Why Enterprise AI Deployments Often Fall Short of Pre-Sale Promises

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A recurring pattern in enterprise AI procurement sees organizations invest heavily in vendor evaluations—including demos, reference calls, and contract negotiations—only to find real-world performance significantly below expectations six to twelve months after deployment. The gap is not attributed to deliberate deception but rather to structural features of how AI products are sold and evaluated. Demo environments use curated data, optimized hardware, and pre-vetted queries that rarely reflect the messy, inconsistent conditions of actual enterprise data. Vendor-provided reference customers are selectively chosen from positive experiences, leaving buyers without insight into common deployment struggles or product limitations. Experts recommend probing failure modes during evaluations and independently seeking out non-reference customers for more candid assessments.

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