{"id":113018,"date":"2026-02-03T12:18:20","date_gmt":"2026-02-03T11:18:20","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&#038;p=113018"},"modified":"2026-06-29T19:33:24","modified_gmt":"2026-06-29T17:33:24","slug":"employee-human-centered-ai","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/employee-human-centered-ai\/","title":{"rendered":"Human-Centered AI in Companies with Employee Representation"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">The use of artificial intelligence (AI) in companies has been considered a key factor for the successful transformation of work and the economy for years. Although the actual spread of such systems remained limited for a long time [1], AI applications were recognized early on as having the potential to profoundly impact work processes, which is why the first regulatory debates began as early as 2016 with the \u201cWhite Paper on Work 4.0\u201d published by the Federal Ministry of Labor and Social Affairs [2].<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The actual use of AI has changed with the advent of <a href=\"https:\/\/industry-science.com\/en\/articles\/language-models-llm-production\/\">large language models (LLM)<\/a> such as ChatGPT. The chatbot from the US software company OpenAI broke the one million user mark after just five days [3] and, according to internal statements, is now used by over 400 million people every week. This means that the topic has arrived in German companies, and more and more employees are regularly confronted with AI systems in their everyday work.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Generative AI (GenAI) such as ChatGPT has enormous potential to bring about social and economic change [4]. The German government addressed AI in its current coalition agreement, emphasizing not only its potential but also the need for (social partnership-based) regulation [5]. Current OECD figures point to a lack of the AI skills [6] necessary in business practice for the widespread, trustworthy use of AI at work. In order to truly leverage the opportunities offered by AI, clear rules are needed on how employees can participate, gain qualifications, and have their interests taken into account after AI is introduced.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Early white papers already recognized the fact that cooperation between company management and employees (or their representatives) is essential for the successful and humane introduction of AI in companies [7, 8]. This insight was also later recorded in concrete guidelines for practitioners [9, 10]. The AI Regulation (EU AI Act) adopted by the European Union in May 2024 created a legal framework that requires the regulated and responsible use of AI in companies. At the same time, it reinforced the importance of a social partnership approach, building on established structures in Germany.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Due to the path dependency of past transformations [11], trade unions, works councils, and employers play a central role in concrete implementation. \u00a777 of the Works Constitution Act (BetrVG) permits the parties within a company to conclude works agreements. Such regulations are not only necessary to compensate for structural power imbalances between employers and employees, but also create legal certainty for companies, prevent unilateral decisions, increase acceptance among employees, and thus contribute to the successful introduction of AI systems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Against this backdrop, this article introduces <a href=\"https:\/\/humaine.info\/en\/\" target=\"_blank\" rel=\"noopener\">HUMAINE<\/a> MBV KI, which was developed as a practical tool for the social partnership-based regulation of AI. The MBV KI is an extension of the RBV KI, which was developed in HUMAINE as a collaboration between the Joint Working Group RUB\/IGM and the project partner Doncasters Precision Castings Bochum GmbH (DPC) and tested in a number of multiplier cases.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Methodologically, this process was supported by the co-determination dialogues of the HUMAINE toolbox (<strong>Fig. 1<\/strong>; see Wann\u00f6ffel et al. in this issue). The project uses a transfer research methodology [12, 13] in order to take into account the fact that operational change processes are path-dependent and that successful implementation can only be achieved with targeted further training that accounts for the experiences of the actors involved.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"572\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-1-1024x572.jpeg\" alt=\"Figure 1: Relationship between co-determination dialogues and MBV KI (employees).\" class=\"wp-image-113023\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-1-1024x572.jpeg 1024w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-1-671x375.jpeg 671w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-1-768x429.jpeg 768w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-1-514x287.jpeg 514w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-1-1536x859.jpeg 1536w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-1-510x285.jpeg 510w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-1-64x36.jpeg 64w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-1.jpeg 2000w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Figure 1: Relationship between co-determination dialogues and MBV KI.<\/em><\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Employee participation in AI based on the BetrVG<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">According to BetrVG , the works council plays an intermediary role in shaping the structural conflict of interest between employer and employees. The description of the works council as a boundary institution [14] also applies in the context of the introduction of AI [14] (<strong>Fig. 2<\/strong>).