This article critically reappraises three prior publications by the author concerning the use of English in negotiation and marketing, the evolution of business-English learning, and the exporting challenges experienced by non-Anglophone business owners. Interpreted alongside independent scholarship on business communication, cross-cultural competence, generative artificial intelligence (AI), and machine translation, these works are reconsidered in light of the rapid development of AI-mediated multilingual communication since 2023. The reappraisal identifies linguistic, cultural, psychological, and situational barriers that interact in shaping the experiences and outcomes of non-native business communicators. It then develops a two-layer model of communicative competence. The surface layer comprises vocabulary, grammar, pronunciation, spelling, and literal comprehension—capabilities that generative AI and machine translation can increasingly support at low cost, most reliably in routine business registers and in high-resource language pairs involving English, and that such support raises in performance without necessarily developing in the communicator. The deep layer, termed cultural fluency, is specified as three antecedent capabilities—cultural intelligence, pragmatic competence, and relational competence—whose joint exercise produces counterpart-rated trust as an outcome rather than as a fourth component. Whether the deep layer remains human-led is treated as contingent on the present reliability of automated systems, on the location of accountability, and on the relational context of communication rather than as a permanent boundary. On this basis, the article advances five testable propositions: that AI will commoditize surface competence; shift competitive value toward cultural fluency; restructure rather than eliminate the English-language premium; increase the relative value of developed bilingual and bicultural competence in culturally demanding tasks; and potentially reinforce the structural dominance of English through inequalities in language-technology performance. A practical framework is also proposed for allocating communicative tasks across automation, human-AI collaboration, and human control according to task layer, consequences of error, and relational stakes. The article concludes that generative AI is unlikely to eliminate the importance of English competence but may transform what that competence means and where its strategic value resides.
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