Paper Number

ECIS2026-2218

Paper Type

SP

Abstract

Organizations increasingly invest in artificial intelligence (AI), yet many initiatives stall before becoming formal projects. In this pre-project phase, stakeholders must align on the value and feasibility of an AI initiative despite uncertainty. We introduce readiness work to describe the collective, artefact-mediated efforts through which heterogeneous actors determine when alignment is “good enough” to justify project initiation. We conceptualize the Design Challenge Brief (DCB) as a coordination and gatekeeping artefact that structures this work. Drawing on a preliminary qualitative analysis of successive DCB versions across five AI innovation projects, we identify a recurring three-phase progression: divergent exploration, negotiation and translation, and convergent alignment. Across these phases, the DCB helps teams navigate AI-specific uncertainty related to data conditions, non-deterministic outcomes, and emergent feasibility. Our study theorizes how readiness for AI innovation is organized prior to design and modeling and clarifies the coordination and gatekeeping role of artefacts in early-stage AI innovation.

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Jun 14th, 12:00 AM

Structuring Readiness For AI Innovation: The Role Of The Design Challenge Brief As A Coordination and Gatekeeping Artefact

Organizations increasingly invest in artificial intelligence (AI), yet many initiatives stall before becoming formal projects. In this pre-project phase, stakeholders must align on the value and feasibility of an AI initiative despite uncertainty. We introduce readiness work to describe the collective, artefact-mediated efforts through which heterogeneous actors determine when alignment is “good enough” to justify project initiation. We conceptualize the Design Challenge Brief (DCB) as a coordination and gatekeeping artefact that structures this work. Drawing on a preliminary qualitative analysis of successive DCB versions across five AI innovation projects, we identify a recurring three-phase progression: divergent exploration, negotiation and translation, and convergent alignment. Across these phases, the DCB helps teams navigate AI-specific uncertainty related to data conditions, non-deterministic outcomes, and emergent feasibility. Our study theorizes how readiness for AI innovation is organized prior to design and modeling and clarifies the coordination and gatekeeping role of artefacts in early-stage AI innovation.

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