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Works councils are confronted with conflicts with regard to the efficiency and rationalization potential associated with the use of AI. Even if the works council wants to protect the interests and, ultimately, the jobs of employees, it must also take the profitability of the company into account. Section 2 (1) of the Works Constitution Act (BetrVG) obliges both sides to work together for the benefit of the employees and the company. This creates a mandate for both parties [15, 16].<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Looking at the works council\u2019s experience in shaping digital transformation processes within companies to date, three key findings can be applied to the implementation of AI:&nbsp;<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Operational change processes are incremental rather than disruptive and revolutionary [10]<\/li>\n\n\n\n<li>Operational change processes follow operational path dependencies in their design [17]<\/li>\n\n\n\n<li>An accompanying and framework-setting labor policy is important for the design of these transformation processes [18].<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">In the latest amendment to the Works Constitution Act (BetrVG)\u2014the 2021 Works Council Modernization Act\u2014the scope of action and discretion of works councils was explicitly expanded to include the topic of AI. Against the backdrop of the partial enactment the EU AI Act in February 2025, the options for action and organization available to works councils will in future become an obligation to act that applies to both parties in the workplace: According to Art. 4, they are obliged to demonstrate the competencies of employees with regard to the AI systems and applications used in the workplace.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This obligation represents an important lever for co-determination and directly links European legislation to the reality of the workplace in Germany: Sections 96-98 of the Works Constitution Act (BetrVG) grant the works council extensive rights to information, consultation, and co-determination with regard to the qualification of employees. In view of this change, the parties in the workplace must take action. However, studies show that around half of employees do not feel sufficiently informed about AI [19] and only just under a fifth are considered by their superiors to be sufficiently qualified for basic work with AI [20].<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"618\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-2-1024x618.jpeg\" alt=\"Figure 2: Works council as a boundary institution (based on [14]).\" class=\"wp-image-113021\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-2-1024x618.jpeg 1024w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-2-621x375.jpeg 621w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-2-768x463.jpeg 768w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-2-484x292.jpeg 484w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-2-1536x927.jpeg 1536w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-2-510x308.jpeg 510w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-2-64x39.jpeg 64w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-2.jpeg 2000w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Figure 2: Works council as a boundary institution (based on [14]).<\/em><\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The works council has co-determination rights in the event of fundamental changes to the company\u2019s organization (Section 111 (1) No. 4 BetrVG) or the introduction of new working methods and production processes (Section 111 (1) No. 5 BetrVG). These legal rights must also be examined in the context of AI implementation [21]. The same applies to an application-specific assessment of the potential risk of AI controlling employee behavior and\/or performance (Section 87 (1) No. 6 BetrVG) [22]. If this is the case, the introduction of AI is subject to the works council\u2019s co-determination rights [21].&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Furthermore, other standards safeguard the role of the works council: According to Section 90 (1) No. 3 BetrVG, the works council must be informed in good time about plans to introduce AI. According to Section 91 BetrVG, there is a right of co-determination if planned changes contradict ergonomic findings. In conjunction with Section 87 (1) No. 7 BetrVG and Section 5 (1) ArbSchG, there are also enforceable co-determination rights in occupational health and safety\u2014for example, in the ergonomic design of workplaces.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In addition to the BetrVG, there are other links between German labor law and European management rights, such as the EU AI Act or the General Data Protection Regulation (GDPR) [21, 23]. In order to implement the resulting obligations at the operational level, the parties within the company can conclude agreements on the basis of Section 77 BetrVG, which, from a transaction cost theory perspective, provides the basis for tapping the potential associated with AI and safeguarding the interests of the parties at the company.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Specific contents of the MBV KI<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">With HUMAINE MBV KI, RUB\/IGM has collaborated with DPC to develop an action support tool that companies can use to develop their own works agreement (WA) for a human-centered AI planning, implementation, and application process. As DPC is a former subsidiary of Thyssen Guss AG, the company has its roots in coal and steel co-determination. This means that the works council\u2014in line with path dependency and the historical relevance of past transformation processes [11]\u2014plays a significant role in the solidarity-based design of these very same transformation processes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These circumstances are just as unique as the fact that the intention to introduce AI was proactively initiated by the works council. A works council that acts in this way has a positive influence on the implementation of operational transformation processes but requires specific qualifications [24].