{"id":31764,"date":"2026-06-19T10:20:03","date_gmt":"2026-06-19T08:20:03","guid":{"rendered":"https:\/\/www.digital-chiefs.de\/?p=31764"},"modified":"2026-07-22T13:47:33","modified_gmt":"2026-07-22T11:47:33","slug":"four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations","status":"publish","type":"post","link":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/","title":{"rendered":"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations"},"content":{"rendered":"<p style=\"display:inline-block;background:#d65663;color:#fff;padding:4px 14px;border-radius:20px;font-size:0.85em;margin-bottom:18px;\">7 Min. Reading Time<\/p>\n<p><strong>The most dangerous moment in an AI project comes after a successful pilot.<\/strong> In the completed test run, everything looks promising: a clean dataset, a motivated team, an impressive demo result. Then the model is supposed to go into regular operation-and that&#8217;s exactly where most projects get stuck. Gartner estimates that at least 30 percent of GenAI projects are abandoned after the proof of concept. The RAND Corporation reports a failure rate of around 80 percent for AI projects overall. The causes range from poor data quality through unclear goals to a lack of integration within the organization. The transition from pilot to everyday use is one of the most critical moments for CIOs.<\/p>\n<div style=\"background:#f6f1e6;border-left:4px solid #d65663;border-radius:0 8px 8px 0;padding:22px 26px;margin:28px 0 34px;\">\n<p style=\"margin:0 0 14px 0;font-size:0.76em;font-weight:700;text-transform:uppercase;letter-spacing:0.16em;color:#c0414e;\">Key Takeaways<\/p>\n<ul style=\"margin:0;padding-left:22px;color:#23303f;line-height:1.6;\">\n<li style=\"margin-bottom:10px;\"><strong>The break lies in the transition:<\/strong> According to Gartner, at least 30 percent of GenAI projects are abandoned after the proof of concept, while RAND estimates that around 80 percent of all AI projects fail. The pilot proves feasibility, not operational readiness.<\/li>\n<li style=\"margin-bottom:10px;\"><strong>Four recurring stumbling blocks:<\/strong> Data does not scale, no one owns the model in production, operating costs eat into the business case, and the organization does not keep up.<\/li>\n<li><strong>CIOs actively steer the transition:<\/strong> Those who clarify the data pipeline, ownership, cost model, and change management before rollout avoid the pilot trap. Those who try afterward pay twice.<\/li>\n<\/ul>\n<\/div>\n<p style=\"font-size:0.88em;color:#666;margin:20px 0 32px 0;border-top:1px solid #e5e5e5;border-bottom:1px solid #e5e5e5;padding:10px 0;\"><span style=\"color:#d65663;font-weight:700;text-transform:uppercase;font-size:0.72em;letter-spacing:0.14em;margin-right:14px;\">Related:<\/span><a href=\"https:\/\/www.digital-chiefs.de\/en\/blind-spot-transformation-pitch\/\" style=\"color:#333;text-decoration:underline;\">The blind spot in the transformation pitch<\/a>&nbsp;&nbsp;<span style=\"color:#ccc;\">\/<\/span>&nbsp;&nbsp;<a href=\"https:\/\/www.digital-chiefs.de\/en\/ai-governance-2026-only-14-percent-responsible\/\" style=\"color:#333;text-decoration:underline;\">87% more AI budget, 14% clear accountability<\/a><\/p>\n<h2 style=\"margin-top:32px;margin-bottom:16px;\">Why the transition becomes a stress test<\/h2>\n<p>A pilot is a controlled exception. It runs with curated data, a carefully selected use case, and the full attention of the best people. Regular operations are the opposite: fluctuating data quality, many parallel users, limited budgets, and no more project glamour. What works in the pilot mainly shows that it can be done in principle. It says little about operational maturity.<\/p>\n<p>The numbers underline this. Gartner forecasts that a significant portion of AI initiatives will fail due to a lack of AI-ready data, and that many GenAI projects won&#8217;t survive the testing phase. For CIOs, this means: success in the pilot is not a guarantee of success in production. The following four stumbling blocks determine whether a project bridges the gap. A concise overview with three central levers is provided in the analysis <a href=\"https:\/\/www.digital-chiefs.de\/en\/from-ai-pilot-to-regular-operations-why-most-miss-the-leap\/\" style=\"color:#d65663;text-decoration:underline;\">why the majority miss the leap<\/a>.