{"id":21679,"date":"2026-09-10T20:00:40","date_gmt":"2026-09-10T20:00:40","guid":{"rendered":"https:\/\/8657085.xyz\/?p=21679"},"modified":"2026-09-10T20:00:40","modified_gmt":"2026-09-10T20:00:40","slug":"the-friction-between-scaling-enterprise-ai-and-business-integration","status":"publish","type":"post","link":"https:\/\/8657085.xyz\/?p=21679","title":{"rendered":"The friction between scaling enterprise AI and business integration"},"content":{"rendered":"<p> <div style=\"display: grid; grid-template-columns: 300px 160px; gap: 2px; width: 460px; background: #eee; padding: 2px;\">\r\n\r\n  <!-- \u6574\u884c\u5bbd\u5e7f\u544a -->\r\n  <div style=\"grid-column: 1\/-1; width: 460px; height: 250px; background: #ccc; display: grid; place-items: center;\">\r\n  <script async type=\"application\/javascript\" src=\"https:\/\/a.magsrv.com\/ad-provider.js\"><\/script> \r\n <ins class=\"eas6a97888e2\" data-zoneid=\"5876674\"><\/ins> \r\n <script>(AdProvider = window.AdProvider || []).push({\"serve\": {}});<\/script>\r\n  <\/div>\r\n  <div style=\"grid-column: 1\/-1; width: 460px; height: 90px; background: #ccc; display: grid; place-items: center;\">\r\n  <script async type=\"application\/javascript\" src=\"https:\/\/a.magsrv.com\/ad-provider.js\"><\/script> \r\n <ins class=\"eas6a97888e2\" data-zoneid=\"5876676\"><\/ins> \r\n <script>(AdProvider = window.AdProvider || []).push({\"serve\": {}});<\/script>\r\n  <\/div>\r\n\r\n  <!-- \u5de6\u4fa7\u7ad6\u6392 -->\r\n  <div style=\"height: 250px; background: #ccc; display: grid; place-items: center;\">\r\n  <script async type=\"application\/javascript\" src=\"https:\/\/a.magsrv.com\/ad-provider.js\"><\/script> \r\n <ins class=\"eas6a97888e2\" data-zoneid=\"5876672\"><\/ins> \r\n <script>(AdProvider = window.AdProvider || []).push({\"serve\": {}});<\/script>\r\n  <\/div>\r\n  <div style=\"height: 500px; background: #ccc; display: grid; place-items: center;\">\r\n  <script async type=\"application\/javascript\" src=\"https:\/\/a.magsrv.com\/ad-provider.js\"><\/script> \r\n <ins class=\"eas6a97888e2\" data-zoneid=\"5876680\"><\/ins> \r\n <script>(AdProvider = window.AdProvider || []).push({\"serve\": {}});<\/script>\r\n  <\/div>\r\n\r\n  <!-- \u53f3\u4fa7\u6469\u5929\u697c\uff08\u548c\u5de6\u4fa7\u5b8c\u5168\u5bf9\u9f50\uff09 -->\r\n  <div style=\"grid-row: 3\/5; height: 750px; background: #ccc; display: grid; place-items: center;\">\r\n  <script async type=\"application\/javascript\" src=\"https:\/\/a.magsrv.com\/ad-provider.js\"><\/script> \r\n <ins class=\"eas6a97888e2\" data-zoneid=\"5876678\"><\/ins> \r\n <script>(AdProvider = window.AdProvider || []).push({\"serve\": {}});<\/script>\r\n  <\/div>\r\n  \r\n  <script async type=\"application\/javascript\" src=\"https:\/\/a.magsrv.com\/ad-provider.js\"><\/script> \r\n <ins class=\"eas6a97888e6\" data-zoneid=\"5876682\"><\/ins> \r\n <script>(AdProvider = window.AdProvider || []).push({\"serve\": {}});<\/script>\r\n<\/div><br \/>\n<\/p>\n<div style=\"padding-right:0;padding-left:0\">\n<p class=\"wp-block-paragraph\"><em>This article has been reviewed by a Digital Journal editor and may be licensed for reuse.<\/em><\/p>\n<p class=\"wp-block-paragraph\">Artificial intelligence appears to be moving from an emerging technology story into an enterprise spending story, yet the business case is still proving harder to deliver at scale. Nearly nine in ten organizations now report regular AI use, according to McKinsey\u2019s latest global survey, while most remain stuck in experimentation or early scaling stages. Only 37% report an enterprise-level EBIT impact from AI.<\/p>\n<p class=\"wp-block-paragraph\">The gap can be more consequential as companies may be moving AI into operational environments where decades of infrastructure cannot be replaced. Enterprise applications tend to carry years of business logic, proprietary data, and carefully constructed workflows. A model can generate an impressive answer in isolation, but putting that capability inside a production environment could introduce questions around access, reliability, security, and integration.<\/p>\n<p class=\"wp-block-paragraph\">Recent developments are making the distinction harder to ignore. McKinsey found that fewer than 1% of executives considered their organizations mature in AI deployment in early 2025. Across existing research, the same operational bottleneck continues to appear, where organizations are using AI widely yet struggling to embed it deeply enough into the processes that yield enterprise performance.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Security can add another layer of complexity. Enterprise AI can involve customer information, internal documentation, proprietary processes, and intellectual property moving through systems that were never designed around generative models. The rise of so-called shadow AI has sharpened the concern, with 2026 Deloitte research highlighting widening gaps between employee AI use and the governance structures intended to control corporate data.