{"id":98303,"date":"2026-04-25T11:39:30","date_gmt":"2026-04-25T11:39:30","guid":{"rendered":"https:\/\/mybusinessfuture.com\/enterprise-ai-adoption-impact-index-april-2026-medium-scale\/"},"modified":"2026-07-12T20:18:05","modified_gmt":"2026-07-12T20:18:05","slug":"enterprise-ai-adoption-impact-index-april-2026-medium-scale","status":"publish","type":"post","link":"https:\/\/mybusinessfuture.com\/en\/enterprise-ai-adoption-impact-index-april-2026-medium-scale\/","title":{"rendered":"AI Adoption Gap: What It Means for Mid-Sized Businesses"},"content":{"rendered":"<div style=\"background:#202528;color:#fff;padding:6px 14px;border-radius:999px;display:inline-block;font-size:13px;font-weight:600;letter-spacing:0.4px;margin-bottom:16px;\">TREND \u00b7 AI SCALING<\/div>\n<div class=\"lesezeit\" style=\"font-size:13px;color:#202528;margin-bottom:20px;\">8 Min. Reading Time<\/div>\n<p><strong>On April 22, 2026, Infor released the Enterprise AI Adoption Impact Index: 1,000 decision-makers from the US, UK, Germany, and France, 49 percent of organizations are stuck in the early deployment phase, although 80 percent claim to have the internal capabilities to implement AI. In parallel, Infor presented new AI orchestration tools on April 22, which specifically address this scale gap. For German SMEs, three key figures from the index are immediately relevant for the current budget rounds.<\/strong><\/p>\n<div class=\"tldr-box\" style=\"background:#f4f5f6;border-left:4px solid #202528;padding:18px 22px;margin:24px 0;border-radius:8px;\">\n<strong style=\"color:#202528;\">Key Takeaways (as of 24.04.2026):<\/strong><\/p>\n<ul style=\"margin:8px 0 0 18px;padding:0;\">\n<li>Infor Enterprise AI Adoption Impact Index, 22.04.2026, 1,000 decision-makers DE\/UK\/US\/FR: More than half of the companies cannot productively scale AI.<\/li>\n<li>49 percent in early deployment, 80 percent believe in their own internal capabilities. This is the largest discrepancy between self-image and reality that a benchmark report has shown in the last twelve months.<\/li>\n<li>Top 3 barriers: data security and compliance (36 percent), lack of AI talent (25 percent), unclear ROI (23 percent).<\/li>\n<li>Infor has announced new AI orchestration tools in parallel, which are specifically aimed at ERP-integrated AI scaling and are intended to directly address the data quality gap.<\/li>\n<li>For SMEs, this means three concrete steps: honest scale status check, data homework before buying new models, checking ERP integration as a scale platform.<\/li>\n<\/ul>\n<\/div>\n<h2 style=\"color:#202528;padding-top:64px;margin-bottom:20px;\">What the Index Specifically Shows<\/h2>\n<p><strong>What is the Enterprise AI Adoption Impact Index?<\/strong> The index is an industry study published by Infor on April 22, 2026, with 1,000 decision-makers from the US, UK, Germany, and France. It does not measure the presence of AI projects, but rather the maturity of scaling and value contribution. The methodological strength: the study asks for both self-assessment and hard indicators (productive deployments, ROI measurement, data management maturity) and contrasts both. The result is one of the sharpest pictures of the execution gap in enterprise AI available in 2026.<\/p>\n<p>The central statement: more than half of all surveyed organizations do not get beyond early deployment. Two-thirds of respondents have at least one AI pilot project, but only one-third have productive AI in a core business process in use. This is a number that contradicts the 80 percent who claim to have the internal capabilities to implement it. The difference between capability and actual scaling is the gap that will be the focus in the coming twelve months.<\/p>\n<p>For German SMEs, the index acts as a wake-up call in two directions. Firstly, the numbers match what we have observed in DACH consulting mandates since the beginning of the year: the pilot wave is through everywhere, the scaling wave is stalling. Secondly, the barriers (data security, talent, ROI) are not the ones that are solved by buying another model. They are organizational and data structural. Anyone who primarily buys new licenses in Q2 and Q3 instead of working on these three topics will fall back into the 49 percent category according to the index.