{"id":19220,"date":"2026-09-17T14:43:18","date_gmt":"2026-09-17T12:43:18","guid":{"rendered":"https:\/\/on.de\/?p=19220"},"modified":"2026-09-17T14:43:18","modified_gmt":"2026-09-17T12:43:18","slug":"one-call-is-enough-ai-gets-maintenance-moving","status":"publish","type":"news","link":"https:\/\/on.de\/en\/news\/one-call-is-enough-ai-gets-maintenance-moving\/","title":{"rendered":"One call is enough: AI gets maintenance moving"},"content":{"rendered":"<p>An AI phone assistant connects production directly to the EAM system. Employees report faults faster, capture information completely and thereby relieve the maintenance team.<\/p>\n<p>Idle equipment, long walks to the nearest terminal and incomplete fault reports: for a medium-sized manufacturing company, these challenges were part of everyday life for a long time. Information on machines, equipment and maintenance processes was already available in the Enterprise Asset Management (EAM) system; for employees, however, accessing it was often cumbersome.Together with the Munich-based IT and AI service provider Handz.on, the company therefore introduced an AI-based phone assistant. Today employees can handle faults, queries and maintenance orders simply by phone. This speeds up processes, improves the quality of the information and relieves the burden on production staff.<\/p>\n<h3 class=\"inf-xheading table-of-content\">Starting point: every minute of downtime counts<\/h3>\n<p class=\"inf-body-text\">In a production environment, response speed often determines how severe the consequences of a fault are. If equipment fails or no longer works reliably, the incident has to be recorded quickly and passed on to maintenance.<br \/>\nThe necessary information was generally available in the company: equipment master data, orders, locations and maintenance information were already managed in the EAM system. Even so, everyday work kept raising the question of how employees could access this data as simply as possible and report a fault completely:<\/p>\n<ul class=\"inf-list inf-list--circle\" data-inf-type=\"inf-list\">\n<li class=\"inf-list__item\" data-inf-type=\"inf-list__item\">Employees were often standing right at the affected equipment, while the nearest input terminal was in a different hall.<\/li>\n<li class=\"inf-list__item\" data-inf-type=\"inf-list__item\">The service desk was not always immediately reachable.<\/li>\n<li class=\"inf-list__item\" data-inf-type=\"inf-list__item\">Mobile applications did not always prove practical during ongoing production either \u2013 for example when wearing protective gloves.<\/li>\n<\/ul>\n<p class=\"inf-body-text\">The result was delays and, in part, incomplete reports. Valuable time passed before the actual problem-solving could begin.<\/p>\n<h3 class=\"inf-xheading table-of-content\">Challenge: capturing faults quickly and completely<\/h3>\n<p class=\"inf-body-text\">The problem became especially clear with technical defects. If an employee notices unusual noises on a conveyor belt, for example, further information has to be documented in addition to the fault symptoms. This includes the exact location, the affected equipment, the serial number and as precise a description of the fault as possible. The more manual input is required, the greater the risk of missing or incorrect details. At the same time, employees in day-to-day production often have neither the time nor the opportunity to deal with complex input forms. What was needed was a solution that lowers the barrier to reporting a fault while improving data quality.<\/p>\n<h3 class=\"inf-xheading table-of-content\">The solution: an AI phone assistant as the gateway to the EAM<\/h3>\n<p class=\"inf-body-text\">The company chose an AI assistant from Handz.on. The solution can be adapted to different use cases, answers recurring questions by voice or text and can be integrated into existing business processes.<br \/>\nTechnically, the assistant is based on a Large Language Model (LLM) combined with a Retrieval-Augmented Generation (RAG) system. The AI draws on defined company data and knowledge sources. Alternatively, the solution can be operated entirely locally. In that case, the whole AI assistant, including the required server components, runs offline on a workstation \u2013 a compact hardware box. Local operation is particularly interesting for security-critical use cases. Companies thereby retain control over data, infrastructure and access paths.<br \/>\nFor the manufacturing company, the assistant was configured as a phone solution and connected to the existing data and processes. It serves as an additional access channel to the existing EAM system and thereby simplifies the use of information that is already available. The assistant knows the company&#8217;s equipment, locations, workflows and technical terms. Employees can reach it by phone and in natural language. The solution is available around the clock.<\/p>\n<blockquote class=\"inf-blockquote\"><p>&#8220;The assistant knows the company&#8217;s equipment, locations, workflows and technical terms. Employees can reach it by phone and in natural language.&#8221;<\/p><\/blockquote>\n<h3 class=\"inf-xheading table-of-content\">From phone call to maintenance order<\/h3>\n<p class=\"inf-body-text\">A typical use case shows how the process works in practice: an employee notices that a conveyor belt is making unusually loud noises and that there may be a motor failure. Instead of first going to a terminal or entering a report manually, he calls the AI phone assistant. It guides him through the conversation in a structured way. It asks, for example, about the location and identifies the affected machine from the stored master data. The assistant then records further details of the defect and assigns the fault to a suitable order type. Before the data is transmitted to the EAM system, it summarises all the information in an understandable way. The employee can check, correct or add to the details. Only after his confirmation is a complete maintenance order created automatically and handed over to the EAM system. The caller receives the order number immediately. This keeps employees in control at all times while manual capture steps are eliminated.