adsAlmost every large company in a 2025 EY survey reported financial losses from AI-related risks. Sixty-four percent lost more than US$1 million, while the estimated average loss was US$4.4 million. These figures should unsettle boards not because every AI failure can be prevented, but because the cost of AI is increasingly determined by what happens after confidence is challenged.
The most consequential AI failure may not be an incorrect output. It may be the loss of the organisation’s ability to justify continued use of the system.
When an organisation cannot demonstrate why a consequential system was appropriate, which risks were accepted, what evidence supported its use, or why intervention came when it did, a technical problem becomes an enterprise problem. The immediate loss may come from an incorrect decision. The greater loss can include remediation, suspended deployment, regulatory restrictions, damaged reputation and a reluctance to pursue the next valuable innovation.
This is the AI Trust Gap: the distance between an organisation’s confidence in its AI and its ability to demonstrate that such confidence is justified.
The consequences are no longer theoretical. A national pharmacy chain deployed facial-recognition technology to identify people suspected of shoplifting. According to the US Federal Trade Commission, the system generated thousands of false matches, leading to customers being followed, searched, removed from stores or reported to the police. Women and some racial and ethnic groups were disproportionately affected. The company was subsequently prohibited from using facial recognition for surveillance for five years. The loss extended beyond a faulty system: the organisation lost permission to use the technology.adsads
