{"id":25897,"date":"2026-11-03T09:00:00","date_gmt":"2026-11-03T08:00:00","guid":{"rendered":"https:\/\/kcpdynamics.com\/?p=25897"},"modified":"2026-11-03T09:00:00","modified_gmt":"2026-11-03T08:00:00","slug":"ai-projects-that-dont-scale","status":"publish","type":"post","link":"https:\/\/kcpdynamics.com\/en\/ai-projects-that-dont-scale\/","title":{"rendered":"Why AI Projects That Don&#8217;t Scale Get Stuck in Demo (and How to Change That)"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"25897\" class=\"elementor elementor-25897 elementor-25878\" data-elementor-settings=\"{&quot;ha_cmc_init_switcher&quot;:&quot;no&quot;}\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-bd361d16 e-flex e-con-boxed e-con e-parent\" data-id=\"bd361d16\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f6d0078c elementor-widget elementor-widget-text-editor\" data-id=\"f6d0078c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The demo dazzles. The pilot launches with enthusiasm. And three months later, the project is stalled \u2014 or worse, it survives in a PowerPoint that nobody opens again. This pattern has a name: it&#8217;s the gap between pilot and production, and it&#8217;s the most expensive problem in enterprise AI in LATAM today. AI projects that don&#8217;t scale are, in practice, the main obstacle to real <a title=\"digital transformation\" href=\"https:\/\/kcpdynamics.com\/en\/?p=22631\">digital transformation<\/a> in the region.<\/p><p>The numbers are uncomfortable but necessary: <strong>95% of generative AI pilots generate no measurable return<\/strong>, according to the MIT report &#8220;The GenAI Divide: State of AI in Business 2025.&#8221; This is not a technology failure. It&#8217;s a failure in how projects are designed, governed, and scaled. At KCP Dynamics we see this repeat with a regularity that no longer surprises us \u2014 though it remains avoidable. Every time an AI initiative fails to reach production, it joins the growing list of AI projects that don&#8217;t scale and drain budget without return.<\/p><aside class=\"nseo-callout nseo-callout--data\" style=\"background: #eff6ff;border-left: 4px solid #2563eb;padding: 12px 16px;margin: 1rem 0\">\n  <strong>Key fact:<\/strong> In LATAM, 78% of companies already use AI in some form, but only 12% have a formal governance system. That&#8217;s not adoption: it&#8217;s improvisation at industrial scale. And it&#8217;s precisely the breeding ground where AI projects that don&#8217;t scale proliferate.\n<\/aside>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-be6844e3 dce_masking-none elementor-widget elementor-widget-image\" data-id=\"be6844e3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<picture><source type=\"image\/avif\" srcset=\"https:\/\/kcpdynamics.com\/wp-content\/uploads\/avif\/proyectos-de-ia-que-no-escalan.avif\"><img decoding=\"async\" src=\"https:\/\/kcpdynamics.com\/wp-content\/uploads\/proyectos-de-ia-que-no-escalan.jpg\" title=\"\" alt=\"Gap between demo and production: AI project stuck in prototype without scaling to a production environment.\" loading=\"lazy\" \/><\/picture>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">95% of AI pilots never transcend the experimental phase, revealing a systematic pattern in enterprise adoption.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-df30c149 elementor-widget elementor-widget-heading\" data-id=\"df30c149\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The real problem: AI projects that don't scale don't fail because of technology<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6b754752 elementor-widget elementor-widget-text-editor\" data-id=\"6b754752\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>When an AI pilot fails, the first instinct is to look for technical culprits: the model wasn&#8217;t accurate, the data was dirty, the infrastructure couldn&#8217;t handle it. But <strong>70% of AI adoption problems are tied to people and processes<\/strong>, not algorithms. The technology, in most cases, works. What doesn&#8217;t work is the <a href=\"https:\/\/kcpdynamics.com\/dynamics-365-ai-agents-2\/\">organizational context in which it tries to operate<\/a>. AI initiatives that never reach production \u2014 AI projects that don&#8217;t scale \u2014 almost always share this same pattern of organizational, not technological, failure.<\/p><figure id=\"nv92a58942\" class=\"nseo-visual nseo-visual--stats\" data-nseo-visual=\"stats\" data-nseo-visual-id=\"6\" style=\"margin:2em 0;padding:22px 24px 16px;background:#fff;border:1px solid #ececf1;border-radius:16px;box-shadow:0 1px 2px rgba(24,24,27,0.04),0 8px 24px -12px rgba(24,24,27,0.08);font-family:Inter,ui-sans-serif,system-ui,-apple-system,'Segoe UI',Roboto,'Helvetica Neue',Arial,sans-serif;color:#18181b;line-height:1.4;text-align:left\"><figcaption style=\"margin:0 0 18px;padding:0;font-style:normal;text-align:left;font-size:inherit;color:inherit;line-height:1.4\"><span style=\"display:block;margin:0;font-size:17px;line-height:1.35;font-weight:600;letter-spacing:-0.01em;color:#18181b\">La ra\u00edz del problema por qu\u00e9 los pilotos de IA no llegan a producci\u00f3n<\/span><span style=\"display:block;margin:4px 0 0;font-size:14px;line-height:1.5;color:#52525b\">Tres cifras que revelan que el freno es organizacional, no tecnol\u00f3gico.