I developed this cost comparison to ground the AI discussion in economic reality instead of assumptions and marketing slides. Too often, generative and agentic AI are framed as inevitable next steps—something you add "on top" of existing systems—as if the only risk is moving too slowly. In truth, these approaches introduce substantial new costs: specialized skills, LLM usage, vector infrastructure, orchestration platforms, and ongoing governance. By putting three approaches—traditional development, generative AI–enhanced systems, and agentic AI solutions—side by side with approximate Year‑1 costs, I wanted to make that premium obvious and impossible to ignore.

The calculation is intentionally simple: if AI costs more, it must do more, and that "more" has to be expressed in concrete business terms. It's designed to prove that the right question is not, "Can we use AI in inventory control?" but, "When does AI outperform a well‑engineered traditional system on measurable outcomes such as labor savings, error reduction, margin improvement, or resilience?" This framework forces enterprises to build a defensible business case, set clear KPIs, and justify AI as one investment among many—not as a foregone conclusion.