"Most organizations are not failing at AI because of technology. They are failing because they do not know which data actually matters, and they are scaling that confusion faster than ever."
"Clean data has limited value if it is not relevant, connected, or usable in the context of real decisions. Over time, organizations have accumulated dashboards, reports, and tracking systems that create the appearance of visibility while leaving critical questions unresolved."
"The volume of data has expanded faster than the systems used to interpret it. Teams track what they can, often without a clear view of why it matters, and the result is an environment filled with metrics that compete for attention."
Organizations are failing at AI not due to technology but because they cannot identify relevant data. Despite increased investment, only 14% of CFOs report measurable returns, and 42% of companies abandoned AI pilots. The issue lies in distinguishing signal from noise, leading to poor decision-making. Clean data is insufficient if it lacks relevance and usability. Accumulated dashboards create an illusion of visibility while leaving critical questions unanswered. The rapid expansion of data outpaces interpretation systems, resulting in competing metrics and inconsistent reporting across departments.
Read at TNW | Opinion
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