Analytical Frameworks for Digital Transformation
Three evidence-based frameworks that provide systematic approaches for understanding, planning, and evaluating digital transformation initiatives in learning organizations.
Why frameworks matter: Systematic reviews find that 70–84% of AI and digital transformation initiatives underperform or fail during scaling (Oludapo et al., 2024; Syed et al., 2023). A systematic review of public sector failures attributed the leading causes to organizational factors (42%), cultural resistance (38%), and leadership gaps (35%) — not technology (Syed et al., 2023). These frameworks directly address those human and organizational dimensions.
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Schmarzo's Laws of Digital Transformation
Bill Schmarzo (2020) · Economics of Data, Analytics, and Digital Transformation
Schmarzo's Laws establish foundational principles for understanding how data, analytics, and AI create cumulative, self-reinforcing value — and why organizations that treat digital transformation as a product rather than a process consistently underperform.
- The Law of Increasing Returns on Data: Unlike physical assets that depreciate, data assets appreciate in value as they are used — enabling predictive analytics, identifying patterns, and powering decisions. Organizations must treat data as a strategic, reusable asset.
- The Law of Decreasing Marginal Cost of AI: The cost of applying AI to new use cases drops as existing data and models are reused. Organizations gain compounding advantages by building AI infrastructure around reusable data assets rather than siloed applications.
- The Law of Digital Transformation Economics: Digital transformation initiatives generate the highest ROI when they shift organizational culture toward continuous learning, stakeholder empowerment, and value co-creation — not merely automation.
2
Warren's Dynamic Systems Engineering (DSE)
Warren, S. J. et al. (2025). Dynamic Systems Engineering for Corporate Training Media Development. Decision Sciences Institute Annual Meeting, Orlando, FL. Program p. 369.
Dynamic Systems Engineering adapts systems engineering principles from aerospace and defense to organizational digital transformation. DSE provides iterative modeling, comprehensive stakeholder engagement, and systematic documentation that addresses both technical and human dimensions of change.
- Define System Boundaries: Identify the transformation system's scope — stakeholders, technology components, organizational units, and interdependencies — using rich picture modeling.
- Stakeholder Requirements Analysis: Conduct structured engagement with all affected groups (executives, managers, end-users, IT) to capture needs, constraints, and success criteria.
- Iterative Modeling: Develop and test transformation models in cycles, using feedback to refine assumptions and update system designs before full implementation.
- Risk Integration: Apply FMEA alongside DSE analysis to quantify failure risks at each system interface and transition point.
- Documentation and Knowledge Transfer: Maintain living documentation of decisions, trade-offs, and rationale to support organizational learning and future transformation cycles.
3
Soft Systems Methodology (SSM)
Checkland, P. (1981, 2000) · Applied by Warren et al. in educational and organizational contexts
SSM provides a structured seven-step approach for analyzing complex, human-centered systems where multiple stakeholders hold competing worldviews. It is particularly powerful for digital transformation because it surfaces hidden assumptions and conflicting mental models before technical implementation begins.
- Enter & Express the Problem Situation: Develop a rich picture of the current state — relationships, conflicts, processes, and stakeholder positions — without premature problem definition.
- Name Relevant Systems: Identify candidate systems that might address the problem situation, expressed as "a system to do X in order to achieve Y in the context of Z."
- Root Definition (CATWOE): Define the system's Customers, Actors, Transformation, Worldview, Owners, and Environment to surface underlying assumptions.
- Build Conceptual Models: Model the minimum set of activities needed to accomplish the transformation described in the root definition.
- Compare Models to Reality: Structured debate between conceptual models and real-world situation — identifying gaps and opportunities.
- Identify Desirable & Feasible Changes: From the debate, identify changes that are both systemically desirable and culturally feasible.
- Take Action: Implement changes and cycle back through the process — SSM is iterative, not linear.
