Busting the Myth: AI That Needs Perfect Data

Many organisations hesitate to adopt AI because they believe their data is not clean or structured enough. This myth creates delays and stalls progress. In reality, modern AI systems are built to work with messy, unstructured, and semi-structured data.

This session explores why waiting for “perfect” data is the wrong approach. Instead, AI adoption works best in iterative cycles: start small, prove value, and let your data practices improve alongside the process. By addressing this myth, businesses can unlock AI’s value sooner and avoid being held back by perfectionism.