{"id":67,"date":"2026-03-31T16:26:45","date_gmt":"2026-03-31T20:26:45","guid":{"rendered":"https:\/\/blogs.mathworks.com\/digitaleng\/?p=67"},"modified":"2026-04-20T14:19:15","modified_gmt":"2026-04-20T18:19:15","slug":"are-you-focusing-on-the-wrong-parts-of-digital-transformation","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/digitaleng\/2026\/03\/31\/are-you-focusing-on-the-wrong-parts-of-digital-transformation\/","title":{"rendered":"Are You Focusing on the Wrong Parts of Digital Transformation?"},"content":{"rendered":"

Surprising Insights from Aerospace & Defense Engineers<\/span>\u00a0\u00a0<\/span><\/b>\u00a0<\/span><\/p>\n

If you are leading digital transformation in engineering, it is easy to assume the next step is more tools, more infrastructure, and more modernization. In many cases, that is part of the answer. But our recent survey of aerospace and defense engineers suggests it is not the whole answer. The bigger issues may be less about getting new tools into the organization and more about making engineering work\u00a0actually connect\u00a0across teams, artifacts, and decisions.<\/span>\u00a0<\/span><\/p>\n

That is what stood out to us in the\u00a0<\/span>survey results<\/span><\/a>. Yes, respondents pointed to investment in new tools and environments as a top request\u00a0to\u00a0leadership. But they also pointed, very clearly, to interoperability, traceability, steep learning curves, and the challenge of moving away from legacy artifacts and processes. And one result\u00a0stood out\u00a0even more: strong interest in using system-level architecture models for simulation, tradeoff analysis, and validation.<\/span>\u00a0<\/span><\/p>\n

That last point is worth sitting with for a minute. It suggests that many teams are not just looking for more models or more digital artifacts.\u00a0They are looking for models they can actually use.\u00a0Models that help them explore alternatives, make decisions earlier, and\u00a0validate\u00a0system behavior with more confidence. That feels like a much more revealing signal than a generic statement about digital transformation.<\/span>\u00a0<\/span><\/p>\n

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What the survey is really saying<\/b><\/span>\u00a0<\/span><\/p>\n

Respondents could select more than one response, so percentages do not\u00a0sum to\u00a0100%.<\/span><\/i>\u00a0<\/span><\/p>\n

Tools are only part of the story.\u00a0<\/span><\/b>A few survey results tell the story pretty quickly.\u00a0More than half of respondents, 56%, said investment in new tools and environments is a top request\u00a0to\u00a0leadership. At the same time, the top digital transformation initiatives underway were digital twin at 55% and digital thread at 53%. Those are big, strategic efforts. No surprise there.<\/span>\u00a0<\/span><\/p>\n

The biggest gaps are still foundational.\u00a0<\/span><\/b>But here is the tension. The biggest pain points were not a lack of named initiatives. They were tool and data interoperability at 56% and traceability between engineering artifacts at 50%. In other words, many organizations are aiming for digital\u00a0twin\u00a0and digital\u00a0thread\u00a0while still struggling with the basics that make those efforts useful in practice.\u00a0If tools do not connect and artifacts do not trace cleanly, the bigger vision starts to wobble pretty quickly.<\/span>\u00a0<\/span><\/p>\n

Engineers want models they can actually use.\u202f<\/span><\/b>Then there is the result we found especially interesting: 60% of respondents said they want to learn more about using system-level architecture models for simulation, tradeoff analysis, and validation. To us, that says something important. It says that for many engineers, the next step is not just having architecture\u00a0models,\u00a0it is making those models executable enough to support real engineering work.<\/span>\u00a0<\/span><\/p>\n

That is a meaningful distinction. A model that documents architecture is useful. A model that helps you analyze tradeoffs,\u00a0validate\u00a0behavior, and inform decisions is much more powerful. If a lot of engineers are asking for that capability, it suggests many transformation efforts still have a gap between creating digital artifacts and getting real engineering value from them.<\/span>\u00a0<\/span><\/p>\n

The\u202fpeople\u202fside may be the hardest part.\u202f<\/span><\/b>The\u00a0people\u00a0side of the story is just as clear. Eighty percent of respondents cited a significant learning curve as a major hurdle to\u00a0adopting\u00a0new processes and technologies. Seventy-nine percent pointed to the challenge of transitioning from legacy artifacts and processes. That should be a reminder that digital transformation is not just\u00a0a tooling\u00a0problem. It is also a workflow and adoption problem. If the new way of working is too hard to\u00a0adopt, teams will keep falling back to the old one.<\/span>\u00a0<\/span><\/p>\n

And all of this is happening under time pressure.\u00a0Nearly all\u00a0respondents, excluding those who answered \u201cunsure,\u201d said they need to achieve a digital transformation goal within the next\u00a018 months.\u00a0So\u00a0there is urgency, but the survey suggests that moving faster is\u00a0not the same as\u00a0moving effectively. If the foundation is weak, speed just exposes the cracks sooner.<\/span>\u00a0<\/span><\/p>\n

Why\u00a0this matters<\/b><\/span>\u00a0<\/span><\/p>\n

Digital transformation only matters if engineering work improves.\u00a0<\/span><\/b>One thing we have talked about before is that digital transformation is a broad term, while digital engineering is much more specific. Digital transformation can mean many things across an organization. Digital engineering is about how engineering work\u00a0actually gets\u00a0done: how teams design, connect, verify,\u00a0maintain, and improve complex systems over time. That distinction matters here. These survey results are interesting precisely because they point to the practical frictions inside engineering work, not just the broad ambition around transformation.<\/span>\u00a0<\/span><\/p>\n

If that is true, then the takeaways are fairly straightforward.<\/span>\u00a0<\/span><\/p>\n

Interoperability matters because disconnected tools and data make it hard to create usable workflows across disciplines. Traceability matters because teams need to understand how a change in one place affects the rest of the system, without resorting to manual detective work. System-level models matter most when they can support simulation, tradeoff analysis, and validation, not just documentation. And skills matter because even the best workflow will stall if teams cannot realistically adopt it.<\/span>\u00a0<\/span><\/p>\n

The real goal is\u202fbetter\u202fconnected engineering work.\u00a0<\/span><\/b>Taken together, the survey points to a simple but important idea: digital transformation in engineering is not just about modernizing the toolset. It is about making engineering work more connected, more analyzable, and more practical to execute.<\/span>\u00a0<\/span><\/p>\n

A few questions worth asking<\/b><\/span>\u00a0<\/span><\/p>\n

If you are leading digital transformation in engineering, this survey raises a few useful questions.<\/span>\u00a0<\/span><\/p>\n