Evolving the OVB Service Platform Approach to Overcome the AI Experimentation Trap

Abstract

OVB, a leading organization in the financial services sector, is proactively leveraging artificial intelligence (AI) to drive innovation and deliver measurable benefits in both customer experience and operational efficiency. During this AI-driven transformation, the company encountered the “AI Experimentation Trap” (Furr and Shipolov, 2025, Huang et al., 2025), the difficulty of converting promising AI prototypes into scalable, compliant, and value-generating solutions. In an era of increasing technological densification, many organizations face similar challenges, compounded by phenomena such as “Shadow AI” and the “Governance Drift Zone” (Silic et al., 2025). To address these challenges, and particularly to embed AI effectively into OVB’s core processes while maintaining customer relevance, the organization adopts a Service-Dominant (S-D) mindset, treating services as the central structuring paradigm. Complementing this approach, OVB employs Service Dominant Architecture (SDA), (Spohrer et al., 2022) as enterprise architecture and organizing logic for both its process design and its technical implementation as core platform. This architectural approach enables the seamless integration of AI-enabled services into the broader business ecosystem. The research picked the Translational Service Research and Design Methodology (TSRDM) (Warg et al., 2025) to systematically generate, translate, and apply knowledge that bridges the persistent gap between scientific advances in AI and their practical, value-creating implementation. In this way the work also contributes to the development of the unifying service language of TSRDM.

Keywords: Experimentation Trap, Service Dominant Architecture, Translational Service Research And Design Methodology.

DOI: 10.54941/ahfe1007708