<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Through co-determination dialogues (see Wann\u00f6ffel et al. in this issue), the contents of an AI WA were developed in collaboration with the works council of DPC. Following this process, this specific AI WA was abstracted into an initial model form and further tested and evaluated. Through collaboration with Gebr. Eickhoff Maschinenfabrik und Eisengie\u00dferei GmbH and IHK-Gesellschaft f\u00fcr Informationsverarbeitung mbH (IHK-GfI), 24 paragraphs were created in sample form that can be used to create company-specific WAs (<strong>Fig. 3<\/strong>).<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"508\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-3-1024x508.jpeg\" alt=\"Figure 3: Overview of the contents of the humAIne MBV KI.\" class=\"wp-image-113019\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-3-1024x508.jpeg 1024w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-3-755x375.jpeg 755w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-3-768x381.jpeg 768w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-3-514x255.jpeg 514w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-3-1536x763.jpeg 1536w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-3-510x253.jpeg 510w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-3-64x32.jpeg 64w, https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_I4S-26-1_Figure-3.jpeg 2000w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Figure 3: Overview of the contents of the HUMAINE MBV KI.<\/em><\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The paragraphs developed in this way do not claim to be exhaustive. An AI WA may vary in terms of the scope and granularity of its content and the number and content of its paragraphs due to existing company regulations, such as WAs on IT or EDP systems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With regard to the eight criteria for human-centered AI [24], the following criteria can be addressed using an AI policy:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Trustworthiness, confidentiality, and ethics<\/li>\n\n\n\n<li>Human action and augmentation<\/li>\n\n\n\n<li>Avoiding job losses<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Paragraph in the WA on AI impact assessment<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">All AI processes in the company are monitored from the outset by the works council and, if necessary, by experts, whereby alternatives to the planned use of AI must also be examined. In order to institutionalize this continuous exchange between the employer and the works council, an AI commission is established, which meets as required. This commission is composed of two representatives each from the works council and the employer, as well as the data protection officer. As soon as the purchase of an AI application is finalized, the employer provides the AI commission with all relevant documents relating to the AI application.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Once the AI Commission has received these documents, a test or pilot phase of the AI application can begin. If this trial phase is successful, the commissioning department and the client must assess the specific risk categorization in accordance with the EU AI Act on the basis of the AI WA annex. This risk assessment is evaluated by the AI Commission and the works council. In the course of this, the AI Commission decides whether and to what extent further regulation is necessary and whether this must be addressed in an application-specific WA. This applies in particular to AI applications that are classified in risk category 3 (high risk) of the EU AI Act.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In this context, transparency regarding the use of AI must also be established for employees. If potential changes to activities and work processes are known, these must be clearly identified. In addition, clear objectives and evaluation criteria must be defined before the pilot phase begins, and the results of these used for the final risk assessment. Before an AI application is integrated into regular work and business processes, all necessary documents, attachments, and any application-specific WA must be signed by the operating parties. With regard to the project partner IHK-GfI, the importance of a balanced approach to the practical implementation of the AI Commission described in the paragraph of the German Industrial Code becomes clear:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>\u201cThe establishment of an AI commission presents both opportunities and challenges. As stipulated in the paragraph, the commission should meet \u201cas required.\u201d The extent of this need can only be guessed at this point. We are caught between the conflicting priorities of innovation and security risks:<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>On the one hand, it is important for us, as it is for many commercial enterprises, to be able to react quickly and adapt. The short innovation cycles in AI put pressure on companies. In the area of software-as-a-service (SaaS) in particular, suppliers often introduce AI components as part of continuous deployments, to which companies then have to react ex post. In addition, AI has now become a commodity; providers are increasingly trying to integrate AI into almost every product or service.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>On the other hand, in addition to the requirements for response speeds, there is the security and integrity of our own employees and infrastructure. The AI Commission\u2019s task is therefore not an easy one: our challenge will be to find an acceptable balance between speed in decision-making and implementation cycles and security for our employees, our data, and our infrastructure.