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From open source to data-driven platform: Innovating business models through AI integration

Simone De Ruosi et al.

Journal of Engineering and Technology Management2026https://doi.org/10.1016/j.jengtecman.2026.101949article
AJG 2ABDC B
Weight
0.50

Abstract

This paper, using the case of PrestaShop, investigates how an open-source platform could innovate and redesign its business model to capitalise on the opportunities presented by AI adoption and data analytics capabilities building. Existing literature emphasises that AI is one of the most transformative digital technologies, enabling platforms to enhance operational and financial efficiency, driving product innovation, fostering network synergies, and more broadly, fuelling business model innovation. However, open-source platforms face unique challenges, particularly in data collection, as their business models may not be structured to gather user data effectively. In such cases, rather than directly leveraging AI for innovation, these platforms may need to first innovate their business models to facilitate data collection, thereby unlocking AI’s potential. This study seeks to unveil such dynamics, by examining the case of PrestaShop, a French e-commerce platform with over 250,000 merchants and thousands of expert partners, showing how the platform transformed its operations, product development, training, and stakeholder relationships to create the conditions necessary for the strategic deployment of AI at scale.

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https://doi.org/https://doi.org/10.1016/j.jengtecman.2026.101949

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@article{simone2026,
  title        = {{From open source to data-driven platform: Innovating business models through AI integration}},
  author       = {Simone De Ruosi et al.},
  journal      = {Journal of Engineering and Technology Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jengtecman.2026.101949},
}

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From open source to data-driven platform: Innovating business models through AI integration

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.