Cyber Security
- 2 Apr, 2024
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Most AI pilots don’t fail because the model was wrong. They fail in the six months after the demo, when the model meets real, messy, inconsistently-defined production data — and nobody budgeted time to fix that first.
The pattern is familiar. A team picks a promising use case, gets access to a clean, hand-curated dataset, and builds something impressive in a few weeks. Leadership is enthusiastic. Then the project moves toward production, and the data source changes: instead of a curated CSV, the model now has to work against the live system, with its duplicate customer records, inconsistent category labels, and fields that mean something slightly different in every team that touches them. The model’s accuracy quietly drops. Trust erodes faster than anyone expected, and the project stalls — not dead, exactly, but never quite finished either.
This isn’t a modeling problem. It’s a sequencing problem. Organizations that get AI into production reliably tend to do the unglamorous work first: they define what each critical field actually means, agree on who owns that definition, and build monitoring that catches drift before a business user does. Only then do they scope the AI use case — because by that point, they actually know what the data can support.
The uncomfortable part is that this foundational work rarely shows up in a pilot’s success metrics. A demo doesn’t care whether your customer ID is consistent across five systems. Production does, immediately and unforgivingly.
If there’s one question worth asking before any AI initiative gets budget: not “can we build this,” but “do we already trust the data this will run on, or are we assuming we will by the time it matters.” The projects that answer that question honestly, early, are the ones still running a year later.
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