\u201d (Mathias Preu\u00df, IHK-GfI)<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Qualification paragraph in the MBV KI<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The qualifications required for working with the proposed AI systems are determined in advance on the basis of the RBV-KI annex. Accordingly, the employees affected must be comprehensively informed about the technical or organizational changes and new work processes or methods before the AI is introduced. In this context, employees must be trained both in the specific application and in general on the topic of AI. To ensure the quality of work, a needs-based schedule for refresher and training measures for employees, works council members, and the AI commission will be established. These measures will take place exclusively during working hours and with continued payment of regular wages.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In the event of foreseeable changes in activities as a result of the use of AI, retraining or <a href=\"https:\/\/industry-science.com\/en\/articles\/ai-skills-responsible-use\/\">further training<\/a> shall be carried out in order to maintain the employability of the employees affected. In addition, works council members and managers shall be trained to be able to counter any fears and concerns that employees may have. The AI Commission can also support employers in developing a documentation system for verifying AI competencies in accordance with Art. 4 of the AI Act. The project partner IHK-GfI also found that training issues play a key role in the successful use of AI in the workplace:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>\u201cThere is no question that training and further education on the functioning and application of AI is necessary. It is important to meet all employees where they are individually and to empower them in the long term to use the technologies correctly and safely and to critically ensure the quality of the results. For us, the key point of the paragraph lies in the changing framework conditions for cooperation. Workplaces and work processes for all employees in the company are potentially changing.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>This opportunity gives us additional options for addressing demographic change, in particular generational change and the increasing shortage of skilled workers. At the same time, training and continuing education are necessary aspects of change and transformation management and thus essential for organizational development.\u201d (Mathias Preu\u00df, IHK-GfI)<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">AI change processes often proceed incrementally&nbsp;<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Regulating the use of AI in companies with works councils requires the systematic linking of European and national legal requirements, in particular the EU AI Act, the GDPR, and the BetrVG. The MBV KI developed in the HUMAINE project is a practice-oriented tool that takes legal requirements into account while promoting the human-centered design of AI in the workplace.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Empirical findings from the development and testing of MBV KI show that operational change processes in the context of AI are often incremental, build on existing structures and are only effective to a limited extent without accompanying training measures. The obligation to demonstrate AI competencies in accordance with Art. 4 of the EU AI Act opens up new design options, which can be systematically exploited by the MBV KI.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The MBV KI thus offers a proven orientation framework that combines legal certainty, ethically sound, human-centered guidelines, and co-determination into an integrated design approach. It promotes employee acceptance of and trust in AI systems, ensures their employability, and contributes to the sustainable design of digital transformation in the spirit of a social partnership-based working relationship. Its transferability to different operational contexts, even beyond existing works council structures, underscores its innovative and forward-looking character, essential in the age of AI.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>This research and development project is funded by the Federal Ministry of Research, Technology, and Space (BMFTR) FKZ 02L19C200 and supervised by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the content of this publication.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The original German version of this article can be accessed via <a href=\"https:\/\/doi.org\/10.30844\/I4SD.26.1.14\" target=\"_blank\" rel=\"noopener\">DOI: 10.30844\/I4SD.26.1.14<\/a><\/strong><\/p>\n<hr><div class=\"gito-pub-content-bibliography\"><h2>Bibliography <\/h2>[1] Giering, O.: K\u00fcnstliche Intelligenz und Arbeit: Betrachtungen zwischen Prognose und betrieblicher Realit\u00e4t. In: Zeitschrift f\u00fcr Arbeitswissenschaft 76 (2022) 1, pp. 50-64. DOI: https:\/\/doi.org\/10.1007\/s41449-021-00289-0.\r<br>[2] Federal Ministry of Labor and Social Affairs: Wei\u00dfbuch Arbeiten 4.0. Berlin 2016.\r<br>[3] Makhov, V.: ChatGPT-Statistiken enth\u00fcllt \u2013 Vom Benutzerwachstum bis zur wirtschaftlichen Auswirkung. URL: https:\/\/doit.software\/en\/blog\/chatgpt-statistiken#screen4, accessed 01.07.2025.\r<br>[4] Engels, B.; Scheufen, M.; Schmitz, E.: K\u00fcnstliche Intelligenz als Wettbewerbsfaktor f\u00fcr die deutsche Wirtschaft (IW Report 33\/2025).\r<br>[5] CDU, CSU &amp; SPD: Verantwortung f\u00fcr Deutschland. Koalitionsvertrag zwischen CDU, CSU und SPD, 21. Legislaturperiode. Berlin Munich 2025.\r<br>[6] OECD: OECD-Bericht zu K\u00fcnstlicher Intelligenz in Deutschland, OECD Publishing, Paris 2024.\r<br>[7] Stowasser, S.; Suchy, O.; Huchler, N.; M\u00fcller, N.; Peissner, M.; et al.: Einf\u00fchrung von KI-Systemen in Unternehmen: Gestaltungsans\u00e4tze f\u00fcr das Change-Mangement (2020).\r<br>[8] Huchler, N.; Adolph, L.; Andr\u00e9, E.; Bauer, W.; Bender, N.; et al. (eds.): Kriterien f\u00fcr die menschengerechte Gestaltung der Mensch-Maschine-Interaktion bei Lernenden Systemen \u2013 Whitepaper aus der Plattform Lernende Systeme (2020).\r<br>[9] Schr\u00f6der, L.; H\u00f6fers, P.: Praxishandbuch K\u00fcnstliche Intelligenz. Handlungsanleitungen, Praxistipps, Pr\u00fcffragen, Checklisten. Frankfurt am Main 2022.