Failure Modes & Effects Analysis (FMEA)
A real applied example of SSM-FMEA integration in higher education — analyzing academic program recruiting and advising operations at a university department to identify and prioritize system failures using Risk Priority Numbers (RPN).
Risk Priority Number (RPN) = Occurrence × Severity × Detectability
Each dimension is rated on a 1–10 scale. A higher RPN indicates a higher-priority target for intervention. RPN scores are used to rank and compare failure modes so that scarce resources can be directed toward the greatest risks first (Xiao et al., 2011; Beck & Warren, 2019).
Each dimension is rated on a 1–10 scale. A higher RPN indicates a higher-priority target for intervention. RPN scores are used to rank and compare failure modes so that scarce resources can be directed toward the greatest risks first (Xiao et al., 2011; Beck & Warren, 2019).
Applied Case Study: Academic Program Marketing & Advising Operations
The following FMEA results are drawn directly from published research: Warren, S. J., Bonhamgregory, C. M., & McGuffin, K. T., A case study of higher education academic program marketing operations: Analysis of system responsiveness and reliability to improve the service provision. University of North Texas. The study applied SSM (Soft Systems Methodology) coupled with FMEA to analyze the recruiting and advising operations of one university department, identifying four major system components at risk of failure and prioritizing improvements using RPN scores.
The following FMEA results are drawn directly from published research: Warren, S. J., Bonhamgregory, C. M., & McGuffin, K. T., A case study of higher education academic program marketing operations: Analysis of system responsiveness and reliability to improve the service provision. University of North Texas. The study applied SSM (Soft Systems Methodology) coupled with FMEA to analyze the recruiting and advising operations of one university department, identifying four major system components at risk of failure and prioritizing improvements using RPN scores.
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| System Component / Failure Mode | Key Problems Identified | RPN | Priority | Recommended Improvement |
|---|---|---|---|---|
| Marketing Activities Program visibility & web communication |
No functional program webpage; incoherent messaging across multiple university-controlled sites; web forms with incorrect addresses not reaching department; no social media presence; staff sent to conferences without training or materials; ~$10K annual budget (~0.6% of dept. budget) insufficient to grow enrollment | 800 | CRITICAL | Rebuild web presence with correct contact info; develop coherent value proposition messaging; establish social media strategy; train conference staff; capture prospective student data for follow-up |
| Student Expresses Interest Lead capture & initial response |
Most student interest communications went to non-existent or unmonitored email addresses; no standardized inter-university process for directing student inquiries; no strategic marketing plan; no process for capturing or following up on conference leads; students who did not call directly received no response | 720 | CRITICAL | Audit and correct all web forms and email routing; establish monitored program inbox; create standardized response protocol with defined time-to-response expectations; implement CRM or tracking for prospective student leads |
| Staff Use of Operations Guide Process standardization & documentation |
No operations guide existed for recruiting tasks; staff received highly variable, inconsistent answers to student questions; no standardized messaging, maximum response time expectations, or documented task responsibilities; staff relied on informal guidance from coordinator with no written record | 700 | CRITICAL | Develop comprehensive written operations guide covering all recruiting tasks, response scripts, escalation procedures, and role responsibilities; establish service-level standards; use guide to onboard new staff and reduce knowledge loss from turnover |
| Pre-Admissions Advising Prospect-to-applicant conversion |
When students successfully reached an advisor by phone, advising quality was high (>50% application capture rate); however, only a small fraction of interested students successfully made contact; secondary advisors lacked transparent, standardized follow-up processes; conference attendees rarely followed up due to missing contact documentation | 576 | HIGH | Standardize pre-admissions advising procedures for all staff; create documented follow-up workflow for all prospective student contacts; establish conference data collection protocol; define clear handoffs between recruiting and advising staff |
Study Outcome: The SSM-FMEA analysis revealed that the two most critical system failures — marketing and lead capture — were not technology problems but operations and process standardization problems. The department lacked documented workflows, defined staff roles, and a coherent communications strategy. The research recommended developing a formal operations plan as the highest-priority intervention, directly addressing the top three RPN scores. This finding illustrates a core principle of digital transformation: technology cannot compensate for the absence of process design.
