Driven by the rapid ascent of artificial intelligence (AI), organizations are at the epicenter of a seismic shift, facing a crucial question: How can AI be successfully integrated into existing operations? To help answer it, manage expectations and mitigate frustration, this article introduces Computational Management, a systematic approach to task automation for enhancing the ability of organizations to harness AI's potential within existing workflows. Computational Management acts as a bridge between the strategic insights of management science with the analytical rigor of computational thinking. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow. Such procedures focus on task (re)formulation, on the assessment of the automation potential of tasks, on the completion of task specification templates for AI selection and adaptation. Included in the article there are manual and automated methods, with prompt suggestions for publicly available LLMs, to complete these three procedures. The first procedure, task (re)formulation, focuses on breaking down work activities into basic units, so they can be completed by one agent, involve a single well-defined action, and produce a distinct outcome. The second, allows the assessment of the granular task and its suitability for automation, using the Task Automation Index to rank tasks based on whether they have standardized input, well-defined rules, repetitiveness, data dependency, and objective outputs. The third, focuses on a task specification template which details information on 16 critical components of tasks, and can be used as a checklist to select or adapt the most suitable AI solution for integration into existing workflows. Computational Management provides a roadmap and a toolkit for humans and AI to thrive together, while enhancing organizational efficiency and innovation.

翻译：在人工智能（AI）快速崛起的推动下，组织正处于深刻变革的中心，面临一个关键问题：如何将AI成功融入现有运营？为回答这一问题、管理预期并减少挫败感，本文提出“计算管理”这一系统化任务自动化方法，以增强组织在现有工作流中发挥AI潜力的能力。计算管理在管理科学的战略洞见与计算思维的严谨分析之间架起桥梁。本文提供了三个循序渐进的简易流程，用以启动AI在工作流中的实施。这些流程聚焦于任务（重新）表述、任务自动化潜力评估，以及为AI选择与适配而完成的任务规范模板。文中包含了手动与自动化方法，并针对公开可用的LLM提供了提示建议，以完成这三个流程。第一个流程——任务（重新）表述——致力于将工作活动分解为基本单元，使其可由单个智能体完成、涉及单一明确动作并产生独特结果。第二个流程可利用任务自动化指数对细粒度任务进行评估，依据任务是否具备标准化输入、明确规则、重复性、数据依赖性及客观输出等特征进行排序。第三个流程则聚焦于一个任务规范模板，该模板详述了任务的16个关键组成部分，可作为清单用于选择或调整最适合现有工作流的AI解决方案。计算管理为人类与AI协同发展提供了路线图和工具包，同时提升组织效率与创新能力。