The practitioners who treat that shift as an engineering problem (automating, testing, measuring, iterating) will build systems that outperform anything a manual workflow can produce. The discipline is closer to ML engineering than traditional marketing. Eval sets, error analysis, feedback loops, version-controlled prompts, systematic testing before anything touches a live account.
Apply them, and you go from prompting an LLM to orchestrating autonomous pipelines that compound improvements over time.