Understanding where your systems sit — across value, architecture, and control — reveals exactly where to invest, what to rescue, and what to retire.
Does this system solve a real problem? Does it serve the business? Systems on the right end of this axis have clear purpose and deliver measurable value. Those on the left are solutions looking for a problem.
Is the system architected to last? Engineered for extensibility and scale? At the bottom: vibe-coded prototypes and proofs of concept. At the top: well-tested, documented, scalable systems that won't hit tech-debt brick walls.
Can you actually operate, maintain, and evolve this system tomorrow? In the foreground: active SMEs, current documentation, healthy dependencies. In the background: orphaned systems where consultants left, libraries aged out, and institutional knowledge evaporated.
Every system occupies a position in this space. Click a node or drag to rotate. Green edges point toward good. Red edges warn of risk.
Each position in the space implies a different intervention. Knowing where a system sits tells you what to do next.
Right problem, right architecture, right team. Protect this. Invest in keeping it here — entropy pulls everything toward the origin.
Great system, nobody left who can operate it. The consultant delivered and moved on. Knowledge transfer is urgent before the next incident.
Everyone depends on it. SMEs keep it alive through heroics. The POC that became production. Refactor before the heroes burn out.
Over-engineered solution to the wrong problem. Expensive to maintain, hard to kill because of sunk cost. Redirect or sunset.
Should have been decommissioned yesterday. Nobody knows what depends on it. Map the dependencies and schedule the funeral.
Still solving the right problem, but poorly built and nobody owns it. Highest risk — one failure cascade away from a crisis.
AI doesn't just push systems toward the gold standard. It amplifies movement in all directions — including the wrong ones. Without expert guidance, AI is a force multiplier for risk as much as for value.
AI compresses the time from idea to implementation. Powerful when the idea is right — dangerous when it isn't.
AI makes sophisticated code accessible to everyone. But "runs in demo" and "survives production" are very different bars.
AI's biggest promise is defeating entropy. Its biggest risk is creating a new kind — systems that only an AI session can explain.
AI without expert guidance is a force multiplier for chaos. The organizations that win aren't the ones using AI — everyone will use AI. The winners are the ones who pair AI acceleration with the strategic framework to ensure it moves systems toward the gold standard, not away from it.
Plot your systems in the space. The position reveals the prescription.
Map every system to its coordinates. Be honest about where things really sit, not where you wish they were.
Each archetype has a specific failure mode and a specific intervention. The framework eliminates guesswork.
High-X systems drifting on Z are urgent. Low-X systems consuming Y investment are waste. Triage accordingly.
Every intervention aims to push systems toward the gold standard — or gracefully retire them from the space entirely.
Three axes. Eight octants. One clear framework for understanding where every system lives — and where it needs to go.