<\/p>\n<h2 style=\"margin-top:32px;margin-bottom:16px;\">1. Data that sufficed in the pilot cannot carry the operation<\/h2>\n<p>In the test, a clean excerpt is enough. In production, the model faces the full reality of enterprise data: duplicates, missing fields, outdated master data, systems never designed for machine learning. This is precisely where Gartner identifies one of the main reasons for failure-namely, the lack of AI-ready data.<\/p>\n<p>The consequence for the transition is clear. Before a model goes live, it needs a reliable data pipeline with quality control instead of manual exports from the pilot phase. Those who only clean up the data foundation after rollout operate a model whose results no one trusts.<\/p>\n<h2 style=\"margin-top:32px;margin-bottom:16px;\">2. No one owns the model in regular operation<\/h2>\n<p>In the pilot, responsibility is clear: the project team. After that, it becomes diffuse. Who monitors model quality, who reacts to drift, who decides on an update? Without clear ownership, the model becomes orphaned, and its quality declines unnoticed. The Logicalis CIO Report clearly highlights the gap: AI budgets are rising broadly, yet <a href=\"https:\/\/www.digital-chiefs.de\/en\/ai-governance-2026-only-14-percent-responsible\/\" style=\"color:#d65663;text-decoration:underline;\">only 14 percent have clarified who bears responsibility<\/a>.<\/p>\n<p>Regular operation demands roles that did not exist in the project: model owner, monitoring, and an escalation path. At its core, this is an organizational question. CIOs who fill these roles before rollout avoid the silent decline after go-live.<\/p>\n<h2 style=\"margin-top:32px;margin-bottom:16px;\">3. Operating costs eat the business case<\/h2>\n<p>A pilot is cost-effective because it is small. In production, inference costs, infrastructure, and maintenance scale with usage. Suddenly, the bill comes into focus, something no one accounted for in the demo. How quickly GenAI spending escalates and which questions justify the rollout are shown in the analysis <a href=\"https:\/\/www.digital-chiefs.de\/en\/from-pilot-to-production-three-questions-cios-must-answer\/\" style=\"color:#d65663;text-decoration:underline;\">GenAI Costs Explode: Who Pays the Bill?<\/a>.<\/p>\n<p>For production deployment, the business case must include operating costs from the start, beyond pure project costs. A model that costs more per query than it saves should not be in regular operation. This calculation must be done before rollout, so it doesn\u2019t later lead to decommissioning.<\/p>\n<div style=\"background:#f6f1e6;border-radius:8px;padding:22px 26px;margin:28px 0;\">\n<p style=\"margin:0 0 6px;font-size:0.72em;font-weight:700;text-transform:uppercase;letter-spacing:0.14em;color:#c0414e;\">AI Projects in Numbers<\/p>\n<p style=\"margin:0;color:#23303f;line-height:1.7;\"><strong style=\"font-size:1.15em;color:#0a1e3d;\">at least 30 %<\/strong> &nbsp;of GenAI projects are abandoned after the proof of concept, according to Gartner.<br \/>\n<strong style=\"font-size:1.15em;color:#0a1e3d;\">around 80 %<\/strong> &nbsp;of AI projects fail to deliver the expected business value, according to RAND.<br \/>\n<strong style=\"font-size:1.15em;color:#0a1e3d;\">14 %<\/strong> &nbsp;of companies have clarified who is responsible for AI, according to Logicalis.<\/p>\n<\/div>\n<h2 style=\"margin-top:32px;margin-bottom:16px;\">4. Without change management, the model remains unused<\/h2>\n<p>The fourth stumbling block lies with people. A technically functioning model fails when the workforce bypasses it. Employees distrust recommendations they don\u2019t understand, and revert to their old workflows. Then the model runs, but no one uses it.<\/p>\n<p>Therefore, production deployment needs more than a deployment. It needs training, transparency about the model\u2019s boundaries, and honest communication about what the AI decides and what the human does. Skipping this part results in a costly tool with no impact.