The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows
CoRR(2024)
摘要
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.
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