Authored by Daniel Dennis. Featured in CIOReview Europe.
Most enterprise AI programmes are not failing because the models are wrong. They are failing because the data underneath them was never built for the job. The records feeding these systems were collected for dealer management workflows, project tracking, clinical reference. Each was fit for its original purpose. None was designed with the accuracy, freshness, and domain specificity that a production AI system demands. The model runs. The outputs quietly mislead. Nobody immediately knows why.
The research makes the scale of the problem visible. Informatica's 2025 CDO Insights survey found that 43% of organisations cite data quality as their number one obstacle to AI success - not model capability, not infrastructure, not talent. Only 12% report data of sufficient quality to run AI applications effectively. IBM's Institute for Business Value puts a financial figure on it: more than a quarter of organisations lose over $5 million annually from poor data quality alone, before any AI investment is factored in. Volume disguises the problem. Ten million records of varying accuracy and freshness is not a data advantage.
The organisations closing this gap share one characteristic. They stopped measuring their data strategy by volume and started measuring it by quality, asking whether the data they hold is accurate enough, current enough, and sector-specific enough to trust a commercial decision made on the back of it. McKinsey's 2025 research confirms the pattern: organisations achieving real financial returns from AI are twice as likely to have redesigned their data workflows before selecting modelling techniques. The intelligence gap is not between enterprises with more data and those with less. It is between those that built their data to be useful and those that accumulated data and hoped it would be.
Read the full article here: The Intelligence Gap: Why More Data Is Not the Same as Better...
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