Firms embracing an 'algorithmic turn'—combining AI, platform orchestration and human-centric Industry 5.0 practices—report different foreign-entry strategies and redesigned accounting and control systems linked to stronger international performance. The evidence, drawn from four case studies and a 218‑firm survey, is associative rather than causal.

Main Finding

The paper introduces the "algorithmic turn" in firm internationalization and shows that digital, algorithmic, and human-centric forces jointly reshape how firms enter foreign markets, structure management accounting and control systems (MACS), and realize international performance. Specifically, higher AI maturity and platform orchestration change entry-mode choice and speed (toward more asset-light, distant, or complex modes), and Industry 5.0 human-centric practices systematically moderate those effects—mitigating risks, improving trust/legitimacy, and strengthening long-run performance.

Key Points

• Three complementary dimensions of the algorithmic turn:
  • AI-driven internationalization: firms leverage algorithmic decision-making (ML, predictive analytics, automation) to inform market selection, pricing, supply chains, and localization.
  • Platform-orchestrated internationalization: digital platforms enable rapid, asset-light cross-border expansion but shift coordination and performance assessment onto platform metrics and rules.
  • Industry 5.0 human-centric internationalization: emphasis on human oversight, ethics, worker involvement, and resilience that counterbalances algorithmic externalities.
• Mechanisms:
  • Entry decisions: AI maturity increases capacity to target and scale in distant or complex markets quickly; platforms lower entry costs and time-to-market but may lead to dependency.
  • MACS configuration: advanced analytics drive finer-grained, real-time controls and decentralized decision rights; platform firms rely more on platform KPIs and algorithmic governance; human-centric approaches reintroduce qualitative, stakeholder-oriented controls.
  • Performance outcomes: AI and platform use can boost short-term international sales and efficiency; benefits are larger and more sustainable when coupled with human-centric governance (ethics, worker skills, transparency).
• Trade-offs and risks:
  • Overreliance on algorithms may reduce managerial judgement, amplify biases, and create regulatory and reputational exposure.
  • Platform dependence can create bargaining power asymmetries and value capture by platform owners.
  • Human-centric investments mitigate risks but require organizational change and upskilling.
• Managerial implications:
  • Align AI investments with MACS and governance structures.
  • Use platforms strategically while safeguarding data and negotiating governance terms.
  • Invest in human-centric practices (transparency, worker involvement, ethics) to sustain performance gains.

Data & Methods

• Mixed-methods design combining:
  • Four in-depth case studies chosen for contrasting digital profiles (e.g., AI-native exporter, platform-dependent firm, Industry 5.0 exemplar, legacy internationalizer). Qualitative data: interviews, internal documents, process observations.
  • A quantitative survey of 218 internationalized firms measuring AI maturity, platform orchestration intensity, Industry 5.0 human-centric orientation, entry-mode characteristics, MACS sophistication, and international performance outcomes.
• Analysis approaches:
  • Qualitative coding to build process-level insights and mechanisms.
  • Statistical analyses on survey data (multivariate regressions, moderation tests, robustness checks) controlling for firm size, age, industry, and prior international experience.
• Limitations noted by the authors:
  • Cross-sectional survey design limits causal claims.
  • Self-reported measures and potential sample bias by industry/geography.
  • Four cases provide depth but not exhaustive coverage of sectoral variation.

Implications for AI Economics

• Theory development:
  • Classical internationalization models (e.g., Uppsala incrementalism, Dunning’s OLI) need extensions to account for algorithmic capabilities, platform intermediaries, and the human-centric counterweights shaping firm boundaries, market access, and control rights.
  • AI maturity becomes a strategic asset akin to intangible capital; models should incorporate algorithmic capabilities as determinants of comparative advantage and entry timing.
• Measurement and productivity:
  • Economists should develop standardized metrics for AI capital, platform dependence, and human-centric governance to better capture their contributions to multifactor productivity and value capture.
  • Platform-driven value capture requires attention in national accounts and trade statistics (digital intermediaries mediate cross-border flows differently from goods/FDI).
• Labor and distributional effects:
  • The algorithmic turn reshapes demand for skills, task composition, and bargaining positions—policy must address reskilling, social protections, and potential inequality from platform value concentration.
• Regulation and policy:
  • Competition, data-flow, and digital governance policies should consider how platforms and algorithmic decisioning affect firm-level internationalization choices and market structure.
  • Standards for transparency, auditability, and accountability of algorithmic systems will matter for cross-border trust and legitimacy.
• Future research directions:
  • Longitudinal and causal studies linking AI adoption, platform strategies, and sustained international performance.
  • Microdata on algorithmic decision rules and platform contracts to quantify value capture and bargaining dynamics.
  • Cross-country comparisons to assess how regulatory regimes moderate the algorithmic turn’s effects.