Confidently Wrong: What Happens When AI Runs on Messy Data

I’ve helped many companies fix failed AI projects.

Most had the same pattern.

Most "AI failures" are actually data and integration failures. But here's what makes AI different from traditional analytics - when your data is messy, AI doesn't just give you bad results. It delivers the bad results with unwavering confidence.

That's what happens when you skip the foundation work on an AI project.

The real blockers:

→ Your customer data lives in 6 different systems with 6 different formats

→ Nobody knows which version of "revenue" is the source of truth

→ Your APIs break, but your AI keeps running on stale data

→ Security was an afterthought, so now you can't use your most valuable datasets

Three things that actually work:

→ Start with one clean dataset before going wide. Pick your most reliable dataset and prove the new AI capability works end-to-end.

→ Build data quality checks that fail loudly. If your source data changes unexpectedly, your AI should stop rather than keep hallucinating.

→ Assign a data owner (with real authority) to every dataset feeding your AI. Someone needs to be accountable when things break.

The AI capabilities you're chasing are probably achievable, but only with the right foundation. This old saying still holds true today with AI projects: garbage in →  garbage out.