The Strategic Value of Artificial Intelligence in Global Business: What the Evidence Shows

A systematic review of the most influential literature from 2016 to 2025 finds that artificial intelligence has moved from an operational tool to a determinant of competitive positioning — while governance, skills and uneven knowledge production remain open questions.

Executive Summary

A systematic review published in Frontiers in Artificial Intelligence maps the most influential research on artificial intelligence in global business strategy across the decade to 2025. Combining bibliometric analysis with a qualitative synthesis of the fifty most-cited studies, the authors find that AI is increasingly framed not as a discrete technology deployment but as a strategic resource that reshapes competitive dynamics, organisational capabilities and knowledge-intensive processes.

The evidence base is expanding quickly and diversifying beyond purely digital sectors into manufacturing, logistics, agribusiness, energy, financial services and supply chain management. It also reveals structural weaknesses: research remains fragmented across disciplines, concentrated geographically, unevenly distributed between descriptive findings and actionable policy frameworks, and thin on the governance, transparency and workforce questions that determine whether AI adoption translates into durable strategic advantage.

Introduction

The integration of machine learning, explainable AI and generative models into corporate decision-making has become one of the defining features of the current phase of global economic transformation. What distinguishes the present moment from earlier waves of enterprise technology adoption is the breadth of the reallocation: AI systems now sit inside forecasting, pricing, maintenance, customer engagement, risk assessment and product development processes that were previously the preserve of human judgement.

The academic literature has responded. Yet the field has grown faster than its conceptual coherence, producing a body of work that is rich in case-level insight but inconsistent in method, geography and theoretical framing. The value of a bibliometric and qualitative review of this kind lies in establishing what is genuinely established, what remains contested, and where the gaps fall.

Main Analysis

From productivity tool to strategic resource

The review's central finding is a shift in how the literature classifies AI. Early contributions treated algorithmic systems largely as efficiency instruments — automation of routine tasks, cost reduction, incremental process improvement. More recent influential work positions AI as a source of organisational capability, influencing how firms sense their environment, allocate resources and reconfigure themselves in response to turbulence.

This reframing matters for competitive analysis. If AI is a capability rather than a purchase, then differences in firm performance cannot be explained by access to models alone. The review points to leadership commitment, organisational culture, data governance and institutional preparedness as mediating factors — an argument consistent with the technology–organisation–environment tradition in information systems research.

Where the evidence is concentrated

Applications examined in the most influential studies span a wide industrial range. In manufacturing and production systems, algorithmic optimisation, predictive maintenance and digital twins support simulation-heavy engineering workflows. In logistics and supply chains, AI is associated with resilience and coordination capacity across organisational boundaries, with evidence drawn in part from the semiconductor industry. In knowledge-intensive services and marketing, generative tools are linked to creativity, productivity and decision quality — but only where adoption is supported by appropriate structures.

Environmental and climate-related applications form a distinct and growing cluster. Research using interpretable machine learning methods has identified non-linear relationships between digital transformation and carbon emissions, with implications for low-carbon policy design. Related work associates digital maturity and fintech development with environmental outcomes in advanced economies, and AI-enabled green innovation with sustainable growth trajectories in emerging ones.

The evidence also extends into the internal architecture of firms. Studies of business-to-business account management indicate that leadership style, personality traits and organisational culture moderate AI adoption and mediate its effects on performance. In human resource management, the emphasis falls on building the competencies needed to detect and mitigate bias arising from data collection, model training and algorithmic deployment — capabilities increasingly understood as part of a firm's dynamic capabilities.

The governance and acceptance constraint

A recurring theme across the influential literature concerns the limits of technical capability. Questions of transparency, algorithmic bias, digital skills and customer acceptance recur across sectors. Research on service delivery suggests that how organisations communicate their AI capabilities materially shapes trust: an emphasis on algorithmic superiority can erode willingness to engage when customers expect human judgement or empathy.

Similar tensions appear in financial digitalisation and cybersecurity, where AI is combined with behavioural techniques such as gamification and personalisation to strengthen risk mitigation. The same digital infrastructure that enables these approaches introduces new vulnerabilities, reinforcing demands for adaptive governance and more robust regulation.

A widening skills gap

Several influential studies document a divergence between the analytical and digital competencies labour markets require and those students and recent graduates believe they possess. The gap is especially salient in debates around human-centric industrial models that prioritise interdisciplinary collaboration and ethical technology development. Closing it, the literature argues, requires coordinated effort among universities, industry and policymakers rather than curricula reform in isolation.

Limits of the evidence base

The review is candid about the weaknesses of the field it maps. Findings are often fragmented across disciplines, limiting cumulative theory-building. Research production is concentrated in certain regions, with Global South contexts under-represented — a structural asymmetry in knowledge production that shapes which managerial and policy questions are asked. The literature also shows an imbalance between descriptive accounts of adoption and the development of frameworks that can be operationalised by decision-makers.

Global Implications

Economic development and productivity. If AI's strategic value depends on organisational and institutional conditions rather than technology access, the productivity gains from adoption will be distributed unevenly across economies. Countries with strong data infrastructure, competition policy and skills pipelines are better positioned to convert deployment into measurable output growth.

