An AI‑assisted Design of Experiments (DoE) workflow is only as powerful as its weakest step: if objectives are poorly framed, data poorly executed, or models uncritically accepted, AI will amplify error rather than insight. A rigorous, repeatable workflow must therefore embed mechanistic thinking, statistical coherence, and scale‑up awareness from the outset. The following sections analyze each stage of aseven‑step end‑to‑end workflow, illustrating how AI can accelerate, but not replace, expert judgment.

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