[{"data":1,"prerenderedAt":125},["ShallowReactive",2],{"NV-qmXwUZNTkW8erCcUDNl0LysuKC9aepA06p5TOQQU":3,"_apollo:default":123,"_apollo:identified":124},{"seo":4,"posts":15},{"social":5,"openGraph":11,"__typename":14},{"twitter":6,"__typename":10},{"cardType":7,"username":8,"__typename":9},"summary_large_image","dassault3ds","SEOSocialTwitter","SEOSocial",{"defaultImage":12,"__typename":13},null,"SEOOpenGraph","SEOConfig",{"nodes":16,"__typename":122},[17],{"id":18,"slug":19,"title":20,"uri":21,"excerpt":22,"locale":23,"featuredImage":26,"tableOfContents":34,"content":46,"date":47,"translations":48,"author":49,"tags":63,"globalTags":77,"brands":89,"keywords":100,"seo":110,"__typename":121},"cG9zdDozMTY1ODE=","catia-magic-automation-david-fields-enola","From 90,000 Requirements to Minutes: How CATIA Magic Automation and AI Are Changing MBSE","\u002Fbrands\u002Fcatia\u002Fcatia-magic-automation-david-fields-enola","\u003Cp>David Fields of Enola Technologies on how CATIA Magic’s Open API turned a 90,000-requirement nightmare into a repeatable process — and where AI and SysML v2 fit next.\u003C\u002Fp>\n",{"locale":24,"__typename":25},"en_US","Locale",{"node":27,"__typename":33},{"large":28,"__typename":29,"medium_large":28,"thumbnail":30,"srcSet":31,"sizes":32},"https:\u002F\u002Fblog-assets.3ds.com\u002Fuploads\u002F2026\u002F09\u002Fitw-david-fields-enola-768x408.png","MediaItem","https:\u002F\u002Fblog-assets.3ds.com\u002Fuploads\u002F2026\u002F09\u002Fitw-david-fields-enola-150x150.png","https:\u002F\u002Fblog-assets.3ds.com\u002Fuploads\u002F2026\u002F09\u002Fitw-david-fields-enola-300x159.png 300w, https:\u002F\u002Fblog-assets.3ds.com\u002Fuploads\u002F2026\u002F09\u002Fitw-david-fields-enola-768x408.png 768w, https:\u002F\u002Fblog-assets.3ds.com\u002Fuploads\u002F2026\u002F09\u002Fitw-david-fields-enola.png 889w","(max-width: 300px) 100vw, 300px","NodeWithFeaturedImageToMediaItemConnectionEdge",[35,36,37,38,39,40,41,42,43,44,45],"Why connected engineering data matters|why-connected-engineering-data-matters-0","The problem of bringing legacy data into the model|the-problem-of-bringing-legacy-data-into-the-model-1","Using the CATIA Magic Open API to automate the work|using-the-catia-magic-open-api-to-automate-the-work-2","CATIA Magic brings modeling, simulation and integration together|catia-magic-brings-modeling-simulation-and-integration-together-3","AI can take repetitive MBSE tasks further|ai-can-take-repetitive-mbse-tasks-further-4","SysML v2 makes textual modeling particularly interesting|sysml-v2-makes-textual-modeling-particularly-interesting-5","MBSE adoption is spreading beyond aerospace and defense|mbse-adoption-is-spreading-beyond-aerospace-and-defense-6","A CATIA Magic deployment needs more than a SysML course|a-catia-magic-deployment-needs-more-than-a-sysml-course-7","Automation, AI and expertise point toward a different MBSE workflow|automation-ai-and-expertise-point-toward-a-different-mbse-workflow-8","Conclusion|conclusion-9","FAQ|faq-10","\u003Cdiv class=\"ds-video\">\u003Ca data-3ds-videoplayer=\"modal\" href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GhEBCcM4p6Q\" target=\"_blank\">\u003Cspan class=\"ImageCover Border Block\" style=\"background-image: url(https:\u002F\u002Fimg.youtube.com\u002Fvi\u002FGhEBCcM4p6Q\u002Fhqdefault.jpg); width:100%; height: 100%;\">\u003Cspan class=\"Btn--circle isCenter\">\u003Ci class=\"Icon Icon--playBig\">\u003C\u002Fi>\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fa>\u003Cspan>\u003Cfigure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\">\u003Cdiv class=\"wp-block-embed__wrapper\">\n\u003Ciframe loading=\"lazy\" title=\"Engineering excellence through advanced model-based solutions - ENOLA TECHNOLOGIES\" width=\"640\" height=\"360\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FGhEBCcM4p6Q?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen>\u003C\u002Fiframe>\n\u003C\u002Fdiv>\u003C\u002Ffigure>\u003C\u002Fspan>\u003C\u002Fdiv>\n\n\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fsystems-engineering\">\u003Cstrong>Model-based systems engineering\u003C\u002Fstrong> \u003Cstrong>(MBSE)\u003C\u002Fstrong>\u003C\u002Fa> is often presented as a way to improve traceability, accuracy and efficiency. But moving large volumes of existing engineering information into a model can create a significant practical challenge.