AI adoption drives firm value when backed by strong organizational capabilities

A new systematic published in FinTech review offers the most comprehensive analysis to date of how artificial intelligence (AI) contributes to firm value. The study, titled “Artificial Intelligence and Firm Value: A Bibliometric and Systematic Literature Review,” collects findings from 219 peer-reviewed articles published between 2013 and May 2025, providing fresh insights into the theories, trends, and global collaborations shaping the link between AI adoption and business performance.

The researchers, affiliated with the Hellenic Open University and the National and Kapodistrian University of Athens, combine PRISMA-guided systematic review protocols with bibliometric science-mapping techniques to chart the intellectual evolution of this emerging field. Their analysis underscores that AI is no longer viewed merely as a technology but as a strategic, intangible resource whose impact depends on organizational alignment, workforce skills, and the integration of advanced data-driven capabilities.

Theoretical foundations of AI–firm value research

The review reveals that research on AI’s contribution to firm value is rooted in strategic management theories, particularly the Resource-Based View (RBV) and dynamic capabilities. These frameworks, historically used to study the business impact of IT investments, have been extended to AI, portraying it as a resource that can drive competitive advantage when effectively integrated into a company’s operations.

The authors highlight how these classical theories have been enriched by the Knowledge-Based View (KBV), which emphasizes knowledge integration and application as key drivers of value. AI amplifies this logic by enabling scalable, automated knowledge generation that transforms managerial decision-making. The Technology–Organization–Environment (TOE) framework further explains how environmental pressures, organizational readiness, and technological infrastructure interact to shape the value realized from AI.

This theoretical convergence underscores that AI’s performance impact cannot be explained solely by access to technology. Instead, it is tied to the firm’s ability to develop complementary organizational capabilities, such as strategic intent, data infrastructure, workforce expertise, and adaptability, that unlock AI’s potential to improve productivity, decision quality, and market positioning.

Global research trends and collaboration patterns

The study tracks a sharp rise in AI–firm value research after 2020, mirroring rapid advances in AI technologies and growing industry adoption. The surge coincides with the broader digital transformation wave that has reshaped business models across sectors. Leading academic outlets contributing to the literature include the Journal of Business Research, Industrial Marketing Management, Sustainability, Technological Forecasting and Social Change, MIS Quarterly, and the Strategic Management Journal. These journals have provided a platform for bridging classical strategic management insights with cutting-edge studies on AI-enabled decision-making and organizational agility.

Geographically, China, the United States, and the United Kingdom dominate research output, with China leading in publication volume and the United States and the UK serving as hubs for collaborative projects. Notably, China–USA collaborations account for the strongest bilateral partnerships, followed by significant linkages between the UK and France, and the UK and India. Regional clusters are also emerging in Europe, the Middle East, and Asia, though the study notes that many countries in the Global South remain marginally connected to the core research network.

Influential scholars, including P. Mikalef, S.F. Wamba-Taguimdje, M. Gupta, S. Bag, and R. Dubey, have played pivotal roles in bridging traditional theories with applied research on AI capabilities and their role in enhancing organizational performance. These researchers’ work highlights that AI’s value lies not only in the technology itself but in the development of AI capability (AIC), a multidimensional construct combining technical infrastructure, workforce skills, and organizational readiness.

From theoretical insights to practical implications

The analysis shows a strong alignment between co-word and co-citation networks, indicating that the field is evolving cohesively around shared concepts such as firm performance, information technology, dynamic capabilities, decision-making, innovation, and sustainability. This alignment suggests that research is moving toward a consensus: firms that embed AI within their strategic and operational processes, supported by robust infrastructure and governance, are more likely to achieve measurable performance gains.

According to the study, AI capability is not simply about acquiring advanced tools. Instead, it reflects a firm’s ability to integrate these tools into decision-making processes, adapt them to dynamic environments, and align them with strategic objectives. Companies that invest in both technological and organizational dimensions of AI are better positioned to realize improvements in financial metrics, operational efficiency, and long-term competitiveness.

The authors note that the literature increasingly recognizes AI’s role in supporting sustainability and innovation. AI-driven initiatives in areas such as energy efficiency, supply chain optimization, and risk mitigation are emerging as pathways for creating durable value while addressing societal and environmental challenges. Entrepreneurial orientation, when combined with AI, further accelerates business model innovation, allowing firms to capture and sustain value.

For policymakers, the findings highlight the importance of addressing regional capability gaps to ensure equitable diffusion of AI benefits. This includes investing in skills development, research collaboration networks, and infrastructural support in less advanced economies. For business leaders, the study underscores the need to view AI as part of a broader ecosystem involving people, processes, and technologies rather than a stand-alone investment.

Future directions and remaining gaps

While the review provides a structured foundation for future empirical studies, the authors acknowledge certain limitations. The reliance on a single bibliographic database, the Web of Science, may have excluded relevant work published elsewhere. The relatively small sample size—219 articles—reflects the emerging nature of the field, and the bibliometric approach, while effective for mapping trends, does not capture the full qualitative depth of individual studies.

The authors call for more empirical research to validate the conceptual linkages identified between AI adoption and firm value. They emphasize the need to move beyond binary measures of adoption to assess AI as a multi-dimensional strategic resource, capturing its dynamic interactions with organizational and environmental factors. Developing reliable AI adoption key performance indicators (KPIs) and conducting longitudinal studies could provide actionable insights for both researchers and practitioners.