Author
Listed:
- Tayyab Warraich
(University of Vaasa, School of Management)
- Rimsha Naeem
(University of Vaasa, School of Management)
Abstract
The rapid advancement of Artificial Intelligence (AI) presents transformative opportunities for firms, particularly within manufacturing and solution-oriented industries, by enabling digitalization, automation, and intelligent decision-making. While AI has the potential to redefine business offerings and foster the evolution from traditional product-service systems to smart product-service systems (SPSS), its successful commercialization remains a persistent challenge. This chapter proposes a multilevel, iterative framework for commercializing AI-enabled sustainable solutions. It begins at the strategy level, where firms make foundational decisions about value propositions. These decisions shape the business model, which guides how firms structure and align operations to support AI-enabled value creation, delivery and capture. The framework then moves to the activity system level, involving the selection, design, prototyping, and advancement of AI solutions for market adoption. The chapter identified that the process is not linear; feedback from implemented solutions loops back to inform strategic adjustments. It contributed theoretically by addressing the limitations of linear commercialization models, emphasizing adaptability, feedback integration, and sustainability. For managers, it will offer guidance on adopting agile, cross-functional strategies that align innovation with digitally evolving markets and sustainability demands. Furthermore, it equips firms to navigate the complexities of AI commercialization better while supporting the development of scalable and sustainable business models.
Suggested Citation
Tayyab Warraich & Rimsha Naeem, 2026.
"Commercializing AI-Eabled Sustainable Solutions,"
Springer Books, in: Marko Kohtamäki & Rodrigo Rabetino & Vinit Parida & David Sjödin & Tim Baines & Ali Ziaee Bigdeli (ed.), Sustainable Product-Service Systems, pages 185-200,
Springer.
Handle:
RePEc:spr:sprchp:978-3-032-07765-3_11
DOI: 10.1007/978-3-032-07765-3_11
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