<\/span><\/figcaption><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 720 178\" width=\"100%\" role=\"img\" aria-labelledby=\"nv92a58942-t\" style=\"display:block;width:100%;max-width:720px;height:auto;margin:0 auto;overflow:visible;font-family:Inter,ui-sans-serif,system-ui,-apple-system,'Segoe UI',Roboto,'Helvetica Neue',Arial,sans-serif\"><title id=\"nv92a58942-t\">La ra\u00edz del problema por qu\u00e9 los pilotos de IA no llegan a producci\u00f3n<\/title><defs><radialgradient id=\"nv92a58942-sph-stat\" cx=\"35%\" cy=\"30%\" r=\"75%\"><stop offset=\"0%\" stop-color=\"#a9abf7\"><\/stop><stop offset=\"55%\" stop-color=\"#6366f1\"><\/stop><stop offset=\"100%\" stop-color=\"#4345a4\"><\/stop><\/radialgradient><lineargradient id=\"nv92a58942-lg-card\" x1=\"0\" y1=\"1\" x2=\"0\" y2=\"0\"><stop offset=\"0%\" stop-color=\"#ffffff\"><\/stop><stop offset=\"100%\" stop-color=\"#f6f6fe\"><\/stop><\/lineargradient><\/defs><g class=\"nv-item nv-node\" data-nv-label=\"95 %\" data-nv-value=\"de los pilotos de IA generativa no genera retorno medible\"><rect x=\"0\" y=\"2\" width=\"230.7\" height=\"172\" rx=\"14\" fill=\"url(#nv92a58942-lg-card) #f9f9fe\" stroke=\"#e9eafd\"><\/rect><circle class=\"nv-ball\" cx=\"24\" cy=\"24\" r=\"6\" fill=\"url(#nv92a58942-sph-stat) #6366f1\"><\/circle><text x=\"18\" y=\"76\" font-size=\"38\" font-weight=\"700\" letter-spacing=\"-1\" fill=\"#18181b\" style=\"font-variant-numeric:tabular-nums\">95<\/text><text x=\"64.1\" y=\"76\" font-size=\"17\" font-weight=\"600\" fill=\"#52525b\">%<\/text><text x=\"18\" y=\"102\" font-size=\"13.5\" fill=\"#52525b\" text-anchor=\"start\"><tspan x=\"18\" dy=\"0\">de los pilotos de IA<\/tspan><tspan x=\"18\" dy=\"18.9\">generativa no genera<\/tspan><tspan x=\"18\" dy=\"18.9\">retorno medible<\/tspan><\/text><\/g><g class=\"nv-item 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class=\"nv-item nv-node\" data-nv-label=\"12 %\" data-nv-value=\"de las empresas en LATAM tiene un sistema formal de gobernanza de IA\"><rect x=\"489.3\" y=\"2\" width=\"230.7\" height=\"172\" rx=\"14\" fill=\"url(#nv92a58942-lg-card) #f9f9fe\" stroke=\"#e9eafd\"><\/rect><circle class=\"nv-ball\" cx=\"513.3\" cy=\"24\" r=\"6\" fill=\"url(#nv92a58942-sph-stat) #6366f1\"><\/circle><text x=\"507.3\" y=\"76\" font-size=\"38\" font-weight=\"700\" letter-spacing=\"-1\" fill=\"#18181b\" style=\"font-variant-numeric:tabular-nums\">12<\/text><text x=\"553.4\" y=\"76\" font-size=\"17\" font-weight=\"600\" fill=\"#52525b\">%<\/text><text x=\"507.3\" y=\"102\" font-size=\"13.5\" fill=\"#52525b\" text-anchor=\"start\"><tspan x=\"507.3\" dy=\"0\">de las empresas en LATAM<\/tspan><tspan x=\"507.3\" dy=\"18.9\">tiene un sistema formal de<\/tspan><tspan x=\"507.3\" dy=\"18.9\">gobernanza de IA<\/tspan><\/text><\/g><\/svg><div style=\"margin-top:14px;padding-top:12px;border-top:1px solid #efeff3\"><span style=\"display:block;margin:0 0 6px;font-size:12.5px;color:#8b8b94\">Fuente: MIT, The GenAI Divide: State of AI in Business 2025<\/span><details style=\"margin:0;font-size:13px;color:#52525b\"><summary style=\"cursor:pointer;color:#6366f1;font-weight:500;list-style-position:inside\">Ver los datos<\/summary><div style=\"margin-top:10px\"><table><thead><tr><th>Dato<\/th><th>Qu\u00e9 significa<\/th><\/tr><\/thead><tbody><tr><td>95 %<\/td><td>de los pilotos de IA generativa no genera retorno medible<\/td><\/tr><tr><td>70 %<\/td><td>de los problemas de adopci\u00f3n de IA est\u00e1n ligados a personas y procesos, no a algoritmos<\/td><\/tr><tr><td>12 %<\/td><td>de las empresas en LATAM tiene un sistema formal de gobernanza de IA<\/td><\/tr><\/tbody><\/table><\/div><\/details><\/div><\/figure><p>In the projects we accompany at KCP Dynamics, we have identified a clear pattern: <strong>pilots are designed to impress, not to scale<\/strong>. Use cases are chosen that shine in a demo \u2014 response speed, clean interface, eye-catching outputs \u2014 but that are not connected to a real business process, to production data, or to the workflows of the team that should use them every day. That is how AI projects that don&#8217;t scale are born: with a great presentation and no roots in operations.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a29dbc8d elementor-widget elementor-widget-heading\" data-id=\"a29dbc8d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The three warning signs that appear before the pilot dies<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bf994279 elementor-widget elementor-widget-text-editor\" data-id=\"bf994279\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>There are early signals that announce a project is going to stay in demo. Recognizing them in time makes the difference between correcting course or losing the budget. Ignoring them is the most common reason why AI projects that don&#8217;t scale multiply year after year in organizations across the region.