<\/p>\n<div style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;margin:28px 0;\">\n<table style=\"width:100%;border-collapse:collapse;font-size:0.95em;\">\n<thead>\n<tr style=\"background:#0a1e3d;color:#fff;\">\n<th style=\"text-align:left;padding:10px 14px;\">Dimension<\/th>\n<th style=\"text-align:left;padding:10px 14px;\">Pilot<\/th>\n<th style=\"text-align:left;padding:10px 14px;\">Regular Operation<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"border-bottom:1px solid #e5e5e5;\">\n<td style=\"padding:10px 14px;\">Data<\/td>\n<td style=\"padding:10px 14px;\">curated slice<\/td>\n<td style=\"padding:10px 14px;\">pipeline with quality control<\/td>\n<\/tr>\n<tr style=\"border-bottom:1px solid #e5e5e5;background:#faf7f0;\">\n<td style=\"padding:10px 14px;\">Responsibility<\/td>\n<td style=\"padding:10px 14px;\">project team<\/td>\n<td style=\"padding:10px 14px;\">model owner and monitoring<\/td>\n<\/tr>\n<tr style=\"border-bottom:1px solid #e5e5e5;\">\n<td style=\"padding:10px 14px;\">Costs<\/td>\n<td style=\"padding:10px 14px;\">manageable<\/td>\n<td style=\"padding:10px 14px;\">scale with usage<\/td>\n<\/tr>\n<tr style=\"border-bottom:1px solid #e5e5e5;background:#faf7f0;\">\n<td style=\"padding:10px 14px;\">Acceptance<\/td>\n<td style=\"padding:10px 14px;\">enthusiasm within the team<\/td>\n<td style=\"padding:10px 14px;\">change across the board<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2 style=\"margin-top:32px;margin-bottom:16px;\">The Transition as a Distinct Project Phase<\/h2>\n<p>The shared lesson from these four stumbling blocks: the step from pilot to regular operation is a distinct phase with its own budget, roles, and key performance indicators. Anyone who treats it as a mere appendage to the pilot systematically underestimates it. CIOs who plan it for what it truly is significantly increase the likelihood of success.<\/p>\n<figure class=\"evm-inline-figure inarticle-visual\" style=\"display:block;max-width:100%;width:100%;margin:28px auto;border-radius:8px;overflow:hidden;border:1px solid #d6566333;\"><img decoding=\"async\" src=\"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/post-iav0-hero-4.jpg\" alt=\"A team works with focus during the pilot phase, while the hectic regular operation with many users and complex data becomes visible in the background.\" style=\"width:100%;height:auto;display:block;\" loading=\"lazy\"\/><figcaption style=\"font-size:.85em;color:#667;margin-top:.5em;font-style:italic;line-height:1.45;\">From pilot project to hectic regular operation &#8211; the leap challenges teams.<\/figcaption><\/figure>\n<p>In concrete terms, this means establishing four prerequisites before rollout: a production-ready data pipeline, a designated model owner with monitoring capabilities, a business case that includes operating costs, and a change management plan for users. These four points bridge the exact gap where the majority of projects get stuck.<\/p>\n<p>The budget reveals whether a company has truly grasped this. If the entire investment flows into the pilot and nothing is reserved for the transition, failure is preordained. A rough rule of thumb from past projects: the effort required for production deployment often matches the scale of the pilot itself, and sometimes exceeds it. CIOs who openly declare this in their investment requests protect themselves against awkward requests for additional funding that leave a project vulnerable at the worst possible moment. The transition deserves its own line item in the budget, its own accountability, and a dedicated milestone where the decision to continue or abort is made.<\/p>\n<h2 style=\"margin-top:32px;margin-bottom:16px;\">Frequently Asked Questions<\/h2>\n<details>\n<summary><strong>Why do so many AI projects fail after the pilot?<\/strong><\/summary>\n<p style=\"margin:8px 0 4px 24px;color:#555;line-height:1.6;\">Because a pilot only demonstrates feasibility. In everyday operations, the model then faces fluctuating data quality, unclear responsibilities, rising costs, and a lack of acceptance. According to Gartner, at least 30 percent of GenAI projects are abandoned after the proof of concept.