Interpretability Engine Optimization (IEO): The Missing Optimization Layer in AI Commerce

Abstract

Commerce is increasingly mediated by machines. A machine may retrieve an offer it does not fully understand; it cannot reliably constrain, compare, recommend, or transact on that offer without sufficient interpretation. Existing optimization disciplines — SEO, AEO, GEO and related AI optimization practices — address whether a business is found, extracted, cited, or acted upon. None addresses the question that precedes them all: can the machine correctly interpret what the business sells? This paper proposes Interpretability Engine Optimization (IEO): the practice of structuring, resolving, enriching, validating, and representing commerce data so AI systems can correctly interpret offers before retrieval, comparison, recommendation, or action occurs. We define the discipline, its three objects of interpretation (product, brand, transaction), its core operations, a boundary test that separates it from neighboring practices, and a measurement approach implemented in a working instrument. We report empirical evidence from an operational audit corpus showing that interpretability failures are common within the audited corpus, measurable, and correctable — and that their correction produces large projected improvements in machine-facing readiness. IEO is proposed as an open discipline: anyone can practice it, with any tooling. IEO does not propose a new data format, protocol, model, or enrichment technique. Its proposal is the discipline itself: treating machine interpretability of source business data as an independent optimization objective that can be measured, improved, and tested. Semantic discovery is downstream of interpretation.