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Trustworthy and explainable artificial intelligence is frequently evaluated through model-performance benchmarks, laboratory experiments, robustness tests, fairness measures, or explanation metrics. Although these forms of evidence can reveal important technical properties, they do not independently establish that an AI system is suitable for consequential decision use. This article proposes a conceptual benchmarking framework for assessing what may count as sufficient pre-deployment evidence when field experiments, operational-performance observations, and real-world user-outcome data are unavailable. The framework distinguishes benchmark validity and coverage from technical readiness, separates robustness evidence from operational safety, and treats explanation usefulness as different from explanation availability or plausibility. It further integrates evidence quality, relevance, completeness, uncertainty, traceability, failure testing, boundary-condition analysis, human evaluation, appropriate reliance, residual-risk documentation, and governance review. Non-empirical decision-readiness criteria are introduced to support structured reasoning about whether an AI system has been characterized sufficiently to proceed to further evaluation, rather than to certify deployment. The framework emphasizes that benchmark scores must be interpreted in relation to the intended use case, decision context, stakeholder context, and risk environment. It also requires explicit identification of evidence gaps, unsupported inferences, monitoring needs, and claims that remain dependent on field evaluation. The principal contribution is an evidence-to-decision structure that helps researchers and decision makers organize pre-deployment trustworthy-AI evidence without collapsing technical performance, human-centered evaluation, decision readiness, and deployment approval into a single score or conclusion.