That study claiming \u201c95% of AI POCs fail\u201d has been making the rounds. It\u2019s clickbait nonsense, and frankly, it\u2019s not helping anyone. The real number? Nobody knows, because nobody\u2019s tracking it properly. But here\u2019s what I do know after years of watching teams build AI systems: the study masks a much more important problem.<\/p>\n
Teams are confused about how to design POCs that survive beyond the demo stage. There is no playbook.<\/strong><\/p>\n\n Most AI POCs die because they were designed to die. They\u2019re built as disposable demos, optimized for executive presentations rather than production reality. They burn through cloud credits, rely on perfect conditions and perfectly structured data, and quickly collapse when real users start to touch them. If they don\u2019t collapse then, often under scale they collapse when the design problems emerge under strain, leading to more serious failure.<\/p>\n\n But it doesn\u2019t have to be this way.\u00a0<\/p>\n\n After watching hundreds of AI projects at Docker and beyond, I\u2019ve seen the patterns that separate the 5% that make it from the 95% that don\u2019t. Here\u2019s the playbook I wish every platform and MLOps team had from day one.<\/p>\n\n Before we dive into the rules, let\u2019s talk about the biggest shift in how successful teams approach AI development: remocal workflows<\/strong> (remote + local).<\/p>\n\n Running AI locally isn\u2019t just about saving money\u2014though it absolutely does that. It\u2019s about maintaining developer velocity and avoiding the demo theater trap. Here\u2019s how the best teams structure their work:<\/p>\n\n Test locally on laptops<\/strong> for fast iteration. No waiting for cloud resources, no surprise bills, no network latency killing your flow. The nature of building with AI should be making the process feel very interactive.<\/p>\n Burst to remote resources<\/strong> for scale testing, production-like validation, or when you actually need those H100s. It should feel easy to move AI workloads around.<\/p>\n Keep costs transparent<\/strong> from day one. You know exactly what each experiment costs because you\u2019re only paying for remote compute when you choose to.<\/p>\n POCs that incorporate this pattern from day zero avoid both runaway bills and the classic \u201cit worked in the demo\u201d disaster. They\u2019re grounded in reality because they\u2019re built with production constraints baked in.<\/p>\n\n Your first instinct is wrong. You don\u2019t need the biggest model, the complete dataset, or every possible feature. Bite-sized everything: models that fit on a laptop, datasets you can actually inspect, and scope narrow enough that you can explain the value in one sentence.<\/p>\n\n Early wins compound trust. A small thing that works beats a big thing that might work.<\/p>\n\n Logging, monitoring, versioning, and guardrails aren\u2019t \u201cnice to haves\u201d you add later. They\u2019re the foundation that determines whether your POC can grow up to be a real system.<\/p>\n\n If your POC doesn\u2019t have structured logging and basic metrics \u2013 observability \u2013\u00a0 from the first commit, you\u2019re building a disposable demo, not a prototype of a production system.<\/p>\n\n Infrastructure should be templated. Prompt testing should be in CI\/CD. Model comparisons should be apples-to-apples benchmarks, not \u201cit felt better this time.\u201d What\u2019s more, POC designs can and should assume existing model families will continue to rapidly improve. That includes larger context windows, greater accuracy, lower latency and smaller resource consumption.<\/p>\n\n The sexiest part of AI isn\u2019t the novel algorithm\u2014it\u2019s how we\u2019re learning to frame problems in ways\u00a0 that make AI more reliable at scale.<\/p>\n\n This is the big one that separates amateur hour from production-ready systems. Separate your non-deterministic AI components from your deterministic business logic. Build in layers of control and validation. Domain knowledge is still your magic ingredient.<\/p>\n\n In a remocal setup, this becomes natural: your agent loops can run locally for fast iteration, while tool execution and heavy compute burst to remote resources only when needed. You get reliability from layered control, not from hoping your model has a good day.