Enterprise AI deployments are failing due to fragmented corporate data, not flawed algorithms, according to Patrick O'Connor, Founder and CEO of procurement software firm Gatekeeper. O'Connor highlighted the phenomenon of "jagged intelligence," a term coined by AI researcher Andrej Karpathy, where a large language model might score 95 percent on the 2026 US Math Olympiad but fail visual logic puzzles that 98 percent of children solve. In B2B SaaS, when an AI agent evaluates supplier costs across multiple contracts, it requires clean, relational data; if vendor data is in one system, legal contracts in a shared drive, and risk assessments in an isolated spreadsheet, the AI lacks the contextual pattern for accurate answers. The enterprise software sales cycle obscures this deficiency with demos using pristine, fully joined datasets, while most enterprises house operational data across six or more disparate systems. Wiring these systems via surface-level APIs does not synthesize the data, forcing the AI to attempt complex data-joining operations it is inherently bad at. The result is inaccurate outputs six months after purchase, leading executives to wrongly conclude the AI model is flawed. O'Connor argues that data readiness before AI readiness is the critical differentiator.