The most expensive AI projects we see are the ones built where simple automation would have worked. Here's how to tell the difference.
By The CoolNerd team
A lot of the AI work we get asked to do shouldn't be AI work at all.
That sounds bad coming from an AI consulting firm, but the math is straightforward: most business processes that look "AI-shaped" are actually just "automation-shaped" wearing a fancier hat. And the cost difference between the two is enormous — usually about an order of magnitude.
When you're staring at a workflow and trying to decide what to throw at it, run this check:
If the task has deterministic inputs and deterministic outputs, it's automation. A new Shopify order should always trigger the same five things. Automate it.
If the task requires interpretation, it's AI. Reading a customer email and figuring out what they actually want — that's an interpretation problem. Use AI.
If the task is mostly automation with a single interpretation step, build it as automation and let AI handle just that one decision. This is where the highest ROI usually lives.
The most expensive mistakes we see:
The wins we see in production tend to look like this:
Notice these are all about reducing the cognitive load on a human, not eliminating the human. That's usually the sweet spot.
A good gut check: estimate what it costs you per month to run the AI version, then multiply by 36 months. Compare that to what it would cost to build and maintain the deterministic automation version. Most of the time, the automation wins on three-year cost, even when AI feels faster to ship today.
The exception — and it's a big one — is when the task is genuinely open-ended. There, AI isn't competing with automation. It's competing with hiring a human. And there, AI is usually the better answer.
Our default playbook for a new engagement now looks like this:
This is boring. It's also what works.
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