Source: Warren, S. J., Bonhamgregory, C. M., & McGuffin, K. T. A case study of higher education academic program marketing operations: Analysis of system responsiveness and reliability to improve the service provision. University of North Texas, Department of Learning Technologies.
Source: Warren, S. J., Bonhamgregory, C. M., & McGuffin, K. T. A case study of higher education academic program marketing operations: Analysis of system responsiveness and reliability to improve the service provision. University of North Texas, Department of Learning Technologies.
Decision Support Tools & Models
Practical frameworks for evaluating AI and technology adoption decisions — combining technical assessment, ethical evaluation, and stakeholder considerations.
DTAD Framework
Digital Transformation Adoption Decision framework evaluates technology across six criteria: Explainable & Trustworthy, Educationally Useful, Adaptable, Fair & Equitable, Usable, and Safe. Assessment occurs at IDEA, FEASIBILITY, and ETHICS levels.
Evaluation Matrix
Service-Dominant Logic (SDL)
Reframes AI from a product to a living collaborator in value co-creation. SDL requires designing for transparency, adaptability, and stakeholder engagement — shifting from automation to augmentation. Foundation: Vargo & Lusch (2004); Schmarzo (2020).
Conceptual Model
Proactive Quality Management
AI-enhanced QA shifts from reactive monitoring to predictive analytics — identifying risks before they manifest. Combines predictive modeling, automated early warning systems, and evidence-based decision support for continuous improvement.
QA Framework
Stakeholder Engagement Matrix
Differentiated strategies for four stakeholder groups: Executives (ROI framing, strategic alignment), Managers (process efficiency, team impact), End-Users (usability, autonomy, training), Technical Teams (integration, standards, security).
Change Management
DT Roadmap (8-Phase)
Access current state → Strategize future vision → Identify opportunities → Document business case → Commit to cases/targets → Pilot and evaluate → Scale successes → Review and revise. Transformation as a journey, not a destination.
Implementation
SSM-FMEA Integration
Coupling SSM's rich picture and stakeholder modeling with FMEA's quantified risk prioritization. SSM surfaces the "what and why" of failure modes; FMEA quantifies and prioritizes them for intervention planning.
Integrated Methodology
Selected Presentations by Dr. Warren
Invited and refereed presentations on digital transformation, AI integration, and evidence-based decision support in learning and organizational contexts.
2026
Digital Transformation in Learning Organizations with AI: Opportunities, Risks, and Decision Support
Department Presentation · University of North Texas
2025
Reasoning in the Age of Machines
Invited Presentation · Lubbock Christian University
lcu2025st.systemly.space
2025
Digital Transformation in the Age of AI: Navigating Challenges and Embracing Opportunities in K-12, Higher Education, and Business
Lubbock Economics Forum · Lubbock, TX
lcu2025.systemly.net
2025
The Wild West of Generative AI: Considering Gen AI's Impact on User Experience
Invited Presentation · Lubbock Christian University
2025
Dynamic Systems Engineering for Corporate Training Media Development
Decision Sciences Institute Annual Meeting · Mon. Nov. 24, 1:00–2:30 p.m. · Magnolia 4, Orlando World Center Marriott · Program p. 369
With Annette Fog (Globe Life), Janetta Robins Boone (NASA), Brent Tincher (Lockheed Martin), Stephanie L. Robinson (UNT)
2025 Conference Program
2025
Engineering Reliability into Digital Transformation: Applying FMEA in Learning Organizations
SWDSI Annual Conference (Refereed)
2025
Learning from Failure: Applying SSM and FMEA to Digital Transformation Challenges
SWDSI Annual Conference (Refereed)
2023
AI Support Tools for Teaching, Learning, and Research
UNT College of Information CODE Series · University of North Texas, Denton, TX
2023
Challenges and Opportunities with AI for Trust in Higher Education
LCU Scholars Colloquium · Lubbock Christian University, Lubbock, TX
Curated Reference Library
Key sources supporting the frameworks and evidence-based practices on this site.
