intelligence is making a real impact in everyday CAD workflows:

                  • Automated drawing creation with AI-driven views, hole callouts, dimensioning, detailing, and even sheet format selection.

                  • AI assembly recognition can now automatically detect and insert fastener-like components (nuts, bolts, washers), reducing repetitive tasks and improving assembly accuracy.

                  These enhancements deliver faster detailing and more accurate assemblies with less manual effort.

                  What's New in SOLIDWORKS 2026 - Design 1

                  2. Large Assembly Performance

                  Large assemblies are now easier to manage:

                  • Open massive designs faster by filtering only what you need from the 3DEXPERIENCE platform.

                  • Skip rebuilds when only cosmetic changes are made.

                  • Disable auto-resolve for lightweight components to improve responsiveness.

                  Together, these updates create smarter workflows that keep even the largest projects running smoothly.

                  What's New in SOLIDWORKS 2026 - Design 2

                  3. Improved User Experience

                  • Offline mode ensures uninterrupted productivity during internet disruptions.

                  • A redesigned UI highlights common commands, helping new users get up to speed quickly.

                  • Command Search now includes an expanded, customizable set of keywords.

                  The result is a smoother learning curve and less frustration for both new and experienced users.

                  What's New in SOLIDWORKS 2026 - Design 3

                  4. Streamlined Part Design

                  • Define a custom start point for sheet metal base flanges.

                  • Break internal corners on folded geometry.

                  • Quickly create square sketch geometry in one click.

                  • Track maturity changes and drawing history with Evaluated Attributes.

                  These improvements enable faster sketching and greater flexibility in sheet metal design.

                  What's New in SOLIDWORKS 2026 - Design 4

                  5. Collaboration & Data Management

                  • Share designs directly via 3DDrive and 3DSwym.

                  • Update files to the latest version on the 3DEXPERIENCE platform with a single action.

                  • Access the SOLIDWORKS User Forum from within the software.

                  With these tools, staying in sync with your team and your data has never been easier.

                  What's New in SOLIDWORKS 2026 - Design 5

                  6. Drawing Detailing & MBD

                  • Insert Family Tables into drawings for configuration details.

                  • Use magnetic lines to align not just balloons, but notes, weld symbols, and other annotations.

                  • Propagate DimXpert dimensions to library features and selectively manage annotation visibility.

                   This gives designers more control and clarity when documenting their designs.

                  What's New in SOLIDWORKS 2026 - Design 6

                  7. Routing, Electrical, and Piping

                  • Combine routing BOMs across subassemblies for a clearer overall materials list.

                  • Place clips, mounts, and hangers directly in assemblies for flexible routing.

                  • Visual indicators help guide splice placement in harnesses and wire bundles.

                  This gives designers more control and clarity when documenting their designs.

                  What's New in SOLIDWORKS 2026 - Design 7

                  8. Rendering Enhancements

                  • Control tessellation for a balance of geometry quality and performance.

                  • Improved denoising in CPU mode reduces render noise in fewer passes.

                  • Expanded format support with PBR materials in USDZ and glTF.

                   This gives designers more control and clarity when documenting their designs.

                  What's New in SOLIDWORKS 2026 - Design 8

                  9. ECAD/MCAD Collaboration

                  • Track parent/child PCB data like keep-in, keep-out, plated and non-plated holes.

                  • Use CircuitWorks™ with IDX 3.0 support to review and even undo MCAD changes before final ECAD updates.

                   This gives designers more control and clarity when documenting their designs.

                  What's New in SOLIDWORKS 2026 - Design 9

                  10. Import/Export

                  • Simplify complex multibody parts faster by using advanced selection tools to efficiently isolate and manage bodies based on similarity or size.

                  • Share assemblies saved on the 3DEXPERIENCE platform using the Export as Package option in the Share dialog box.

                  • Streamline exports by choosing whether to include drawings in your package, for greater control.

                   This gives designers more control and clarity when documenting their designs.

                  What's New in SOLIDWORKS 2026 - Design 10

                  Final Thoughts

                  With AI-powered automation, large assembly improvements, smarter part tools, and seamless collaboration, SOLIDWORKS 2026 makes it easier than ever to move from concept to finished product. Whether you’re creating complex assemblies, detailing drawings, or collaborating across disciplines, this release delivers tools to help you work smarter, faster, and with greater confidence.

                  Take your designs to the next level with SOLIDWORKS 2026. Contact us today to unlock smarter, faster, AI-powered engineering.

                  Michael Habrich

                  3DEXPERIENCE Specialist

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                    SOLIDWORKS AI: The Future of CAD is Already Here

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                    SOLIDWORKS AI: The Future of CAD is Already Here

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                    When we talk about AI, certain fears arise: could AI replace the engineer or the designer? The answer is no. Dassault Systèmes’ philosophy is very clear: AI does not replace, it enhances. After 11 years at S0lidxperts and more than 15 years of using SOLIDWORKS, I am well aware of this fact.

                    SOLIDWORKS, one of the most trusted CAD platforms globally, has already begun integrating AI-driven tools that assist engineers in reducing repetitive tasks, improving design quality, and accelerating time-to-market. Through the 3DEXPERIENCE platform, Dassault Systèmes is also paving the way for AI-powered collaboration and predictive design in the cloud.

                    In this article, we’ll explore how AI is shaping the present and future of SOLIDWORKS, highlight the business benefits, and share how Solidxperts, over 40 years in the industry and more than 25 years as a SOLIDWORKS partner, supports companies in embracing this transformation.