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>David Fields\u003C\u002Fstrong>, President, co-founder and Chief Technology Officer of \u003Ca href=\"https:\u002F\u002Fenola.com\u002F\">\u003Cstrong>Enola Technologies\u003C\u002Fstrong>\u003C\u002Fa>, has seen that challenge firsthand. His company specializes in model-based systems engineering consulting, services and training, with particular expertise in \u003Cstrong>\u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fcatia-magic\" data-type=\"link\" data-id=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fcatia-magic\">CATIA Magic\u003C\u002Fa>\u003C\u002Fstrong> and \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fno-magic\" data-type=\"link\" data-id=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fno-magic\">\u003Cstrong>No Magic\u003C\u002Fstrong> \u003C\u002Fa>tools.\u003C\u002Fp>\n\n\n\n\u003Cp>For David, the value of \u003Cstrong>MBSE\u003C\u002Fstrong> comes from connecting engineering information and making it usable across \u003Cstrong>modeling\u003C\u002Fstrong>, \u003Cstrong>simulation\u003C\u002Fstrong> and \u003Cstrong>other disciplines\u003C\u002Fstrong>. But the real productivity gains appear when organizations go beyond basic modeling and take advantage of automation, open\u003Cstrong>APIs\u003C\u002Fstrong>, \u003Cstrong>AI\u003C\u002Fstrong> and \u003Cstrong>the capabilities of \u003C\u002Fstrong>\u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fcatia-magic\u002Fsysmlv2\">\u003Cstrong>SysML v2\u003C\u002Fstrong>\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"why-connected-engineering-data-matters-0\">\u003Cstrong>Why connected engineering data matters\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>David came to \u003Cstrong>MBSE\u003C\u002Fstrong> after working in traditional systems engineering environments where much of the work was document-based. On some of the programs he worked on at the Navy, performing impact analysis and understanding the full scope of requirements and systems could be difficult.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>MBSE\u003C\u002Fstrong> provides a \u003Cstrong>different approach\u003C\u002Fstrong> by \u003Cstrong>capturing\u003C\u002Fstrong> engineering information in a model and \u003Cstrong>connecting\u003C\u002Fstrong> that information.\u003C\u002Fp>\n\n\n\n\u003Cp>For David, the model is \u003Cstrong>valuable\u003C\u002Fstrong> not only because it provides a structured description of the system, but because it gives engineers a way to \u003Cstrong>communicate\u003C\u002Fstrong> that information to \u003Cstrong>stakeholders\u003C\u002Fstrong> and \u003Cstrong>leadership\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>The connections between model data, simulation and other engineering disciplines can also \u003Cstrong>improve\u003C\u002Fstrong> \u003Cstrong>efficiency\u003C\u002Fstrong> and \u003Cstrong>accuracy\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>This \u003Cstrong>interconnected structure\u003C\u002Fstrong> is one of the fundamental reasons David sees \u003Cstrong>MBSE\u003C\u002Fstrong> as more than simply a replacement for documents. The model becomes an environment in which engineering information can be \u003Cstrong>related\u003C\u002Fstrong>, \u003Cstrong>analyzed\u003C\u002Fstrong> and \u003Cstrong>communicated\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"the-problem-of-bringing-legacy-data-into-the-model-1\">\u003Cstrong>The problem of bringing legacy data into the model\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Once an organization decides to adopt \u003Cstrong>MBSE\u003C\u002Fstrong>, however, there is an immediate practical question: \u003Cem>what happens to all the information it already has?