Core Frameworks & Foundational Works
- Checkland, P. (1981). Systems Thinking, Systems Practice. John Wiley & Sons.
- Checkland, P. (2000). Soft Systems Methodology: A thirty year retrospective. Systems Research and Behavioral Science, 17(S1), S11–S58.
- Schmarzo, B. (2020). The Economics of Data, Analytics, and Digital Transformation. Packt Publishing.
- Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1–17.
- Warren, S. J., Fog, A., Boone, J. R., Tincher, B., & Robinson, S. L. (2025). Dynamic systems engineering for corporate training media development. Decision Sciences Institute Annual Meeting, Orlando, FL. Program p. 369. Conference Program
- Warren, S. J., Sauser, B., & Nowicki, D. (2019). A bibliographic and visual exploration of the historic impact of Soft Systems Methodology. Systems, 7(1), 10. https://doi.org/10.3390/systems7010010
FMEA Applications in Education & Organizations
- Warren, S. J., Bonhamgregory, C. M., & McGuffin, K. T. A case study of higher education academic program marketing operations: Analysis of system responsiveness and reliability to improve the service provision. University of North Texas, Department of Learning Technologies & Department of Philosophy & Religion.
- Beck, D. E., & Warren, S. J. (2019). Rural art teachers' access: A failure modes and effects analysis of one museum's online art curriculum. Pedagogies.
- Chrysostom, J., & Dwivedi, S. N. (2013). Failure mode and effects analysis. Engineering Management Journal.
- Bustard, D. W., Greer, D., & Tate, G. (1994). Enhancing the soft systems methodology with risk management techniques. Transactions on Information and Communications Technologies, 9, 145–157.
Digital Transformation Research
- Oludapo, S., Carroll, N., & Helfert, M. (2024). Why do so many digital transformations fail? A bibliometric analysis and future research agenda. Journal of Business Research, 174, 114528. https://doi.org/10.1016/j.jbusres.2024.114528
- Syed, R., Bandara, W., Arthur, D., French, E., & Ferrer, M. (2023). Digital transformation failure factors in public sector organizations: A systematic literature review. Information Polity, 28(3), 355–372. https://doi.org/10.3233/IP-220017
- Gartner. (2025). 2025 CIO and Technology Executive Survey. Gartner, Inc.
- McKinsey & Company. (2018). Unlocking success in digital transformations. mckinsey.com
- Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, Not Technology, Drives Digital Transformation. MIT Sloan Management Review and Deloitte.
- Xiao, N., Huang, H. Z., Li, Y., He, L., & Jin, T. (2011). Multiple failure modes analysis and weighted risk priority number evaluation in FMEA. Engineering Failure Analysis, 18(4), 1162–1170. https://doi.org/10.1016/j.engfailanal.2011.02.004
Warren — Selected Journal Articles & Book Chapters
- Warren, S. J., Gratch, J., & Robinson, H. (2018). Soft Systems Methodology: Application of an engineering analytic approach to complex educational problems.
- Warren, S. J., Roy, M., & Robinson, H. (2021). Business simulation games: Three cases from supply chain management, marketing, and business strategy. In D. Ifenthaler (Ed.), Game-based learning across the disciplines. Springer.
- Warren, S. J., & Jones, G. (2017). Learning games: The science and art of development. (D. Ifenthaler, S. Warren, & D. Esreyel, Eds.). Springer.
- Grotewold, K. S., Warren, S. J., & Beck, D. (2024). Ethical choices in educational technology framework for AI: Applied examples for decision-making with scoring. AECT 2024 Annual Meeting. Kansas City, MO.