{"id":5028,"date":"2025-10-13T20:47:08","date_gmt":"2025-10-13T20:47:08","guid":{"rendered":"https:\/\/www.aviator.co\/blog\/?p=5028"},"modified":"2026-06-07T13:34:40","modified_gmt":"2026-06-07T13:34:40","slug":"ai-2025-dora-report","status":"publish","type":"post","link":"https:\/\/www.aviator.co\/blog\/ai-2025-dora-report\/","title":{"rendered":"AI Won\u2019t Fix Broken Systems: Lessons from the 2025 DORA Report"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">TLDR<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AI adoption is nearly universal<\/strong>, but productivity gains are mostly <em>perceived<\/em>, not always measured.<\/li>\n\n\n\n<li><strong>Individual speed \u2260 system performance<\/strong>. Faster code means little if pipelines, reviews, and release processes can\u2019t keep up.<\/li>\n\n\n\n<li><strong>Delivery instability is rising<\/strong>, especially where teams adopt AI without rethinking workflows or quality gates.<\/li>\n\n\n\n<li><strong>Strong systems amplify AI\u2019s value<\/strong>: mature version control, healthy data ecosystems, and robust internal platforms make the biggest difference.<\/li>\n\n\n\n<li><strong>AI mirrors the system it enters\u2014fixing<\/strong> processes and culture is the real unlock for long-term impact.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">AI is rapidly reshaping software engineering. The 2025 <a href=\"https:\/\/cloud.google.com\/resources\/content\/2025-dora-ai-assisted-software-development-report\" target=\"_blank\" rel=\"noopener\" title=\"\">DORA report<\/a> shows that adoption in software engineering has become nearly universal: <strong>90% of survey respondents use AI<\/strong>, and more than 80% believe it has increased their productivity.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The keyword here is \u2018believe,\u2019 as it has been shown in various studies that AI productivity gains can be deceptive. One study (<a href=\"https:\/\/metr.org\/blog\/2025-07-10-early-2025-ai-experienced-os-dev-study\/\" target=\"_blank\" rel=\"noopener\" title=\"\">METR<\/a>) showed that developers believed AI tools made them 20% more efficient, while in reality they were slowed down by AI tools by 19%. Also, individual productivity gains in writing code often do not reflect as increased productivity across the entire software delivery lifecycle, and the DORA reports dedicated a whole section of this year\u2019s report to that.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Engineering organizations don&#8217;t need faster typers<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">AI adoption, as per the DORA research, correlates with improvements in several key areas:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Higher levels of individual effectiveness<br><\/li>\n\n\n\n<li>Higher code quality<br><\/li>\n\n\n\n<li>Better team and organizational performance<br><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Individual developers report <strong>producing more code, faster.<\/strong> The top use case for AI tools is writing new code, stated by 71% of coding respondents. Yet <strong>software delivery remains a system problem<\/strong>. As Chris Westerhold, Global Practice Director for Engineering Excellence at Thoughtworks, put it in <a href=\"https:\/\/www.aviator.co\/podcast\" target=\"_blank\" rel=\"noopener\" title=\"\">The Hangar podcast<\/a>:<\/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\"><em>Most engineering organizations do not need faster typers. <br><\/em><br><em>The common engineering bottlenecks are flaky pipelines, no testing strategy, poor documentation, or organizational structures, the usual roadblocks to getting to business value. <\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Your team might get marginally faster at writing code, but unless you address those systemic issues, you\u2019re never going to realize the full value of AI tools.<\/em><\/p>\n<\/blockquote>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Throwing AI at Developers Won&amp;apos;t Solve Their Problems with Chris Westerhold\" width=\"1200\" height=\"675\" src=\"https:\/\/www.youtube.com\/embed\/kc2Bfb_yTT4?start=1676&amp;feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The DORA report also found no clear link between AI adoption and reductions in friction or burnout and even observed increased delivery instability in some organizations.<\/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\"><em>While a tool designed to automate repetitive duties might seem like a clear path to a smoother workflow, our data indicates that workplace friction is a much larger and more complex issue than the mere completion of rote tasks. As we\u2019ve indicated, some research points to friction as a product of processes beyond the individual.<\/em><\/p>\n<\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\">AI Engineering Waste<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In his analysis on the<a href=\"https:\/\/www.linkedin.com\/company\/thoughtworks\/\" target=\"_blank\" rel=\"noopener\" title=\"\"> <\/a><a href=\"https:\/\/www.thoughtworks.com\/insights\/articles\/the-dora-report-2025--a-thoughtworks-perspective\" target=\"_blank\" rel=\"noopener\" title=\"\">Thoughtworks blog<\/a>, Chris warns that AI tools can even contribute to the emergence of a new kind of waste in engineering organizations &#8211; AI engineering waste. <br><br>Examples of AI engineering waste could be:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Prompt-response latency: <\/strong>Engineers spend valuable time waiting for AI models to generate responses, delaying workflows and breaking focus.