{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T03:30:10Z","timestamp":1791430210172,"version":"4.3.3"},"reference-count":25,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T00:00:00Z","timestamp":1754352000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Information Systems Journal"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) brings great potential for manufacturers, but clashes with the established culture due to the unexplainable and opaque nature of the solutions it provides. Having little experience in AI and machine learning (ML), most manufacturing leaders experience barriers implementing AI. This is especially true in lean environments, since these are often nearly perfected and hence intolerant of failure. Cultural barriers to adoption abound, even if the quality of the AI is assured. Deeply grounded in the case of Siemens' Digital Industries division, this 5\u2010year practitioner research investigates AI adoption in lean quality management. Collaborating with key executives involved in the case and fellow researchers, we investigate the challenges and solutions of AI adoption in lean quality management. We identify three distinct paradoxes: (1) Human\u2010driven versus AI\u2010driven analysis, decisions and actions, (2) transparency versus opacity and (3) specification\u2010driven versus discovery\u2010driven processes to achieve quality. Rooted in Siemens' experience, we derive four strategies for overcoming these paradoxes. Our findings have important implications for practice, as we present clear and realistic steps and guidance for effective AI integration into lean quality management environments. Our results contribute to the discourse on AI adoption, pointing to the importance of recognising potential cultural clashes as well as strategies for overcoming these which go beyond the technology itself.<\/jats:p>","DOI":"10.1111\/isj.70006","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T10:44:50Z","timestamp":1754390690000},"page":"315-339","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["The Culture Clash of AI Adoption in Lean Quality Management. Resolving the Tensions at Siemens Electronics Works Amberg"],"prefix":"10.1111","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6753-8399","authenticated-orcid":false,"given":"Benjamin","family":"van Giffen","sequence":"first","affiliation":[{"name":"University of Liechtenstein  Vaduz Liechtenstein"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gunter","family":"Beitinger","sequence":"additional","affiliation":[{"name":"Siemens AG  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helmuth","family":"Ludwig","sequence":"additional","affiliation":[{"name":"Southern Methodist University  Dallas Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bill","family":"Schiano","sequence":"additional","affiliation":[{"name":"Bentley University  Waltham Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantin","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Siemens AG  Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0071-3719","authenticated-orcid":false,"given":"Jan","family":"vom Brocke","sequence":"additional","affiliation":[{"name":"University of M\u00fcnster  M\u00fcnster Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,5]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.2307\/248684"},{"issue":"3","key":"e_1_2_10_3_1","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.25300\/MISQ\/2021\/16274","article-title":"Managing Artificial Intelligence","volume":"45","author":"Berente N.","year":"2021","journal-title":"Management Information Systems Quarterly"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/qshc.2006.018093"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/isj.12316"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/0960085X.2018.1524418"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2021.102317"},{"key":"e_1_2_10_8_1","volume-title":"Transforming Quality and Warranty Through Advanced Analytics","author":"Duncan P.","year":"2021"},{"issue":"3","key":"e_1_2_10_9_1","first-page":"98","article-title":"Heavy Machinery Meets AI","volume":"103","author":"Govindarajan V.","year":"2024","journal-title":"Harvard Business Review"},{"key":"e_1_2_10_10_1","article-title":"How AI Fits Into Lean Six Sigma","author":"Holweg M.","year":"2023","journal-title":"Harvard Business Review"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.2307\/249410"},{"key":"e_1_2_10_12_1","unstructured":"Lean Enterprise Institute.2020.\u201cLean Thinking and Practice.\u201dAccessed June 23 2024.https:\/\/www.lean.org\/lexicon\u2010terms\/lean\u2010thinking\u2010and\u2010practice\/."},{"key":"e_1_2_10_13_1","unstructured":"McKinsey.2024.\u201cWhat Is AI (Artificial Intelligence)?\u201dhttps:\/\/www.mckinsey.com\/featured\u2010insights\/mckinsey\u2010explainers\/what\u2010is\u2010ai."},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/isj.12274"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.2307\/258559"},{"key":"e_1_2_10_16_1","unstructured":"Ransbotham S. andS.Khodabandeh.2024.\u201cDriving Manufacturing Efficiency With AI: Pirelli's Daniele Petecchi.\u201dMIT Sloan Management Review. Accessed June 23 2024.https:\/\/sloanreview.mit.edu\/audio\/driving\u2010manufacturing\u2010efficiency\u2010with\u2010ai\u2010pirellis\u2010daniele\u2010petecchi\/."},{"issue":"2","key":"e_1_2_10_17_1","first-page":"167","article-title":"How Audi Scales Artificial Intelligence in Manufacturing","volume":"23","author":"Sagodi A.","year":"2024","journal-title":"MIS Quarterly Executive"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.5465\/19416520.2016.1162422"},{"issue":"4","key":"e_1_2_10_19_1","first-page":"13","article-title":"The CRISP\u2010DM Model: The New Blueprint for Data Mining","volume":"5","author":"Shearer C.","year":"2000","journal-title":"Journal of Data Warehousing"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00146-020-01004-z"},{"issue":"3","key":"e_1_2_10_21_1","first-page":"88","article-title":"What Makes Change Harder\u2014or Easier","volume":"58","author":"Spiegel M.","year":"2017","journal-title":"MIT Sloan Management Review"},{"issue":"4","key":"e_1_2_10_22_1","first-page":"23","article-title":"Creating a Culture of Quality","volume":"92","author":"Srinivasan A.","year":"2014","journal-title":"Harvard business review"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.17705\/2msqe.00072"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1057\/ejis.1995.9"},{"key":"e_1_2_10_25_1","unstructured":"World Economic Forum.2022.Unlocking Value From Artificial Intelligence in Manufacturing.https:\/\/www.weforum.org\/publications\/unlocking\u2010value\u2010from\u2010artificial\u2010intelligence\u2010in\u2010manufacturing\/."},{"key":"e_1_2_10_26_1","volume-title":"Case Study Research: Design and Methods, (4th Ed.), Applied Social Research Methods","author":"Yin R. 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