Togaf

🎯 The Original Motivation The DBJ Method exists to provide a single, unifying, and open standard for steering organizations at the speed of AI. It addresses the gap left by traditional frameworks (like TOGAF) that were designed for slower, pre-AI operating rhythms. The method bridges business, process, and technology domains through a shared taxonomy, maturity model, and iterative decision cycle—so enterprises can hold everything together while moving fast. 🛠️ What Problem It Solves ...

I was asked to produce a DBJ Method “one-pager” at the end of June 2026. This is my opinion, revised on 2026-09-28. AI makes it fast. DBJ Method makes it possible. Distribution of efforts to deliver the product under the pressure of AI. Might be BTP in one sentence. The DBJ Method gives an organisation operational efficiency in the presence of AI. The onboarding is matured: first the organisation becomes efficient without AI, then it becomes efficient through automation and AI. AI accelerates an organisation that already runs well. It does not repair one that does not. ...

Someone described DBJ Taxonomy this way: “a taxonomy that maps to Zachman-style layers (Strategic/Conceptual → Logical → Physical → Implementations).” Sounds credible. Sadly, it isn’t true. That image is the simplified four-layer version quoted in the claim. It’s not what Zachman actually defined — his framework is a 5×6 matrix (Scope/Business/System/Technology/Component crossed with What/How/Where/Who/When/Why), not a single top-to-bottom stack. The linear “Strategic → Logical → Physical → Implementations” progression is a loose paraphrase, which is also why the mapping claim doesn’t hold up. ...

Clear roles == Clear communication == Safe Business Sailing Otherwise. “if the underlying system does not evolve…” : AI mirrors broken structures: in a big ball of mud with messy models and fuzzy boundaries, it propagates inconsistencies and hallucinates domain meaning AI amplifies organizational frictions: with repeated handoffs between teams, AI-accelerated code generation creates bigger queues at the handoff. It does not increase throughput, but inventory. AI builds the wrong things faster: without strategic guidance, it custom-builds commodities for non-differentiating problems that already have off-the-shelf alternatives. For details: Building Foundations for Continuous (AI-accelerated) Change ...

Question: Why does DBJ begin with Capability Maturity Model assessment rather than jumping straight into technical delivery? Answer: Because transformation fails without measurable organizational readiness. The Foundation Problem Most enterprises attempt AI-assisted modernization while operating at ad-hoc levels. This creates: Misaligned expectations between business and technology Inconsistent terminology across stakeholder groups Undefined accountability for architectural decisions No repeatable process for evaluating technical risk Architecture-led, AI-assisted delivery requires stable foundations. CMM onboarding establishes those foundations before any work begins. ...