<br><\/li>\n\n\n\n<li><strong>Context loss:<\/strong> If AI systems lose track of conversations or project-specific context, developers must repeatedly re-explain issues, leading to frustration and wasted effort.<br><\/li>\n\n\n\n<li><strong>AI toolchain fragmentation:<\/strong> Teams juggle multiple, disconnected AI tools and platforms, which leads to frequent context switching and increased cognitive load.<br><\/li>\n\n\n\n<li><strong>Validation overhead: <\/strong>Thoroughly reviewing and validating AI-generated code for correctness, security, and coherence adds significant effort to the process.<\/li>\n<\/ul>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><em>Without the right structure and processes, AI can turn speed into chaos.<\/em><\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">The DORA report also acknowledges that successfully adopting AI in software development is <strong>not as simple as just using new tools.<\/strong><br><br>The research identifies seven <strong>DORA AI Capabilities<\/strong> that influence positive impacts of AI adoptions in certain organizations:&nbsp;<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Clear AI strategy and communication<br><\/li>\n\n\n\n<li>A healthy, accessible data ecosystem<br><\/li>\n\n\n\n<li>Strong version control practices<br><\/li>\n\n\n\n<li>Working in small batches<br><\/li>\n\n\n\n<li>User-centered design focus<br><\/li>\n\n\n\n<li>High-quality internal platforms<br><\/li>\n\n\n\n<li>Tight alignment between teams and systems<br><\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">Organizations that have these capabilities in place tend to amplify AI\u2019s impact; those that don\u2019t often see uneven or unstable results.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For example, strong <a href=\"https:\/\/www.aviator.co\/merge-queue\" target=\"_blank\" rel=\"noopener\" title=\"\">version control<\/a> becomes even more critical when AI-generated code dramatically increases the volume of commits. Similarly, <a href=\"https:\/\/www.aviator.co\/stacked-prs\" target=\"_blank\" rel=\"noopener\" title=\"\">working in small batches<\/a> reduces friction for AI-assisted teams and supports faster, safer iteration.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How to Adopt AI Well<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">AI doesn\u2019t inherently make engineering better\u2014it magnifies whatever system it operates within. In teams with well-defined processes and clean architectures, AI can enhance quality and flow. In teams with tangled pipelines or unclear governance, it can accelerate chaos.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To translate AI adoption into lasting organizational performance, teams must treat it as a systems design problem, not a tooling upgrade.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The report points to several key enablers, some&nbsp;of which are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Redesign workflows<\/strong> to match new development speeds. Don\u2019t assume existing processes can carry increased output.<br><\/li>\n\n\n\n<li><strong>Invest in internal platforms<\/strong> that centralize documentation, tools, and data.<br><\/li>\n\n\n\n<li><strong>Clarify governance<\/strong> and roles so that AI usage aligns with quality and compliance standards.<br><\/li>\n\n\n\n<li><strong>Use Value Stream Management (VSM)<\/strong> to ensure local productivity gains translate to system-level improvement.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The AI Mirror<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The DORA 2024 explicitly states that AI reflects and <strong>amplifies your organization\u2019s true capabilities<\/strong>. This is why AI functions both as a mirror and a multiplier. It shines a light on what\u2019s working, accelerating what\u2019s already in motion, but it also surfaces what needs to change.<\/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\"><em>We are seeing that AI\u2019s effects on performance depend on the system in which the work takes place.<br><br>Without<strong> intentional changes to workflows, roles, governance, and cultural expectations<\/strong>, AI tools are likely to remain isolated boosts in an otherwise unchanged system\u2014a missed opportunity.<\/em><\/p>\n<\/blockquote>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"970\" height=\"250\" src=\"https:\/\/www.aviator.co\/blog\/wp-content\/uploads\/2025\/10\/runbooks-cta.png\" alt=\"\" class=\"wp-image-5070\" srcset=\"https:\/\/www.aviator.co\/blog\/wp-content\/uploads\/2025\/10\/runbooks-cta.png 970w, https:\/\/www.aviator.co\/blog\/wp-content\/uploads\/2025\/10\/runbooks-cta-300x77.png 300w, https:\/\/www.aviator.co\/blog\/wp-content\/uploads\/2025\/10\/runbooks-cta-768x198.png 768w\" sizes=\"auto, (max-width: 970px) 100vw, 970px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI adoption is nearly universal, but the 2025 DORA Report shows that faster coding doesn\u2019t always mean increased productivity. <\/p>\n","protected":false},"author":8,"featured_media":5029,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[29,35],"tags":[93,85,66,39,40],"class_list":["post-5028","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-developer-productivity","tag-ai","tag-ai-developer-productivity","tag-chris-westerhold","tag-dora","tag-dora-metrics"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - 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