The Strategic Value of Artificial Intelligence in Global Business

Subheadline: A systematic review of influential literature reveals how enterprises are moving from AI experimentation to systemic adoption.

Executive Summary

Artificial intelligence (AI) has evolved from an operational tool to a strategic asset. A systematic review published in Frontiers in Artificial Intelligence in April 2026 analyzed the most influential academic research on AI-driven strategic value. The review finds that AI is now embedded in decision support, predictive analytics, and digital business models across industries, but success depends on governance, transparency, and organizational readiness. This article examines the review's findings and their implications for enterprise strategy, investment, and innovation policy.

Introduction

The integration of AI into global business is no longer a matter of experimentation; it is a strategic imperative. The systematic review by Zambonino-Torres et al. provides a comprehensive mapping of the research landscape from 2016 to 2025, drawing on bibliometric and qualitative analysis. It highlights how AI is reshaping competitive dynamics, operational processes, and customer engagement across manufacturing, financial services, and knowledge-intensive sectors.

Technology Context

The review focuses on AI technologies including machine learning, explainable AI (XAI), and generative models. In enterprise settings, these technologies enable large-scale data processing, predictive decision-making, and the automation of routine and complex tasks. The review positions AI not as a standalone tool but as a system that interacts with organizational structures, leadership, and institutional readiness. Importantly, the literature stresses that AI adoption demands digital competencies and ethical governance, particularly in high-stakes environments.

Main Analysis

The systematic review identifies several thematic patterns:

  • Strategic resource view: AI is increasingly framed as a capability that underpins competitive advantage.
  • Operational optimization: Applications include predictive maintenance, digital twins, and supply chain resilience.
  • Customer-facing AI: Generative AI enhances creativity and productivity, but trust is fragile.
  • Governance and ethics: Transparency, algorithmic bias, and accountability are recurring concerns.
  • Sustainability and macro dynamics: AI intersects with environmental transitions, fintech, and green innovation.

The review also reveals significant gaps: limited representation of Global South contexts, fragmentation across disciplines, and an imbalance between descriptive research and actionable policy frameworks.

Industry Impact

For enterprises, the review's findings are both strategic and operational. In manufacturing, AI is facilitating Industry 5.0, which emphasizes human-centric and sustainable production. In financial services, AI-driven personalization and risk management are reshaping customer engagement, while cybersecurity threats demand adaptive governance. In human resources and key account management, AI adoption is moderating firm performance, leadership styles, and organizational culture. The review underscores that AI success is not automatic; it requires deliberate investments in talent, data infrastructure, and change management.

Strategic Insights

Technology leaders and investors can draw several insights:

  • Enterprise strategy: AI must be embedded in corporate strategy, not bolted on as a technology project.
  • Investment trends: Capital is flowing toward AI infrastructure, explainability, and governance platforms.
  • Competitive dynamics: Trust and transparency may become differentiators as important as algorithmic performance.
  • Engineering challenges: Model bias, data quality, and system interoperability remain unresolved.
  • Regulation and policy: The review calls for interdisciplinary collaboration and adaptive governance.
  • Innovation ecosystems: Academic, industrial, and public-sector partnerships are essential for scaling AI responsibly.

Future Outlook

Over the next 5–10 years, AI is expected to become further integrated into enterprise architecture. Advances in AI agents, multimodal systems, and edge infrastructure will broaden the scope of automation. However, the review warns that research is still fragmented and that policy and governance frameworks lag behind technological progress. Enterprises that invest early in responsible AI, dynamic capabilities, and cross-sector ecosystems will be better positioned for long-term resilience. Policymakers will face pressure to align digital sovereignty, cross-border data rules, and innovation policies with the pace of AI development.

Conclusion

The strategic value of AI in global business is not guaranteed; it must be deliberately engineered. The systematic review provides an evidence-based foundation for understanding AI's current and future role in enterprise transformation. For executives, investors, and policymakers, the findings reinforce the need for integrated approaches that balance innovation, governance, and human-centric values.

Key Takeaways

  • AI is a strategic resource, not merely a technology investment.
  • Governance and transparency are critical determinants of AI-driven value.
  • Adoption varies widely across industries and regions; digital readiness is a competitive differentiator.
  • Investment is shifting toward responsible AI and enterprise-grade infrastructure.
  • Interdisciplinary collaboration and policy innovation are essential for realizing AI's long-term value.

Sources

  • Zambonino-Torres, M.J., Coello-Viejó, J.M., & Zambonino-Torres, S.C. (2026). Strategic value driven by artificial intelligence in global businesses: a bibliometric and qualitative analysis of the most influential literature. Frontiers in Artificial Intelligence, 9, Article 1800412. <https://doi.org/10.3389/frai.2026.1800412>