Artificial Intelligence in Collaborative Data Sharing for Intra-Company Logistics
on scaling collaborative data ecosystems emphasizes that trust, incentive alignment, and organizational coordination must develop in parallel with technical expansion [ 3 ]. Deliberate ecosystem orchestration can support this alignment as objectives become more diverse [ 11 ]. Sustained value therefore depends not on technical scalability alone, but on the continued alignment of data quality, operational routines, partner incentives, and governance arrangements. Immediate operational effects are more directly observable, whereas longer-term strategic and cumulative effects will become clearer as further use cases and participants are integrated.
6.4 OVERALL IMPACT ON AI APPLICATION AND DIGITAL TWIN CAPABILITIES
Collectively, the findings show that collaborative data sharing provides more than operational visibility. It establishes a shared foundation through which AI applications and Digital Twins can become embedded in cross-organizational logistics processes. Reliable data provisioning supports the continuous synchronization of digital representations with physical assets and activities, while AI-supported analytics transforms the shared data into decision-relevant information. The reuse and expansion of these capabilities can progressively increase the scope and strategic relevance of the resulting applications.
Their impact nevertheless depends on more than technical functionality. Benefits must be sufficiently relevant to the participating organizations, analytical outputs must be integrated into operational routines, and governance arrangements must evolve as the ecosystem expands. When these elements are developed together through a cooperative development approach [ 2 ] and clearly defined data-sharing roles [ 8 ], AI can move beyond isolated applications toward a scalable ecosystem capability. Digital Twins consequently develop not only through the addition of data, but through the sustained alignment of technical integration, organizational participation, and shared value creation.
7 DISCUSSION
The findings suggest that collaborative AI in logistics is a socio-technical ecosystem capability rather than a collection of isolated applications. Prior research identifies clear roles, contribution rules, and neutral facilitation as important for overcoming trust and data-volume barriers [ 8 ]. The ICL Testbed substantiates these requirements and adds a longitudinal perspective: AI and Digital Twin capabilities emerged through the alignment of technical integration, partner responsibilities, and operational use. The coordinating role of the research institute [ 13 ] supported this process. Governance and cooperative development are therefore continuing components of collaborative AI deployment.
7.1 LIMITATIONS AND BOUNDARY CONDITIONS
The Testbed and the frameworks it draws on also reveal clear limitations. The consortium is relatively small and stable, with only three core partners. Scaling to larger and more diverse ecosystems, such as those envisioned in European Data Space initiatives [ 14 ], will likely face greater coordination complexity, varied incentives, and stronger data-sovereignty concerns.
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