{"id":48327,"date":"2025-07-04T17:10:00","date_gmt":"2025-07-04T15:10:00","guid":{"rendered":"https:\/\/www.cybus.io\/?p=48327"},"modified":"2025-08-18T17:45:14","modified_gmt":"2025-08-18T15:45:14","slug":"the-foundation-of-ai-in-manufacturing-getting-your-data-right","status":"publish","type":"post","link":"https:\/\/www.cybus.io\/en\/the-foundation-of-ai-in-manufacturing-getting-your-data-right\/","title":{"rendered":"The Foundation of AI in Manufacturing: Getting Your Data Right"},"content":{"rendered":"\n<div class=\"wp-block-nb-onecol ce ce-onecol narrow-2\"><div class=\"container\"><div class=\"row\"><div class=\"col\">\n<p class=\"wp-block-paragraph\"><strong>A robust data foundation is the single most critical success factor for AI in manufacturing. Discover the architectural principles, core metrics and implementation roadmap that enable global producers to scale machine learning, AI assistants and agents across plants and supply chains.<\/strong><\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-nb-onecol ce ce-onecol vertically-centered\"><div class=\"container\"><div class=\"row\"><div class=\"col\">\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-default\"\/>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-top is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:10%\"><div class=\"wp-block-image\">\n<figure class=\"alignleft size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"247\" height=\"247\" src=\"https:\/\/www.cybus.io\/wp-content\/uploads\/2022\/10\/Jasmin-skenderi-cto-cyus.png\" alt=\"Ein rundes Bild von Jasmin Skenderi, CTO von Cybus\" class=\"wp-image-29116\" style=\"width:83px;height:auto\" srcset=\"https:\/\/www.cybus.io\/wp-content\/uploads\/2022\/10\/Jasmin-skenderi-cto-cyus.png 247w, https:\/\/www.cybus.io\/wp-content\/uploads\/2022\/10\/Jasmin-skenderi-cto-cyus-150x150.png 150w\" sizes=\"auto, (max-width: 247px) 100vw, 247px\" \/><\/figure>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:90%\">\n<p class=\"is-style-sub-headline wp-block-paragraph\">Expert article by <\/p>\n\n\n\n<p class=\"is-style-default wp-block-paragraph\">Jasmin Skenderi<br><em>CTO, Cybus<\/em><\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-default\"\/>\n<\/div><\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-nb-onecol ce ce-onecol narrow-2\"><div class=\"container\"><div class=\"row\"><div class=\"col\">\n<h2 class=\"wp-block-heading\" id=\"h-the-paradox-of-industrial-ai-adoption\"><strong>The paradox of Industrial AI adoption<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Everyone is talking about Generative AI, yet <strong>70\u202f% of companies struggle<\/strong> to move pilot projects into production. The reason: Their underlying data infrastructure cannot deliver clean, contextual, real\u2011time information. Even the most advanced machine\u2011learning models remain academic proofs of concept without trustworthy data pipelines.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Meanwhile, the opportunity cost is exploding: analysts project the market for Industrial AI analytics to grow from <strong>USD\u202f1.7\u202fbn in\u202f2023 to over 5\u202fbn by\u202f2028<\/strong>. Manufacturers that wait risk permanent competitive gaps, lost ROI on digital investments and compliance exposure under directives like the EU <strong>CSRD<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-why-ai-fails-without-proper-data-foundation\">Why AI fails without proper data foundation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI fails when it can\u2019t access the data it needs. In industrial environments, the main causes are missing structure, data silos, lack of context or unreliable access to high quality data. A Data Foundation addresses these issues. It is the architectural layer that abstracts, contextualizes and governs OT and IT data before it is consumed by analytics, AI assistants or autonomous agents. Without it, even advanced AI models cannot operate reliably \u2013 making projects fragile, fragmented or unsustainable.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In practice, a robust Data Foundation includes:<\/p>\n\n\n\n<ul class=\"wp-block-list is-style-arrow-list\">\n<li><strong>Unified Namespace (UNS)<\/strong> \u2013 a single, event\u2011driven representation of all machines, lines and processes.<\/li>\n\n\n\n<li><strong>Infrastructure as Code (IaC)<\/strong> \u2013 so that data pipelines are version\u2011controlled, repeatable and auditable.<\/li>\n\n\n\n<li><strong>Edge\u2011grade Performance<\/strong> \u2013 sub\u2011second latency, buffering and local fail\u2011over for 24\u202f\u00d7\u202f7 production.<\/li>\n\n\n\n<li><strong>Secure\u2011by\u2011Design Governance<\/strong> \u2013 fine\u2011grained access, token\u2011based auth and encrypted transport (TLS\/mTLS).<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Without these pillars, data quality degrades, silos persist and every new AI use case becomes a bespoke IT project.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-from-generic-ai-use-cases-to-intelligent-assistants-and-agents\">From generic AI use cases to intelligent assistants and agents<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Industrial AI spans a wide spectrum.<br>All four layers mentioned in the Table 1 <strong>consume the same foundational data<\/strong> \u2013 the differentiation lies in the algorithmic logic. A sound Data Foundation therefore future\u2011proofs your roadmap: invest once in connectivity and governance, then iterate on models at minimal marginal cost.