                    1. AI in SOLIDWORKS: An Engineer’s New Ally

                    Dassault Systèmes’ vision has always been clear: AI does not replace engineers. It enhances their capabilities. For decades, SOLIDWORKS has incorporated automation features designed to simplify CAD workflows:

                    • Smart Mates for automatic assembly constraints.

                    • Command Prediction suggesting the most likely next tool.

                    These were early steps toward intelligent CAD. Today, SOLIDWORKS goes further with AI-driven assistants like Design Assistant, and cloud-based tools powered by machine learning through the 3DEXPERIENCE platform.

                    The philosophy is simple: let AI handle the repetitive and time-consuming tasks, so engineers can focus on innovation, creativity, and solving real engineering challenges.

                    2. Current AI-Driven Features in SOLIDWORKS

                    a) Design Assistant

                    • Mate Helper and Selection Helper: automatically detects similar components and applies constraints, drastically reducing clicks in large assemblies.

                    • Example: When assembling dozens of fasteners, Design Assistant identifies similar geometry and instantly applies consistent mates.

                    b) Automatic Drawings (SOLIDWORKS 2025)

                    • Automatically generates 2D drawings from 3D models.

                    • Ensures compliance with drawing standards.

                    • Saves significant time in documentation, especially for design offices managing large projects.

                    c) Fastener Recognition (FD03)

                    • Automatically detects standard fasteners in assemblies.

                    • Applies appropriate mates without manual intervention.

                    • Direct impact: reduced errors, fewer repetitive operations.

                    d) AURA: The Conversational AI Assistant

                    • Integrated into the 3DEXPERIENCE platform.

                    • Provides contextual guidance to users, learns from workflows, and offers predictive insights.

                    • Example: guiding new users through advanced features or assisting with design standards.

                    3. The Future of AI in SOLIDWORKS: A Roadmap

                    Dassault Systèmes continues to push boundaries, and the AI roadmap promises groundbreaking advancements:

                    • Mesh to Parametric Conversion (Reverse Engineering): AI will streamline converting mesh files into fully parametric models, accelerating reverse engineering processes.

                    • AI-Optimized Assemblies: Automatic detection of redundant constraints, performance optimization, and manufacturability checks.

                    • AI-Generated Photorealistic Renders: Leveraging generative AI to instantly create marketing-ready visuals without lengthy manual rendering.

                    • xDesign and Cloud-Ready AI: The 3DEXPERIENCE xDesign app represents the next generation of CAD: cloud-based, AI-enhanced, and fully collaborative.

                    • Vision: Predictive, Collaborative CAD: An environment where the system anticipates design intent, suggests optimizations, and improves with each project.

                    4. Strategic Benefits for Companies

                    Adopting AI in SOLIDWORKS is not just about convenience—it drives measurable business impact:

                    • Productivity: Significant time savings through automation of repetitive tasks.

                    • Quality: Consistent designs, fewer human errors, and adherence to standards.

                    • Innovation: Engineers gain more time to focus on R&D and creative problem-solving.

                    • Competitiveness: Faster time-to-market and optimized workflows lead to a competitive edge.

                    Use Cases:

                    • Design offices managing large assemblies save hours on constraints.

                    • Manufacturers preparing technical documentation see accelerated drawing creation.

                    • Teams leveraging AURA gain training efficiency and reduced onboarding time.

                    5. Data Security and Confidentiality

                    AI adoption often raises concerns about data protection. Dassault Systèmes has addressed this head-on:

                    • Your proprietary data is not shared or trained externally without explicit consent.

                    • Companies can configure private AI models trained on their own part libraries and workflows.

                    • This ensures both innovation and confidentiality which is critical for SMEs and global enterprises alike.

                    6. Solidxperts: 40 Years of Experience, Always Evolving with AI

                    The evolution of Solidxperts over the past four decades reflects the same philosophy as SOLIDWORKS: constant growth, adaptation, and customer focus.

                    • 1998: Solidxperts opened its main office in Montreal, introducing clients to SOLIDWORKS back then, delivered on physical CDs.

                    • Today: Solidxperts delivers the full Dassault Systèmes portfolio, from SOLIDWORKS to the cloud-based 3DEXPERIENCE platform, supporting every step of the design-to-manufacturing journey.

                    • Values: transparency, dedication to customer success, and a personalized approach embedded in every interaction.

                    • Expansion: with offices in Quebec City (QC) and Nashua (NH), Solidxperts serves engineers and businesses across Eastern Canada and New England.

                    This long-term experience positions Solidxperts as the partner of choice for companies looking to adopt AI-powered CAD and manufacturing solutions.

                    The Future is Now with SOLIDWORKS AI

                    Artificial Intelligence is no longer an abstract concept. Iit is already part of the tools engineers use daily in SOLIDWORKS. From automated mates to predictive design and cloud collaboration, AI is redefining the engineering experience.

                    For businesses, embracing AI in SOLIDWORKS means:

                    • Faster workflows

                    • Fewer errors

                    • More time for innovation

                    • Stronger market competitiveness

                    At Solidxperts, we’ve supported thousands of engineers and companies for over 25 years with SOLIDWORKS, and we are ready to help you take the next step: integrating AI into your design and manufacturing workflows.

                    Contact our experts today to schedule a personalized demo and discover how AI in SOLIDWORKS can transform your business.

                    Alain

                    Alain Provost

                    Senior Technical Sales Executive

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