Paper Type

Short

Paper Number

PACIS2026-1323

Description

Artificial intelligence (AI) is increasingly embedded in organizational operations, yet prior AI adoption research often conceptualizes adoption through stage-based or capability-oriented models, with limited attention to tensions arising during embedding. This paper examines AI embedding as a processual phenomenon shaped by tensions between experimentation with evolving AI applications and their integration into existing systems, routines, and decision processes. Drawing on 13 qualitative interviews with managers and executives involved with AI in an emerging-market manufacturing context characterized by infrastructural constraints, IT ambidexterity (ITA) is adopted as an analytical lens to examine how exploratory and exploitative IT practices are enacted during AI embedding. The findings identify three recurring organizational action patterns: iterative AI infrastructure development, workforce alignment and behavioral reinforcement, and multi-level coordination and alignment. The study contributes to ITA and organizational AI research by showing how exploratory and exploitative IT practices are intertwined through a processual framework of AI embedding.

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11-Strategy

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Jul 5th, 12:00 AM

Enacting IT Ambidexterity in Organizational AI Embedding

Artificial intelligence (AI) is increasingly embedded in organizational operations, yet prior AI adoption research often conceptualizes adoption through stage-based or capability-oriented models, with limited attention to tensions arising during embedding. This paper examines AI embedding as a processual phenomenon shaped by tensions between experimentation with evolving AI applications and their integration into existing systems, routines, and decision processes. Drawing on 13 qualitative interviews with managers and executives involved with AI in an emerging-market manufacturing context characterized by infrastructural constraints, IT ambidexterity (ITA) is adopted as an analytical lens to examine how exploratory and exploitative IT practices are enacted during AI embedding. The findings identify three recurring organizational action patterns: iterative AI infrastructure development, workforce alignment and behavioral reinforcement, and multi-level coordination and alignment. The study contributes to ITA and organizational AI research by showing how exploratory and exploitative IT practices are intertwined through a processual framework of AI embedding.