Service-Dominant Logic as a Framework for Collaborative Digital Transformation
The Problem
Despite massive global investment, AI and digital transformation initiatives fail at staggering rates. The bottleneck is never the algorithm.
AI capabilities exceed what most organizations can meaningfully integrate. The bottleneck is organizational logic, not technical sophistication.
Treating AI as a product to acquire, install, and optimize for autonomy — people become obstacles, not assets. Value is assumed to be embedded in the technology itself.
Measuring by automation rates and cost savings instead of value creation guarantees the wrong outcomes. Efficiency metrics capture performance, but not value.
Two Paradigms
Understanding why AI projects fail requires examining the assumptions embedded in their design. These two paradigms represent opposing models of how organizations conceptualize value creation.
| Dimension | Goods-Dominant (GDL) | Service-Dominant (SDL) |
|---|---|---|
| Primary Goal | Replace human labor through automation | Enhance human capability through collaboration |
| Value Location | Embedded in the AI system | Emerges through human–AI interaction |
| Success Metric | Efficiency, cost reduction | Stakeholder value & relationship quality |
| System Design | Autonomous, closed | Collaborative, transparent |
| Transparency | Black-box acceptable | Explainability required for trust |
| Adaptation | Static optimization | Continuous learning and evolution |
| Stakeholder Role | Passive recipients | Active co-creators of value |
| Failure Response | Technical debugging | Collaborative redesign and learning |
Five Failure Patterns
Five persistent, predictable failure modes emerge when organizations approach AI through a goods-dominant framework.
Full autonomy removes feedback loops; independence creates fragility. Autonomous systems lack context awareness — they continue to optimize for goals that may no longer align with current conditions. Without human feedback, neither the system nor the organization learns.
Black-box systems break trust; explainability must be an architectural priority. Organizations cannot improve or govern what they cannot interpret. Transparency enables learning, builds trust, and supports integration of AI insights with contextual human knowledge. (Kleinaltenkamp et al., 2012)
Optimizing speed and cost sacrifices adaptive capacity — efficiency without adaptability is fragility. Efficiency-focused design sacrifices stakeholder relationships and adaptive capacity, the very foundations of sustainable performance. (Ostrom et al., 2015)
AI as replacement triggers resistance; inclusion transforms opponents into co-creators. When people understand how AI supports rather than replaces them, trust develops naturally, adoption accelerates, and the system reflects organizational realities. (Edvardsson et al., 2011)
Fixed designs degrade in dynamic environments; effective AI is a living capability. Systems optimized for today's conditions will inevitably fail tomorrow. Goods-dominant logic treats AI as a finished product — but effective AI functions as a dynamic capability that evolves through continuous interaction. (Teece, 2007)
Goods-Dominant Logic in Practice
Each abstract failure pattern maps directly onto a documented organizational collapse. In every case, the technology performed as designed — the logic failed.
Designed to replace oncologist judgment autonomously. Physicians could not understand recommendations or override errors. MD Anderson terminated the $62M project in 2017 after safety concerns arose from unsafe treatment suggestions.
GDL trap: value embedded in system; humans as passive recipients (Abdurrahman et al., 2024)
Black-box hiring algorithm downgraded women's applications for 5 years, undetected. No interpretable outputs for HR to audit or override. Scrapped in 2018 after internal discovery of systematic gender bias.
GDL trap: black-box acceptable; explainability not a design priority (Kleinaltenkamp et al., 2012)
Automated trading system optimized entirely for speed and volume. A 45-minute runaway event in August 2012 cost $440M. No adaptive override, no human feedback loop. The firm collapsed within days.
GDL trap: efficiency without adaptability is fragility (Ostrom et al., 2015)
COIN was deployed without involving legal staff. Lawyers saw it as a threat; many began seeking other roles. Credit officers couldn't explain decisions to clients. Trust eroded. The bank later rebuilt COIN as a collaborative intelligence platform.
GDL trap: stakeholders as passive recipients, not co-creators (Warren, 2025, Ch. 3)
In the mid-2010s Maersk invested heavily in AI for route optimization and port efficiency. Despite technical sophistication, these initiatives failed to create transformational value — AI could not adapt to what customers actually needed: integrated logistics ecosystems.
GDL trap: AI as finished product, not dynamic capability (Teece, 2007; Warren, 2025, Ch. 6)
Service-Dominant Solutions
Originating in marketing and systems theory (Vargo & Lusch, 2004, 2008, 2016), SDL asserts that value is co-created through relationships and interactions, not delivered through static products.
Interpretable models and visible reasoning chains. Explainability is an architectural priority, not a compliance checkbox. Users need to understand how AI arrives at outputs — transparency builds the trust that makes collaboration possible.
Modular designs with feedback mechanisms and iterative retraining so AI evolves alongside the business. Effective AI functions as a dynamic capability — a resource that evolves through continuous interaction and learning, not a finished product.