<\/p>\n<\/details>\n<details>\n<summary><strong>What is the most important step for the transition?<\/strong><\/summary>\n<p style=\"margin:8px 0 4px 24px;color:#555;line-height:1.6;\">A production-ready data pipeline with quality control. Without reliable data, the model will deliver results in operation that no one trusts, undermining the entire implementation.<\/p>\n<\/details>\n<details>\n<summary><strong>Who should be responsible for an AI model in regular operation?<\/strong><\/summary>\n<p style=\"margin:8px 0 4px 24px;color:#555;line-height:1.6;\">A designated model owner with a clear monitoring and escalation path. According to Logicalis, only 14 percent of companies have clarified who bears responsibility for AI, and this exact gap leads to silent deterioration.<\/p>\n<\/details>\n<details>\n<summary><strong>How do I prevent operating costs from eating up the benefits?<\/strong><\/summary>\n<p style=\"margin:8px 0 4px 24px;color:#555;line-height:1.6;\">By ensuring the business case includes operating costs from the very beginning. Inference, infrastructure, and maintenance scale with usage and must be calculated before the rollout, not after.<\/p>\n<\/details>\n<details>\n<summary><strong>What role does change management play?<\/strong><\/summary>\n<p style=\"margin:8px 0 4px 24px;color:#555;line-height:1.6;\">A crucial one. A technically functioning model remains ineffective if the workforce bypasses it. Training, transparency, and clear communication about the limitations of AI are all part of the transition.<\/p>\n<\/details>\n<h2 style=\"margin-top:40px;margin-bottom:16px;\">Recommended Reading<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.digital-chiefs.de\/en\/autonomous-ai-agents-enterprise-productivity-governance-2026\/\" style=\"color:#d65663;text-decoration:underline;\">AI Agents: Caught Between a Productivity Leap and Loss of Control<\/a><\/li>\n<li><a href=\"https:\/\/www.digital-chiefs.de\/en\/when-an-ai-model-disappears-overnight-why-cios-need-a-plan-b\/\" style=\"color:#d65663;text-decoration:underline;\">When an AI Model Vanishes Overnight: Why CIOs Need a Plan B<\/a><\/li>\n<li><a href=\"https:\/\/www.digital-chiefs.de\/en\/records-management-cio-thema-information-governance\/\" style=\"color:#d65663;text-decoration:underline;\">Records Management as a CIO Priority: Why Governance Needs Clear Ownership<\/a><\/li>\n<\/ul>\n<p style=\"margin:40px 0 12px 0;font-size:0.78em;font-weight:700;text-transform:uppercase;letter-spacing:0.18em;color:#666;\">More from the MBF Media Network<\/p>\n<div style=\"display:flex;flex-direction:column;gap:12px;margin-bottom:8px;\">\n<div style=\"border-left:3px solid #0bb7fd;background:#fafafa;padding:12px 16px;\"><span style=\"display:block;font-size:0.7em;font-weight:700;text-transform:uppercase;letter-spacing:0.12em;color:#0bb7fd;margin-bottom:4px;\">cloudmagazin<\/span><a href=\"https:\/\/www.cloudmagazin.com\/2026\/06\/11\/wenn-die-ki-80-prozent-des-codes-schreibt-wer-prueft\/\" style=\"color:#222;text-decoration:none;font-weight:600;\">When AI Writes 80 Percent of the Code, Who Reviews It?<\/a><\/div>\n<div style=\"border-left:3px solid #202528;background:#fafafa;padding:12px 16px;\"><span style=\"display:block;font-size:0.7em;font-weight:700;text-transform:uppercase;letter-spacing:0.12em;color:#202528;margin-bottom:4px;\">MyBusinessFuture<\/span><a href=\"https:\/\/mybusinessfuture.com\/ki-vertrauen-unter-druck-anthropic-macht-verdeckte-eingriffe-sichtbar\/\" style=\"color:#222;text-decoration:none;font-weight:600;\">Trust in AI Under Pressure: Anthropic Exposes Covert Interventions<\/a><\/div>\n<div style=\"border-left:3px solid #0098b5;background:#fafafa;padding:12px 16px;\"><span style=\"display:block;font-size:0.7em;font-weight:700;text-transform:uppercase;letter-spacing:0.12em;color:#0098b5;margin-bottom:4px;\">SecurityToday<\/span><a