Nearly 90% of companies now have AI running somewhere in their stack. Way fewer have anything that's actually load-bearing in production, tied to real metrics, with someone on-call for it.
That gap isn't a model problem. It's an engineering and org problem, and it looks the same almost everywhere I've seen it:
The pilot works. Then it hits the boring stuff:
🔌 Data plumbing nobody owns — the pilot ran on a clean CSV someone hand-curated. Production means real pipelines, schema drift, and someone getting paged when the upstream data changes shape.
🔄 Workflow integration nobody scoped — the demo was a notebook. Shipping it means touching four other services, adding monitoring, handling the failure modes nobody thought about in week one.
🧑💻 A human whose job just changed — someone's task got partially automated, and the tooling/process around that shift usually lags months behind the model itself.
None of this shows up in a benchmark. All of it determines whether the thing you built survives past Q2.
"We use AI" and "AI is in production and load-bearing" are very different sentences. Most orgs are only truthfully saying the first one right now.
If you've actually shipped something past the pilot stage — genuinely curious what broke first for you: the data layer, the integration surface, or getting buy-in to change the workflow around it?
What's been the real blocker on your team — infra, trust, or scope creep?
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