{"id":114677,"date":"2026-08-24T14:36:08","date_gmt":"2026-08-24T12:36:08","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=114677"},"modified":"2026-08-24T14:48:03","modified_gmt":"2026-08-24T12:48:03","slug":"designing-effective-ai-certification","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/designing-effective-ai-certification\/","title":{"rendered":"Designing Effective AI Certification"},"content":{"rendered":"

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Certification for human-centered AI is becoming increasingly important\u2014as a source of competitive differentiation, a response to regulatory expectations, and a mechanism for fostering human-centered work environments. But how can organizations determine whether the underlying requirements are truly embedded in practice? Drawing on expert interviews from established certification domains, this paper shows that the decisive question is answered not during the audit itself, but in the period between audit cycles. Ultimately, it is not the certificate that matters, but the commitment of organizational leadership and the organization as a whole to engage seriously in the certification process and its continuous implementation.<\/p>\n

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\n\n\n\n<\/thead>\n\n\n\n\n\n\n\n\n<\/tbody>\n<\/table>\n

Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
Digital<\/td>\n−<\/td>\n27,55 \u20ac<\/td>\n
Expert<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
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AI-Driven Organization as a New Work Paradigm<\/h4>\n
Implications for individual and organizational change<\/div>
Wolfgang Beinhauer<\/a>, Josephine Hofmann<\/a>, Leonie Krauch<\/a>, Katharina Morsch<\/a>, Carsten Schmidt<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Is it sufficient to train employees in the use of AI tools, or does the AI organization require an entirely new set of competencies? This paper introduces a digital enablement model comprising three competency dimensions and demonstrates, through an upskilling program implemented at the Fraunhofer Institute for Industrial Engineering IAO, how organizations can sustainably bridge the AI adoption gap. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 44 | 2026 | Edition 5 | Pages 94-100<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Data-Driven\n <\/picture>\n <\/div>\n
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\t\t

Data-Driven Supplier Selection in Industry 4.0<\/h4>\n
Towards an industrial platform for selection and configuration<\/div>
Purushothaman Ganesh<\/a> \n \"ORCID<\/a>, Baris E. Albayrak<\/a> \n \"ORCID<\/a>, J\u00f6rg Franke<\/a> \n \"ORCID<\/a>, Till Sindel<\/a> \n \"ORCID<\/a>, Jens F\u00fcrst<\/a>, Sebastian Lang<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial supply chains must repeatedly reconfigure sourcing strategies in response to disruptions, yet supplier capability information remains heterogeneous and difficult to operationalize. Existing research addresses supplier selection, simulation, and interoperability standards separately, treating discovery, evaluation, and optimization as disconnected steps requiring manual data transformation. This paper presents a framework for a data-driven industrial platform integrating three components: Asset Administration Shell-based supplier profiles structured by an Actor-Service ontology for semantic discovery, automatic discrete-event simulation model generation from layout data for performance evaluation, and KPI-driven configuration using optimization algorithms. The main contributions are an end-to-end interoperable workflow, a two-tier supplier profile concept separating semantic descriptions from simulation parameters, and a standardized KPI interface maintaining consistency ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 50-61 | DOI 10.30844\/I4SE.26.4.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Open\n <\/picture>\n <\/div>\n
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Open Source as Enabler for Smart Data Ecosystems<\/h4>\n
How collaboration shapes sovereignty and interoperability across industrial applications<\/div>
Anna Maria Schleimer<\/a> \n \"ORCID<\/a>, Julia Pampus<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n From automotive supply chains to smart factories, data ecosystems promise seamless collaboration across company boundaries. But how can industries build the necessary infrastructure without creating new dependencies? This article explores how open-source software can serve as a foundation and impactful tool for sovereign, interoperable technologies and standards. Yet, open-source software is not a silver bullet; rather, it poses challenges for digital sovereignty in burgeoning data ecosystems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 6-13 | DOI 10.30844\/I4SE.26.4.1<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"From\n <\/picture>\n <\/div>\n
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From Private Law to Private Ordering<\/h4>\n
Rule-making through industrial digital platforms<\/div>
Pia C. Romeike<\/a>, Frederik B\u00e4r<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial digital platforms are considered the driving force behind digital value creation, yet legally they largely operate in a gray area. This article examines what defines industrial digital platforms and what legal framework applies to them. Existing platform regulations apply only to a limited extent to industrial digital platforms, which is why these platforms often establish their own legal framework through their terms and conditions. The article also explores the implications of European regulation, power asymmetries, and the opportunities and limitations of private regulation. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 98-105 | DOI 10.30844\/I4SD.26.4.11<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Industry\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Industry 4.0\u2014Progress and Digitalization in Limbo<\/h4>\n
Status of sustainable transformation and digitalization in production engineering<\/div>
Christian Donhauser<\/a> \n \"ORCID<\/a>, Daniel Riepl<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Digitalization projects help users represent complex processes more simply and efficiently. However, there are many obstacles to implementation. Reluctance to implement these projects is palpable. This affects, among others, employers and employees, who may fall behind economically by waiting or avoiding change. These observations can be traced back to an overarching research question: What barriers and systemic challenges hinder sustainable transformation within the context of Industry 4.0, particularly when considering human labor in production engineering? What questions are the affected stakeholders asking? The primary goal of this long-term research project is to define these questions decisively and in detail in order to develop a conceptual foundation that integrates research, teaching, and technological development and thus combines the potential of digital technologies with the experiential and practical knowledge of production workers. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 56-60<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Certification can institutionalize normative requirements, but the degree to which this is achieved varies considerably. Drawing on expert interviews from established certification domains, this paper identifies indicators and conditions for the effective embedding of standards and transfers these insights to the context of AI. Substantial embedding is not evident in the certification event itself, but rather across audit cycles. Leadership commitment, the role of the internal representative, and practically applicable indicators emerge as key enabling factors. The paper derives three areas of action for the process certification of human-centered AI deployed in workplace processes.<\/p>\n","protected":false},"featured_media":114672,"menu_order":0,"template":"","categories":[79167,79168,79298],"tags":[74012,80231,86084,83874],"product_cat":[],"topic":[68206],"technology":[67790],"knowhow":[],"industry":[],"writer":[86083],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":["post-114677","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-translate-en","category-typeset","tag-certification","tag-human-centered-ai","tag-norm-embedding","tag-standardization-en","topic-industry-4-0","technology-artificial-intelligence","writer-katharina-morsch","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow.png",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-150x150.png",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-666x375.png",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-768x432.png",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-1024x576.png",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-1032x320.png",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-764x376.png",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-392x320.png",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-608x496.png",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-640x325.png",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-274x376.png",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-514x292.png",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-320x440.png",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-514x289.png",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-196x180.png",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow.png",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow.png",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-510x510.png",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-510x287.png",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-100x100.png",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/morsch_AdobeStock_1860648731_InfiniteFlow-64x36.png",64,36,true]},"uagb_author_info":{"display_name":"Malou Baumann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Certification can institutionalize normative requirements, but the degree to which this is achieved varies considerably. Drawing on expert interviews from established certification domains, this paper identifies indicators and conditions for the effective embedding of standards and transfers these insights to the context of AI. Substantial embedding is not evident in the certification event itself, but…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/114677","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/114672"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=114677"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=114677"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=114677"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=114677"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=114677"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=114677"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=114677"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=114677"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=114677"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=114677"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=114677"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=114677"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=114677"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}