\r<br>[10] ver.di: Digitalisierung und K\u00fcnstliche Intelligenz. Gute Arbeit 2025. Berlin 2024.\r<br>[11] Hirsch-Kreinsen, H.: Digitale Transformation von Arbeit. Entwicklungstrends und Gestaltungsans\u00e4tze. Stuttgart 2020.\r<br>[12] Sch\u00e4fer, M.; Wann\u00f6ffel, M.; Virgillito, A.: Transferforschung \u2013 ein methodisches Konzept f\u00fcr die Analyse der Industriellen Beziehungen. In: Industrielle Beziehungen 2 (2020), pp. 127-149.\r<br>[13] Wann\u00f6ffel, M.: Transferforschung im Feld der Mitbestimmung. In: Wann\u00f6ffel, M.; Niewerth, C.; Hoose, F.; Urban, H.-J. (eds.): Co Mitbestimmung und Partizipation 2030: Demokratische Perspektiven auf Arbeit und Besch\u00e4ftigung. Baden-Baden 2025, pp. 475-498.\r<br>[14] F\u00fcrstenberg, F.: Der Betriebsrat. Strukturanalyse einer Grenzinstitution. In:\u00a0K\u00f6lner Zeitschrift f\u00fcr Soziologie und Sozialpsychologie 10 (1958), pp. 418-429.\r<br>[15] Gerst, D.: Autonome Systeme und K\u00fcnstliche Intelligenz Herausforderungen f\u00fcr die Arbeitssystemgestaltung: Perspektiven, Herausforderungen und Grenzen der K\u00fcnstlichen Intelligenz in der Arbeitswelt. In: H. Hirsch-Kreinsen, A. Karacic (eds.): Autonome System und Arbeit. Perspektiven, Herausforderungen und Grenzen der K\u00fcnstlichen Intelligenz in der Arbeitswelt. Bielefeld 2019, pp. 101-138.\r<br>[16] Niehues, S.; Sandrock, S.; Shahinfar, F.; Sch\u00fcth, N. J.; Conrad, R.: Gestaltung eines KI-Arbeitssystems. In: Stowasser, S. (ed.): K\u00fcnstliche Intelligenz (KI) und Arbeit. Leitfaden zur soziotechnischen Gestaltung von KI-Systemen. Berlin Heidelberg 2023, pp. 141-166.\r<br>[17] Haipeter, T.; Hoose, F.; Rosenbohm, S.: Arbeitspolitik in digitalen Zeiten. Entwicklungslinien einer nachhaltigen Regulierung und Gestaltung von Arbeit. Baden-Baden 2021.\r<br>[18] Kuhlmann, M.: Digitalisierung und Arbeit. Eine Zwischenbilanz als Einleitung. In: WSI Mitteilungen 76 (2023) 5, pp. 331-336. DOI: https:\/\/doi.org\/10.5771\/0342-300X-2023-5-331.\r<br>[19] Pfeiffer, S.: (Generative) K\u00fcnstliche Intelligenz (KI) als Kollegin? Gestaltung und Mitbestimmung aus Sicht der Besch\u00e4ftigten. In: Wann\u00f6ffel, M.; Niewerth, C.; Hoose, F.; Urban , H.-J. (eds.): Mitbestimmung und Partizipation 2030: Demokratische Perspektiven auf Arbeit und Besch\u00e4ftigung. Baden-Baden 2025, pp. 247-264.\r<br>[20] Rampelt, F.; Klier, J.; Kirchher, J.; Ruppert, R.: KI-Kompetenzen in Deutschen Unternehmen. Schl\u00fcssel zu einer Jahrhundertchance f\u00fcr Deutschland. Essen 2025.\r<br>[21] Wedde, P.: K\u00fcnstliche Intelligenz und Mitbestimmung: M\u00f6glichkeiten und Grenzen. In: Ver.di (ed.): Digitalisierung und K\u00fcnstliche Intelligenz \u2013 Gute Arbeit 2025. Berlin 2024, pp. 28-39.\r<br>[22] Hoppe, M.; Suriano, G.: Das humAIn work.lab: Erkenntnisse aus betrieblichen Praxislaboratorien zur menschenzentrierten KI-Gestaltung. In: Ver.di (ed.): Digitalisierung und K\u00fcnstliche Intelligenz \u2013 Gute Arbeit 2025. Berlin 2024, pp. 78-89.\r<br>[23] Von dem Bussche, A. F: Datenschutz 4.0. In: Frenz, W. (ed.): Handbuch Industrie 4.0: Recht, Technik, Gesellschaft. Berlin Heidelberg 2020, pp. 155-180.\r<br>[24] Niewerth, C.; Massolle, J.: Betriebliche Interessenvertretung in der doppelten Transformation &#8211; Einblicke in neue Gestaltungsformen betriebsr\u00e4tlicher Arbeit. Mitbestimmungspraxis No. 36. D\u00fcsseldorf 2020.\r<br>[25] Wilkens, U.; Lupp, D.; Langholf, V.: Configurations of human-centered AI at work: seven actor-structure engagements in organizations. In: Front. Artif. Intell. 7 (2023). DOI: https:\/\/doi.org\/10.3389\/frai.2023.1272159.<\/div><div id=\"download-section\" class=\"gito-pub-download-section\" style=\"text-align:center;margin:20px;\"><h2>Your downloads<\/h2><button style=\"font-size:14px;margin-right:15px;\" class=\"button gito-pub-cpt-download-button\" data-postid=\"113018\" data-userid =\"0\" data-filename=\"I4S_01-2026_DE_Ranft.pdf\"><span style=\"margin-top:5px !important;\" class=\"dashicons dashicons-download\"><\/span>&nbsp;&nbsp;PDF (DE)<\/button><button style=\"font-size:14px;margin-right:15px;\" class=\"button gito-pub-cpt-download-button\" data-postid=\"113018\" data-userid =\"0\" data-filename=\"I4S_01-2026_ENG_Ranft.pdf\"><span style=\"margin-top:5px !important;\" class=\"dashicons dashicons-download\"><\/span>&nbsp;&nbsp;PDF (EN)<\/button><\/div><br>Potentials: <span class=\"gito-pub-tag-element\"><a href=\"\/potentials\/innovation-en\/\">Innovation<\/a><\/span> <span class=\"gito-pub-tag-element\"><a href=\"\/potentials\/management-en\/\">Management<\/a><\/span> \n<h2 class=\"gito-pub-frontend-post-headline\">You might also be interested in<\/h2>\n<!-- GITO_PUB_POST start flex-container -->\n<div class=\"gito-pub-flex-container\">\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/tacit-talk\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/brandhoff_AdobeStock_598538618_Gorodenkoff-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/brandhoff_AdobeStock_598538618_Gorodenkoff-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/brandhoff_AdobeStock_598538618_Gorodenkoff-196x180.webp\" alt=\"Tacit Talk\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Tacit Talk\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Tacit Talk<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">A speech-based hybrid AI system for capturing tacit maintenance knowledge<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/vincent-philipp-brandhoff\/\">Vincent Philipp Brandhoff<\/a> <a href=\"https:\/\/orcid.org\/0009-0006-9410-5285\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/philipp-besinger\/\">Philipp Besinger<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/joscha-zaremba\/\">Joscha Zaremba<\/a> <a href=\"https:\/\/orcid.org\/0009-0008-8921-6823\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/daniel-valtiner\/\">Daniel Valtiner<\/a> <a href=\"https:\/\/orcid.org\/0009-0005-3475-8738\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/michael-necemer\/\">Michael Necemer<\/a> <a href=\"https:\/\/orcid.org\/0009-0003-8814-1755\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/safa-omri\/\">Safa