From voice-of-field to product strategy: Operationalizing sales feedback loops in AI product go-to-market systems

In AI product go-to-market contexts, sales feedback should be treated as a formal component of product planning, rather than a peripheral input. This topic is important because it sits at the intersection of sales digitalization, artificial intelligence adoption in customer relationship management, product innovation governance, and data-driven market sensing. This review explores how voice-of-field data derived from sales conversations can be translated into replicable product-strategy mechanisms for AI products, because customer objections, adoption barriers, implementation challenges, and feature requests often emerge before they are captured in formal market research. The literature reviewed suggests that structured capture, semantic interpretation, prioritization and governance, and feedback return to the sales organization are essential for effective feedback loops. Fragmented evidence across sales, marketing, and innovation research, the lack of longitudinal validation, insufficient consideration of bias in salesperson-generated data, and the limited empirical research in AI-product contexts remain unresolved issues. This review presents a concise framework for feedback-loop maturity and identifies methodological priorities for future research on AI product go-to-market execution.