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The following works and case studies demonstrate how TSRDM is applied to industry-agnostic challenges, such as digital transformation, and across different industries to overcome translational gaps.
Digital Transformation
The work: "Service Ecosystem Engineering to Overcome Translational Gaps in Digital Transformation" (Gruhn, V., Warg, M. (2026)
Abstract. Digital transformation initiatives continue to fail at rates between 70% and 95%, as technology‑driven efforts further densify already complex application landscapes instead of translating technological potential into realized value (Koczerga, 2024, Bughin et al., 2019). To systematically address these translational gaps (Sung et al., 2003, Woolf, 2008), this paper introduces Service Ecosystem Engineering (SESE) as a paradigm shift from technology‑dominated to structure‑dominated transformation strategies grounded in the centrality of service (Spohrer et al., 2022) and the Translational Service Research and Design Methodology (Warg et al., 2025). SESE adopts a unifying service language in which service provides the overarching grammar and services act as the primary structuring paradigm. Drawing on Service Dominant Architecture (SDA) as a reference structure, SESE organizes value-creation systems as actors, roles, processes, and services across five systems for interaction,
data, participation, institutions, and operant resources. In doing so, SDA serves both as medium for service design and outcome of software engineering (Gruhn and Striemer, 2018). This SESE approach decouples value-creation systems from specific technologies, and enables pace‑controlled modernization, interoperability, and
ecosystem‑wide value cocreation, helping organizations overcome translational gaps and evolve as learning organizations.
 | Gruhn-Volker_PaperID_117.pptx (7.18MB) |
| Gruhn-Volker_PaperID_117.pptx (7.18MB) |
Financial Services
The work: "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.
THE AI EXPERIMENTATION TRAP
The AI experimentation trap (Furr and Shipolov, 2025; Huang et al., 2025) is a strategic failure mode in which organizations rely on widespread AI experiments without anchoring them in clear customer or business outcomes, digital foundations, or scale-up pathways, so that most initiatives remain isolated pilots that consume resources, fragment effort, and ultimately erode confidence in AI’s value. It mirrors earlier “digital transformation” mistakes where a “let 10,000 flowers bloom” approach produced numerous local successes but very few enterprise-level gains. Recent surveys demonstrate that the vast majority of AI initiatives fail to take off and companies are seemingly stuck in proof-of-concept purgatory (Haefner et al., 2023).
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