The accountability gap behind one of AI's biggest risks

The rise of AI has brought about a new kind of accountability gap that is becoming increasingly problematic for businesses. While companies have traditionally invested in data management and governance, the same level of rigor has not been applied to the information their businesses actually operate on. This discrepancy can lead to inconsistencies and inaccuracies that AI systems pick up and propagate to customers, causing damage to the business.

For example, a company may update a policy in one system, but fail to update it in others. If an AI system reads this out-of-date information, it can confidently share it with a customer, even though the information is no longer accurate. This can lead to customer dissatisfaction and loss of business. The problem is compounded when multiple teams within a company own different aspects of the information, leading to contradictory statements that no longer align.

The issue is not necessarily about AI being incorrect, but rather about the lack of accountability for keeping information up-to-date. While a human may notice a stale page or outdated policy and correct it, AI systems do not have this capability. They simply repeat what is present in the documents they are fed, without understanding the context or ability to verify the information.

To address this issue, companies need to establish clear ownership for ensuring that information remains accurate and up-to-date. This means defining who is responsible for catching and correcting stale information, rather than just publishing it. By taking proactive steps to manage information ownership, businesses can avoid the pitfalls of AI exposing quiet inconsistencies and protect their reputation and customer trust.