We identify and describe episodes of sensemaking around challenges in modern AI-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI's inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.

翻译：我们识别并描述了在IBM与客户公司合作项目中出现的、围绕现代基于人工智能的系统开发挑战的意义建构过程。所有项目均采用IBM Watson作为开发平台，旨在构建定制化的基于人工智能的解决方案以支持客户公司的员工或客户。然而，许多项目的实施难度远超IBM及其客户的预期。分析表明，项目成员在确立关于技术、项目、背景及可操作数据的可靠认知方面面临显著困难。项目成员报告了多个未曾预料却需要理解的问题维度。许多问题源于当前一代人工智能的固有特性，例如对大规模数据集的依赖以及随着数据增加而持续改进的需求。这些特性增加了项目的复杂性，要求保持审慎的平衡意识以避免意外问题。