\u003C\u002Fem>\u003C\u002Fp>\n\n\n\n\u003Cp>David identifies data ingestion as one of the \u003Cstrong>major challenges\u003C\u002Fstrong> facing his customers. \u003Cstrong>Legacy programs\u003C\u002Fstrong> can contain thousands or even tens of thousands of requirements stored in traditional requirements management systems such as \u003Cstrong>DOORS\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>Requirements are not the only issue. \u003Cstrong>Test data\u003C\u002Fstrong> \u003Cstrong>and other engineering information\u003C\u002Fstrong> may also need to be brought into the \u003Cstrong>MBSE environment\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>The challenge is therefore not simply importing a file. Organizations need to \u003Cstrong>get large quantities of information \u003C\u002Fstrong>into the model \u003Cstrong>accurately\u003C\u002Fstrong> and then make that information useful within the \u003Cstrong>modeling environment\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>For large programs, doing this manually can quickly become a major drain on engineering resources.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"using-the-catia-magic-open-api-to-automate-the-work-2\">\u003Cstrong>Using the CATIA Magic Open API to automate the work\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>This is where David sees a particular advantage in \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fcatia-magic\">\u003Cstrong>CATIA Magic\u003C\u002Fstrong>\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fno-magic\u002Fcameo-systems-modeler\">\u003Cstrong>Cameo\u003C\u002Fstrong>\u003C\u002Fa>: their Open API capabilities.\u003C\u002Fp>\n\n\n\n\u003Cp>He highlights both the Open \u003Cstrong>API\u003C\u002Fstrong> and \u003Cstrong>REST API\u003C\u002Fstrong>, with particular emphasis on the\u003Cstrong> Java Open API, \u003C\u002Fstrong>as powerful tools for automation and integration.\u003C\u002Fp>\n\n\n\n\u003Cp>These \u003Cstrong>APIs\u003C\u002Fstrong> can be used for custom imports, integration with other engineering tools, and manipulation or refinement of information that has already been brought into the environment through \u003Cstrong>DataHub\u003C\u002Fstrong> or manual imports.\u003C\u002Fp>\n\n\n\n\u003Cp>The significance becomes clear in one customer example.\u003C\u002Fp>\n\n\n\n\u003Cp>The customer had received more than \u003Cstrong>90,000 requirements\u003C\u002Fstrong> that needed to be incorporated into the model. Many of those requirements were outdated and referred to a system that no longer existed.\u003C\u002Fp>\n\n\n\n\u003Cp>Manually updating that volume of information would have been a huge undertaking.\u003C\u002Fp>\n\n\n\n\u003Cp>Instead, \u003Cstrong>Enola Technologies\u003C\u002Fstrong> helped the customer develop automation using the \u003Cstrong>CATIA Magic Open API\u003C\u002Fstrong>. The scripts could automatically adjust requirement text where necessary and create relationships for verification, test cases and other elements.\u003C\u002Fp>\n\n\n\n\u003Cp>The result was a dramatic change in the workflow: a process that had previously taken \u003Cstrong>months could be executed in minutes\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>Even more importantly, the automation could be \u003Cstrong>reused\u003C\u002Fstrong>. When the customer received another data drop, the scripts could be run again to prepare the information for the next release.\u003C\u002Fp>\n\n\n\n\u003Cp>That example illustrates how an \u003Cstrong>MBSE tool’s API \u003C\u002Fstrong>can become more than a development feature. It can provide a \u003Cstrong>mechanism\u003C\u002Fstrong> for turning repetitive systems engineering work into a repeatable process.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"catia-magic-brings-modeling-simulation-and-integration-together-3\">\u003Cstrong>CATIA Magic brings modeling, simulation and integration together\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>David also points to the breadth of capabilities available through the \u003Cstrong>CATIA Magic\u003C\u002Fstrong> and \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fno-magic\">\u003Cstrong>No Magic\u003C\u002Fstrong>\u003C\u002Fa> product family.\u003C\u002Fp>\n\n\n\n\u003Cp>From his perspective, one of its strengths is the \u003Cstrong>combination\u003C\u002Fstrong> of advanced simulation, API capabilities and integration with third-party platforms.\u003C\u002Fp>\n\n\n\n\u003Cp>Rather than focusing on one isolated capability, David describes \u003Cstrong>Cameo\u003C\u002Fstrong> as providing a \u003Cstrong>broad set of functions\u003C\u002Fstrong> across the \u003Cstrong>MBSE workflow.