<\/p>\n<h2 style=\"color:#202528;padding-top:64px;margin-bottom:20px;\">Three key figures from the index that belong in the executive round<\/h2>\n<div class=\"stat-block-row\" style=\"display:grid;grid-template-columns:repeat(3,1fr);gap:16px;margin:24px 0;\">\n<div style=\"background:#202528;color:#fff;padding:20px;border-radius:10px;\">\n<div style=\"font-size:32px;font-weight:700;color:#fff;\">49%<\/div>\n<div style=\"font-size:14px;color:#fff;margin-top:6px;\">get stuck in early deployment despite having pilots. Infor Enterprise AI Adoption Impact Index, April 2026.<\/div><\/div>\n<div style=\"background:#202528;color:#fff;padding:20px;border-radius:10px;\">\n<div style=\"font-size:32px;font-weight:700;color:#fff;\">80%<\/div>\n<div style=\"font-size:14px;color:#fff;margin-top:6px;\">believe they have the internal capabilities to implement AI. The discrepancy with the actual scaling rate is remarkably high.<\/div><\/div>\n<div style=\"background:#202528;color:#fff;padding:20px;border-radius:10px;\">\n<div style=\"font-size:32px;font-weight:700;color:#fff;\">36\/25\/23<\/div>\n<div style=\"font-size:14px;color:#fff;margin-top:6px;\">Percent: Top 3 barriers are data security, lack of AI talent, and unclear ROI. Three levers for Q2\/Q3 2026.<\/div><\/div>\n<\/div>\n<blockquote style=\"border-left:4px solid #202528;background:#f4f5f6;padding:18px 24px;margin:28px 0;font-style:italic;color:#202528;\"><p>View Transitions API sounds like niche tinkering. Until you&#8217;ve integrated it into a real product page for the first time and can&#8217;t live without it. Enterprise AI sounds like hype. Until you&#8217;ve cleanly integrated it into an ERP for the first time and can&#8217;t live without it.<\/p><\/blockquote>\n<div style=\"display:flex;flex-wrap:wrap;gap:16px;margin:40px 0;\">\n<div style=\"flex:1;min-width:200px;background:#fff5f5;border-radius:8px;padding:20px 24px;\">\n<div style=\"font-size:11px;font-weight:700;color:#c0392b;text-transform:uppercase;letter-spacing:0.5px;margin-bottom:8px;\">KEY FIGURE<\/div>\n<div style=\"font-size:36px;font-weight:800;color:#c0392b;line-height:1;white-space:nowrap;\">49 Percent<\/div>\n<div style=\"font-size:13px;color:#555;margin-top:8px;opacity:0.8;line-height:1.3;\">of organizations get stuck in the early deployment phase<\/div>\n<\/div>\n<div style=\"flex:1;min-width:200px;background:#f8f9fa;border-radius:8px;padding:20px 24px;\">\n<div style=\"font-size:11px;font-weight:700;color:#c0392b;text-transform:uppercase;letter-spacing:0.5px;margin-bottom:8px;\">KEY FIGURE<\/div>\n<div style=\"font-size:36px;font-weight:800;color:#c0392b;line-height:1;white-space:nowrap;\">80 Percent<\/div>\n<div style=\"font-size:13px;color:#555;margin-top:8px;opacity:0.8;line-height:1.3;\">claim to have the internal capabilities to implement AI<\/div>\n<\/div>\n<div style=\"flex:1;min-width:200px;background:#f8f9fa;border-radius:8px;padding:20px 24px;\">\n<div style=\"font-size:11px;font-weight:700;color:#c0392b;text-transform:uppercase;letter-spacing:0.5px;margin-bottom:8px;\">KEY FIGURE<\/div>\n<div style=\"font-size:36px;font-weight:800;color:#c0392b;line-height:1;white-space:nowrap;\">36 Percent<\/div>\n<div style=\"font-size:13px;color:#555;margin-top:8px;opacity:0.8;line-height:1.3;\">), lack of AI talent (25 Percent), unclear ROI (23 Percent)<\/div>\n<\/div>\n<\/div>\n<h2 style=\"color:#202528;padding-top:64px;margin-bottom:20px;\">Three steps for mid-sized businesses by July 2026<\/h2>\n<p>The practical consequence of the index is not &#8220;spend more money.&#8221; The index reveals that the bottleneck is structural in nature. Three concrete steps that can be achieved in the next three months and have a direct impact.