<\/p>\n\n Anchor everything to measurable business pain. Real users with real problems they\u2019re willing to pay to solve. If your POC\u2019s main value proposition is \u201clook how cool this is,\u201d you\u2019re building the wrong thing.<\/p>\n\n Kill the vanity demos that only look good in slideware. Build the boring solutions that save people actual time and money.<\/p>\n\n Track unit economics from day one. What does each request cost? Each user? Each workflow?<\/p>\n\n Benchmark small vs. large models. Cloud vs. local execution. Know your trade-offs with real numbers, not hand-waving about \u201ccloud scale.\u201d<\/p>\n\n Who owns this thing when it breaks at 2 AM? What are the SLAs? Who\u2019s responsible for retraining the model? Who pays for the compute?<\/p>\n\n Don\u2019t let POCs drift in the organizational void between research labs and operations teams. Assign owners, responsibilities, and lifecycle management from day one.<\/p>\n\n Transparent cost per request, user, and workflow. Hard budget caps and kill switches. No surprises in the monthly cloud bill.<\/p>\n\n Remocal workflows make this natural: you default to local execution and only burst remote when you consciously choose to spend money. Your costs are predictable because they\u2019re intentional.<\/p>\n\n Co-design with real users, not executives who saw a ChatGPT demo and want \u201cAI for everything.\u201d Measure adoption and time saved, not just accuracy scores.<\/p>\n\n The best AI POCs feel like natural extensions of existing workflows because they were built with the people who actually do the work.<\/p>\n\n Most failed AI POCs never had a chance. They were too big, too expensive, too disconnected from real problems, and too optimized for demo day rather than daily use.<\/p>\n By flipping the script\u2014starting small, designing for production, involving real users, and building on remocal workflows\u2014you dramatically increase your odds of building something that ships and scales.<\/p>\n\n The difference between a successful AI POC and a failed one isn\u2019t the sophistication of the model. It\u2019s the boring engineering decisions you make on day zero.<\/p>\n\n Stop treating AI POCs as disposable demos. Treat them as the first draft of a production system.<\/strong><\/p>\n\n Jim Clark is Principal Engineer for AI at Docker, where he helps teams build AI systems that actually make it to production. He\u2019s spent the last decade watching the gap between AI demos and AI products, and occasionally bridging it.<\/em><\/p>","protected":false},"excerpt":{"rendered":" That study claiming \u201c95% of AI POCs fail\u201d has been making the rounds. It\u2019s clickbait nonsense, and frankly, it\u2019s not […]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[4],"tags":[],"class_list":["post-2478","post","type-post","status-publish","format-standard","hentry","category-docker"],"_links":{"self":[{"href":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/wp-json\/wp\/v2\/posts\/2478","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/wp-json\/wp\/v2\/comments?post=2478"}],"version-history":[{"count":0,"href":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/wp-json\/wp\/v2\/posts\/2478\/revisions"}],"wp:attachment":[{"href":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/wp-json\/wp\/v2\/media?parent=2478"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/wp-json\/wp\/v2\/categories?post=2478"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rssfeedtelegrambot.bnaya.co.il\/index.php\/wp-json\/wp\/v2\/tags?post=2478"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}The New Foundation: Remocal Workflows<\/strong><\/h2>\n\n
The Nine Rules of POC Survival<\/strong><\/h2>\n\n
1. Start Small, Stay Small<\/strong><\/h3>\n\n
2. Design for Production from Day Zero<\/strong><\/h3>\n\n
3. Optimize for Repeatability and Model Improvement, Not Novelty<\/strong><\/h3>\n\n
4. Think in Feedback Loops<\/strong><\/h3>\n\n
5. Solve Pain, Not Impress<\/strong><\/h3>\n\n
<\/strong>6. Embed Cost and Risk Awareness Early<\/strong><\/h3>\n\n
7. Make Ownership Clear<\/strong><\/h3>\n\n
8. Control Costs Upfront<\/strong><\/h3>\n\n
9. Involve Users From Day Zero<\/strong><\/h3>\n\n
Why This Actually Matters<\/strong><\/h2>\n\n