href=\"https:\/\/www.securitytoday.de\/2026\/06\/17\/searchleak-microsoft-365-copilot-parameter-injection-datenleck\/\" style=\"color:#222;text-decoration:none;font-weight:600;\">SearchLeak: How a Single Link Turned Microsoft 365 Copilot Into a Data Leak<\/a><\/div>\n<\/div>\n<p style=\"text-align:right;font-style:italic;color:#666;\"><em>Image source: AI-generated (June 2026)<\/em><\/p>\n<p style=\"font-size:.8em;color:#888;margin-top:1.5em;\">Images in article: AI-generated (May 2026)<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI projects often fail during the transition from pilot to regular operations. The four stumbling blocks are data, ownership, costs, and change, along\u2026<\/p>\n","protected":false},"author":118,"featured_media":31754,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_focuskw":"AI pilot regular operation","_yoast_wpseo_title":"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations","_yoast_wpseo_metadesc":"AI projects often fail during the transition from pilot to regular operations. Discover the four key stumbling blocks - data, ownership, costs, and\u2026","_yoast_wpseo_opengraph-image":"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg","_yoast_wpseo_opengraph-image-id":0,"_yoast_wpseo_twitter-image":"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg","_yoast_wpseo_twitter-image-id":0,"featured_post_sortierung":0,"featured_post":0,"pre_headline":"","bildquelle":"","teasertext":"","language":"de","_evm_slot_owner":"","_evm_translation_lang":"","_wp_old_slug":["vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte"],"footnotes":""},"categories":[712,708,645,694],"tags":[],"class_list":{"0":"post-31764","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-ai-data","8":"category-ki-daten","9":"category-new-work-leadership","11":"entry"},"wpml_language":"en","wpml_translation_of":31752,"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations<\/title>\n<meta name=\"description\" content=\"AI projects often fail during the transition from pilot to regular operations. Discover the four key stumbling blocks - data, ownership, costs, and\u2026\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations\" \/>\n<meta property=\"og:description\" content=\"AI projects often fail during the transition from pilot to regular operations. Discover the four key stumbling blocks - data, ownership, costs, and\u2026\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/\" \/>\n<meta property=\"og:site_name\" content=\"Digital Chiefs\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/digitalchiefs\/\" \/>\n<meta property=\"article:published_time\" content=\"2026-06-19T08:20:03+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-07-22T11:47:33+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg\" \/>\n<meta name=\"author\" content=\"Jakob Bach\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:image\" content=\"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg\" \/>\n<meta name=\"twitter:creator\" content=\"@digital_chiefs\" \/>\n<meta name=\"twitter:site\" content=\"@digital_chiefs\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Jakob Bach\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/\"},\"author\":{\"name\":\"Jakob Bach\",\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/#\/schema\/person\/257154921d1b05d9f85df00be993699e\"},\"headline\":\"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations\",\"datePublished\":\"2026-06-19T08:20:03+00:00\",\"dateModified\":\"2026-07-22T11:47:33+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/\"},\"wordCount\":1406,\"publisher\":{\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg\",\"articleSection\":[\"AI &amp; Data\",\"KI &amp; Daten\",\"New Work & Leadership\",\"New Work & Leadership\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/\",\"url\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/\",\"name\":\"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations\",\"isPartOf\":{\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg\",\"datePublished\":\"2026-06-19T08:20:03+00:00\",\"dateModified\":\"2026-07-22T11:47:33+00:00\",\"description\":\"AI projects often fail during the transition from pilot to regular