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c80dbf8b elementor-widget elementor-widget-heading\" data-id=\"c80dbf8b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The use case is not connected to a real business pain<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-52071327 elementor-widget elementor-widget-text-editor\" data-id=\"52071327\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>If the pilot was born from an innovation initiative disconnected from the operational area, the warning sign is already active. <strong>An AI agent that automates a process nobody considered a priority does not generate adoption<\/strong> \u2014 it generates temporary curiosity. In manufacturing, retail, or financial services in LATAM, the use cases that scale are those that solve a documented bottleneck: accounting close times, returns management, electronic invoice validation. AI projects that don&#8217;t scale, on the other hand, tend to attack problems that don&#8217;t appear on any business priority list.<\/p><p>At KCP Dynamics we work with business teams before defining the technical use case. Not the other way around. That sequence seems obvious, but most AI projects that don&#8217;t scale invert it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-df672123 elementor-widget elementor-widget-heading\" data-id=\"df672123\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">There is no clear owner of the change<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b14c1089 elementor-widget elementor-widget-text-editor\" data-id=\"b14c1089\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>An AI pilot fails when nobody in the organization is responsible for making it work in production. <strong>Without a &#8220;change owner&#8221; \u2014 someone with authority, budget, and dedicated time \u2014 pilots stay as pilots<\/strong>. The technical team delivers the model. The business area waits for someone to integrate it. And the project dies in the no-man&#8217;s land between IT and operations. This absence of leadership is one of the most frequent causes of AI projects that don&#8217;t scale in companies of all sizes.<\/p><p>This is one of the most frequently documented patterns we see in AI adoption projects at mid-sized and large companies in the region. Governance is not a luxury: it is the minimum condition for any AI initiative to survive the initial enthusiasm and not swell the list of AI projects that don&#8217;t scale.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-74b9004f elementor-widget elementor-widget-heading\" data-id=\"74b9004f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Success metrics are defined after the pilot, not before<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb7606e4 elementor-widget elementor-widget-text-editor\" data-id=\"cb7606e4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>If at the time of launching the pilot there is no clear answer to &#8220;how will we know this worked?&#8221;, the project already has a structural problem. <strong>BCG reported in 2025 that 60% of companies get low value from their AI investments because they measure the wrong metrics from day one.<\/strong> Model response speed, accuracy in a test environment, number of queries processed \u2014 those are lab metrics, not business metrics. And they are precisely the metrics that characterize AI projects that don&#8217;t scale: they measure the system, not the impact.<\/p><p>The metrics that matter are those connected to the bottom line: hours of work freed up, reduction of errors in critical processes, shortened cycle time. If those metrics are not defined before starting, the pilot has no success criterion \u2014 and without a success criterion, there is no argument to scale.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0daa319f elementor-widget elementor-widget-heading\" data-id=\"0daa319f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Organizational factors that block the move to production<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0a28f748 dce_masking-none elementor-widget elementor-widget-image\" data-id=\"0a28f748\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<picture><source type=\"image\/avif\" srcset=\"https:\/\/kcpdynamics.com\/wp-content\/uploads\/avif\/proyectos-de-ia-que-no-escalan-factores-organizacionales-que-bloquean-el-paso-a.avif\"><img decoding=\"async\" src=\"https:\/\/kcpdynamics.com\/wp-content\/uploads\/proyectos-de-ia-que-no-escalan-factores-organizacionales-que-bloquean-el-paso-a.jpg\" title=\"\" alt=\"Organizational obstacles: departmental silos, lack of resources, and poor governance block the implementation of AI projects.