<\/p>\n<h3 style=\"color:#202528;\">Step 1: Honest Scale Status Check<\/h3>\n<p>The 80 percent self-overestimation trap from the index can be easily tested within your own organization. Count the AI systems that are productively running in core business processes (not pilots, not experiments), count the number of users, count the time-to-productivity per ongoing initiative. If you have fewer than two productive systems with more than 100 users each, you&#8217;re in the 49 percent class. If you have four or more, you&#8217;re in the maturity class. The scale check takes half a day and is the basis for everything that follows.<\/p>\n<h3 style=\"color:#202528;\">Step 2: Data homework before buying new models<\/h3>\n<p>The index shows data security and compliance as the number one barrier. In consulting practice, this translates into three concrete topics that should be clarified before the next model purchase: Which data classes are allowed to flow into which model, how is this documented in the data lineage system, and what audit trail runs for each model inference? If you can&#8217;t answer these three questions in writing, you should spend the next 90 days on data infrastructure instead of model licenses. The calculation is simple: Without a clean data foundation, no model scales, no matter how expensive.<\/p>\n<h3 style=\"color:#202528;\">Step 3: ERP Integration as a Scale Platform<\/h3>\n<p>Infor&#8217;s approach with ERP-integrated AI is one of several possible scaling paths. Other providers (SAP Joule, Microsoft Dynamics Copilot, Oracle AI Agents) are pursuing similar strategies. The common thesis: AI scales best where data is already structured and processes are already defined. For mid-sized businesses already operating within Infor, SAP, or Microsoft ecosystems, the ERP-integrated AI option is a natural path. It reduces integration work because data mapping, authorization systems, and workflow engines are already in place. For greenfield organizations, the platform decision requires broader consideration.<\/p>\n<h3 style=\"color:#202528;\">What Infor&#8217;s new tools aim to achieve<\/h3>\n<p>Infor has simultaneously released a series of new AI orchestration tools with its Index Report, targeting the scaling gap. The core building blocks include: an orchestration layer between ERP data and various model providers, a central prompt library for reusable use cases, and a compliance framework that checks model calls against data protection and industry regulations. For Infor customers, this is a logical evolution. For non-Infor organizations, it&#8217;s a signal that major ERP providers will offer scale platforms by 2026, not just models. The subsequent SAP and Microsoft announcements in the coming weeks will likely showcase similar architectures.<\/p>\n<h2 style=\"color:#202528;padding-top:64px;margin-bottom:20px;\">Frequently Asked Questions<\/h2>\n<details style=\"background:#f4f5f6;border-left:3px solid #202528;padding:14px 18px;margin:12px 0;border-radius:6px;\">\n<summary style=\"font-weight:600;color:#202528;cursor:pointer;\"><strong>How valid is the Infor Index&#8217;s base of 1,000 decision-makers?<\/strong><\/summary>\n<p style=\"margin:8px 0 0;padding:0;\">The sample size is solid for a benchmark study of this type. Importantly, Infor is the client, not an independent institute partner. The results are not biased, but the interpretation (&#8220;Infor tools close the gap&#8221;) is sales-related in part of the communication. Those using the figures should keep the sales message and benchmark data separate.<\/p>\n<\/details>\n<details style=\"background:#f4f5f6;border-left:3px solid #202528;padding:14px 18px;margin:12px 0;border-radius:6px;\">\n<summary style=\"font-weight:600;color:#202528;cursor:pointer;\"><strong>Does the study align with Deloitte&#8217;s State of AI 2026?<\/strong><\/summary>\n<p style=\"margin:8px 0 0;padding:0;\">Yes, the direction is consistent. Deloitte cites a 25 percent productivity rate, while Infor mentions a 49 percent early deployment share; both numbers describe the same phenomenon: the gap between pilot and production is the central theme in 2026. Triangulating both studies in the IT Committee paper increases the informative value.<\/p>\n<\/details>\n<details style=\"background:#f4f5f6;border-left:3px solid #202528;padding:14px 18px;margin:12px 0;border-radius:6px;\">\n<summary style=\"font-weight:600;color:#202528;cursor:pointer;\"><strong>How deeply does the German Mittelstand segment get analyzed?