<br><\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><thead><tr><th><strong>Maturity<\/strong><\/th><th><strong>Typical Application Example<\/strong><\/th><th><strong>Data Characteristics<\/strong><\/th><\/tr><\/thead><tbody><tr><td><strong>Descriptive<\/strong><\/td><td>OEE dashboards, anomaly alerts<\/td><td>High\u2011volume time\u2011series, medium latency<\/td><\/tr><tr><td><strong>Predictive<\/strong><\/td><td>Predictive maintenance, energy forecasting<\/td><td>Long historical windows, labelled events<\/td><\/tr><tr><td><strong>Prescriptive<\/strong><\/td><td>Dynamic scheduling, closed\u2011loop SPC<\/td><td>Real\u2011time feedback, optimization targets<\/td><\/tr><tr><td><strong>AI Assistants &amp; Agents<\/strong><\/td><td>Connected\u2011Worker guidance, autonomous material flow (OTSM)<\/td><td>Semantic context, intent recognition, deterministic control<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Table 1: Four maturity layers for data-driven projects in manufacturing industry. <\/em><\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-data-quality-drives-results-even-before-the-first-ai-project\">Data quality drives results \u2013 even before the first AI project<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A Data Foundation pays off long before the first neural network is trained.<em> <\/em>Industry-standard KPIs such as Overall Equipment Effectiveness (OEE), Mean Time to Repair (MTTR), scrap rates and energy cost per unit directly reflect the effectiveness of data quality and availability. Organizations that prioritize their data infrastructure often realize significant improvements, including OEE increases of up to 10 percentage points or MTTR reductions by as much as 25%.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These enhancements translate directly into tangible business benefits: Higher productivity, reduced operating costs and accelerated deployment of new digital applications. Investing in a unified, standardized data infrastructure is thus not merely strategically sound but economically essential.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Companies gain significant benefits from a strong data foundation, even before applying advanced AI. Standard industrial metrics confirm it (see Table 2) and the numbers underline a core truth: <em>connectivity and context<\/em> unlock efficiency levers that are independent of any specific AI model.<\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><thead><tr><th>Metric<\/th><th>Typical Improvement<\/th><th>Business Impact<\/th><\/tr><\/thead><tbody><tr><td><strong>OEE (Overall Equipment Effectiveness)<\/strong><\/td><td>+5\u201310%<\/td><td>Higher productivity, more revenue<\/td><\/tr><tr><td><strong>MTTR (Mean Time to Repair)<\/strong><\/td><td>\u201325%<\/td><td>Fewer stoppages, lower costs<\/td><\/tr><tr><td><strong>Quality (Scrap Rate)<\/strong><\/td><td>+2%<\/td><td>Reduced waste, increased efficiency<\/td><\/tr><tr><td><strong>Energy Usage<\/strong><\/td><td>\u20135\u20138%<\/td><td>Direct savings in operations<\/td><\/tr><tr><td><strong>Deployment Speed<\/strong><\/td><td>Weeks instead of months<\/td><td>Rapid ROI, reduced risks<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Table 2: Benchmarks from IDC Manufacturing Insights 2024, VDMA Digital Transformation Survey 2024, Cybus customer projects 2023\u20132025.<\/em><\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-architectural-blueprint-for-an-ai-data-foundation\">Architectural blueprint for an AI data foundation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Building a solid data foundation for AI in manufacturing requires a structured and scalable approach to industrial data. Traditionally, this architecture is divided into four layers: the Source Layer (data-generating assets like PLCs and sensors), the Unification Layer (protocol normalization), the Context Layer (data modeling and contextualization), and the Consume Layer (AI, MES, analytics). While this model has served its purpose, it often results in fragmented responsibilities and complex integrations.<\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-nb-onecol ce ce-onecol pb-medium narrow-2 vertically-centered\"><div class=\"container\"><div class=\"row\"><div class=\"col\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\"><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1200\" height=\"627\" src=\"https:\/\/www.cybus.io\/wp-content\/uploads\/2025\/07\/unified-layer-as-data-foundation-for-ai.gif\" alt=\"\" class=\"wp-image-48311\"\/><\/figure>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<ol class=\"wp-block-list is-style-square-list\">\n<li><strong>Consume Layer<\/strong> \u2013 Data lakes, MES extensions, AI assistants, optimisation agents.<\/li>\n\n\n\n<li><strong>Context Layer<\/strong> \u2013 Model, enrich and publish datasets into a Unified Namespace.<\/li>\n\n\n\n<li><strong>Unification Layer<\/strong> \u2013 Edge connectors normalise industrial protocols (OPC\u202fUA, MQTT, Modbus, proprietary fieldbuses).<\/li>\n\n\n\n<li><strong>Source Layer<\/strong> \u2013 PLCs, sensors, robots, AGVs, EMS, MES.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\">A central data foundation uniquely consolidates the Connect Layer and the Context Layer into one central data layer, which provides a lean <a href=\"https:\/\/www.cybus.io\/en\/resources\/glossary\/data-architecture\/\">data architecture<\/a>, unified data modeling and namespace management.