Research on the adaptation path of corporate strategy based on the differences in AI governance visions between the Atlantic and China.

Strategic adaptation to divergent AI governance regimes is increasingly central to multinational corporate strategy. Regulatory divergence across the European Union, United States, and China has moved AI governance from a compliance function to a strategy-shaping constraint, yet firm-level adaptation evidence remains disproportionately conceptual and fragmented by single-jurisdiction perspectives. Empirically specifying how multinational firms operationalise cross-regime alignment through changes in deployment architecture, compliance routines, and external signaling remains necessary to support theory-building on strategic adaptation under regulatory fragmentation. To address this empirical gap, the study identifies and quantifies the mechanisms by which multinational firms restructure AI deployment, compliance, and communication in response to fragmented AI governance regimes across the European Union, United States, and China. A comparative multi-case dataset of 12 multinational firms (4 tri-jurisdictional, 4 Atlantic, 4 China-primary) was analyzed, including 48 executive and technical informants and 500 coded adaptation events. This design enables cross-jurisdiction, cross-sector comparison of adaptation intensity and configuration, producing a replicable evidence base for theory-building on how governance exposure and organisational AI maturity jointly shape strategic adaptation pathways. Path-specific composite indices for bifurcation, modularity, ethical signaling, and compartmentalization were quantified using validated scales. Regression models and moderation analyses were performed in R (R Computing, Austria) to examine associations between governance exposure, AI maturity, and adaptation intensity. Tri-jurisdictional firms had larger workforces (5,380 ± 1,245) and higher annual revenues (2,310.4 ± 450.2 million USD) than other groups. Bifurcation scores were highest in the EU (0.84 ± 0.06), while modularity peaked in multinational corporations (0.86 ± 0.04). Ethical signaling intensity was greatest in tech firms (0.82 ± 0.04), and compartmentalization scores were highest for tri-jurisdictional organizations (0.82 ± 0.05). Regression showed governance exposure significantly predicted all adaptation indices (β = 0.35-0.47, R² = 0.29-0.41, all p ≤ 0.004), with AI maturity moderating these effects (p ≤ 0.035). These results clarify why divergent AI governance regimes generate systematically different adaptation profiles, and they provide an empirically grounded basis for aligning corporate strategy, internal controls, and external communication with evolving regulatory and standard-setting expectations across jurisdictions. Firms with higher governance exposure and AI maturity exhibit more advanced, multi-dimensional adaptation across regulatory environments.