{"id":105085,"date":"2025-06-27T11:51:15","date_gmt":"2025-06-27T11:51:15","guid":{"rendered":"https:\/\/www.simscale.com\/?p=105085"},"modified":"2025-07-07T15:14:33","modified_gmt":"2025-07-07T15:14:33","slug":"the-engineering-ai-ambition-execution-gap-what-our-new-global-survey-reveals","status":"publish","type":"post","link":"https:\/\/www.simscale.com\/blog\/the-engineering-ai-ambition-execution-gap-what-our-new-global-survey-reveals\/","title":{"rendered":"The Engineering AI Ambition-Execution Gap: What Our New Global Survey Reveals"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\">AI is everywhere in the <em>conversation <\/em>about engineering today, but how far is it actually in the <em>practice <\/em>of engineering?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">That\u2019s the question that led us to commission our latest global survey: the <em>State of Engineering AI 2025<\/em>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We spoke to <strong>300 senior engineering leaders<\/strong> &#8211; CTOs, VPs of Engineering, Simulation leaders &#8211; across the US and Europe, to understand how prepared engineering organizations really are to adopt and scale AI in their design engineering and simulation workflows.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The results are fascinating, and for me, both a clear validation of the opportunity and a sharp reminder of where the real work lies.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">AI Ambition Is Not the Problem &#8211; Execution Is<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The headline is simple:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>93%<\/strong> of leaders expect AI to drive productivity gains.<\/li>\n\n\n\n<li><strong>30%<\/strong> expect those gains to be \u201c<em>very high\u201d<\/em>.<\/li>\n\n\n\n<li>But only <strong>3%<\/strong> say they are achieving that level of impact today.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This is a massive gap (10:1) between current ambition and experience &#8211; what we\u2019re calling the <strong>Engineering AI expectation v. execution gap<\/strong>.\u00a0 It\u2019s also not unique to Engineering, many industries go through this phase. But the depth of this gap in Engineering is shaped by some very specific challenges:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"993\" height=\"660\" src=\"https:\/\/frontend-assets.simscale.com\/media\/2025\/06\/state-of-engineering-ai-q1.webp\" alt=\"graph showing the current state of engineering AI adoption compared to the possible amount\" class=\"wp-image-104916\" srcset=\"https:\/\/frontend-assets.simscale.com\/media\/2025\/06\/state-of-engineering-ai-q1.webp 993w, https:\/\/frontend-assets.simscale.com\/media\/2025\/06\/state-of-engineering-ai-q1-300x199.webp 300w, https:\/\/frontend-assets.simscale.com\/media\/2025\/06\/state-of-engineering-ai-q1-768x510.webp 768w\" sizes=\"auto, (max-width: 993px) 100vw, 993px\" \/><figcaption class=\"wp-element-caption\"><em>The productivity gains \u201cExpectation-Execution Gap\u201d seen with Engineering AI<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\">Why Engineering Is Different<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Unlike fields where large-scale public data and cloud-native workflows are the norm, <strong>engineering teams face structural barriers<\/strong> that AI alone cannot magically remove:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Siloed data<\/strong>: 55% of leaders cite fragmented, inaccessible data as the top barrier to AI progress.<br><\/li>\n\n\n\n<li><strong>Legacy tools<\/strong>: 42% cite the limitations of traditional desktop CAE tools. Many workflows are not cloud-native or even cloud-connected.<br><\/li>\n\n\n\n<li><strong>Leadership disconnect<\/strong>: Interestingly, <strong>42% <\/strong>of CTOs perceive significant <strong>resistance<\/strong> to AI adoption within their teams, but engineering leaders themselves report this only <strong>25%<\/strong> of the time. In other words: <em>many teams are more AI ready and enthusiastic than leadership assumes.<\/em><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">And finally, engineering data itself is often fundamentally harder to leverage for AI than the text or image data used to train other types of foundation models. This is why I believe the evolution of <strong>Physics AI<\/strong> and <strong>Engineering AI<\/strong> will take a path that is very much grounded in accelerating the adoption of cloud-native tech stacks across engineering workflows.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What the Leaders Are Doing Differently<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The good news is that our survey clearly shows a cohort of engineering leaders who are already achieving transformational results.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These teams share several traits:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>They have <strong>modernized their toolstack<\/strong> &#8211; favoring cloud-native, open platforms.<\/li>\n\n\n\n<li>They have invested in ensuring <strong>centralized, clean engineering data<\/strong> is captured across workflows &#8211; not perfectly, but enough to enable scalable AI.