<\/p>\n<ul class=\"inf-list inf-list--circle\" data-inf-type=\"inf-list\">\n<li class=\"inf-list__item\" data-inf-type=\"inf-list__item\"><strong>Detect the fault<\/strong><br \/>\nAn employee notices that a conveyor belt is making unusually loud noises and that there may be a motor failure.<\/li>\n<li class=\"inf-list__item\" data-inf-type=\"inf-list__item\"><strong>Call the assistant<\/strong><br \/>\nInstead of going to a terminal or entering a report manually, the employee calls the AI phone assistant.<\/li>\n<li class=\"inf-list__item\" data-inf-type=\"inf-list__item\"><strong>Identify the equipment<\/strong><br \/>\nThe assistant guides the employee through the conversation in a structured way, asks about the location and identifies the affected machine from the stored master data.<\/li>\n<li class=\"inf-list__item\" data-inf-type=\"inf-list__item\"><strong>Record the fault<\/strong><br \/>\nThe assistant then gathers further details of the defect and assigns the fault to a suitable order type.<\/li>\n<li class=\"inf-list__item\" data-inf-type=\"inf-list__item\"><strong>Check the details<\/strong><br \/>\nBefore the data is transmitted to the EAM system, the assistant summarises all the information in an understandable way. The employee can check, correct or add to it.<\/li>\n<li class=\"inf-list__item\" data-inf-type=\"inf-list__item\"><strong>Create the order<\/strong><br \/>\nOnly after confirmation is a complete maintenance order created automatically and handed over to the EAM system.<\/li>\n<li class=\"inf-list__item\" data-inf-type=\"inf-list__item\"><strong>Receive the order number<\/strong><br \/>\nThe order number is communicated to the caller immediately. Employees stay in control at all times while manual capture steps are eliminated.<\/li>\n<\/ul>\n<h3 class=\"inf-xheading table-of-content\">Better data, faster response<\/h3>\n<p class=\"inf-body-text\">The AI phone assistant allows employees to capture information right where it arises. They are no longer tied to particular workstations or input devices. At the same time, the structured conversation improves the quality of the reports. Through targeted follow-up questions, the assistant makes sure that important details are not missing. Maintenance thereby receives traceable, uniformly structured information. This speeds up processing and reduces follow-up queries.<br \/>\nIn addition, the assistant can take on further tasks. For example, it can access stored knowledge bases, provide repair instructions or retrieve information on existing orders. This creates a digital point of contact for a variety of questions in everyday production.<\/p>\n<h3 class=\"inf-xheading table-of-content\">Defined data sources ensure consistent answers<\/h3>\n<p class=\"inf-body-text\">The assistant obtains its knowledge from the sources provided by the IT service provider during configuration. These include PDF documents, links, FAQs and specially formatted questionnaires, among others. It can also access connected interfaces and the company&#8217;s internal databases. Because the assistant can be restricted to defined and verified information sources, the risk of contradictory or invented answers can be reduced. The quality of the output depends significantly on the currency, completeness and maintenance of the underlying data.<\/p>\n<h3 class=\"inf-xheading table-of-content\">Conclusion: voice control as a new gateway to maintenance<\/h3>\n<p class=\"inf-body-text\">With the AI phone assistant, the company has created a low-threshold gateway to its maintenance processes. The solution combines the existing functions of the EAM system with intuitive voice interaction and brings digital processes directly into the production hall. The result is faster fault reports, more structured information and noticeable relief for employees. Instead of spending time on administrative work, they can concentrate more on keeping production running smoothly.<br \/>\nThe final decision always stays with people. The assistant supports, structures and accelerates workflows, but does not replace employees&#8217; responsibility. The project thus shows by example how artificial intelligence complements existing systems and increases their practical value in everyday work.<\/p>\n<blockquote class=\"inf-blockquote\"><p>&#8220;The assistant combines the existing functions of the EAM system with intuitive voice interaction and brings digital processes directly into the production hall.&#8221;<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>How an AI phone assistant relieves maintenance: employees report faults by phone and the work order ends up complete in the EAM system.<\/p>\n","protected":false},"featured_media":19116,"template":"","meta":{"footnotes":""},"class_list":["post-19220","news","type-news","status-publish","has-post-thumbnail","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>One call is enough: AI gets maintenance moving - on.de<\/title>\n<meta name=\"description\" content=\"How an AI phone assistant relieves maintenance: employees report faults by phone and the work order ends up complete in the EAM system.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/on.de\/en\/news\/one-call-is-enough-ai-gets-maintenance-moving\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"One call is enough: AI gets maintenance moving - 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