\" loading=\"lazy\" \/><\/picture>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Internal fragmentation and the absence of clear governance frameworks are the main culprits behind pilot failure, not technology.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-10ba3d5b elementor-widget elementor-widget-text-editor\" data-id=\"10ba3d5b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Beyond the early warning signs, there are structural factors that turn the demo-to-production gap into a wall. In LATAM, these factors have their own characteristics that are worth understanding, especially because they most frequently explain why AI projects that don&#8217;t scale remain the norm rather than the exception.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f36cc51 elementor-widget elementor-widget-heading\" data-id=\"3f36cc51\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Data disconnected from the ERP and core systems<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8a07f89a elementor-widget elementor-widget-text-editor\" data-id=\"8a07f89a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>One in four companies has still not connected its internal data to AI tools. They give employees access to a language model, but they don&#8217;t connect the ERP, the CRM, or the operational documentation. The result is an agent that responds with generic information while the team continues to consult the management system separately. <strong>AI that doesn&#8217;t talk to production data doesn&#8217;t change any process<\/strong> \u2014 it only adds a layer of friction. This type of superficial implementation is one of the most common traits of AI projects that don&#8217;t scale: visible presence, zero impact.<\/p><p>In <a href=\"https:\/\/kcpdynamics.com\/copilot-studio-how-to-create-your-personalized-ai\/\">KCP Dynamics projects with Dynamics 365<\/a>, integration with real business data is the first step, not the last. An agent connected to the ERP that answers questions about inventory, order status, or financial closes gets immediate adoption because it solves a problem the user has today, not in a hypothetical scenario.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-440a2bb9 elementor-widget elementor-widget-heading\" data-id=\"440a2bb9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Team resistance that nobody manages<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-97d95b36 elementor-widget elementor-widget-text-editor\" data-id=\"97d95b36\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Resistance to change is not irrational. It is the natural response of a team whose way of working is changed without explaining why, without giving them time to adapt, and without guaranteeing that their role remains relevant. <strong>More than 60% of failure points in AI adoption are human factors<\/strong>: resistance, fear, lack of training, and absence of change leadership. AI projects that don&#8217;t scale almost always have this unmanaged human factor as a common denominator.<\/p><p>In LATAM, this factor is amplified because many organizations treat AI implementation like a software rollout \u2014 they install the tool, run a training session, and expect the team to adopt it. It doesn&#8217;t work that way. AI adoption in companies requires a real change plan: continuous communication, internal champions, visible quick wins, and a feedback loop that allows adjustments before resistance solidifies. Without that plan, AI projects that don&#8217;t scale multiply even in organizations with top-tier technology.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-81a11d04 elementor-widget elementor-widget-heading\" data-id=\"81a11d04\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Lack of post-pilot governance<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-49a8090f elementor-widget elementor-widget-text-editor\" data-id=\"49a8090f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>46% of pilots are discarded before production because nobody defined who is accountable when the agent makes an incorrect decision. <strong>AI governance is not an abstract compliance topic: it is the answer to very concrete questions<\/strong> \u2014 who validates the outputs, how the model is updated when data changes, what happens when the system fails in a critical process. The absence of these answers turns promising initiatives into AI projects that don&#8217;t scale due to lack of structure, not lack of technical capability.<\/p><p>At KCP Dynamics we structure a governance framework before scaling any agent to production. That includes defining supervision roles, establishing human intervention thresholds, and documenting escalation flows. Without that framework, the first system error in production \u2014 and there will be errors \u2014 destroys the team&#8217;s trust and buries the project.