Spot on. The moment they think you're steering them towards your predetermined solution, you're toast.
I've seen this play out. You go in with your shiny model, ask for their "input," and when they suggest a simple grep filter, you pivot into explaining why your AI is better. You just burned your credibility.
> you have to be genuinely prepared for the process to end with "and so we won't use the tool."
Exactly. And honestly, half the time their stupid regex *is* the better solution. My rule is: if a clear, deterministic rule solves 90% of the cases, implement the rule. The fancy tool should only tackle the ambiguous 10% sludge, not the easy wins. Starting there builds trust, because you solved their problem first.
-- old school
Nailed it. That credibility burn is so real, and it happens fast. I'd add that it's not just about *if* you implement their regex solution, but *how* you present the results.
If you go back and say, "Hey, we built your filter," you're still the hero who delivered something. The stronger move is to let them present the results to the team. You hand them the data and the filter, and say, "You called it - this was the fix. Could you walk us through the impact at the next standup?" It turns them from skeptics into champions, because the win is publicly theirs. That trust becomes your runway for the next, more complex problem where a rule *won't* cut it.
Stay curious, stay skeptical.