<\/strong><\/summary>\n<p style=\"margin:8px 0 0;padding:0;\">The Infor Index includes Germany as one of four countries. The Mittelstand dimension is not granularly broken down in the published short version. Those requiring Mittelstand-specific figures can combine the Infor Index with Bitkom or Fraunhofer data, which better represent the DACH segmentation.<\/p>\n<\/details>\n<details style=\"background:#f4f5f6;border-left:3px solid #202528;padding:14px 18px;margin:12px 0;border-radius:6px;\">\n<summary style=\"font-weight:600;color:#202528;cursor:pointer;\"><strong>What if we don&#8217;t have an ERP like Infor, SAP, or Dynamics?<\/strong><\/summary>\n<p style=\"margin:8px 0 0;padding:0;\">Then the scaling path is different, but not impossible. Organizations with best-of-breed setups (Salesforce + Workday + Snowflake + custom tools) need a separate data and orchestration layer that takes on the ERP role. The major hyperscaler AI platforms (Azure AI Foundry, Google Agentspace, AWS Bedrock) are built for this. The effort is higher, but so is the flexibility.<\/p>\n<\/details>\n<details style=\"background:#f4f5f6;border-left:3px solid #202528;padding:14px 18px;margin:12px 0;border-radius:6px;\">\n<summary style=\"font-weight:600;color:#202528;cursor:pointer;\"><strong>How do we determine our organization&#8217;s 80 percent self-image?<\/strong><\/summary>\n<p style=\"margin:8px 0 0;padding:0;\">An internal pulse check with two questions for department heads and IT leaders: &#8220;Can we bring a new AI initiative into production within six weeks today?&#8221; and &#8220;Do we have a clean data basis for it?&#8221; Separating self-assessments and comparing them with actual time-to-production is a good insight generator for the next executive committee.<\/p>\n<\/details>\n<details style=\"background:#f4f5f6;border-left:3px solid #202528;padding:14px 18px;margin:12px 0;border-radius:6px;\">\n<summary style=\"font-weight:600;color:#202528;cursor:pointer;\"><strong>What are the concrete costs of the scaling phase?<\/strong><\/summary>\n<p style=\"margin:8px 0 0;padding:0;\">For an organization aiming to build three to five productive AI applications by the end of the year, we estimate costs between 400,000 and 900,000 Euro in year 1, depending on data maturity and integration needs. About 50 percent goes to data and integration work, 30 percent to model and platform costs, and 20 percent to training, governance, and communication.<\/p>\n<\/details>\n<p><em>Source title image: Pexels \/ Fauxels (px:3184292)<\/em><\/p>\n<h3 style=\"color:#202528;\">Network: Read more on MyBusinessFuture<\/h3>\n<ul>\n<li>Detailed homework in the <a href=\"https:\/\/mybusinessfuture.com\/en\/ai-data-maturity-in-the-midmarket-2026-five-must-dos-before\/\">AI data maturity analysis on the five homework assignments<\/a><\/li>\n<li>Foundational look at the <a href=\"https:\/\/mybusinessfuture.com\/en\/80-ai-failure-rate-2026-how-rand-and-gartner-expose-the-ai\/\">AI failure rate 2026 and the structural question<\/a><\/li>\n<li>Regulatory classification on the <a href=\"https:\/\/mybusinessfuture.com\/en\/franco-german-ai-dialogue-sme-gaia-x-operationalization\/\">Franco-German AI report and IPCEI AI homework<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>TREND \u00b7 AI SCALING 8 Min. Reading Time On April 22, 2026, Infor released the Enterprise AI Adoption Impact Index: 1,000 decision-makers from the US, UK, Germany, and France, 49 percent of organizations are stuck in the early deployment phase, although 80 percent claim to have the internal capabilities to implement AI. In parallel, Infor [&hellip;]<\/p>\n","protected":false},"author":182,"featured_media":98263,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_focuskw":"Infor Enterprise AI Adoption Index remains the same, as it appears to be an English phrase already. However, if you meant to provide a German phrase, you didn't. If you provide the German phrase, I'd be happy to translate it. In case you are referring to a keyword similar to: \"In","_yoast_wpseo_title":"Infor Enterprise AI Adoption Impact Index April 22, 2026: The Scale Gap and What","_yoast_wpseo_metadesc":"Infor Index 04\/22\/2026: 49% early deployment, 80% overconfidence, 36% data security barriers. 