International trade and investment. Divergence in AI governance regimes — spanning data localisation, model transparency, liability and procurement rules — is likely to influence where multinationals site data-intensive operations. Compliance architecture becomes a factor in foreign direct investment decisions alongside cost, market access and talent.

Supply chains and industrial policy. Evidence linking AI to supply chain resilience reinforces existing government interest in semiconductor capacity, critical infrastructure and digital logistics. The risk is duplication and fragmentation of industrial policy across blocs at a moment when interoperability would deliver larger aggregate gains.

Energy and climate. The environmental cluster in the literature cuts both ways. AI-enabled analytics can support emissions measurement, grid optimisation and industrial efficiency; the computational footprint of large-scale deployment raises energy security questions that few corporate strategies currently address explicitly.

Governance and cooperation. The recurring themes of bias, transparency and accountability point toward international standard-setting as a practical, rather than aspirational, requirement. Comparability of audit methods and disclosure practices would lower compliance costs for firms operating across jurisdictions and improve the evidence base for regulators.

Regional stability and resilience. Where AI adoption concentrates in a small number of economies and firms, technological asymmetry becomes a geopolitical variable. Capacity-building in less represented regions is therefore both a research and a stability question.

Strategic Insights

Capability, not procurement. The literature's most consistent message is that value accrues to organisations that treat AI as an organisational capability. Investment in data quality, decision rights, process redesign and change management is likely to matter more than model selection.

Governance as competitive infrastructure. Firms that build credible internal mechanisms for bias detection, model documentation and human oversight reduce regulatory exposure and preserve customer trust — both increasingly material to valuation and market access.

Talent pipelines as a binding constraint. The documented mismatch between employer expectations and graduate competencies suggests that corporate strategy should include partnerships with educational institutions, internal reskilling and differentiated recruitment rather than relying on external labour markets.

Environmental applications deserve dedicated scrutiny. The non-linear relationships identified between digital transformation and emissions imply that environmental benefits are conditional rather than automatic, and that measurement frameworks should precede large-scale deployment claims.

Watch the evidence gap. Under-representation of emerging-market contexts limits the generalisability of findings drawn largely from advanced-economy samples. Investors and policymakers operating in those markets should treat imported best practice with appropriate caution.

Future Outlook

Over the next three to ten years, three developments are likely to shape how the relationship between AI and global business strategy evolves.

First, the research agenda will probably shift from adoption to attribution. As deployment becomes near-universal in large firms, the strategically relevant question becomes why returns differ so widely — a question that requires longitudinal, firm-level data that the current literature largely lacks.

Second, governance will move from principle to procedure. Disclosure requirements, audit standards and procurement conditions are likely to converge partially across major markets, creating a compliance layer that shapes market entry and supplier qualification. Regulatory divergence will persist, but the direction of travel favours documented, testable processes.

Third, the geographic distribution of both research and capability will come under pressure. Increased investment in AI research capacity in emerging economies, alongside international cooperation on standards and skills, could gradually correct the asymmetry the review identifies. Absent such investment, the risk is a self-reinforcing concentration of both technological capability and the analytical frameworks used to interpret it.

Underlying these shifts is an unresolved question about skills. If labour markets continue to demand analytical and digital competencies faster than education systems supply them, the constraints on AI-driven value creation will be human rather than computational.

Conclusion

The most influential literature on AI in global business converges on a single proposition: technology availability is not the binding constraint on strategic value. What determines outcomes is the organisational, institutional and regulatory context in which AI systems are deployed. That finding has direct implications for corporate strategy, public policy and international cooperation — and it places governance, skills and evidence quality at the centre of the next phase of the debate rather than at its margins.

Key Takeaways

• A bibliometric and qualitative review of the fifty most influential studies (2016–2025) shows artificial intelligence increasingly analysed as a strategic resource rather than a discrete operational tool.
• Evidence spans manufacturing, logistics, supply chains, financial services, energy, marketing, human resource management and environmental policy.
• Organisational factors — leadership, culture, data governance and institutional preparedness — mediate whether AI adoption produces measurable performance effects.
• Transparency, algorithmic bias, digital skills and customer acceptance recur as constraints across sectors and are not resolved by technical capability alone.
• The field remains fragmented, geographically concentrated and thin on actionable policy frameworks, with Global South contexts under-represented.
• Environmental benefits of AI-enabled digitalisation appear conditional and non-linear, underscoring the need for measurement frameworks before large-scale claims.

SEO Keywords

Artificial Intelligence, Global Business Strategy, Digital Economy, Technology Innovation, Strategic Intelligence, Global Governance, International Cooperation, Supply Chain Resilience, Energy Transition, Climate Change, Foreign Direct Investment, Future of Work, Innovation Ecosystems, Economic Development, Global Trade, Business Strategy, Geopolitics, International Relations

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. https://doi.org/10.3389/frai.2026.1800412
• Article record: https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1800412/full
• Journal section reference (AI in Business): https://www.frontiersin.org/journals/artificial-intelligence/sections/ai-in-business