Before AI, Get the data Right
“Data never lies” is a familiar phrase, but it overlooks an essential truth: data is only as reliable as the process used to collect it. Incomplete records, inconsistent definitions, and inaccurate measurements can create a misleading picture of performance.
When those weaknesses feed into artificial intelligence, automation can amplify the problem. AI can identify patterns and support decisions, but its outputs still require validation against reliable information and operational reality.
Consider a warehouse measuring employee productivity. If completed units are recorded accurately but downtime is not, productivity reports may suggest an employee performance issue when the actual cause is equipment failure or material shortages. Applying AI to those records could reinforce the wrong conclusion.
Accurate data begins with clear definitions, consistent collection methods, and routine verification. Leaders should establish what each metric measures, who records it, and how errors are identified and corrected. They should also confirm that the metric reflects the process they want to improve.
Before investing in AI, organizations should strengthen this foundation. Better technology delivers greater value when paired with disciplined processes and sound judgment.
Garbage in, garbage out still applies even with AI. Process improvement starts with making the data trustworthy.