jon snow

jon snow
posted in mentor circle: Charlotte City Circle

Jan 5, 2026 at 19:02

The challenges of scaling AI solutions We recently built a small AI prototype in our team that actually worked pretty well in tests, but once we tried to move it closer to production, everything slowed down. Data pipelines weren’t ready, edge cases kept popping up, and suddenly the excitement faded. It made me wonder if this is a common wall teams hit or if we just underestimated the gap between a demo and a real product. Curious how others deal with that messy middle stage.

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  • Denis Zheleznyi

    Denis Zheleznyi

    Jan 7, 2026 at 12:45

    I’ve noticed that a lot of technical initiatives stumble not because of tools, but because priorities shift midstream. One quarter everyone is focused, the next there’s a new roadmap and half the context is gone. Documentation gets outdated fast, and the people who understood the logic move on to something else. Over time, even solid ideas lose momentum if they’re not anchored into day-to-day operations and planning cycles.
  • ben bemer

    ben bemer

    Jan 7, 2026 at 09:19

    That gap is very real. In my experience, prototypes usually live in a controlled bubble where assumptions hold and data behaves nicely. Once you go production-level, things like data quality, monitoring, scalability, and ownership become unavoidable. On one project, the biggest blocker wasn’t the model but figuring out who maintains it and how it fits into existing systems. We looked at how teams structure that transition on this website and it helped us see why discovery, validation, and gradual rollout matter so much. Without that structure, prototypes tend to stall or quietly get shelved.

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