Omri<\/a> <a href=\"https:\/\/orcid.org\/0009-0000-9668-1418\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/jens-neuhuettler\/\">Jens Neuh\u00fcttler<\/a> <a href=\"https:\/\/orcid.org\/0000-0001-8403-5451\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/fazel-ansari-en\/\">Fazel Ansari<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/katharina-hoelzle\/\">Katharina H\u00f6lzle<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-9733-4650\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     The tacit expertise required for industrial maintenance is increasingly at risk due to demographic change and system complexity. This article presents Tacit-TALK, a speech-based system that externalizes tacit knowledge through AI-guided conversation. Spoken input is transcribed, analyzed by large language models (LLMs), and persisted in a knowledge graph, linking new insights to existing organizational data. Using a design science research process grounded in sociotechnical theory and instantiated for semiconductor maintenance, the article contributes design principles for AI-supported tacit knowledge capture derived from value-based stakeholder engagement, a reference architecture combining LLMs with knowledge graphs, and preliminary prototyping insights into user acceptance and organizational implications.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 82-90 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.16\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.16<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/ai-knowledge-transfer\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Beitragsbild-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Beitragsbild-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/Beitragsbild-196x180.webp\" alt=\"AI-Based Identification of Knowledge Transfer Situations\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"AI-Based Identification of Knowledge Transfer Situations\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">AI-Based Identification of Knowledge Transfer Situations<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">An adaptive multi-agent system for agile product development in engineering<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/georg-david-ritterbusch-en\/\">Georg David Ritterbusch<\/a> <a href=\"https:\/\/orcid.org\/0000-0001-9151-5074\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/ravil-goetzke\/\">Ravil Goetzke<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/norbert-gronau-en\/\">Norbert Gronau<\/a> <a href=\"https:\/\/orcid.org\/0000-0001-8966-0731\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     Although it has been demonstrated that organizational knowledge transfer can be improved in principle, there is still no automated approach for identifying patterns in complex, context-dependent, and domain-specific knowledge transfer situations. This conceptual article therefore examines and characterizes knowledge transfer situations using the real-world example of product development in engineering. Furthermore, a concept for an adaptive, AI-based, real-time multi-agent system uses data to recognize recurring patterns in knowledge transfer situations and enables context-sensitive interventions. Finally, an outlook is provided on AI-based learning mechanisms (reinforcement learning) that can be used to adapt interventions for higher effectiveness in the long term.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 110-116 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.13\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.13<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/experiential-knowledge-powered-ai\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schmauder_AdobeStock_2036790511_DC-Studio-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schmauder_AdobeStock_2036790511_DC-Studio-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schmauder_AdobeStock_2036790511_DC-Studio-196x180.webp\" alt=\"Experiential Knowledge Powered by AI\u00a0\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Experiential Knowledge Powered by AI\u00a0\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Experiential Knowledge Powered by AI\u00a0<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Practical insights from industry<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/martin-schmauder\/\">Martin Schmauder<\/a> <a href=\"https:\/\/orcid.org\/0009-0002-8796-5093\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/gritt-ott\/\">Gritt Ott<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-6208-6546\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/bianca-windisch\/\">Bianca Windisch<\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     The use of experiential knowledge is a key success factor for companies. Based on four corporate case studies, this article analyzes the technical, organizational, and personnel challenges associated with the use of retrieval-augmented generation (RAG) systems. The results show that the success of such systems depends on the strategic development and maintenance of the knowledge base, as well as on employee engagement.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 128-135 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.15\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.15<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/inclusive-work-system-design\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schlund_AdobeStock_2046886237_InfiniteFlow-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schlund_AdobeStock_2046886237_InfiniteFlow-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/schlund_AdobeStock_2046886237_InfiniteFlow-196x180.webp\" alt=\"Inclusive Work System Design\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"Inclusive Work System Design\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">Inclusive Work System Design<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Automation, standardization, and adaptability<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/sebastian-schlund-en\/\">Sebastian