--- title: Why AI projects fail: MLOps lessons for leaders | daily.dev description: Enterprise AI projects fail at rates of 80-95% for the same organizational reasons ML projects failed a decade ago: no clear path to production, governance as... canonical: https://daily.dev/posts/why-ai-projects-fail-mlops-lessons-for-leaders-vshplj4kv twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Why AI projects fail: MLOps lessons for leaders | daily.dev og:description: Enterprise AI projects fail at rates of 80-95% for the same organizational reasons ML projects failed a decade ago: no clear path to production, governance as... og:url: https://daily.dev/posts/why-ai-projects-fail-mlops-lessons-for-leaders-vshplj4kv og:image: https://api.daily.dev/og/posts/VSHPLj4Kv.png og:image:alt: Why AI projects fail: MLOps lessons for leaders og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Why AI projects fail: MLOps lessons for leaders **[Domino Data Lab](https://daily.dev/sources/ddl)** · 11 min read · 1 upvotes · 0 comments ## Summary Enterprise AI projects fail at rates of 80-95% for the same organizational reasons ML projects failed a decade ago: no clear path to production, governance as an afterthought, and velocity mistaken for strategy. Leaders who built MLOps maturity have a structural advantage because the same disciplines — defined production paths, embedded governance, reproducibility, and platform-enforced standards — apply directly to AI-assisted application development. The post outlines three core failure modes (pilots without production paths, velocity without strategy, and late-stage governance), draws a direct parallel between today's vibe coding culture and the research notebook era that gave rise to MLOps, and provides a diagnostic checklist for assessing organizational readiness. The key shift for leaders is recognizing that when AI can generate prototypes in hours, the bottleneck moves upstream to strategy, prioritization, and governance — not execution speed. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Why Enterprise AI Projects Fail: 5 Root Causes & Fixes](https://daily.dev/posts/why-enterprise-ai-projects-fail-5-root-causes-fixes-roxbg76ej) · Netguru · 0 upvotes · 0 comments - [Operational AI: Scaling AI in government agencies](https://daily.dev/posts/operational-ai-scaling-ai-in-government-agencies-sqkiumbjk) · Domino Data Lab · 0 upvotes · 0 comments - [Critical Mistakes Companies Make When Integrating AI/ML into Their Processes](https://daily.dev/posts/critical-mistakes-companies-make-when-integrating-ai-ml-into-their-processes-1oopopkx8) · Towards Data Science · 0 upvotes · 0 comments - [Why Most Machine Learning Projects Fail to Reach Production](https://daily.dev/posts/why-most-machine-learning-projects-fail-to-reach-production-opcx6gdce) · InfoQ · 0 upvotes · 0 comments - [Escaping the AI Pilot Trap](https://daily.dev/posts/escaping-the-ai-pilot-trap-ing2agjbz) · The Register · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#mlops](https://daily.dev/tags/mlops) [View this post on daily.dev](https://daily.dev/posts/why-ai-projects-fail-mlops-lessons-for-leaders-vshplj4kv) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Why AI projects fail: MLOps lessons for leaders","url":"https://daily.dev/posts/why-ai-projects-fail-mlops-lessons-for-leaders-vshplj4kv","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/why-ai-projects-fail-mlops-lessons-for-leaders-vshplj4kv"},"datePublished":"2026-06-08T23:15:29.976Z","dateModified":"2026-06-15T06:19:44.067Z","description":"Enterprise AI projects fail at rates of 80-95% for the same organizational reasons ML projects failed a decade ago: no clear path to production, governance as...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/cb551d44a0ee0817f3c8409b20f53ce7?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/cb551d44a0ee0817f3c8409b20f53ce7?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Domino Data Lab","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Domino Data Lab","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/76153f08f32c439ebb12eba7aa3b714f","url":"https://daily.dev/sources/ddl"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/why-ai-projects-fail-mlops-lessons-for-leaders-vshplj4kv","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,mlops","timeRequired":"PT11M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Domino Data Lab","item":"https://daily.dev/sources/ddl"},{"@type":"ListItem","position":3,"name":"Why AI projects fail: MLOps lessons for leaders"}]} ```