Humanistic Orchestration of Artificial Intelligence Applications in Real-Time Business Platform Contexts: A Rhythmic Fabric Theory
by
Omar El-Sawy
Kenneth King Stonier Professor of Business Administration
Professor of Data Sciences and Operations Department
Marshall School of Business
University of Southern California
Friday, February 21
10:30 – 12:00 pm | Speakman 200
Abstact:
AI applications are increasingly being deployed in real-time business platform contexts in which tempo is very fast and digital connectivity is very high. This creates a number of new challenges that go beyond the typical challenges and perspective of the deployment of digital technologies, as AI applications come with a different level of engagement and learning that require more complex orchestration. This presentation develops and exposits the elements of a theory for the humanistic orchestration of AI applications in real-time management business platform contexts that is based on rhythms.
We use an abductive theory building approach that moves from empirical observation to inductive insights to empirical diagnosis to theory development. Our recent real-time management study (Rydén & El Sawy, “How Managers Perceive Real-Time Management: Thinking Fast & Flow”, California Management Review, Feb 2019) has uncovered a phenomenon that is key to humanistic engagement in real-time contexts that we have called Fast & Flow. In this presentation, we draw on a case study of a renter’s insurance AI-enabled application (Lemonade) to couple AI engagement with Fast & Flow behavior. We argue that a rhythmic perspective using Fast & Flow is more appropriate in real-time business platform settings, and we develop a rhythmic fabric ontology for AI engagement. Using this rhythmic perspective, we build elemental propositions of a rhythmic theory for humanistic AI orchestration in real-time business platform settings. We use another case study context (Microsoft Outlook) to illustrate the rhythmic theory in action and make recommendations for the way forward for AI orchestration in organizations. Implications for information systems researchers and management researchers are provided.
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