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Simulation\u003C\u002Fstrong> is particularly important within that portfolio. Combined with the API and external integrations, it provides organizations with ways to \u003Cstrong>connect\u003C\u002Fstrong> modeling activities to other engineering environments.\u003C\u002Fp>\n\n\n\n\u003Cp>David also notes the familiarity of these tools within the Department of Defense and aerospace communities. For \u003Cstrong>Enola Technologies\u003C\u002Fstrong>, that existing familiarity can make it easier to engage with customers who already understand the environment and its capabilities.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"ai-can-take-repetitive-mbse-tasks-further-4\">\u003Cstrong>AI can take repetitive MBSE tasks further\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>David is already experimenting with \u003Cstrong>AI\u003C\u002Fstrong> in \u003Cstrong>MBSE\u003C\u002Fstrong> and sees significant potential in \u003Cstrong>combining\u003C\u002Fstrong> artificial intelligence with the structured data found in engineering models.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>MBSE\u003C\u002Fstrong> environments can contain structural information in formats such as \u003Cstrong>XMI\u003C\u002Fstrong> and \u003Cstrong>XML\u003C\u002Fstrong>. David believes \u003Cstrong>large language models\u003C\u002Fstrong> are well suited to understanding this kind of structured information.\u003C\u002Fp>\n\n\n\n\u003Cp>One potential application is checking modeling standards. \u003Cstrong>AI\u003C\u002Fstrong> could help identify whether models conform to an organization’s expected practices.\u003C\u002Fp>\n\n\n\n\u003Cp>Requirements are another area of interest. \u003Cstrong>AI\u003C\u002Fstrong> could \u003Cstrong>assist\u003C\u002Fstrong> engineers in writing better requirements and \u003Cstrong>identifying\u003C\u002Fstrong> areas where the system may not have been fully considered because requirements were incomplete.\u003C\u002Fp>\n\n\n\n\u003Cp>For David, these capabilities could eventually become a normal part of \u003Cstrong>modeling workflows.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>The key benefit is not that AI can perform tasks engineers are incapable of doing. It is that AI can potentially \u003Cstrong>perform repetitive activities\u003C\u002Fstrong> much \u003Cstrong>faster\u003C\u002Fstrong>, allowing engineers to \u003Cstrong>concentrate\u003C\u002Fstrong> on problems that require human judgment.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cem>Note that Dassault Systemes is rapidly expanding out of the box enterprise level AI capabilities to above mentioned and other use case.\u003C\u002Fem>\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"sysml-v2-makes-textual-modeling-particularly-interesting-5\">\u003Cstrong>SysML v2 makes textual modeling particularly interesting\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>David is especially enthusiastic about one aspect of \u003Cstrong>SysML v2\u003C\u002Fstrong>: its textual notation.\u003C\u002Fp>\n\n\n\n\u003Cp>Coming from a computer engineering and programming background, he finds the textual representation particularly natural to work with.\u003C\u002Fp>\n\n\n\n\u003Cp>But his interest goes beyond personal preference. He identifies \u003Cstrong>two practical advantages.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>First, textual \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fcatia-magic\u002Fsysmlv2\">\u003Cstrong>SysML v2\u003C\u002Fstrong>\u003C\u002Fa> can work well with \u003Cstrong>AI\u003C\u002Fstrong>. Structured textual representations give \u003Cstrong>AI systems \u003C\u002Fstrong>a form of model information they can process and generate.\u003C\u002Fp>\n\n\n\n\u003Cp>Second, \u003Cstrong>textual notation\u003C\u002Fstrong> can make it \u003Cstrong>easier\u003C\u002Fstrong> to share pieces of a model. Instead of exchanging an entire model, engineers could potentially \u003Cstrong>share\u003C\u002Fstrong> \u003Cstrong>snippets\u003C\u002Fstrong>, \u003Cstrong>compartments\u003C\u002Fstrong> or examples more easily.