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Photo by Dietmar Leutnecker.
Arriving in New York at the peak of the Covid-19 Omicron wave in late 2021 was like stepping into a parallel universe. The city that never sleeps had dozed off – empty streets, shuttered shops, and snow instead of tourists. I lived in a hotel because, strangely, it was more affordable than renting. And yet, amid the pandemic fatigue, the infamous New Yorker resilience was still thriving. At the hospital where I conducted my fieldwork, people greeted challenges with a smile and a “can-do-attitude” – it was this attitude that shaped my research on human–AI collaboration in the U.S.
Learning from a hospital on the digital frontier
Learning from a hospital on the digital frontier
My Postdoc.Mobility grant focused on human–AI collaborations. To explore this, I conducted qualitative fieldwork in a large U.S.-based hospital, where digital transformation was not just a buzzword – it was a daily reality. Over the course of two years, I observed and interviewed people employing different AI tools in their care work, and I happened to be there just as Generative AI (GenAI) (e.g., ChatGPT) broke into the mainstream.
My key takeaways
My key takeaways
Looking back, I’d like to share key lessons for leaders and managers considering the integration of AI into their organizational workflows:
Fail fast, adapt quickly. AI prototypes should be tested out in a timely manner, and user feedback helps to directly shape improvements.
Talent drives innovation. Developers require unique insights into pioneering models that have an impact on real-world applications.
Diversity from the start. Projects should explicitly consider how AI tools can serve diverse users and support equity goals.
Ethics isn’t an afterthought. Stakeholders are advised to openly discuss how AI tools might disrupt existing ethical values and what transparency measures are required.
Integration needs everyone. Developers, designers, users, HR, and strategy teams all need a seat at the table from the start.
Pay attention to mindsets and emotions. People’s beliefs and feelings strongly influence how AI is used in practice – often diverging from what designers or managers intended.
What I found most valuable was not just the technology itself, but the way people approached it – with openness, courage, and resilience. At the hospital, I experienced firsthand how integrating AI tools to predict diseases or support administrative tasks can be fueled by positivity, alliances, and a genuine willingness to make it work despite challenges. True to the New Yorker spirit.
If I had to sum it up, my two years in New York taught me that AI is as much about people as it is about technology. The workflows may be digital, but it’s the emotions, ethics, collaboration, and mindsets of people that play a key role in whether AI tools succeed or fail.
Disclaimer: the views expressed in this article are those of the authors and do not necessarily reflect the opinions or policies of the GSFN.
Swiss National Science Foundation (SNSF) Postdoc.Mobility Fellow 2021
Assistant Professor, University of St. Gallen
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