Paper Number

ICIS2025-1954

Paper Type

Complete

Abstract

Artificial Intelligence (AI) holds promise for enhancing management within complex innovation ecosystems, yet its practical implementation faces significant hurdles. This study explores the barriers hindering the implementation of AI-driven management practices in these multi-actor environments. Employing an exploratory qualitative design, we conducted semi-structured interviews with 27 diverse experts and analyzed the data using thematic analysis. The findings reveal four interconnected core themes: (1) the ecosystem's amplification of foundational AI barriers (data, skills, trust); (2) pervasive governance voids and accountability dilemmas specific to ecosystem AI; (3) misaligned incentives and value conflicts among actors; and (4) synchronization challenges stemming from actor heterogeneity. These results highlight a systemic, context-dependent barrier landscape where challenges are mutually reinforcing. This research provides a framework for understanding these unique impediments, offering critical insights for facilitating effective AI integration in innovation ecosystems.

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Dec 14th, 12:00 AM

Exploring Barriers to Implementing AI-Driven Management in Innovation Ecosystems

Artificial Intelligence (AI) holds promise for enhancing management within complex innovation ecosystems, yet its practical implementation faces significant hurdles. This study explores the barriers hindering the implementation of AI-driven management practices in these multi-actor environments. Employing an exploratory qualitative design, we conducted semi-structured interviews with 27 diverse experts and analyzed the data using thematic analysis. The findings reveal four interconnected core themes: (1) the ecosystem's amplification of foundational AI barriers (data, skills, trust); (2) pervasive governance voids and accountability dilemmas specific to ecosystem AI; (3) misaligned incentives and value conflicts among actors; and (4) synchronization challenges stemming from actor heterogeneity. These results highlight a systemic, context-dependent barrier landscape where challenges are mutually reinforcing. This research provides a framework for understanding these unique impediments, offering critical insights for facilitating effective AI integration in innovation ecosystems.

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