Schlund<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-8142-0255\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     The design of inclusive work systems is gaining importance due to demographic change and the increasing digital penetration of value creation processes. While traditional ergonomic approaches are based primarily on percentile logic and thus address only a portion of the user population, the integration of digital technologies opens up new possibilities for the dynamic and individualized adaptation of work systems. This article presents a conceptual framework for inclusive work system design that integrates standardization, automation, and adaptability. Methodologically, the article is based on a conceptual analysis of existing approaches from ergonomics and human-centered design. The article makes a theoretical contribution to the systematization of inclusive work system design and identifies areas of focus for further research and industrial practice.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 70-76 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.8\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.8<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/mtm-analyses-ai-rule-algorithms\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/eckart_AdobeStock_1923313286_noppadon-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/eckart_AdobeStock_1923313286_noppadon-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/08\/eckart_AdobeStock_1923313286_noppadon-196x180.webp\" alt=\"MTM Analyses with AI and Rule-Based Algorithms\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"MTM Analyses with AI and Rule-Based Algorithms\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">MTM Analyses with AI and Rule-Based Algorithms<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">An approach to interpreting textual process descriptions<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/constantin-eckart\/\">Constantin Eckart<\/a> <a href=\"https:\/\/orcid.org\/0009-0002-9922-0603\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/martin-benter-en\/\">Martin Benter<\/a> <a href=\"https:\/\/orcid.org\/0000-0002-9336-0739\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/peter-kuhlang-en\/\">Peter Kuhlang<\/a> <a href=\"https:\/\/orcid.org\/0009-0005-3706-7588\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     MTM methods are a proven standard for analyzing and designing human work processes. However, work planners continue to face challenges in applying these methods correctly and efficiently. Artificial intelligence \u2014 particularly in the case of large language models \u2014 holds significant potential for combatting these challenges. However, the use of AI raises legitimate questions regarding the reliability and traceability of the results. The approach presented here combines LLMs with a rule-based algorithm to extract the information required for MTM analyses from textual process descriptions such as work instructions. This information is then translated into MTM analyses in accordance with the MTM methodology. This approach ensures that the resulting analyses can be transparently traced back to the original input data by the user.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 5 | Pages 62-69 | DOI <a style=\"font-weight:bold !important;\" href=\"https:\/\/doi.org\/10.30844\/I4SE.26.5.7\" target=\"_blank\" rel=\"noopener\">10.30844\/I4SE.26.5.7<\/a><\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n   <div class=\"gito-pub-frontend-post-card gito-pub-flex-item gito-pub-flex-item-1\">\n      <a href=\"https:\/\/industry-science.com\/en\/articles\/ai-lubrication-thread-forming\/\">\n         <div class=\"gito-pub-frontend-post-card-row\">         <div class=\"gito-pub-frontend-post-card-column gito-pub-frontend-post-card-column-image\">\n            <picture>\n               <source media=\"(max-width:640px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-640x325.webp\">\n               <source media=\"(min-width:641px)\" srcset=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-196x180.webp\">\n               <img decoding=\"async\" class=\"gito-pub-frontend-post-card-image\" src=\"https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/Donhauser_AdobeStock_1969238171_Gorodenkoff-196x180.webp\" alt=\"AI-Powered Lubrication Strategies for Thread Forming\">\n            <\/picture>\n         <\/div>\n            <div class=\"gito-pub-frontend-post-card-column\">               <div class=\"ellipsis\" style=\"height:166px !important;overflow:hidden;\" title=\"AI-Powered Lubrication Strategies for Thread Forming\">                  <table class=\"gito-pub-frontend-post-card-header\">\n            \t     <tr>\n                        <td>                  \t\t   <h4 class=\"gito-pub-frontend-post-card-title\" style=\"line-height:1.2em;\">AI-Powered Lubrication Strategies for Thread Forming<\/h4>\n                        <div class=\"gito-pub-frontend-post-card-subtitle\">Adaptive spray jet control to increase process reliability and tool life<\/div>                        <div class=\"gito-pub-frontend-post-card-author\"><a href=\"https:\/\/industry-science.com\/en\/authors\/reinhard-schmied\/\">Reinhard Schmied<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/marco-susic\/\">Marco Susic<\/a>, <a href=\"https:\/\/industry-science.com\/en\/authors\/christian-donhauser\/\">Christian Donhauser<\/a> <a href=\"https:\/\/orcid.org\/0009-0009-0366-1828\" target=\"_blank\" title=\"ORCID eintrag \u00f6ffnen.