operations. Discover the four key stumbling blocks - data, ownership, costs, and\u2026\",\"breadcrumb\":{\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#primaryimage\",\"url\":\"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg\",\"contentUrl\":\"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg\",\"width\":1792,\"height\":1024,\"caption\":\"AI project transition from pilot to production: a centered, broad editorial illustration of a gap or break point between a small test lab an\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Startseite\",\"item\":\"https:\/\/www.digital-chiefs.de\/en\/home\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/#website\",\"url\":\"https:\/\/www.digital-chiefs.de\/en\/\",\"name\":\"Digital Chiefs\",\"description\":\"Architekten des digitalen Deutschlands\",\"publisher\":{\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.digital-chiefs.de\/en\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/#organization\",\"name\":\"Digital Chiefs\",\"url\":\"https:\/\/www.digital-chiefs.de\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2020\/05\/cropped-digital-chiefs-logo-klein.jpg\",\"contentUrl\":\"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2020\/05\/cropped-digital-chiefs-logo-klein.jpg\",\"width\":190,\"height\":190,\"caption\":\"Digital Chiefs\"},\"image\":{\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/digitalchiefs\/\",\"https:\/\/x.com\/digital_chiefs\",\"https:\/\/www.linkedin.com\/company\/digital-chiefs\/\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.digital-chiefs.de\/en\/#\/schema\/person\/257154921d1b05d9f85df00be993699e\",\"name\":\"Jakob Bach\",\"description\":\"Partner bei Evernine Media, davor Jahre im Motorsport-Umfeld. Schreibt \u00fcber F\u00fchrung unter Druck: Entscheidungen mit unvollst\u00e4ndigen Daten, Comebacks nach Fehlern, Teams die in kurzen Zeitfenstern funktionieren m\u00fcssen.\",\"sameAs\":[\"https:\/\/www.linkedin.com\/in\/jakobbach\/\"],\"url\":\"https:\/\/www.digital-chiefs.de\/en\/author\/jakob-bach\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations","description":"AI projects often fail during the transition from pilot to regular operations. Discover the four key stumbling blocks - data, ownership, costs, and\u2026","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/","og_locale":"en_US","og_type":"article","og_title":"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations","og_description":"AI projects often fail during the transition from pilot to regular operations. Discover the four key stumbling blocks - data, ownership, costs, and\u2026","og_url":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/","og_site_name":"Digital Chiefs","article_publisher":"https:\/\/www.facebook.com\/digitalchiefs\/","article_published_time":"2026-06-19T08:20:03+00:00","article_modified_time":"2026-07-22T11:47:33+00:00","og_image":[{"url":"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg","type":"","width":"","height":""}],"author":"Jakob Bach","twitter_card":"summary_large_image","twitter_image":"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg","twitter_creator":"@digital_chiefs","twitter_site":"@digital_chiefs","twitter_misc":{"Written by":"Jakob Bach","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"NewsArticle","@id":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#article","isPartOf":{"@id":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/"},"author":{"name":"Jakob Bach","@id":"https:\/\/www.digital-chiefs.de\/en\/#\/schema\/person\/257154921d1b05d9f85df00be993699e"},"headline":"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations","datePublished":"2026-06-19T08:20:03+00:00","dateModified":"2026-07-22T11:47:33+00:00","mainEntityOfPage":{"@id":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/"},"wordCount":1406,"publisher":{"@id":"https:\/\/www.digital-chiefs.de\/en\/#organization"},"image":{"@id":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#primaryimage"},"thumbnailUrl":"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg","articleSection":["AI &amp; Data","KI &amp; Daten","New