Despite the current popularity of artificial intelligence (AI) and a steady increase in publications over time, few studies have investigated AI in public contexts. As a result, assumptions about the drivers, challenges, and impacts of AI in government are far from conclusive. By using a case study that involves a large research university in England and two different county councils in a multiyear collaborative project around AI, we study the challenges that interorganizational collaborations face in adopting AI tools and implementing organizational routines to address them. Our findings reveal the most important challenges facing such collaborations: a resistance to sharing data due to privacy and security concerns, insufficient understanding of the required and available data, a lack of alignment between project interests and expectations around data sharing, and a lack of engagement across organizational hierarchy. Organizational routines capable of overcoming such challenges include working on-site, presenting the benefits of data sharing, reframing problems, designating joint appointments and boundary spanners, and connecting participants in the collaboration at all levels around project design and purpose.

尽管人工智能（AI）的当前流行以及随着时间的推移出版物的稳步增长，但很少有研究在公共场合研究AI。结果，关于人工智能在政府中的驱动因素，挑战和影响的假设远非结论性的。通过使用涉及英格兰一所大型研究型大学和两个不同县议会的案例研究，围绕AI进行了多年的合作项目，我们研究了组织间协作在采用AI工具和实施组织例程以应对这些挑战方面所面临的挑战。我们的发现揭示了此类协作面临的最重要挑战：由于隐私和安全问题而导致无法共享数据，对所需数据和可用数据的了解不足，项目兴趣和对数据共享的期望之间缺乏一致性，并且缺乏跨组织层次的参与。能够克服此类挑战的组织惯例包括在现场工作，展示数据共享的好处，重新组织问题，指定联合任命和边界扳手，以及在围绕项目设计和目的的各个级别的协作参与者之间建立联系。