\" rel=\"noopener\">\n        <img decoding=\"async\" src=\"https:\/\/orcid.org\/assets\/vectors\/orcid.logo.icon.svg\" alt=\"ORCID Icon\" style=\"width:16px;height:16px;vertical-align:middle;\"><\/a><\/div>\n                        <\/td>\n                     <\/tr>\n                  <\/table>\n                  <div class=\"gito-pub-frontend-post-card-text\">\n                     <div class=\"gito-pub-frontend-post-card-abo-sign gito-pub-login-register-link\" data-targetabo=\"expert\" data-targeturl=\"https:\/\/industry-science.com\/en\/articles\/ai-lubrication-thread-forming\/\" title=\"please login or register - content can only be read in its entirety with a subscription  expert\">\n\t\t\t                         <img decoding=\"async\" src=\"https:\/\/industry-science.com\/wp-content\/plugins\/gito-publisher\/img\/i4s-login.png\">\n\t\t\t                      <\/div>Thread forming requires precise lubricant application because high contact pressures and process temperatures strongly influence tool loading, friction, and process stability. Although minimum quantity lubrication (MQL) systems are widely used, current spray-based approaches can still suffer from spray losses, insufficient wetting of the thread grooves, and unstable droplet transport. This article presents a concept for adaptive precision lubrication in thread forming based on computational fluid dynamics (CFD)-supported flow analysis, experimental validation, and artificial intelligence (AI)-assisted optimization. The focus is on droplet size, spray jet geometry, nozzle position, ambient flow conditions, and their influence on wetting intensity. Preliminary simulation-based investigations indicate that data-driven optimization can help identify wetting deficiencies and support the development of future control strategies for resource-efficient lubricant application.                  <\/div>\n               <\/div>\n               <div class=\"gito-pub-frontend-post-card-scientific\"><strong>Industry 4.0 Science<\/strong> | Volume 42 | 2027 | Edition 3 | Pages 76-83<\/div>            <\/div>\n         <\/div>\n      <\/a>\n   <\/div>\n<\/div>\n<!-- GITO_PUB_POST end flex-container -->\n","protected":false},"excerpt":{"rendered":"<p>The introduction of artificial intelligence (AI) in companies poses new challenges for regulation and co-determination. Binding requirements have been in force since the 2025 EU AI Act, which must be linked nationally with the Works Constitution Act (BetrVG). The regional competence center humAine has developed a model works agreement on AI (MBV KI) in accordance with Section 77 BetrVG, which strengthens co-determination rights in companies and implements European regulations in a practical way. Flanked by co-determination dialogues, the MBV KI enables company-specific adaptation for responsible and human-centered AI use. Using selected parts of the MBV KI as examples, this article shows how a framework works agreement on AI can be designed and discusses its transferability to companies without a works council. The MBV KI presented here contributes to the sustainable, socially secure design of the digital transformation.<\/p>\n","protected":false},"featured_media":112916,"menu_order":0,"template":"","categories":[79167,79298],"tags":[],"product_cat":[79304],"topic":[79491,79333],"technology":[67790,68059],"knowhow":[],"industry":[79494],"writer":[85748,83780,82330],"content-type":[83932],"potential":[67894,68057],"solution":[],"glossary":[],"class_list":["post-113018","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-typeset","product_cat-articles","topic-change-management-en","topic-process-optimization","technology-artificial-intelligence","technology-training","industry-manufacturing-en","writer-claudia-niewerth","writer-fabian-hoose-en","writer-manfred-wannoeffel-en","content-type-article","potential-innovation-en","potential-management-en","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\/02\/Ranft_AdobeStock_921970766_Nirusmee.jpg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-150x150.jpg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-666x375.jpg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-768x432.jpg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-1024x576.jpg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-1032x320.jpg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-764x376.jpg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-392x320.jpg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-608x496.jpg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-640x325.jpg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-274x376.jpg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-514x292.jpg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-320x440.jpg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-514x289.jpg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-196x180.jpg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee.jpg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee.jpg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-510x510.jpg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-510x287.jpg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-100x100.jpg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/02\/Ranft_AdobeStock_921970766_Nirusmee-64x36.jpg",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"The introduction of artificial intelligence (AI) in companies poses new challenges for regulation and co-determination. Binding requirements have been in force since the 2025 EU AI Act, which must be linked nationally with the Works Constitution Act (BetrVG). The regional competence center humAine has developed a model works agreement on AI (MBV KI) in accordance&hellip;","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/113018","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\/112916"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=113018"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=113018"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=113018"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=113018"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=113018"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=113018"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=113018"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=113018"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=113018"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=113018"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=113018"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=113018"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=113018"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}