3 steps for mid-sized businesses by July.","_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_meta-robots-nofollow":"","_yoast_wpseo_meta-robots-adv":"","_yoast_wpseo_canonical":"","_yoast_wpseo_opengraph-title":"","_yoast_wpseo_opengraph-description":"","_yoast_wpseo_opengraph-image":"","_yoast_wpseo_opengraph-image-id":0,"_yoast_wpseo_twitter-title":"","_yoast_wpseo_twitter-description":"","_yoast_wpseo_twitter-image":"","_yoast_wpseo_twitter-image-id":0,"featured_post_sortierung":0,"featured_post":0,"pre_headline":"","bildquelle":"","teasertext":"","language":"de","_evm_translation_lang":"","_wp_old_slug":[],"footnotes":""},"categories":[2214,2263,1146,1164],"tags":[],"class_list":["post-98303","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-digital-economy","category-life-sciences-health-care","category-retail-consumer-goods","entry"],"evm_reading_time_minutes":9,"wpml_language":"en","wpml_translation_of":98265,"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Infor Enterprise AI Adoption Impact Index April 22, 2026: The Scale Gap and What<\/title>\n<meta name=\"description\" content=\"Infor Index 04\/22\/2026: 49% early deployment, 80% overconfidence, 36% data security barriers. 3 steps for mid-sized businesses by July.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mybusinessfuture.com\/en\/enterprise-ai-adoption-impact-index-april-2026-medium-scale\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Infor Enterprise AI Adoption Impact Index April 22, 2026: The Scale Gap and What\" \/>\n<meta property=\"og:description\" content=\"Infor Index 04\/22\/2026: 49% early deployment, 80% overconfidence, 36% data security barriers. 3 steps for mid-sized businesses by July.\" \/>\n<meta property=\"og:url\" content=\"https%3A%2F%2Fmybusinessfuture.com%2Fen%2Fenterprise-ai-adoption-impact-index-april-2026-medium-scale%2F\/\" \/>\n<meta property=\"og:site_name\" content=\"MyBusinessFuture\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/MyBusinessFuture\" \/>\n<meta property=\"article:published_time\" content=\"2026-04-25T11:39:30+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-07-12T20:18:05+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/mybusinessfuture.com\/wp-content\/uploads\/2026\/04\/mbf-infor-260424.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1880\" \/>\n\t<meta property=\"og:image:height\" content=\"1057\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Adrian Garcia-Kunz\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@mbusinessfuture\" \/>\n<meta name=\"twitter:site\" content=\"@mbusinessfuture\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Adrian Garcia-Kunz\" \/>\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:\/\/mybusinessfuture.com\/en\/enterprise-ai-adoption-impact-index-april-2026-medium-scale\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/mybusinessfuture.com\/en\/enterprise-ai-adoption-impact-index-april-2026-medium-scale\/\"},\"author\":{\"name\":\"Adrian Garcia-Kunz\",\"@id\":\"https:\/\/mybusinessfuture.com\/en\/#\/schema\/person\/0b45f122199986cd35edc7e1347509b2\"},\"headline\":\"AI Adoption Gap: What It Means for Mid-Sized Businesses\",\"datePublished\":\"2026-04-25T11:39:30+00:00\",\"dateModified\":\"2026-07-12T20:18:05+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/mybusinessfuture.com\/en\/enterprise-ai-adoption-impact-index-april-2026-medium-scale\/\"},\"wordCount\":1481,\"publisher\":{\"@id\":\"https:\/\/mybusinessfuture.com\/en\/#organization\"},\"image\":{\"@id\":\"https:\/\/mybusinessfuture.com\/en\/enterprise-ai-adoption-impact-index-april-2026-medium-scale\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/mybusinessfuture.com\/wp-content\/uploads\/2026\/04\/mbf-infor-260424.jpg\",\"articleSection\":[\"Artificial Intelligence\",\"Digital Economy\",\"Life Sciences &amp; 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