  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.
