Many enterprise AI initiatives fail not because of insufficient compute power or lack of talent, but because they tackle the wrong data problem. According to the article in Fast Company, organisations often optimise for controlled, ideal-case data scenarios—while what really breaks models are the messy “edge cases” of real-world deployment.
For example, one retailer assumed that a bigger dataset would bridge the gap, but discovered too late that their model failed in conditions that weren’t represented in the training data: lighting changes, rare events, non-standard objects.
The core message: success hinges less on “more data” and more on the right data. Companies that succeed curate datasets with the same rigor applied to modelling—identifying and labelling the hard cases (e.g., rare disease presentations, unusual lighting conditions, mis-placed inventory) before deployment.
That means investing in tooling, metrics, and processes not just for building models, but for measuring data quality, diversity, and completeness across all relevant real-world conditions.
For executives evaluating AI investments, the path forward is clear: treat data maturity as foundational. Prioritise data collection/curation infrastructure and embed scenario-analysis into your strategy so that you can anticipate where models will fail.
In short, the companies that win at enterprise AI are those that recognise the “hidden data problem” and build data practices as the primary lever—not just the flashy AI models.
