{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T19:20:36Z","timestamp":1787512836855,"version":"build-2736575974"},"reference-count":43,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"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":["Nursing Inquiry"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Artificial intelligence is increasingly integrated across nursing education, practice, research, and healthcare systems, yet its long\u2010term sustainability, economic viability, and organizational implications remain uncertain. This raises the question of how the profession should adapt when both rapid integration and cautious delay carry risk. In this discussion paper, I introduce two concepts to examine that question. The AI adaptation paradox describes the simultaneous pressure on nursing to accelerate AI integration to preserve professional relevance while operating under substantial technological, organizational, and economic uncertainty. Adaptive overcommitment describes how a profession may embed technologies into competencies, workflows, educational systems, and organizational infrastructures faster than evidence on their long\u2010term sustainability and value can emerge. Drawing on contemporary literature and interdisciplinary examples from healthcare, education, and technology, I trace these dynamics across workforce preparedness, economic feasibility, organizational resilience, equity, and professional influence, and propose reflexive adaptation as a way of preserving the capacity to revise commitments as evidence evolves. The paper does not argue for slower or faster adoption; it argues that nursing's central challenge is adapting while retaining the flexibility to shape that transformation.<\/jats:p>","DOI":"10.1111\/nin.70158","type":"journal-article","created":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T18:49:36Z","timestamp":1787510976000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The AI Adaptation Paradox: Navigating Professional Relevance and Sustainable Integration in Nursing"],"prefix":"10.1111","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0972-8212","authenticated-orcid":false,"given":"Amina","family":"Silva","sequence":"first","affiliation":[{"name":"School of Nursing Brock University St. Catharines Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,23]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.outlook.2026.102775"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/23779608261424597"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.63278\/jicrcr.vi.2467"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.12968\/hmed.2024.0972"},{"key":"e_1_2_12_6_1","doi-asserted-by":"crossref","first-page":"200","DOI":"10.5220\/0005658202000212","volume-title":"Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies. Volume 5, HEALTHINF","author":"Asiri H.","year":"2016"},{"key":"e_1_2_12_7_1","unstructured":"Berlin G. A.Bilazarian J.Chang andS.Hammer.2023. \u201cReimagining the Nursing Workload: Finding Time to Close the Workforce Gap.\u201dMcKinsey & Company May 26.https:\/\/www.mckinsey.com\/industries\/healthcare\/our-insights\/reimagining-the-nursing-workload-finding-time-to-close-the-workforce-gap."},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.52711\/2349-2996.2026.00004"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/haschl\/qxae123"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n1190"},{"key":"e_1_2_12_11_1","doi-asserted-by":"crossref","unstructured":"Calvino F. D.Haerle andS.Liu.2025. \u201cIs Generative AI a General Purpose Technology? Implications for Productivity and Policy.\u201d OECD Artificial Intelligence Papers. No. 40. OECD Publishing.https:\/\/doi.org\/10.1787\/704e2d12-en.","DOI":"10.1787\/704e2d12-en"},{"key":"e_1_2_12_12_1","volume-title":"The GenAI Divide: State of AI in Business 2025","author":"Challapally A.","year":"2025"},{"key":"e_1_2_12_13_1","unstructured":"Chandrasekaran A.2026. \u201cWhy 50% of GenAI Projects Fail\u2014And How to Beat the Odds.\u201dGartner January 26.https:\/\/www.gartner.com\/en\/articles\/genai-project-failure."},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/20565623.2025.2527505"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/1748-5908-4-50"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2024.60637"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.3912\/OJIN.Vol18No03Man04"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare13070701"},{"key":"e_1_2_12_19_1","unstructured":"Gartner.2024. \u201cGartner Predicts 3% of Generative AI Projects Will Be Abandoned After Proof of Concept by End of 2025.\u201dGartner Newsroom July 29.https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025."},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0042-1757157"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2019.00064"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/chp.47"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1093\/inthealth\/ihaa007"},{"key":"e_1_2_12_24_1","unstructured":"Hertogh K.2025. \u201cMeet Microsoft Dragon Copilot: Your New AI Assistant for Clinical Workflow.\u201dMicrosoft Cloud Blog March 3.https:\/\/www.microsoft.com\/en-us\/microsoft-cloud\/blog\/healthcare\/2025\/03\/03\/meet-microsoft-dragon-copilot-your-new-ai-assistant-for-clinical-workflow\/."},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12909-025-07416-z"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.1111\/jan.70296"},{"key":"e_1_2_12_27_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-081554"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.26550\/2209-1092.1366"},{"key":"e_1_2_12_29_1","unstructured":"Madai V. 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