<\/p><aside class=\"nseo-callout nseo-callout--importante\" style=\"background: #eff6ff;border-left: 4px solid #2563eb;padding: 12px 16px;margin: 1rem 0\">\n  <strong>Important:<\/strong> Shadow AI \u2014 the use of AI tools not sanctioned by IT \u2014 is a direct consequence of pilots that don&#8217;t scale. When the official project stalls, teams look for solutions on their own, outside the organization&#8217;s security and compliance perimeter. It is the most visible symptom that AI projects that don&#8217;t scale carry an indirect cost that few organizations quantify.\n<\/aside>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ea7dc48f elementor-widget elementor-widget-heading\" data-id=\"ea7dc48f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The pattern that differentiates pilots that scale from those that don't<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0e1c480f elementor-widget elementor-widget-text-editor\" data-id=\"0e1c480f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In projects that do reach production, there is a common denominator: <strong>deep system integration from day one<\/strong>. Not superficial access to a language model, but real connection with the data, processes, and workflows of the business. That is what turns a pilot into a tool the team uses because it makes their lives easier, not because the CTO asked for it. It is also what structurally separates AI projects that don&#8217;t scale from those that do reach production and generate return.<\/p><p>The second differentiating factor is real executive leadership. Not the endorsement in the launch email, but assigned resources, a roadmap with quarterly milestones, and visibility in the steering committee. <strong>AI projects that don&#8217;t scale tend to have enthusiastic sponsors but without the authority to unblock organizational bottlenecks<\/strong> \u2014 data access, process changes, team time.<\/p><p>The third factor is iteration speed. Successful projects don&#8217;t wait to have the perfect system before launching it. They launch a scoped version, measure, adjust, and expand. That short cycle maintains momentum, generates real learning, and builds team trust which, in the end, is what sustains adoption. AI projects that don&#8217;t scale, on the contrary, tend to wait for perfection and get lost in review cycles that never end.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-83d32c30 elementor-widget elementor-widget-heading\" data-id=\"83d32c30\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">How to structure the move from pilot to production: a 30\/60\/90 approach<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-586cfb48 elementor-widget elementor-widget-text-editor\" data-id=\"586cfb48\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<figure id=\"nv7a531ea9\" class=\"nseo-visual nseo-visual--steps\" data-nseo-visual=\"steps\" data-nseo-visual-id=\"5\" style=\"margin:2em 0;padding:22px 24px 16px;background:#fff;border:1px solid #ececf1;border-radius:16px;box-shadow:0 1px 2px rgba(24,24,27,0.04),0 8px 24px -12px rgba(24,24,27,0.08);font-family:Inter,ui-sans-serif,system-ui,-apple-system,'Segoe UI',Roboto,'Helvetica Neue',Arial,sans-serif;color:#18181b;line-height:1.4;text-align:left\"><figcaption style=\"margin:0 0 18px;padding:0;font-style:normal;text-align:left;font-size:inherit;color:inherit;line-height:1.4\"><span style=\"display:block;margin:0;font-size:17px;line-height:1.35;font-weight:600;letter-spacing:-0.01em;color:#18181b\">Marco 30\/60\/90 para pasar de piloto a producci\u00f3n<\/span><span style=\"display:block;margin:4px 0 0;font-size:14px;line-height:1.5;color:#52525b\">Cada fase tiene un objetivo concreto; sin completarla, la siguiente pierde sentido.<\/span><\/figcaption><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 720 175\" width=\"100%\" role=\"img\" aria-labelledby=\"nv7a531ea9-t\" style=\"display:block;width:100%;max-width:720px;height:auto;margin:0 auto;overflow:visible;font-family:Inter,ui-sans-serif,system-ui,-apple-system,'Segoe UI',Roboto,'Helvetica Neue',Arial,sans-serif\"><title id=\"nv7a531ea9-t\">Marco 30\/60\/90 para pasar de piloto a producci\u00f3n<\/title><defs><radialgradient id=\"nv7a531ea9-sph-step\" cx=\"35%\" cy=\"30%\" r=\"75%\"><stop offset=\"0%\" stop-color=\"#a9abf7\"><\/stop><stop offset=\"55%\" stop-color=\"#6366f1\"><\/stop><stop offset=\"100%\" stop-color=\"#4345a4\"><\/stop><\/radialgradient><radialgradient id=\"nv7a531ea9-shadow\"><stop offset=\"0%\" stop-color=\"#18181b\" stop-opacity=\"0.16\"><\/stop><stop offset=\"100%\" stop-color=\"#18181b\" stop-opacity=\"0\"><\/stop><\/radialgradient><radialgradient id=\"nv7a531ea9-halo\"><stop offset=\"0%\" stop-color=\"#6366f1\" stop-opacity=\"0.22\"><\/stop><stop offset=\"100%\" stop-color=\"#6366f1\" stop-opacity=\"0\"><\/stop><\/radialgradient><\/defs><line x1=\"152\" y1=\"30\" x2=\"328\" y2=\"30\" stroke=\"#a9abf7\" stroke-width=\"1.5\" stroke-dasharray=\"2 5\" stroke-linecap=\"round\"><\/line><path d=\"M324 26l5 4-5 4\" fill=\"none\" stroke=\"#9294f5\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><line x1=\"392\" y1=\"30\" x2=\"568\" y2=\"30\" stroke=\"#a9abf7\" stroke-width=\"1.5\" stroke-dasharray=\"2 5\" stroke-linecap=\"round\"><\/line><path d=\"M564 26l5 4-5 4\" fill=\"none\" stroke=\"#9294f5\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><g class=\"nv-item nv-node\" data-nv-label=\"Primeros 30 d\u00edas\" data-nv-value=\"Validar el caso de uso con datos reales, definir m\u00e9tricas de negocio e identificar al due\u00f1o del cambio.