Work & Leadership","New Work & Leadership"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/","url":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/","name":"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations","isPartOf":{"@id":"https:\/\/www.digital-chiefs.de\/en\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#primaryimage"},"image":{"@id":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#primaryimage"},"thumbnailUrl":"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg","datePublished":"2026-06-19T08:20:03+00:00","dateModified":"2026-07-22T11:47:33+00:00","description":"AI projects often fail during the transition from pilot to regular operations. Discover the four key stumbling blocks - data, ownership, costs, and\u2026","breadcrumb":{"@id":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#primaryimage","url":"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg","contentUrl":"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2026\/06\/vom-ki-pilot-zum-regelbetrieb-warum-so-viele-projekte-scheitern-cover-hero.jpg","width":1792,"height":1024,"caption":"AI project transition from pilot to production: a centered, broad editorial illustration of a gap or break point between a small test lab an"},{"@type":"BreadcrumbList","@id":"https:\/\/www.digital-chiefs.de\/en\/four-stumbling-blocks-why-ai-projects-fail-to-transition-to-regular-operations\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Startseite","item":"https:\/\/www.digital-chiefs.de\/en\/home\/"},{"@type":"ListItem","position":2,"name":"Four Stumbling Blocks: Why AI Projects Fail to Transition to Regular Operations"}]},{"@type":"WebSite","@id":"https:\/\/www.digital-chiefs.de\/en\/#website","url":"https:\/\/www.digital-chiefs.de\/en\/","name":"Digital Chiefs","description":"Architekten des digitalen Deutschlands","publisher":{"@id":"https:\/\/www.digital-chiefs.de\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.digital-chiefs.de\/en\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.digital-chiefs.de\/en\/#organization","name":"Digital Chiefs","url":"https:\/\/www.digital-chiefs.de\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.digital-chiefs.de\/en\/#\/schema\/logo\/image\/","url":"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2020\/05\/cropped-digital-chiefs-logo-klein.jpg","contentUrl":"https:\/\/www.digital-chiefs.de\/wp-content\/uploads\/2020\/05\/cropped-digital-chiefs-logo-klein.jpg","width":190,"height":190,"caption":"Digital Chiefs"},"image":{"@id":"https:\/\/www.digital-chiefs.de\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/digitalchiefs\/","https:\/\/x.com\/digital_chiefs","https:\/\/www.linkedin.com\/company\/digital-chiefs\/"]},{"@type":"Person","@id":"https:\/\/www.digital-chiefs.de\/en\/#\/schema\/person\/257154921d1b05d9f85df00be993699e","name":"Jakob Bach","description":"Partner bei Evernine Media, davor Jahre im Motorsport-Umfeld. Schreibt \u00fcber F\u00fchrung unter Druck: Entscheidungen mit unvollst\u00e4ndigen Daten, Comebacks nach Fehlern, Teams die in kurzen Zeitfenstern funktionieren m\u00fcssen.","sameAs":["https:\/\/www.linkedin.com\/in\/jakobbach\/"],"url":"https:\/\/www.digital-chiefs.de\/en\/author\/jakob-bach\/"}]}},"_links":{"self":[{"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/posts\/31764","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/users\/118"}],"replies":[{"embeddable":true,"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/comments?post=31764"}],"version-history":[{"count":2,"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/posts\/31764\/revisions"}],"predecessor-version":[{"id":31767,"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/posts\/31764\/revisions\/31767"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/media\/31754"}],"wp:attachment":[{"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/media?parent=31764"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/categories?post=31764"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.digital-chiefs.de\/en\/wp-json\/wp\/v2\/tags?post=31764"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}