Transformation Engineering: A Method-Engineering Architecture for Reasoning Continuity in Socio-Technical Transformation

Organizational transformation requires consequential decisions before the transformed system exists, while part of the knowledge needed to assess those decisions emerges only after intervention. The methodological challenge is therefore not only to design and execute change, but to preserve the reasoning that connects evidence about the current socio-technical system to transformation commitments and, subsequently, to evidence produced by implementation. Existing traditions already provide most of the necessary mechanisms: socio-technical design, systems assurance, program theory, realist evaluation, method engineering, experimentation, verification and validation, enterprise traceability, and adaptive learning. A structured prior-art review and 2026 public-priority scan further identify close contemporary comparators, ruling out novelty claims based on lifecycle breadth, closed-loop transformation, traceability in general, or understand–design–execute–learn sequences. This paper formalizes Transformation Engineering as a transformation-specific method-engineering artifact assembled through exaptation and integration of established mechanisms. The framework connects three domains: an epistemic domain for observing, interpreting, modeling, representing, and reviewing the current system; a commitment and design domain for translating reviewed understanding into a Transformation Design and coordinated initiative portfolio; and an empirical domain for implementing, evaluating, and learning from interventions. Its focal design contribution is the formalization of three lifecycle interface contracts—Understanding-to-Commitment, Commitment-to-Initiative, and Evidence-to-Revision—and a typed bidirectional reasoning-traceability model connecting evidence, warrants, context, hypotheses, models, representations, decisions, transformation assumptions, initiatives, expected outcomes, and validation evidence. The proposed artifact is intended to preserve reasoning continuity across lifecycle boundaries and to support evidence-weighted diagnostic localization when outcomes diverge from expectations. The paper derives design propositions and testable empirical expectations, but does not claim empirical superiority. Its scientific contribution is therefore a method artifact whose utility, usability, diagnostic value, and comparative effectiveness remain to be demonstrated.