The biggest challenge facing artificial intelligence today is not computing power or advanced algorithms. It is data quality. AI systems are only as reliable as the information they learn from, and when that foundation is weak, the consequences spread far beyond technology into finance, advertising, healthcare, and hiring.
Studies show that nearly 87% of AI projects fail before reaching production due to poor data quality. In digital advertising alone, almost one-third of the $750 billion spent annually is lost to fraud and inefficiency because transaction data cannot be verified. Even major technology companies are affected. Amazon famously abandoned its AI recruiting tool after discovering that biased training data led to unfair outcomes. The algorithm itself was not flawed; the data behind it was.
As AI becomes critical infrastructure, data quality can no longer be treated as an afterthought. Many datasets lack clear records of where the data came from, how it was modified, or whether it is complete. When an AI system approves a loan, diagnoses a patient, or recommends a candidate, there is often no way to audit the data that shaped that decision.
This creates a trust gap. Just as no one would trust a self-driving car trained on unsafe driving behavior, AI systems trained on biased or unverifiable data cannot be trusted at scale. Solving the AI problem starts with solving the data problem.
