journal article·2025-11-11Open access

Innovation Flow: A Human–AI Collaborative Framework for Managing Innovation with Generative Artificial Intelligence

Michelle Catta-Preta, Alex Trejo Omeñaca, Jan Ferrer i Picó, Josep Maria Monguet-Fierro

Applied Sciences· Volume 15· Issue 22

Abstract

Conventional innovation management methodologies (IMMs) often struggle to respond to the complexity, uncertainty, and cognitive diversity that characterise contemporary innovation projects. This study introduces Innovation Flow (IF), a human-centred and adaptive framework grounded in Flow Theory and enhanced by Generative Artificial Intelligence (GenAI). At its core, IF operationalises Personalised Innovation Techniques (PInnTs)—adaptive variations of established methods tailored to project genetics and team profiles, generated dynamically through a GenAI-based system. Unlike traditional IMMs that rely on static toolkits and expert facilitation, Innovation Flow (IF) introduces a dynamic, GenAI-enhanced system capable of tailoring techniques in real time to each project’s characteristics and team profile. This adaptive model achieved a 60% reduction in ideation and prototyping time while maintaining high creative performance and autonomy. IF thus bridges the gap between human-centred design and AI augmentation, providing a scalable, personalised, and more inclusive pathway for managing innovation. Using a mixed-methods design that combines grounded theory with quasi-experimental validation, the framework was tested in 28 innovation projects across healthcare, manufacturing, and education. Findings show that personalisation improves application fidelity, engagement, and resilience, with 87% of cases achieving high efficacy. GenAI integration accelerated ideation and prototyping by more than 60%, reduced dependence on expert facilitators, and broadened participation by lowering the expertise barrier. Qualitative analyses emphasised the continuing centrality of human agency, as the most effective teams critically adapted rather than passively adopted AI outputs. The research establishes IF as a scalable methodology that augments, rather than replaces, human creativity, accelerating innovation cycles while reinforcing motivation and autonomy.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Flow Experience in Various Fields · Facilities and Workplace Management · Innovative Approaches in Technology and Social Development

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Innex Labs, 08800 Vilanova i la Geltrú, Spain

Institut pel Futur, 17190 Salt, Spain

Departament d’Enginyeria Gràfica i Disseny, Escola Politècnica Superior d’Enginyeria de Vilanova i la Geltrú (EPSEVG), Universitat Politècnica de Catalunya, 08800 Vilanova i la Geltrú, Spain

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