An old rule that still holds
"Garbage in, garbage out" is a principle from the 1950s – and it holds unchanged in the AI age. A language model can deliver an impressively worded, well-structured answer that still rests on incomplete, outdated, or biased input data. The linguistic quality of an AI output says nothing about the quality of the underlying data.
The competency gap threatening many projects
A current survey shows a clear perception gap: 75 percent of executives believe their employees are proficient working with data – but only 21 percent of employees actually feel confident doing so. For project leads, that means: the assumption that the team can already judge data quality on its own often doesn't hold up in reality.
Bias is the rule, not the exception
Data collection bias affects 85 percent of AI projects, per current surveys – making it the rule rather than the exception. Gartner goes further: by the end of 2026, organizations are expected to abandon 60 percent of AI projects that aren't built on sufficiently AI-ready data. Data quality is therefore not a downstream IT concern, but a direct project-success risk.
Practice section: four questions for every data foundation
Before an AI-assisted forecast, dashboard, or analysis feeds into a project decision, four questions are worth asking: where does the underlying data come from, and how current is it? Does it fully cover the relevant population, or only a slice of it? Are there known gaps, and how does the system fill them – with a clear flag, or unnoticed? And: who documented the data's provenance, so the answers to the first three questions can be verified if in doubt?