The True Cost of Bad Data
An average of $12.9M per year through correction work, bad decisions, and failed pilots, and AI makes it worse. An agent encodes a data error and repeats it silently across thousands of transactions, so problems surface weeks later as complaints or audit flags.
Four channels of cost
Bad data bleeds money through correction work, operational disruption, wrong decisions, and written-off AI investments. Most of it is invisible until you add it up.
AI is an amplifier, not an editor
A human reviewing a report might catch a bad number. An autonomous agent encodes it into its behavior and executes it thousands of times before anyone notices. AI scales whatever you feed it, including the errors.
This is the core reason data readiness matters more in the AI era than it ever did for dashboards. Reporting errors were bounded, a person eventually noticed. Autonomous errors are not: the agent applies the flawed logic silently and at scale, and the failure shows up downstream as customer complaints or compliance flags that are expensive and slow to trace. Clean data is not hygiene; it is a control on blast radius.
Frequently asked questions.
Why is bad data more dangerous with AI than before?
Because AI acts on it autonomously and at scale. A human might catch a wrong number in a report; an AI agent encodes the error and repeats it across thousands of transactions silently, so the damage is larger and harder to trace than a one-off reporting mistake.