<\/li>\n\n\n\n<li>They are building and integrating<strong> AI agents directly into live workflows <\/strong>&#8211; not as bolt-on tools, but as embedded decision-makers at setup, evaluation, and optimization stages.<\/li>\n\n\n\n<li>They have moved from pilots to <strong>production-grade AI use cases<\/strong> that drive real business value (faster design cycles, improved product performance, faster time to market) &#8211; rapidly, with confidence, and with clear mandates.<\/li>\n\n\n\n<li>Critically, they have fostered <strong>strong alignment between leadership and engineering teams<\/strong> -AI initiatives are not being led in isolation.<br><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Cloud-native users in our survey are <strong>3x more likely<\/strong> to have mature AI programs and <strong>6x more likely<\/strong> to have clean, centralized data &#8211; and they are <strong>twice as confident<\/strong> they\u2019ll achieve their AI goals in the next 12 months. It\u2019s clear that <em>confidence <\/em>in AI follows <em>capability <\/em>with cloud-native CAE tooling, rather than the other way around.\u00a0<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Where We Go From Here<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">One of my favorite lessons from the many conversations with engineering leaders had while creating this report is simple:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\ud83d\udc49 <strong>Don\u2019t aim to \u201cdo AI.\u201d<br><\/strong> \ud83d\udc49 <strong>Aim to solve engineering problems better &#8211; with AI as a transformational enabler.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Teams that start with a clear, high-impact use case &#8211; where collapsing a process from days to seconds changes outcomes &#8211; make the fastest progress. Engineering AI is not about replacing engineers. It\u2019s about creating <strong>machine-in-the-loop workflows<\/strong> that supercharge engineering creativity and productivity.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The goal is not to bypass human insight, but to multiply it, to deliver unseen levels of engineering innovation.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">My Call to Action for Engineering Leaders<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">If you are a VP of Engineering, CTO, or simulation leader reading this:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">\u2705 Be aware of the <strong>expectation-execution gap<\/strong> &#8211; don\u2019t let your organization be part of the \u201c<em>93% hoping, only 3% achieving<\/em>\u201d statistic.<br>\u2705 Look hard at your toolstack, your data readiness, and the leadership alignment needed to move forward. Does your legacy tooling hinder or help AI adoption?<br>\u2705 Start with <strong>one high-value application <\/strong>and push hard; prove out the impact, then scale with confidence.<\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">And above all: <strong>the time to start is now<\/strong>. Engineering AI is no longer a future vision or add-on capability, it is a fundamental enabler and accelerator, and is already transforming how some teams design and innovate today.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We created this report not just to benchmark the market, but to help drive the conversation forward. I encourage you to read it, and more importantly, to act on it!<\/p>\n\n\n\n<div class=\"hw-block hw-btnWrapper hw-btnWrapper--alignLeft \">\n    <a href=\"\" class=\"hw-btn  btn-hs-form hw-btn--blue1  \" data-form-id=\"fed6ae7d-5bbb-4bb0-aa07-3eece3135a2c\" data-sfdc-campaign-id=\"701Se00000Q2LSxIAN\" data-form-title=\"Download Engineering AI Report\" data-form-forward-fields=\"true\" target=\"_blank\"    >\n        Download the full report here    <\/a>\n<\/div>\n\n\n\n\n<p class=\"wp-block-paragraph\">I look forward to hearing what you think.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>David<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI is everywhere in the conversation about engineering today, but how far is it actually in the practice of engineering?...","protected":false},"author":7,"featured_media":104915,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_crdt_document":"","inline_featured_image":false,"footnotes":""},"categories":[1625,11],"tags":[739,1588],"class_list":["post-105085","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-featured","category-product","tag-life-at-simscale","tag-news"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.simscale.com\/wp-json\/wp\/v2\/posts\/105085","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.simscale.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.simscale.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.simscale.com\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.simscale.com\/wp-json\/wp\/v2\/comments?post=105085"}],"version-history":[{"count":0,"href":"https:\/\/www.simscale.com\/wp-json\/wp\/v2\/posts\/105085\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.simscale.com\/wp-json\/wp\/v2\/media\/104915"}],"wp:attachment":[{"href":"https:\/\/www.simscale.com\/wp-json\/wp\/v2\/media?parent=105085"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.simscale.com\/wp-json\/wp\/v2\/categories?post=105085"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.simscale.com\/wp-json\/wp\/v2\/tags?post=105085"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}