<\/p>\n<p class=\"wp-block-paragraph\">Doug Grismore, founder of Acceligent Consulting, approaches the problem from a career spent inside the machinery that AI now has to enter. His more than 30 years in enterprise IT span insurance, banking, manufacturing, CRM, systems integration and cloud platforms, with later work focused on generative AI and AI-enabled workflows. His perspective comes from seeing enterprise technology evolve through several generations of architectural and delivery models.<\/p>\n<p class=\"wp-block-paragraph\">\u201cTechnology is the easy part,\u201d Grismore argues. \u201cThe harder question is identifying the right problem before a project begins.\u201d His experience has convinced him that organizations often approach AI through the technology available to them rather than through the operational problem they need to solve.<\/p>\n<p class=\"wp-block-paragraph\">The failure rate around new technology initiatives reinforces the point. Gartner reports that more than 50% of generative AI projects fail, with poor data quality, inadequate governance, escalating costs, and unclear business value among the leading causes. Grismore\u2019s experience with CRM implementations echoes that pattern, where he encountered failure rates of roughly 75% two decades ago, according to his estimation. He attributes much of the problem to management decisions and organizational culture, particularly when projects are launched because AI has become strategically fashionable rather than because a defined business problem warrants it.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">His preferred approach starts with mapping the problem space against the capabilities AI can realistically address. \u201cYou\u2019ve got to do a mapping of your problem,\u201d he explains. \u201cOrganizations should identify suitable AI use cases, remove problems poorly suited to the technology, then weigh potential returns against implementation difficulty.\u201d\u00a0<\/p>\n<p class=\"wp-block-paragraph\">A recent project offered him a practical example. A customer, he recalls, had an interface that was difficult for less technical users to navigate. Instead of rebuilding the underlying system, Grismore placed a retrieval-augmented generation application in front of existing information so users could ask questions in ordinary language. Training materials, he explains, were incorporated into the RAG system, allowing users to ask how to access reports or perform specific tasks without navigating an intimidating technical interface.<\/p>\n<p class=\"wp-block-paragraph\">The example illustrates where enterprise AI can become useful: inside an existing workflow, connected to information employees already need, with the architecture determining how safely and effectively the capability operates.<\/p>\n<p class=\"wp-block-paragraph\">In his view, call centers can use similar approaches to surface operational knowledge, while manufacturing environments can apply AI to information-heavy processes where workers already depend on established enterprise systems.<\/p>\n<p class=\"wp-block-paragraph\">Integration therefore becomes an architectural question as much as an AI question. Grismore\u2019s background includes more than eight full-lifecycle CRM implementations, along with integration architectures connecting CRM platforms to call-center technologies and mainframe environments. His AI methodology carries the same enterprise discipline into newer technology.<\/p>\n<p class=\"wp-block-paragraph\">Security sits inside that architecture from the outset. Grismore emphasizes security-first design, observability, logging, and error handling as essential elements of production AI. His concern is that moving an experimental model into production can introduce operational requirements that a demonstration environment can easily conceal.<\/p>\n<p class=\"wp-block-paragraph\">The next phase of enterprise AI, as Grismore sees it, will be measured within existing businesses. Organizations will have to determine which problems warrant AI, what information a system should access, how it connects with established infrastructure, and where sensitive data must remain under tighter control.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">McKinsey\u2019s research already points toward workflow redesign and defined performance measures as significant drivers of AI value. Grismore sees that discipline as the foundation for what he calls accelerating intelligence.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">He remarks, \u201cAI has the potential to dramatically increase delivery speed, but only when approached with discipline.\u201d The enterprise AI race is moving into a more consequential phase, where model capability matters, yet the architecture surrounding the model will determine whether that capability becomes an operating advantage.<\/p>\n<\/div>\n<p><!-- \u603b\u5bb9\u5668\uff1a\u6700\u5927\u5bbd908px Grid\u7d27\u51d1\u5e03\u5c40 -->\r\n<div style=\"display: grid; grid-template-columns: 728px 160px; gap:2px; width:908px; background:#eee; padding:2px;\">\r\n\r\n  <!-- \u901a\u680f\u9876\u90e8\uff1a\u6700\u5927\u6a2a\u5e45 908x258 \u8de8\u6574\u884c -->\r\n  <div style=\"grid-column:1\/-1; height:258px; background:#ff6b6b; display:grid; place-items:center;\">\r\n    <!