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One of the most overlooked issues when discussing AI is that the data feeding it must be accurate. When transforming any process, it’s essential to recognize that data often comes from multiple sources, and those datasets must be cleansed and standardized before they can be trusted.
Unfortunately, many organizations still rely on green screen systems that are deeply integrated with other legacy platforms, where data has not been consistently maintained or cleansed. As a result, teams may unknowingly inherit issues like duplicates, mismatched identifiers, missing fields, and conflicting values.
That’s why, when updating or modernizing systems, it’s not enough to simply “upgrade the technology.” Organizations must ensure the data is reconciled for accuracy—so there is a single, verified basis of truth.
While there are many presentations about AI, very few emphasize this foundational requirement: accurate, cleansed data is what determines whether AI outputs are reliable—or dangerously wrong.
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