\"><ellipse cx=\"120\" cy=\"57\" rx=\"18.7\" ry=\"4.4\" fill=\"url(#nv7a531ea9-shadow)\"><\/ellipse><circle cx=\"120\" cy=\"30\" r=\"34.1\" fill=\"url(#nv7a531ea9-halo)\"><\/circle><circle class=\"nv-ball\" cx=\"120\" cy=\"30\" r=\"22\" fill=\"url(#nv7a531ea9-sph-step) #6366f1\" stroke=\"rgba(24,24,27,0.10)\" stroke-width=\"1\"><\/circle><text x=\"120\" y=\"35.5\" font-size=\"16\" fill=\"#ffffff\" text-anchor=\"middle\" font-weight=\"700\"><tspan x=\"120\" dy=\"0\">1<\/tspan><\/text><text x=\"120\" y=\"86\" font-size=\"15\" fill=\"#18181b\" text-anchor=\"middle\" font-weight=\"600\"><tspan x=\"120\" dy=\"0\">Primeros 30 d\u00edas<\/tspan><\/text><text x=\"120\" y=\"108\" font-size=\"13.5\" fill=\"#52525b\" text-anchor=\"middle\"><tspan x=\"120\" dy=\"0\">Validar el caso de uso con<\/tspan><tspan x=\"120\" dy=\"18.9\">datos reales, definir m\u00e9tricas<\/tspan><tspan x=\"120\" dy=\"18.9\">de negocio e identificar al<\/tspan><tspan x=\"120\" dy=\"18.9\">due\u00f1o del cambio.<\/tspan><\/text><\/g><g class=\"nv-item nv-node\" data-nv-label=\"D\u00edas 31 a 60\" data-nv-value=\"Lanzar la versi\u00f3n m\u00ednima viable a usuarios representativos, medir adopci\u00f3n diaria y documentar quick wins.\"><ellipse cx=\"360\" cy=\"57\" rx=\"18.7\" ry=\"4.4\" fill=\"url(#nv7a531ea9-shadow)\"><\/ellipse><circle class=\"nv-ball\" cx=\"360\" cy=\"30\" r=\"22\" fill=\"url(#nv7a531ea9-sph-step) #6366f1\" stroke=\"rgba(24,24,27,0.10)\" stroke-width=\"1\"><\/circle><text x=\"360\" y=\"35.5\" font-size=\"16\" fill=\"#ffffff\" text-anchor=\"middle\" font-weight=\"700\"><tspan x=\"360\" dy=\"0\">2<\/tspan><\/text><text x=\"360\" y=\"86\" font-size=\"15\" fill=\"#18181b\" text-anchor=\"middle\" font-weight=\"600\"><tspan x=\"360\" dy=\"0\">D\u00edas 31 a 60<\/tspan><\/text><text x=\"360\" y=\"108\" font-size=\"13.5\" fill=\"#52525b\" text-anchor=\"middle\"><tspan x=\"360\" dy=\"0\">Lanzar la versi\u00f3n m\u00ednima<\/tspan><tspan x=\"360\" dy=\"18.9\">viable a usuarios<\/tspan><tspan x=\"360\" dy=\"18.9\">representativos, medir<\/tspan><tspan x=\"360\" dy=\"18.9\">adopci\u00f3n diaria y documentar\u2026<\/tspan><\/text><\/g><g class=\"nv-item nv-node\" data-nv-label=\"D\u00edas 61 a 90\" data-nv-value=\"Escalar al equipo completo, presentar resultados al comit\u00e9 de direcci\u00f3n y establecer el marco de gobernanza.\"><ellipse cx=\"600\" cy=\"57\" rx=\"18.7\" ry=\"4.4\" fill=\"url(#nv7a531ea9-shadow)\"><\/ellipse><circle class=\"nv-ball\" cx=\"600\" cy=\"30\" r=\"22\" fill=\"url(#nv7a531ea9-sph-step) #6366f1\" stroke=\"rgba(24,24,27,0.10)\" stroke-width=\"1\"><\/circle><text x=\"600\" y=\"35.5\" font-size=\"16\" fill=\"#ffffff\" text-anchor=\"middle\" font-weight=\"700\"><tspan x=\"600\" dy=\"0\">3<\/tspan><\/text><text x=\"600\" y=\"86\" font-size=\"15\" fill=\"#18181b\" text-anchor=\"middle\" font-weight=\"600\"><tspan x=\"600\" dy=\"0\">D\u00edas 61 a 90<\/tspan><\/text><text x=\"600\" y=\"108\" font-size=\"13.5\" fill=\"#52525b\" text-anchor=\"middle\"><tspan x=\"600\" dy=\"0\">Escalar al equipo completo,<\/tspan><tspan x=\"600\" dy=\"18.9\">presentar resultados al comit\u00e9<\/tspan><tspan x=\"600\" dy=\"18.9\">de direcci\u00f3n y establecer el<\/tspan><tspan x=\"600\" dy=\"18.9\">marco de gobernanza.<\/tspan><\/text><\/g><\/svg><div style=\"margin-top:14px;padding-top:12px;border-top:1px solid #efeff3\"><details style=\"margin:0;font-size:13px;color:#52525b\"><summary style=\"cursor:pointer;color:#6366f1;font-weight:500;list-style-position:inside\">Ver los datos<\/summary><div style=\"margin-top:10px\"><ol><li><strong>Primeros 30 d\u00edas<\/strong>: Validar el caso de uso con datos reales, definir m\u00e9tricas de negocio e identificar al due\u00f1o del cambio.<\/li><li><strong>D\u00edas 31 a 60<\/strong>: Lanzar la versi\u00f3n m\u00ednima viable a usuarios representativos, medir adopci\u00f3n diaria y documentar quick wins.<\/li><li><strong>D\u00edas 61 a 90<\/strong>: Escalar al equipo completo, presentar resultados al comit\u00e9 de direcci\u00f3n y establecer el marco de gobernanza.