Frictionless Futures, Invisible Conflicts: The Case for Scenario Methodology in Human-AI IS Research
Journal
International Conference on Information Systems (ICIS)
Type
conference paper
Date Issued
2026-12-13
Author(s)
Abstract
Information systems (IS) research has established AI as a delegation-capable organizational actor with normative consequences. Yet this body of work is methodologically present-bound: it explains how humans and AI coordinate in existing arrangements, not what competing arrangements are possible before design and governance decisions foreclose them. IS has opened epistemological space for researching not-yet-accessible phenomena, but without specifying a method. I argue that scenario methodology fills this gap: not as a management tool, but as a research method that structurally preserves contested stakeholder positions, unresolved normative conflicts, and structured uncertainty. I call this epistemic friction. A comparative corpus study of 4,320 human-authored and GPT-generated scenarios shows what is lost when friction disappears: high-certainty, low-contention, technology-centric futures. Strategically appealing. Analytically impoverished. This pattern illustrates precisely what IS research on human-AI coexistence cannot afford to reproduce.
Language
English
Keywords
scenario method
Artificial Intelligence
Large Language Models (LLMs)
HSG Classification
contribution to scientific community
Refereed
No
Number
2026
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
ICIS2026_DianaKozachek 2.pdf
Size
752.59 KB
Format
Adobe PDF
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