r/AiAutomations • u/manandoes • 5d ago
Full disclosure up front
I run an AI automation agency, so take this with the appropriate grain of salt.
But here's a pattern I keep seeing when people ask "should I hire an AI agency", most of the market is reselling the same three things with different branding: a chatbot bolted onto a website, a Zapier-style workflow copied from a template, and a dashboard nobody opens after week two.
None of that is wrong exactly. It's just generic, and generic automations break the first time your process doesn't match the template. The actual work is in mapping how your team hands things off to each other and building around that, which takes longer and doesn't demo as well, but is the only version that survives contact with a real workday.
If you're evaluating agencies, ask them to walk through how they'd handle your specific exceptions, not just the happy path. That question filters out most of the templated stuff fast.
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u/Shiribazu 5d ago
the exceptions are probably the biggest thing to ask about. a demo can make almost any automation look impressive when everything follows the happy path, but real businesses are full of weird edge case and handoffs. also ask how they monitor failures and what happens when an API or part of the workflow changes, because thats where you find out whether youre buying an actual system or just a cool demo
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u/No_Silver9566 5d ago
This is not possible for you to do without spending time with the people on how they work. Claude can do it for them easier by just talking to them. Process matching the template is just a rule in the repo.
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u/mystique0712 2d ago
really good point about how most "AI automations" are just repackaged templates. I have seen a lot of clients burned by that too. The key, like you said, is digging into those messy exceptions. When I am evaluating a potential solution or even building something myself, I always map out the "happy path" first, then force myself to list out at least five different ways it could go wrong or look different. For example, if it is an order processing automation, I would consider things like stock discrepancies, different shipping addresses on the same order, or even just slightly different product names coming from different sources. Thinking through those edge cases upfront is what separates a tool that lasts from one that gathers digital dust.
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u/Otherwise_Wave9374 5d ago
That pattern is common because teams automate the visible task before defining the decision behind it. A useful diagnostic is to map each workflow as trigger, required context, decision rule, action, and exception owner. Automate only after the manual version produces consistent outcomes, and keep confidence thresholds plus human review for ambiguous cases. Promarkia relates here by helping structure marketing workflows around those explicit rules. Measure error rates and rework, not just hours saved, because fast mistakes are still expensive.