Most Companies Fail at AI Due to Poor Execution, Not Technology Gaps

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A framework published by Blake Aber of Predicate Ventures argues that most organizations struggle with AI not because of inadequate models or tools, but due to a lack of clear execution plans tied to specific business outcomes. The piece draws a sharp distinction between AI strategy, which defines where and how AI fits, and implementation, which turns that strategy into measurable operational change. According to Aber, separating these two layers too early is a primary reason AI initiatives lose momentum and remain as disconnected pilots. Rather than asking broadly where AI can be applied, leaders are advised to identify workflows already costing the business time, margin, or consistency. The framework recommends starting with narrow, operationally specific initiatives that can show visible results within weeks before scaling to broader transformation goals.

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