Logic hybridization in enterprise platforms: Reconfiguring architecture and governance for generative AI
Journal
Journal of Information Technology
Type
journal article
Date Issued
2026-07-24
Author(s)
Abstract
Enterprise platforms have historically relied on a logic of determinacy to ensure predictable, reproducible, and auditable system behavior through mutually reinforcing architecture and governance mechanisms. However, generative AI (GenAI) introduces a conflicting logic of probabilistic inference that destabilizes these mechanisms by generating variable, context-dependent, and non-deterministic outputs. As platform owners increasingly integrate GenAI into enterprise platforms, this study draws on assemblage theory and an in-depth case study of Salesforce to examine how enterprise platform owners reconfigure architecture and governance to manage the coexistence of deterministic and probabilistic logics. We develop a process model of logic hybridization that explains how platform owners restore system-level stability while accommodating and regulating probabilistic components. Specifically, we identify four complementary mediation mechanisms, that is, dependency mediation, service mediation, interaction-centric control, and dynamic certification and monitoring, through which platform owners reconfigure architectural and governance arrangements. We further theorize that logic hybridization gives rise to a hybrid platform logic characterized by three emergent properties: boundary fluidity, trust mediation, and expanded generativity. This study contributes to research on enterprise platforms and enterprise system evolution by identifying incompatible system behavior as a new source of platform destabilization, extending theories of platform evolution beyond structural change, and theorizing how deterministic and probabilistic logics coexist under mediated conditions in enterprise platforms.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
SAGE Publications
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
Safei et al._2026 JIT.pdf
Size
2.45 MB
Format
Adobe PDF
Checksum (MD5)
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