\u003C\u002Fp>\n\n\n\n\u003Cp>David also welcomes the move away from \u003Cstrong>UML\u003C\u002Fstrong> as the foundation of \u003Cstrong>SysML\u003C\u002Fstrong>. While he acknowledges \u003Cstrong>the value of\u003C\u002Fstrong> \u003Cstrong>UML\u003C\u002Fstrong>, he believes that moving away from that legacy creates an opportunity for a language designed more specifically around the needs of systems engineering.\u003C\u002Fp>\n\n\n\n\u003Cp>Together, \u003Cstrong>textual notation\u003C\u002Fstrong> and a \u003Cstrong>new language foundation\u003C\u002Fstrong> make \u003Cstrong>SysML v2\u003C\u002Fstrong> an important part of the \u003Cstrong>future workflow\u003C\u002Fstrong> David describes.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"mbse-adoption-is-spreading-beyond-aerospace-and-defense-6\">\u003Cstrong>MBSE adoption is spreading beyond aerospace and defense\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>David has spent many years working with \u003Cstrong>MBSE\u003C\u002Fstrong>, and he sees a change in how organizations approach the technology.\u003C\u002Fp>\n\n\n\n\u003Cp>Previously, consultants often had to spend significant effort explaining why MBSE was valuable. Today, he says, more organizations already recognize it as part of the \u003Cstrong>future of engineering.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>That does not mean implementation has become simple. Instead, the focus is increasingly shifting from convincing organizations to helping them implement \u003Cstrong>MBSE\u003C\u002Fstrong> effectively.\u003C\u002Fp>\n\n\n\n\u003Cp>Interest is also spreading beyond traditional aerospace and defense programs.\u003C\u002Fp>\n\n\n\n\u003Cp>David is seeing demand from biomedical organizations, energy companies and smaller manufacturers. In some cases, adoption is driven internally; in others, suppliers or customers are introducing \u003Cstrong>MBSE\u003C\u002Fstrong> and creating pressure further along the \u003Cstrong>supply chain\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>This suggests that MBSE is \u003Cstrong>increasingly\u003C\u002Fstrong> becoming \u003Cstrong>relevant\u003C\u002Fstrong> to organizations that may not have considered \u003Cstrong>model-based engineering\u003C\u002Fstrong> several years ago.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"a-catia-magic-deployment-needs-more-than-a-sysml-course-7\">\u003Cstrong>A CATIA Magic deployment needs more than a SysML course\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>For organizations beginning their \u003Cstrong>MBSE journey\u003C\u002Fstrong>, David emphasizes an issue that can easily be underestimated: \u003Cstrong>training\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>Selecting an MBSE tool is only one part of implementation. Organizations also need to prepare the people who will create, use and consume the models.\u003C\u002Fp>\n\n\n\n\u003Cp>That includes \u003Cstrong>engineers\u003C\u002Fstrong> and \u003Cstrong>model practitioners\u003C\u002Fstrong>, but David argues that \u003Cstrong>leadership\u003C\u002Fstrong>, \u003Cstrong>stakeholders\u003C\u002Fstrong> and \u003Cstrong>other\u003C\u002Fstrong> \u003Cstrong>disciplines\u003C\u002Fstrong> also need appropriate training if they will interact with or use model information.\u003C\u002Fp>\n\n\n\n\u003Cp>He compares this with \u003Cstrong>CAD\u003C\u002Fstrong>. Organizations generally do not expect someone to become an expert \u003Cstrong>CAD\u003C\u002Fstrong> user after a single week of training.\u003C\u002Fp>\n\n\n\n\u003Cp>Yet \u003Cstrong>MBSE\u003C\u002Fstrong> \u003Cstrong>programs\u003C\u002Fstrong> can sometimes make the unrealistic assumption that a practitioner will become an expert after a one-week \u003Cstrong>SysML course.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>David argues that successful teams need a broader range of skills. Beyond the basics, engineers may need to know how to query models, run \u003Cstrong>simulations\u003C\u002Fstrong> and extract model information into the formats required for presentations, documents or contracts.