What are the future ethical dilemmas posed by increasingly autonomous AI systems in service innovation? How can they be addressed to ensure equitable development and deployment of AI?

Builds on ethics and trust theories and issues such as AI autonomy, privacy, bias, fairness and accountability in AI-driven services (Floridi and Cowls, 2022; Wirtz et al., 2023)

Identifying and addressing potential ethical issues will ensure responsible deployment of AI, enable regulatory compliance and help to maintain public trust

What are the specific capabilities needed to enable UICs in AI-driven service innovation? How do they differ from those of traditional product innovation UICs?

What innovative models of UIC will emerge to drive technological advancements? How can future UICs be structured to adapt to rapidly changing technological landscapes?

What role will evolving government policies play in shaping the future of UIC?

How can future IP frameworks be designed to better support collaborative innovation to which AI contribute? What are the challenges associated with IP rights?

What future best practices will emerge for effective knowledge transfer between universities and industries in AI projects?

Builds on theories of dynamic capability and alliance and collaboration to show strategic and adaptive partnerships over time (Teece, 2009)

Based on open innovation and UIC literature that explores new models that can enhance the effectiveness of UICs (Cao et al., 2026)

Grounded in policy and regulatory theories, such as the triple helix framework, exploring how government interventions can facilitate or hinder collaborations (Etzkowitz and Leydesdorff, 2000)

Explores the complex interplay between IP rights, open innovation and knowledge sharing (Perkmann and Walsh, 2007)

Grounded in knowledge management and transfer theories, exploring how best practices can evolve to support AI projects (Argote and Ingram, 2000)

New models based on co-creation principles can enhance the effectiveness of UICs and create both better research questions and faster commercialization

Addressing these challenges can help to facilitate the smooth sharing of knowledge between universities and industry partners, clarifying the role of AI

Understanding the impact of government policies helps stakeholders navigate regulatory environments and leverage policy support to enhance collaboration outcomes

Clarifying the IP rights of different collaborators will streamline negotiations for UIC

Co-creation models will lead to improved science and improved outcomes for service organizations

How can AI-driven service innovation remain adaptive to changing stakeholder needs? What are the value drivers for the various stakeholders in embedding AI innovations? How might AI-driven service innovation dynamics unfold longitudinally as part of a complex innovation process?

How will advancements in AI shape the future of personalized customer experiences in service industries?

What emerging technologies could complement AI to overcome current barriers in traditional service sectors? How can AI be effectively integrated into existing service delivery models to enhance customer experience and operational efficiency?

What success factors will be critical for future interdisciplinary teams working on AI-driven service innovation?

Drawing on theories of technology adoption and human-computer interaction, these questions examine how AI can enhance personalization, a key factor in customer satisfaction and loyalty (Huang and Rust, 2018) and extend on broader innovation process theories of Andrew Van de Ven and others

Builds on theories of technological innovation and diffusion, examining how complementary technologies can enhance AI's impact in service sectors (Vendrell-Herrero et al., 2017)

Delves into the intersection of service innovation and AI, drawing on theories of service innovation (e.g., Vargo and Lusch, 2008) and AI adoption (e.g., Rogers, 2003)

Based on team dynamics and interdisciplinary collaboration, exploring the factors that contribute to successful teamwork (Edmondson and Harvey, 2018)

Enhanced personalization will continue to help businesses tailor their service offerings, but an over-reliance on AI may create the opposite effect

Complementary technologies may help businesses integrate AI more effectively and overcome adoption barriers and enhance overall service delivery

Understanding the effective integration of AI can lead to the development of innovative service solutions, improved customer satisfaction and increased organizational productivity

Teamwork is essential for UICs. Therefore, it is crucial that interdisciplinary teams with diverse backgrounds collaborate well to ensure UIC success