Automating enterprise workflows could unlock $4 trillion/year in productivity
gains. Despite being of interest to the data management community for decades,
the ultimate vision of end-to-end workflow automation has remained elusive.
Current solutions rely on process mining and robotic process automation (RPA),
in which a bot is hard-coded to follow a set of predefined rules for completing
a workflow. Through case studies of a hospital and large B2B enterprise, we
find that the adoption of RPA has been inhibited by high set-up costs (12-18
months), unreliable execution (60% initial accuracy), and burdensome
maintenance (requiring multiple FTEs). Multimodal foundation models (FMs) such
as GPT-4 offer a promising new approach for end-to-end workflow automation
given their generalized reasoning and planning abilities. To study these
capabilities we propose ECLAIR, a system to automate enterprise workflows with
minimal human supervision. We conduct initial experiments showing that
multimodal FMs can address the limitations of traditional RPA with (1)
near-human-level understanding of workflows (93% accuracy on a workflow
understanding task) and (2) instant set-up with minimal technical barrier
(based solely on a natural language description of a workflow, ECLAIR achieves
end-to-end completion rates of 40%). We identify human-AI collaboration,
validation, and self-improvement as open challenges, and suggest ways they can
be solved with data management techniques. Code is available at:
this https URL

自动化企业工作流程可以释放每年 4 万亿美元的生产力增益。通过案例研究医院和大型 B2B 企业，我们发现采用机器人流程自动化受到成本高昂（需 12-18 个月），执行不可靠（初始准确率为 60％）和繁琐的维护（需要多个全职员工）的限制。在这篇文章中，我们提出 ECLAIR，一个能够以较少人力监督自动化企业工作流程的系统，通过初步实验表明，多模态基础模型具有解决传统机器人流程自动化的局限性的潜力。我们还确定了人工智能与人类协作、验证和自我提升作为开放性挑战，并提出了通过数据管理技术解决这些挑战的方式。