{"id":20162,"date":"2026-04-05T08:10:30","date_gmt":"2026-04-05T08:10:30","guid":{"rendered":"https:\/\/www.siveco.com\/?p=20162"},"modified":"2026-05-20T08:13:27","modified_gmt":"2026-05-20T08:13:27","slug":"predictive-maintenance-why-artificial-intelligence-is-not-yet-enough","status":"publish","type":"post","link":"https:\/\/www.siveco.com\/en\/blog-gmao\/predictive-maintenance-why-artificial-intelligence-is-not-yet-enough\/","title":{"rendered":"Predictive maintenance: why artificial intelligence is not (yet) enough"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"20162\" class=\"elementor elementor-20162 elementor-20161\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1c1fabc e-flex e-con-boxed e-con e-parent\" data-id=\"1c1fabc\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-50dedd7b e-con-full e-flex e-con e-child\" data-id=\"50dedd7b\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1667f758 elementor-widget elementor-widget-text-editor\" data-id=\"1667f758\" 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>Despite major technological advances, predictive maintenance is struggling to become an industry standard.<\/p><p>Its success depends less on algorithms than on organisational consistency and human support.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-266cd13f e-con-full e-flex e-con e-child\" data-id=\"266cd13f\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-33063662 elementor-widget elementor-widget-image\" data-id=\"33063662\" 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\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"400\" height=\"400\" src=\"https:\/\/www.siveco.com\/wp-content\/uploads\/2025\/11\/IA-AdobeStock_1191947424-400x400.jpg\" class=\"attachment-thumbnail size-thumbnail wp-image-17850\" alt=\"Maintenance pr\u00e9dictive : pourquoi l\u2019intelligence artificielle ne suffit pas\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\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<div class=\"elementor-element elementor-element-7b744e2d e-flex e-con-boxed e-con e-parent\" data-id=\"7b744e2d\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-20edf03f elementor-widget elementor-widget-text-editor\" data-id=\"20edf03f\" 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>Hailed for its performance promises, predictive maintenance is still struggling to establish itself as an industry standard. While artificial intelligence (AI) and the IoT open up unprecedented opportunities, their effectiveness remains dependent on a delicate balance between data quality, system consistency and human expertise. For companies, the challenge is no longer to test technological building blocks, but to build a sustainable operational strategy.  <\/p>\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<div class=\"elementor-element elementor-element-36bcfd4d e-flex e-con-boxed e-con e-parent\" data-id=\"36bcfd4d\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5c5bf848 elementor-widget elementor-widget-heading\" data-id=\"5c5bf848\" 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\">Predictive maintenance: industrial promise or technological illusion?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-739b88cf elementor-widget elementor-widget-text-editor\" data-id=\"739b88cf\" 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>For a decade, predictive maintenance has been presented as the logical outcome of the connected factory. Maintenance 4.0 combined with Factory 4.0&#8230; However, in 2025, predictive maintenance is still far from being widespread. Admittedly, the technologies are there (smart sensors, embedded AI, etc.), but their implementation remains complex.   <\/p><p>La confusion commence souvent par le mot lui-m\u00eame. \u00ab Pr\u00e9dictif \u00bb recouvre des r\u00e9alit\u00e9s tr\u00e8s diff\u00e9rentes, du simple seuil d\u2019alerte \u00e0 l\u2019analyse crois\u00e9e de donn\u00e9es complexes. Dans certains secteurs tr\u00e8s industrialis\u00e9s, comme l\u2019a\u00e9ronautique ou la d\u00e9fense, les cas d\u2019usage se multiplient. Toutefois dans la majorit\u00e9 des entreprises, les projets restent limit\u00e9s \u00e0 quelques p\u00e9rim\u00e8tres pilotes, faute de donn\u00e9es exploitables, d\u2019int\u00e9gration fluide ou de retour sur investissement mesurable.   <\/p><p>The confusion often starts with the word itself. \u2018Predictive\u2019 covers a wide range of realities, from simple alert thresholds to complex cross-analysis of data. In certain highly industrialised sectors, such as aeronautics and defence, use cases are multiplying. However, in most companies, projects remain limited to a few pilot areas due to a lack of usable data, seamless integration or measurable return on investment.  <\/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-381516f elementor-widget elementor-widget-heading\" data-id=\"381516f\" 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\">Conditions for success that go beyond technology alone<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bf7d723 elementor-widget elementor-widget-text-editor\" data-id=\"bf7d723\" 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 predictive maintenance is struggling to gain acceptance, it is not for lack of technology. Sensors, artificial intelligence, analysis tools: everything is already in place. But these building blocks still need to work together. Isolated, poorly positioned or contextualised data is of little value. It is the overall consistency that makes the difference.    <\/p><p>Integration plays a central role here. When CMMS, IoT, BI, and BIM are considered as a whole, the company gains clarity. It can finally move from simply monitoring activity to a proactive approach.  <\/p><p>However, none of this works without teams. They are the ones who validate, interpret and arbitrate. In this sense, technology cannot replace field experience, because to take full advantage of the tools, it is also necessary to support their use, provide training and develop practices in line with the pace of the business.  <\/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-cdd4686 elementor-widget elementor-widget-heading\" data-id=\"cdd4686\" 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\">More than just a technical project, an industrial strategy<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-70c1dfe elementor-widget elementor-widget-text-editor\" data-id=\"70c1dfe\" 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 properly planned, predictive maintenance optimises interventions, reduces unexpected downtime and helps to better allocate resources. Provided it is part of a genuine business strategy. For a project to be useful, it must target the right equipment, the right sensors and the right thresholds. The aim is not to anticipate all breakdowns, but to intervene where the impact is real.   <\/p><p>Added to this are two key dimensions: data confidentiality and cybersecurity. In a system open to connected objects, each sensor becomes a potential attack surface. However, data from industrial equipment is often sensitive, even critical. It must therefore remain on site to be better protected. This requires architectural choices, strictly controlled access and AI capable of operating locally. Because effective but poorly secured artificial intelligence remains a vulnerability. And in such an environment, security cannot be an option.      <\/p><p>Ultimately, predictive maintenance reveals more than just a new relationship with machines. It is a different way of thinking about industrial performance. More agile and more targeted. And perhaps the real turning point will not come from the technology itself, but from the ability to bring together data, tools and human expertise. This is where the industry of tomorrow will be played out.    <\/p><p><strong>By Carlo Fichera, Chief Executive Officer and Founder of Siveco Group.<\/strong><\/p>\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>Despite major technological advances, predictive maintenance is struggling to become an industry standard. Its success depends less on algorithms than on organisational consistency and human support. Hailed for its performance promises, predictive maintenance is still struggling to establish itself as an industry standard. While artificial intelligence (AI) and the IoT open up unprecedented opportunities, their [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":20173,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45,108],"tags":[],"class_list":["post-20162","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-actualites","category-news"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Predictive maintenance: why artificial intelligence is not (yet) enough - Siveco<\/title>\n<meta name=\"description\" content=\"For Siveco Group, predictive maintenance is another way of thinking about industrial performance. 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