This study aims to examine how artificial intelligence (AI) acts as a transformative force within service ecosystems, particularly in small and medium-sized enterprises (SMEs). It investigates the mechanisms through which AI drives service design innovation, optimizes stakeholder experiences, reduces systemic frictions and fosters inclusive outcomes that contribute to service excellence in resource-constrained contexts.
A sequential mixed-methods approach was employed. In-depth interviews with SME service leaders across healthcare, retail and hospitality sectors informed a structural model, which was subsequently tested using survey data from 444 SMEs. Thematic analysis and partial least squares structural equation modeling were used to integrate qualitative and quantitative insights.
The findings reveal that AI enables adaptive service design and personalized stakeholder engagement, while also minimizing technological and procedural frictions. Crucially, inclusivity emerged not as a byproduct but as a central mechanism linking AI integration to service excellence. However, realizing these benefits requires intentional design, human oversight and context-sensitive implementation – especially in digitally evolving, resource-constrained environments.
This study provides actionable guidance for service managers and policymakers, emphasizing the importance of adopting hybrid AI-human models, implementing participatory design approaches and developing AI governance strategies that promote inclusivity. These practices support innovation while promoting sustained stakeholder engagement and long-term ecosystem adaptability.
This study reconceptualizes AI as a transformative operant resource that drives inclusivity-driven, stakeholder-centered service innovation in resource-constrained contexts. It contributes to service-dominant logic by illustrating how AI technologies support value co-creation across diverse actors within service ecosystems in resource-constrained contexts. By focusing on SMEs in a digitally evolving context in resource-constrained contexts, the study sheds light on how AI-enabled mechanisms can support inclusivity in service excellence, providing an empirically grounded framework relevant to both researchers and practitioners.