&nbsp;<\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-nb-onecol ce ce-onecol pt-medium pb-medium wide bgr-blue-dark bgr-img-fullwidth\" style=\"background-image:url(https:\/\/www.cybus.io\/wp-content\/uploads\/2025\/06\/visual-background-captain-connect-b.jpg)\"><div class=\"container\"><div class=\"row\"><div class=\"col\">\n<h3 class=\"wp-block-heading has-text-align-center\" id=\"h-with-a-robust-data-foundation-in-place-every-algorithm-assistant-or-autonomous-agent-becomes-a-plug-and-play-extension-instead-of-a-multi-year-integration-project\">&#8220;<strong>With a robust Data Foundation in place, every algorithm, assistant or autonomous agent becomes a plug\u2011and\u2011play extension instead of a multi\u2011year integration project.<\/strong>&#8220;<\/h3>\n\n\n\n<h4 class=\"wp-block-heading has-text-align-center is-style-hero-subline\" id=\"h-jasmin-skenderi-cto-cybus\"><em>\u2014 Jasmin Skenderi, CTO Cybus<\/em><\/h4>\n<\/div><\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-nb-onecol ce ce-onecol pt-medium pb-large narrow-2\"><div class=\"container\"><div class=\"row\"><div class=\"col\">\n<h2 class=\"wp-block-heading\" id=\"h-one-simplified-architecture-less-complexity-more-impact\">One simplified architecture: less complexity, more impact<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A unified data foundation combines two critical functions: Connecting industrial devices and organizing data into one easy-to-use digital backbone. By simplifying these layers, manufacturers significantly reduce software complexity, licensing costs and the need for specialized knowledge. The result? Projects start faster, run smoother and deliver quicker financial returns.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-implementation-roadmap-for-an-ai-data-foundation\">Implementation roadmap for an AI data foundation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The successful implementation of a robust Data Foundation for AI follows an easy, structured approach. To provide clarity, the following practical roadmap highlights each implementation phase, its typical duration, and the key deliverables expected at every stage:<\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-fixed-layout\"><thead><tr><th><strong>Phase<\/strong><\/th><th><strong>Duration<\/strong><\/th><th><strong>Key Deliverables<\/strong><\/th><\/tr><\/thead><tbody><tr><td><strong>Discovery<\/strong><\/td><td>1\u202fweek<\/td><td>OT\/IT asset inventory, data\u2011quality assessment<\/td><\/tr><tr><td><strong>Pilot<\/strong><\/td><td>4\u20138\u202fweeks<\/td><td>Set up foundational data structure including UNS and connectivity, test and validate<\/td><\/tr><tr><td><strong>Scale<\/strong><\/td><td>Ongoing<\/td><td>Roll out standardized templates, expand to new use cases<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Table 3: Implementation roadmap for an AI Data Foundation from day 2 to ROI in only 8 weeks.<\/em><\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-from-organization-to-infrastructure\">From organization to infrastructure<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Scaling AI in manufacturing isn\u2019t just a technical undertaking, but an organizational one. It depends on more than tools: It requires coordination between strategy, operations and data governance. Success hinges on committed leadership, a clear roadmap and a Center of Excellence that translates ambition into repeatable execution.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-if-your-organization-is-still-debating-where-to-start-focus-on-the-data-layer-first\">If your organization is still debating where to start, focus on the data layer first.<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Your first strategic AI decision isn\u2019t which model to use \u2013 it\u2019s how to ensure your data is accessible, contextualized and production-ready. No AI initiative delivers business value without a secure, contextual and scalable data backbone. <br><strong>With a robust Data Foundation in place, every algorithm, assistant or autonomous agent becomes a plug\u2011and\u2011play extension instead of a multi\u2011year integration project.<\/strong><\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-nb-onecol ce ce-onecol pt-large pb-large narrow-2 vertically-centered bgr-blue-dark bgr-img-fullwidth\" style=\"background-image:url(https:\/\/www.cybus.io\/wp-content\/uploads\/2024\/03\/Hero-banner-webinar-energy-efficiency-in-manufacturing.jpg)\"><div class=\"container\"><div class=\"row\"><div class=\"col\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<h2 class=\"wp-block-heading\" id=\"h-ready-to-scale-ai-across-every-site\">Ready to scale AI across every site?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Book a 30\u2011minute demo and get a custom ROI projection plus implementation roadmap for your production sites.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-primary\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.cybus.io\/en\/demo\/\">book a demo<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>A robust data foundation is the single most critical success factor for AI in manufacturing. Discover which steps are necessary to dive into AI use cases.<\/p>\n","protected":false},"author":5,"featured_media":48322,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_crdt_document":"","footnotes":""},"categories":[154,219],"tags":[],"class_list":["post-48327","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-en","category-produkt"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>The Foundation of AI in Manufacturing: Getting Your Data Right | Cybus.io<\/title>\n<meta name=\"description\" content=\"A robust data foundation is the single most critical success factor for AI in manufacturing. 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