<\/li><\/ol><\/div><\/details><\/div><\/figure>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d75e6195 dce_masking-none elementor-widget elementor-widget-image\" data-id=\"d75e6195\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<picture><source type=\"image\/avif\" srcset=\"https:\/\/kcpdynamics.com\/wp-content\/uploads\/avif\/proyectos-de-ia-que-no-escalan-como-estructurar-el-paso-de-piloto-a-produccion-u.avif\"><img decoding=\"async\" src=\"https:\/\/kcpdynamics.com\/wp-content\/uploads\/proyectos-de-ia-que-no-escalan-como-estructurar-el-paso-de-piloto-a-produccion-u.jpg\" title=\"\" alt=\"30\/60\/90 roadmap: progressive structure for scaling AI projects from pilot to production with defined milestones.\" loading=\"lazy\" \/><\/picture>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">This phased approach reduces the risk of failure by establishing clear milestones and allowing adjustments before full production investment.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a8442a1 elementor-widget elementor-widget-text-editor\" data-id=\"7a8442a1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>At KCP Dynamics we apply a three-phase scaling framework that reduces the risk of staying in demo and accelerates the time to the first measurable impact in production. This framework was born precisely from analyzing what AI projects that don&#8217;t scale have in common, in order to do exactly the opposite.<\/p><ul>\n  <li><strong>First 30 days:<\/strong> validate the use case with real data, not lab data. Define business success metrics before writing a single line of code. Identify the change owner and the adoption team. Without this step, the project has all the conditions to become one more of the AI projects that don&#8217;t scale.<\/li>\n  <li><strong>Days 31 to 60:<\/strong> launch the minimum viable version to a real pilot group \u2014 not enthusiastic volunteers, but representative users of the target process. Measure daily adoption, collect friction points, and adjust. Document the first quick wins with concrete numbers.<\/li>\n  <li><strong>Days 61 to 90:<\/strong> scale to the rest of the team with the active change plan. Present results to the steering committee with business metrics. Establish the governance framework for production and define the continuous improvement cycle.<\/li>\n<\/ul><p>This approach is not exclusive to large projects. We apply it with mid-sized companies in manufacturing, retail, and financial services in LATAM, and the results are consistent: <strong>projects that follow this framework have a significantly higher rate of reaching production<\/strong> than those that start without a scaling structure. In other words: they have significantly less chance of becoming AI projects that don&#8217;t scale.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c4a362b6 elementor-widget elementor-widget-heading\" data-id=\"c4a362b6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What KCP Dynamics does differently in enterprise AI projects<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d2f7c36a elementor-widget elementor-widget-text-editor\" data-id=\"d2f7c36a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The difference is not in the technology we use \u2014 Dynamics 365, <a title=\"Copilot Studio\" href=\"https:\/\/kcpdynamics.com\/?p=25814\">Copilot Studio<\/a>, and the Microsoft ecosystem are well-known tools. The difference is in how we accompany the entire process: from defining the use case to governance in production, including the adoption plan and team training. That accompaniment is what prevents a promising initiative from becoming an AI project that doesn&#8217;t scale due to lack of support at critical moments.<\/p><p>At KCP Dynamics we don&#8217;t deliver a demo and disappear. <strong>Our working model includes accompaniment through all three phases of scaling<\/strong>, with real adoption metrics and a continuous improvement plan that keeps the project alive beyond launch. That is what we learned from real projects in LATAM: technology is installed in weeks; adoption is built over months. And it is real adoption that separates successful projects from AI projects that don&#8217;t scale.<\/p><p>If your organization has an AI pilot that has been going more than 60 days without advancing toward production, it is time to review whether it has the three elements that projects that do scale always have: a use case connected to a real pain, a change owner with authority, and business metrics defined from the start. Without those three elements, the demo will remain just a demo \u2014 and the project will join the growing list of AI projects that don&#8217;t scale that cost organizations in the region so dearly.<\/p><section class=\"nseo-faq\">\n  <h2>Frequently asked questions<\/h2>\n  <details>\n    <summary>Why do most AI pilots never reach production?<\/summary>\n    <p>The main problem is not technical. AI pilots fail because use cases are not connected to real business processes, because there is no clear owner of the organizational change, and because success metrics are defined late or not at all. The technology, in most cases, works \u2014 what fails is the context in which it tries to operate. That is how AI projects that don&#8217;t scale are born: with functional technology but without the organizational conditions to reach production.