This paper aims to address the fundamental paradox of Artificial Intelligence (AI) in innovation, exploring its dual roles as both a problem-solver for human-led failures and a problem creator of new systemic risks. The paper constitutes a broad, multi-layered framework for understanding this apparent paradox of failure and how it might be resolved.
A literature review is synthesized from different research streams, including management science, systems engineering, human-computer interaction and socio-ethical studies. This paper introduces a conceptual model for AI in innovation failure that disentangles the phenomenon into four cascading layers: social, process, technical and socio-ethical.
The analysis reveals a cascading failure model initiated by social breakdowns (e.g. collaborative disconnects), amplified by process deficiencies (e.g. translational gaps), manifesting in technical fragility and culminating in socio-ethical crises. Crucially, the study identifies near failures not as liabilities but as strategic inflection points that, when managed via specific interventions, stimulate organizational resilience.
As a conceptual framework, the model needs to be empirically verified. One key implication is a need for cross-level research exploring the dynamics of the problem-solver vs. problem-creator paradox within each layer and analyzing how near failures facilitate organizational learning based on empirical data.
This paper’s originality lies in synthesizing siloed academic disciplines through the unifying lens of the problem-solver vs. problem-creator paradox. It proposes an innovative and holistic framework for both academic research and practice to identify, mitigate and manage the complex, dual-sided risks of AI in innovation, moving beyond a traditional cost-benefit analysis to a dynamic model of paradoxical tensions.