Stakeholders embedded at every stage: planning, testing, and refinement. Resistance becomes collaboration. When people understand how AI supports rather than replaces them, adoption accelerates and systems reflect organizational realities.
AI extends human capability rather than replacing it. Human judgment plus machine precision equals resilience. Collaborative architecture integrates human judgment into the decision cycle, allowing AI to analyze, recommend, and learn from human validation.
SDL Transformation in Practice
Two organizations that suffered GDL failures subsequently rebuilt their AI systems under service-dominant principles — with measurably different outcomes.
GDL Problem
Legal staff treated as obsolete; opaque scores eroded client trust. Lawyers experienced the AI as an existential threat and began seeking other positions.
SDL Fix
Stakeholder risk mapping engaged lawyers, credit officers, and compliance teams as co-designers. Legal staff became "AI collaboration specialists" rather than replacements, combining algorithmic pattern recognition with regulatory knowledge.
Outcome
Risk analysis combining AI pattern recognition with human relationship knowledge — producing results neither could achieve alone. Client trust was rebuilt on a foundation of shared understanding.
Source: Warren (2025), Ch. 3; Edvardsson et al. (2011)
GDL Problem
Static AI for container routing optimized for efficiency. Customers wanted integrated logistics. The AI couldn't adapt — it was optimizing for container transportation while customers needed ecosystem orchestration.
SDL Fix
Customer co-creation research revealed shipping was only 15–20% of total logistics cost. Maersk rebuilt around integrated ecosystems, applying Schmarzo's Law of Increasing Returns on Data and treating the platform as a reusable, evolving asset.
Outcome
Transformed from container carrier to integrated logistics platform. AI evolved alongside customer needs rather than degrading against them — a shift from static optimization to continuous value co-creation.
Source: Warren (2025), Ch. 6; Schmarzo (2020)
| Dimension | Goods-Dominant (Fails) | Service-Dominant (Succeeds) |
|---|---|---|
| Stakeholder role | Passive recipients | Active co-creators |
| Transparency | Black-box acceptable | Explainability required |
| Value location | Embedded in AI system | Emerges through interaction |
| Adaptation | Static optimization | Continuous learning |
| Failure response | Technical debugging | Collaborative redesign |
SDL Digital Transformation Pathway
Capture big data on organizational performance as a system of systems
SDL: stakeholders define what to measure
Analysts and management co-identify actionable insights and technology solutions
SDL: co-creation prevents misframed metrics
Embed analytics and automate operations to improve performance
⚠ GDL stalls here — efficiency without adaptability
Measure transformation value; ensure improvements drive profit and stakeholder outcomes
SDL: relational and adaptive metrics, not just ROI
Deploy analytics to augment human performance via new technologies and processes
SDL goal: augment not replace — Maersk, COIN v2 achieved this
When organizations treat AI as a product, they achieve operational efficiency at Step 3 but cannot reach Steps 4–5. Knight Capital optimized for speed and eliminated resilience. IBM Watson optimized for accuracy and eliminated trust. Without SDL's co-creation and adaptability, systems degrade rather than evolve. (Ostrom et al., 2015; Warren & Pookulangara, 2026)
SDL embeds stakeholders across all five stages so insights are co-created and monetization reflects relational as well as financial value. Maersk reached Step 5 only by moving from container efficiency (Step 3) to integrated ecosystem orchestration (Step 5) — a shift possible only through stakeholder co-design and adaptive AI architecture. (Schmarzo, 2020; Warren, 2025, Ch. 6)
Implementation Roadmap
Transitioning to service-dominant AI involves both cultural and structural transformation. Successful organizations move through four iterative stages.
Executive education, stakeholder analysis, and mindset alignment
Small-scale collaborative AI tests and feedback cycles
Training, governance redesign, and performance system alignment
Cross-functional integration and network scaling
Measuring Success Differently
Assessment criteria must evolve alongside implementation philosophy. Traditional efficiency metrics capture performance but not value. Service-dominant measurement expands evaluation to include learning, trust, and adaptability.
| Category | Traditional Metrics | Service-Dominant Metrics |
|---|---|---|
| Stakeholder Outcomes | Cost per transaction, speed | Satisfaction, value co-creation |
| Innovation Capacity | Number of AI features | Collaborative innovations, new capabilities |
| Adaptation | System uptime | Responsiveness to change, learning speed |
| Relationship Quality | Usage rates | Trust, transparency, partnership depth |
| Long-term Performance | Short-term ROI | Sustained value creation, strategic advantage |
| Cultural Integration | Adoption percentages | Normalization of collaborative practices |
Takeaways
The Sustainable Digital Transformation Decision Framework (SDTDF) is a structured tool to assess AI and digital transformation readiness and reduce the likelihood of implementation failure. Apply SDL principles systematically before committing to deployment.
https://sdtdf.systemly.net →Contact
Department of Learning Technologies · College of Information · University of North Texas
scott.warren@unt.edu warren.systemly.netDepartment of Learning Technologies · College of Information · University of North Texas
ArunPookulangara@my.unt.eduReferences