\u003C\u002Fp>\n\n\n\n\u003Cp>This means that organizations adopting \u003Cstrong>CATIA Magic\u003C\u002Fstrong> need to think about developing \u003Cstrong>MBSE\u003C\u002Fstrong> \u003Cstrong>capability\u003C\u002Fstrong> over time rather than treating initial training as the endpoint.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"automation-ai-and-expertise-point-toward-a-different-mbse-workflow-8\">\u003Cstrong>Automation, AI and expertise point toward a different MBSE workflow\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>David’s experience points to a shift in what \u003Cstrong>MBSE\u003C\u002Fstrong> \u003Cstrong>productivity\u003C\u002Fstrong> can mean.\u003C\u002Fp>\n\n\n\n\u003Cp>The first step is creating a \u003Cstrong>connected model\u003C\u002Fstrong> containing engineering information. The next is making that model useful through \u003Cstrong>simulation\u003C\u002Fstrong>, \u003Cstrong>integrations\u003C\u002Fstrong> and \u003Cstrong>APIs\u003C\u002Fstrong>. Automation can then \u003Cstrong>eliminate\u003C\u002Fstrong> \u003Cstrong>repetitive work\u003C\u002Fstrong>, as demonstrated by the \u003Cstrong>90,000-requirement example\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>AI\u003C\u002Fstrong> adds another potential layer by helping engineers interact with structured model data, review requirements and modeling practices, and identify potential gaps.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>SysML v2\u003C\u002Fstrong>, meanwhile, introduces \u003Cstrong>textual modeling\u003C\u002Fstrong> that David sees as particularly compatible with both \u003Cstrong>AI\u003C\u002Fstrong> and \u003Cstrong>software-oriented workflows.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>Together, these capabilities suggest an \u003Cstrong>MBSE environment\u003C\u002Fstrong> in which engineers spend \u003Cstrong>less time\u003C\u002Fstrong> \u003Cstrong>manually\u003C\u002Fstrong> manipulating information and more time solving the system-level problems that require their expertise.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"conclusion-9\">\u003Cstrong>Conclusion\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>For \u003Cstrong>David Fields\u003C\u002Fstrong>, the future of \u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fsystems-engineering\">\u003Cstrong>MBSE\u003C\u002Fstrong>\u003C\u002Fa> is not simply about creating more models. It is about making those models easier to \u003Cstrong>populate\u003C\u002Fstrong>, \u003Cstrong>connect\u003C\u002Fstrong>, \u003Cstrong>query\u003C\u002Fstrong> and \u003Cstrong>improve\u003C\u002Fstrong>.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fwww.3ds.com\u002Fproducts\u002Fcatia\u002Fcatia-magic\">\u003Cstrong>CATIA Magic\u003C\u002Fstrong>\u003C\u002Fa>\u003Cstrong>‘s open APIs\u003C\u002Fstrong> provide one route to automation today, while \u003Cstrong>AI\u003C\u002Fstrong> and \u003Cstrong>SysML v2\u003C\u002Fstrong> open further possibilities for \u003Cstrong>reducing\u003C\u002Fstrong> repetitive work and \u003Cstrong>interacting\u003C\u002Fstrong> with structured engineering information.\u003C\u002Fp>\n\n\n\n\u003Cp>The goal is ultimately practical: \u003Cstrong>let technology handle more of the repetitive work so systems engineers can focus on the problems that still require engineering judgment.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cp>👉 Watch the full video \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GhEBCcM4p6Q\" data-type=\"link\" data-id=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GhEBCcM4p6Q\">\u003Cstrong>here\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"faq-10\">\u003Cstrong>FAQ\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>What does Enola Technologies specialize in?\u003C\u002Fstrong>\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>Enola Technologies is a model-based systems engineering consulting, services and training company. David Fields says the company has particular expertise in CATIA Magic and No Magic tools, with many of its employees having 10 to 15 years or more of MBSE experience.\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>How did CATIA Magic help with a 90,000-requirement project?