How Many Coffees Does It Take to Explain That AI Still Needs Clean Data?

The models are powerful. The inputs are not. And somehow... this keeps being ignored.

Author

Glenda Carnate
December 19, 2025

Read time: 2.5 minutes

Artificial intelligence is often described as revolutionary, autonomous, and self-improving, yet one inconvenient truth keeps resurfacing no matter how many times it’s explained: AI can only be as good as the data it’s trained on.

The meeting starts with excitement. Someone says “AI can handle that now.” Another person mentions automation, scale, and speed. You nod, take a sip of coffee, and gently point out that the data feeding the system is inconsistent, incomplete, and stitched together from five sources that don’t agree with each other.

There’s a pause. Then the conversation moves on. Later, when the model underperforms, the question comes back: Why didn’t AI catch this? You reach for another coffee. They didn’t hear you the first time.

The Uncomfortable Reality About AI and Data

  • AI does not “fix” messy inputs… it amplifies them.

  • Automation scales errors faster than insights.

  • Advanced models cannot compensate for missing context.

  • Confidence in outputs often hides fragility underneath.

Before intelligence comes discipline.

💡Key Takeaway: 

AI doesn’t fail because it’s too weak… it fails because the data underneath it is ignored.
And no amount of caffeine can make that truth disappear.

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