Not every widely repeated number holds up, and AI Transformation coverage has its share of statistics that sound authoritative mostly because they get cited so often.
The number that lost its context
A frequently cited statistic made the rounds across AI Transformation coverage for months before we looked closely at its original source and realized the methodology behind it did not support the sweeping claim attached to it in most retellings.
What we do instead now
We have shifted toward citing a statistic's original source directly, including its sample size and date, rather than a secondhand summary of it, and we are more willing to simply note that a widely used number cannot be verified instead of repeating it anyway.
Why this matters
A wrong number that sounds right can shape a reader's understanding of AI Transformation coverage for a long time after the original context is forgotten, so being careful about which numbers we repeat is as important as being careful about which stories we run.