\u003C\u002Fstrong>\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>Enola Technologies used the CATIA Magic Open API to automate the processing of more than 90,000 requirements. The automation could adjust outdated requirement text and create relationships for verification and test cases. A process that previously took months could then be performed in minutes.\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>What role does the CATIA Magic Open API play in MBSE?\u003C\u002Fstrong>\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>David uses the Open API, particularly the Java Open API, for automation, custom imports, integrations and manipulation of model data. It can also be used to automate recurring processes when new data needs to be incorporated into a model.\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Why is David Fields particularly interested in SysML v2’s textual notation?\u003C\u002Fstrong>\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>As a computer engineer with a programming background, David finds textual notation natural to work with. He also sees two major benefits: it can work effectively with AI and makes it easier to share smaller pieces of model information, such as snippets and examples.\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>What should companies consider when implementing MBSE?\u003C\u002Fstrong>\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>David recommends looking beyond the software itself. Organizations need to define what they want to accomplish and prepare different groups through appropriate training. Engineers may need skills in modeling, querying, simulation and extracting model data, while leadership and stakeholders also need to understand how they will interact with the models.\u003C\u002Fp>\n","2026-09-28T09:34:26",[],{"node":50,"__typename":62},{"nicename":51,"description":52,"slug":53,"name":54,"firstName":55,"lastName":56,"avatar":57,"__typename":61},"sauliuspavalkis","Saulius Pavalkis is a CATIA CYBER SYSTEMS Industry Process Expert Director, within the CATIA Cyber Systems EE&F AMERICA organization.","SauliusPAVALKIS","Saulius PAVALKIS","Saulius","PAVALKIS",{"default":58,"url":59,"__typename":60},"mm","https:\u002F\u002Fsecure.gravatar.com\u002Favatar\u002F58999c88f8f1524e166a32cbd344b51faa4f7f09e3f3fc343ceca09e3b709acd?s=96&d=mm&r=g","Avatar","User","NodeWithAuthorToUserConnectionEdge",{"edges":64,"nodes":72,"__typename":76},[65],{"isPrimary":66,"node":67,"__typename":71},true,{"slug":68,"name":69,"__typename":70},"design-simulation","Design & Simulation","Taxonomy_topic","PostToTaxonomy_topicConnectionEdge",[73],{"id":74,"name":69,"uri":75,"__typename":70},"dGVybTo4NTU5","\u002Ftopics\u002Fdesign-simulation\u002F","PostToTaxonomy_topicConnection",{"nodes":78,"__typename":88},[79,84],{"id":80,"name":81,"uri":82,"__typename":83},"dGVybTo4ODMy","AI","\u002Ftags\u002Fai\u002F","Taxonomy_tag",{"id":85,"name":86,"uri":87,"__typename":83},"dGVybTo5MTM0","System Engineering","\u002Ftags\u002Fsystem-engineering\u002F","PostToTaxonomy_tagConnection",{"edges":90,"nodes":97,"__typename":99},[91],{"isPrimary":66,"node":92,"__typename":96},{"slug":93,"name":94,"__typename":95},"catia","CATIA","Taxonomy_brand","PostToTaxonomy_brandConnectionEdge",[98],{"name":94,"slug":93,"__typename":95},"PostToTaxonomy_brandConnection",{"nodes":101,"__typename":109},[102,105,107],{"name":103,"__typename":104},"CATIA Magic","Taxonomy_keyword",{"name":106,"__typename":104},"MBSE",{"name":108,"__typename":104},"SysML","PostToTaxonomy_keywordConnection",{"title":111,"metaDesc":112,"opengraphAuthor":113,"opengraphDescription":112,"opengraphTitle":20,"opengraphUrl":114,"opengraphSiteName":115,"opengraphPublishedTime":116,"opengraphModifiedTime":117,"twitterTitle":113,"twitterDescription":113,"readingTime":118,"metaRobotsNoindex":119,"__typename":120},"CATIA Magic Automation: Turning 90,000 Requirements into Minutes","David Fields of Enola Technologies explains how CATIA Magic automation turned a 90,000-requirement MBSE project into a job done in minutes","","https:\u002F\u002Fblog-frontoffice-contrib-prd.itvpc.3ds.com\u002Fbrands\u002Fcatia\u002Fcatia-magic-automation-david-fields-enola\u002F","Dassault Systèmes blog","2026-09-28T09:34:26+00:00","2026-09-28T09:34:31+00:00",9,"index","PostTypeSEO","Post","RootQueryToPostConnection",{},{},1790601937266]