-- JuicyAds v3.0 -->\r\n<script type=\"text\/javascript\" data-cfasync=\"false\" async src=\"https:\/\/poweredby.jads.co\/js\/jads.js\"><\/script>\r\n<ins id=\"1114307\" data-width=\"908\" data-height=\"258\"><\/ins>\r\n<script type=\"text\/javascript\" data-cfasync=\"false\" async>(adsbyjuicy = window.adsbyjuicy || []).push({'adzone':1114307});<\/script>\r\n<!--JuicyAds END-->\r\n  <\/div>\r\n\r\n  <!-- \u7b2c\u4e8c\u901a\u680f\uff1a728\u00d790 \u901a\u680f -->\r\n  <div style=\"grid-column:1\/-1; 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display:grid; place-items:center;\">\r\n\t<!-- JuicyAds v3.0 -->\r\n<script type=\"text\/javascript\" data-cfasync=\"false\" async src=\"https:\/\/poweredby.jads.co\/js\/jads.js\"><\/script>\r\n<ins id=\"1114302\" data-width=\"133\" data-height=\"139\"><\/ins>\r\n<script type=\"text\/javascript\" data-cfasync=\"false\" async>(adsbyjuicy = window.adsbyjuicy || []).push({'adzone':1114302});<\/script>\r\n<!--JuicyAds END-->\r\n\t<\/div>\r\n    <div style=\"height:125px; background:#91e7ac; display:grid; place-items:center;\">\r\n\t\r\n<!-- JuicyAds v3.0 -->\r\n<script type=\"text\/javascript\" data-cfasync=\"false\" async src=\"https:\/\/poweredby.jads.co\/js\/jads.js\"><\/script>\r\n<ins id=\"1114303\" data-width=\"125\" data-height=\"125\"><\/ins>\r\n<script type=\"text\/javascript\" data-cfasync=\"false\" async>(adsbyjuicy = window.adsbyjuicy || []).push({'adzone':1114303});<\/script>\r\n<!--JuicyAds END-->\r\n\t<\/div>\r\n  <\/div>\r\n\r\n  <!-- \u53f3\u4fa7\u7ad6\u680f\uff1a160\u00d7600 \u6574\u5217\u9ad8\u5e7f\u544a -->\r\n  <div style=\"grid-row:3\/8; height:600px;  display:grid; place-items:center;\">\r\n    <!-- JuicyAds v3.0 -->\r\n<script type=\"text\/javascript\" data-cfasync=\"false\" async src=\"https:\/\/poweredby.jads.co\/js\/jads.js\"><\/script>\r\n<ins id=\"1114301\" data-width=\"160\" data-height=\"600\"><\/ins>\r\n<script type=\"text\/javascript\" data-cfasync=\"false\" async>(adsbyjuicy = window.adsbyjuicy || []).push({'adzone':1114301});<\/script>\r\n<!--JuicyAds END-->\r\n  <\/div>\r\n\r\n<\/div><br \/>\n<br \/> The friction between scaling enterprise AI and business integration<br \/>\n<br \/>#friction #scaling #enterprise #business #integration<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article has been reviewed by a Digital Journal editor and may be licensed for&#8230;<\/p>\n","protected":false},"author":1,"featured_media":21680,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[69,11593,15814,14693,15756],"class_list":["post-21679","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-stories","tag-business","tag-enterprise","tag-friction","tag-integration","tag-scaling"],"featured_image_urls":{"full":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33.png",1600,900,false],"thumbnail":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33-150x150.png",150,150,true],"medium":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33-300x169.png",300,169,true],"medium_large":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33-768x432.png",640,360,true],"large":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33-1024x576.png",640,360,true],"1536x1536":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33-1536x864.png",1536,864,true],"2048x2048":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33.png",1600,900,false],"covernews-slider-full":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33-1115x715.png",1115,715,true],"covernews-slider-center":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33-800x500.png",800,500,true],"covernews-featured":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33-1024x576.png",1024,576,true],"covernews-medium":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33-540x340.png",540,340,true],"covernews-medium-square":["https:\/\/8657085.xyz\/wp-content\/uploads\/2026\/09\/TechTO-11-33-400x250.png",400,250,true]},"author_info":{"display_name":"admin","author_link":"https:\/\/8657085.xyz\/?author=1"},"category_info":"<a href=\"https:\/\/8657085.xyz\/?cat=7\" rel=\"category\">Stories<\/a>","tag_info":"Stories","comment_count":"0","_links":{"self":[{"href":"https:\/\/8657085.xyz\/index.php?rest_route=\/wp\/v2\/posts\/21679","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/8657085.xyz\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/8657085.xyz\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/8657085.xyz\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/8657085.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=21679"}],"version-history":[{"count":0,"href":"https:\/\/8657085.xyz\/index.php?rest_route=\/wp\/v2\/posts\/21679\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/8657085.xyz\/index.php?rest_route=\/wp\/v2\/media\/21680"}],"wp:attachment":[{"href":"https:\/\/8657085.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=21679"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/8657085.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=21679"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/8657085.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=21679"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}