When it comes to automation, the right answer depends heavily on what you are trying to achieve and what constraints you are working within.
**If your priority is flexibility to change direction:** then approaching automation by prioritising simplicity over completeness initially makes the most sense.
**If your priority is team familiarity:** then the calculus around jobs shifts significantly toward accepting a steeper learning curve for long-term leverage.
Most practical AI use cases benefit from combining AI output with domain expertise.
For most people asking about automation: start with the simpler option and migrate once you have a real understanding of safe. Beginning complex and simplifying later is far harder than the reverse.
PI models can produce confident-sounding but incorrect information.
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