Paper Type

ERF

Abstract

This paper develops a theory-driven framework to explain the organizational integration of artificial intelligence (AI) into project management (PM). We conceptualize AI integration as a belief-action-outcome (BAO) cycle in which contextual contingencies shape organizational beliefs about AI, those beliefs influence AI-enabled action through dynamic capabilities, and experienced change feeds back into subsequent beliefs. The framework focuses on organizational uses of AI tools embedded in PM routines such as planning, monitoring, coordination, reporting, and risk management. It highlights replacement anxiety as a threat-based belief that may shape the depth and pattern of AI use. By integrating BAO with bounded roles for contingency theory, dynamic capabilities, and punctuated equilibrium, the paper explains why similar AI tools may be interpreted and integrated differently across PM contexts and proposes eleven propositions for future empirical testing.

Paper Number

1814

Comments

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Aug 15th, 12:00 AM

Integrating Artificial Intelligence in Project Management: An Organizational Perspective

This paper develops a theory-driven framework to explain the organizational integration of artificial intelligence (AI) into project management (PM). We conceptualize AI integration as a belief-action-outcome (BAO) cycle in which contextual contingencies shape organizational beliefs about AI, those beliefs influence AI-enabled action through dynamic capabilities, and experienced change feeds back into subsequent beliefs. The framework focuses on organizational uses of AI tools embedded in PM routines such as planning, monitoring, coordination, reporting, and risk management. It highlights replacement anxiety as a threat-based belief that may shape the depth and pattern of AI use. By integrating BAO with bounded roles for contingency theory, dynamic capabilities, and punctuated equilibrium, the paper explains why similar AI tools may be interpreted and integrated differently across PM contexts and proposes eleven propositions for future empirical testing.

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