The AI Value Gap refers to the disconnect between organizations' expectations for transformative business outcomes from AI and the limitations of current AI implementations, which excel at handling routine tasks but struggle with complex, high-value use cases that drive real competitive advantage. Current enterprise AI approaches rely on "in-context" processing, which attempts to handle all aspects of a complex task within an LLM's context window, leading to accuracy numbers dropping rapidly as complexity increases. To bridge this gap, organizations are adopting a new paradigm for Business-Critical AI, which separates planning from execution and uses structured memory beyond context windows to maintain performance at scale. This approach is exemplified by PromptQL, which generates a query plan that composes retrieval, computation, and AI reasoning in a structured, repeatable way, enabling near-perfect accuracy and repeatability even with complex business logic and growing data volumes. By adopting this paradigm shift, organizations can tackle the critical 20% of use cases where true competitive advantage lies, rather than focusing on the easy 80% that traditional approaches excel at handling.