<\/p>\n  <\/details>\n  <details>\n    <summary>How long does it take for an AI project to move from pilot to production?<\/summary>\n    <p>With a structured scaling framework, the first measurable results in production can be achieved in 60 to 90 days. Without structure, projects can drag on indefinitely without reaching real production \u2014 becoming AI projects that don&#8217;t scale by inertia. The key is to launch a minimum viable version quickly, measure, and adjust, rather than waiting to have the perfect system.<\/p>\n  <\/details>\n  <details>\n    <summary>What is the demo-to-production gap in AI?<\/summary>\n    <p>It is the distance between a pilot that works under controlled conditions and a system that operates with real data, real users, and real business processes. The gap occurs when the pilot is designed to impress in a presentation, not to integrate into the daily workflow of the team that should use it. It is the space where most AI projects that don&#8217;t scale are born and die.<\/p>\n  <\/details>\n  <details>\n    <summary>What role does change management play in AI adoption?<\/summary>\n    <p>It is decisive. More than 60% of AI adoption failures are human factors: resistance, lack of training, and absence of change leadership. Treating AI implementation like a software rollout \u2014 install and wait \u2014 is the most common cause of AI projects that don&#8217;t scale. Adoption requires a real change plan, with continuous communication, practical training, and visible quick wins for the team.<\/p>\n  <\/details>\n  <details>\n    <summary>How do you know if an AI project is at risk of staying in demo?<\/summary>\n    <p>There are three clear signals: the use case does not solve a documented business pain, there is no responsible person with authority and dedicated time for adoption, and success metrics are technical rather than operational or financial. If your pilot has been going more than 60 days without advancing toward production, it is likely that at least one of these signals is present \u2014 and that the project is on its way to becoming one more of the AI projects that don&#8217;t scale in the region.<\/p>\n  <\/details>\n<\/section><section class=\"nseo-sources\"><h2>Sources<\/h2><ul><li><a href=\"https:\/\/www.cmm.org.pe\/post\/inteligencia-artificial-por-qu%C3%A9-fracasan-los-proyectos-de-ia-en-las-empresas\" target=\"_blank\" rel=\"noopener\">Artificial Intelligence: Why do AI projects fail in companies?<\/a><\/li><li><a href=\"https:\/\/gcerti.org\/informes\/inteligencia-artificial-organizaciones-2026\" target=\"_blank\" rel=\"noopener\">Artificial Intelligence in Organizations 2026<\/a><\/li><li><a href=\"https:\/\/www.incentro.com\/es-ES\/blog\/por-que-fracasan-proyectos-piloto-ia\" target=\"_blank\" rel=\"noopener\">AI in the enterprise: why AI pilot projects fail (and how to beat the statistics)<\/a><\/li><li><a href=\"https:\/\/es-us.noticias.yahoo.com\/95-proyectos-piloto-ia-fracasan-202200255.html\" target=\"_blank\" rel=\"noopener\">Why 95% of AI pilot projects fail and what business leaders should do instead &#8211; Yahoo News<\/a><\/li><li><a href=\"https:\/\/www.forbesargentina.com\/innovacion\/por-95-proyectos-piloto-ia-fracasan-deberian-hacer-lideres-empresariales-su-lugar-n77164\" target=\"_blank\" rel=\"noopener\">Why 95% of AI pilot projects fail and what business leaders should do instead &#8211; Forbes Argentina<\/a><\/li><\/ul><\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>95% of AI pilots never reach production. Discover the real causes behind AI projects that don&#8217;t scale and what to do to avoid it.<\/p>\n","protected":false},"author":2,"featured_media":25875,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":"","rank_math_description":"95% of AI pilots never reach production. Discover the real causes behind AI projects that don't scale and what to do to avoid it."},"categories":[1],"tags":[],"class_list":["post-25897","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sin-categorizar"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/posts\/25897","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/comments?post=25897"}],"version-history":[{"count":1,"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/posts\/25897\/revisions"}],"predecessor-version":[{"id":25987,"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/posts\/25897\/revisions\/25987"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/media\/25875"}],"wp:attachment":[{"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/media?parent=25897"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/categories?post=25897"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kcpdynamics.com\/en\/wp-json\/wp\/v2\/tags?post=25897"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}